Evidence Based Standards for Measuring Nurse Staffing and

Evidence-based Standards for Measuring Nurse Staffing and Performance Project # RC1 – 0621 – 06

Report for the

Canadian Health Services Research Foundation December, 2003

Revised and Resubmitted, September, 2004

Prepared by

Linda O’Brien-Pallas, RN, PhD

Donna Thomson, RN, MBA

Linda McGillis Hall, RN, PhD

George Pink, PhD

Mickey Kerr, PhD

Sping Wang, PhD

Xiaoqiang Li, PhD

Raquel Meyer, RN, PhD Student

Evidence-based Staffing i

Acknowledgements The investigators wish to thank the Canadian Health Services Research Foundation, the Ontario Hospital Association Change Foundation, the nursing effectiveness, utilization, and outcomes research unit of the faculty of nursing at the University of Toronto, and the contributing hospitals for the financial support that made this research project possible. The advisory committee members are acknowledged for their guidance in the development of the data collection tools and for their assistance in interpreting the results and their input on the feasibility of collecting significant data elements on an ongoing basis. Dr. Judith Shamian — Health Canada Kathleen MacMillan — Health Canada Jill Strachan — Canadian Institute for Health Information Barbara McGill and Nancy Savage — Atlantic Health Sciences Corporation Jane Moser — University Health Network David McNeil — Sudbury Regional Hospital Margaret Keatings — Hamilton Health Sciences Heather Sherrard — Ottawa Heart Institute Carol Wong — London Health Sciences Centre Lucille Auffrey — Canadian Nurses Association Sue Williams — Ontario Joint Provincial Nursing Committee Beverly Tedford — New Brunswick Department of Health and Wellness Sue Matthews — Ontario Ministry of Health and Long-Term Care Hospital and site co-ordinators and data collectors are recognized for their efforts to collect comprehensive and accurate data about their organization, patients, and nurses in order to support this project. Staff and patients at participating hospitals are thanked for their willingness to participate in this study by completing surveys. Health records departments are thanked for providing patient-specific diagnoses and outcomes. Hospitals and Site Co-ordinators: Sudbury Regional Hospital: Claire Gignac London Health Sciences Centre: Nancy Hilborn University Health Network: Elke Ruthig Hamilton Health Sciences: Bernice King Atlantic Health Sciences Corporation: Trevor Fotheringham Ottawa Heart Institute: Judith Sellick A special thank you is given to project co-ordinators Shirliana Bruce and Min Zhang and research assistant Irene Cheung.

Evidence-based Staffing ii

Key Implications for Decision Makers Variations in nursing productivity/utilization and staffing patterns are frequently observed between, as well as within, hospitals. Decision makers are challenged to maximize productivity/utilization and minimize staffing costs, while ensuring the quality of care. Recommendations from this study inform decision-making on these important issues within hospital cardiac and cardiovascular units.

• Nursing unit productivity/utilization levels should target 85 percent, plus or minus five percent. Levels higher than this lead to higher costs, poorer patient care, and poorer nurse outcomes.

• Maximum productivity/utilization is 93 percent (because seven percent of the shift is

made up of paid, mandatory breaks). Units where nurses frequently work at or beyond maximum productivity/utilization must urgently reduce productivity/utilization and implement acceptable standards.

• Productivity/Utilization targets can be met by enhancing nurse autonomy, reducing

emotional exhaustion, and having enough staff to cope with rapidly changing patient conditions.

• Overall costs are reduced when experienced nurses are retained. Retention is more likely

when there is job security, when nurses can work to their full scope of practice, and when productivity/utilization levels are below 83 percent.

• Retention strategies must address the physical and mental health of nurses, balancing the

efforts and rewards associated with work, nurse autonomy, full scope of practice, managerial relationships, innovative work schedules, hiring more nurses into full-time permanent positions, and reasonable nurse-to-patient ratios based on targeted productivity/utilization standards. These will minimize the effect of persistently high job demands and reduce absenteeism and the use of overtime.

• Investment is needed for infrastructure to collect data that will monitor and improve care

delivery processes and measurement of performance outcomes. Data that should be routinely captured, but are not yet, include valid workload measurement; environmental complexity; patient nursing diagnoses and OMAHA ratings of knowledge, behaviour, and status; nurse and patient SF-12 health status; nurse to patient ratios; and productivity/utilization.

Evidence-based Staffing iii

Executive Summary Policy makers and hospital administrators are seeking evidence to support nursing staffing

decisions that includes both the volume and mix of nurses required to provide efficient and effective

care. The principal objective of this study was to examine the interrelationships between variables

thought to influence patient, nurse, and system outcomes. The results provide quality, evidence-based

standards for adjusted ranges of nursing productivity/utilization and for staffing levels for patients

receiving cardiac and cardiovascular nursing care.

Although hospitals have little control over patient severity and complexity, organizations can

manage nurse characteristics, system characteristics and behaviours, and environmental factors that

influence patient, nurse, and system outcomes. Numerous findings provide important evidence to

guide policy and management decisions related to the deployment and use of nursing personnel.

These findings suggest that organizations can implement many strategies to improve the cost and

quality of care.

In the past, actions to minimize expenses have focused on reducing the cost of inputs, the

number of nurses, and the skill level. The findings of this study suggest that to actually reduce the

cost and improve the quality of patient care, organizations will benefit from 1) hiring experienced,

full-time, baccalaureate-prepared nurses; 2) staffing enough nurses to meet workload demands; and

3) creating work environments that foster nurses’ mental and physical health, safety, security, and

satisfaction. The evidence supports the need for a significant change in the way organizations view

costs and suggests that the emphasis on cost of inputs should shift to the cost of outputs and the

quality of care.

The study found nursing productivity/utilization should be kept at 85 percent, plus or minus

five percent. When rates rise above 80%, costs increase and quality of care decreases. Patient health

is more likely to be improved at discharge if productivity/utilization levels are below 80 percent and

Evidence-based Staffing iv

if patients are cared for by nurses who work less overtime. When productivity/utilization levels are

kept below 80 percent, nurses are more likely to be satisfied with their jobs and absenteeism is

reduced, and nurses are less likely to want to leave their jobs when productivity/utilization is less

than 83 percent.

Costs are lower when hospitals maintain productivity/utilization levels below 90% and

implement strategies to improve nurse health and incentives to retain experienced nurses. Autonomy

can be enhanced by balancing the number of patients assigned to each nurse and each nursing unit,

and emotional exhaustion is less likely when nurses are satisfied, mentally and physically healthy,

and feel that they receive appropriate rewards for their efforts. Nurses are more likely to be

physically healthy when there are good relationships with the physicians on the unit, and these

relationships tend to improve when nurses’ autonomy and decision-making abilities are respected.

Aggression- and violence-free workplaces are key to enabling nurses to do their nursing

interventions on time. There also needs to be enough nursing staff to deal with the rapidly changing

conditions in hospitalized patients, so that nurses have enough time to complete patient care.

Patient care is improved when units are staffed with degree-prepared nurses and when nurses

can work to their full scope of practice. This not only improves job satisfaction, but nurses are also

less likely to leave their jobs.

Patients’ health behaviour improves when nurses have a satisfying work environment, secure

employment, and when unit productivity/utilization does not exceed 88 percent. Enhanced nurse

autonomy, full-time employment, and fewer shift changes are shown to improve patients’ knowledge

about their conditions when they are discharged.

Evidence-based Staffing v

Table of Contents

Acknowledgements.......................................................................................................................... i

Key Implications for Decision Makers ........................................................................................... ii

Executive Summary ....................................................................................................................... iii

I. Context......................................................................................................................................... 7

II. Implications................................................................................................................................ 8

System Implications.................................................................................................................... 9

Patient Implications .................................................................................................................. 13

Nursing Implications................................................................................................................. 13

III. Approach................................................................................................................................. 15

IV. Results..................................................................................................................................... 18

Descriptives............................................................................................................................... 18

Research Question 1. ................................................................................................................ 21 Intermediate System Outputs..........................................................................................21 Patient Outcomes............................................................................................................22 Nurse Outcomes..............................................................................................................24 System Outcomes............................................................................................................27

Research Question 2. ................................................................................................................ 30



V. Additional Resources ............................................................................................................... 32

VI. Further Research..................................................................................................................... 32

VII. References ............................................................................................................................. 32

Evidence-based Staffing vi

Appendices

A. Annotated Bibliography...............................................................................................35

B. Patient Care Delivery Model.......................................................................................84

C. Tables.............................................................................................................................85

D. Instruments, Psychometric Properties, and Variables at Individual and Unit

Levels............................................................................................................................117

E. Data Collection Forms................................................................................................124

F. Methods........................................................................................................................162

G. Descriptive Analyses...................................................................................................168

Evidence-based Staffing 7

I. Context

Nurse staffing is closely linked to patient outcomes and system effectiveness. A greater

understanding of the causes and outcomes of hospital nurse staffing is essential to meet

increasing demands for both cost and quality accountability in healthcare. Recent Canadian

reports highlight the urgent need to identify methods for valid measurement of nursing workload

and productivity/utilization, and to understand their relationship with patient, nurse, and system

outcomes,1,2,3,4 a need further underscored by the current and predicted nursing workforce

shortages.2,5

Policy makers and hospital administrators are seeking evidence to support nursing staffing

decisions that includes both the volume and mix of nurses required to provide efficient and

effective care. Prior studies have provided insight into some of the factors contributing to the

need for nurses and the effect of different staffing approaches on patients, providers, and systems

(Appendix A). Recent evidence suggests that adding one patient to each nurse’s caseload in

acute-care hospitals is associated with increases in 30-day mortality (seven percent), failure-to-

rescue (seven percent), nurse burnout (23 percent), and job dissatisfaction (15 percent).6 Another

study demonstrated that an increase of one hour of overtime per week increases the odds of a

work-related injury by 70 percent.7 Part-time and casual employment can also negatively impact

continuity of care and the nurse’s ability to influence clinical and work related decisions.8 A

review of relevant studies is presented in Appendix A.

The principal objective of this study was to examine the interrelationships between variables

thought to influence patient, nurse, and system outcomes, in order to provide quality evidence-

based standards for adjusted ranges of nursing productivity/utilization and for staffing levels for

patients receiving cardiac and cardiovascular nursing care. This evidence will help policy makers


develop mechanisms and policies to measure the need for nursing service in light of appropriate

staffing and productivity/utilization standards. By examining specific cardiac and cardiology

diagnoses, as well as nurse and nursing work indicators within hospital cardiac and

cardiovascular unit settings, this research study examined four questions:

1. To what extent do patient, nurse, and system characteristics and behaviours, and

environmental complexity measures, explain variation in nursing worked hours and

patient, nurse, and system outcomes, such as length of stay?

2. To what extent is there agreement between the estimates generated by a gold standard for

measuring nursing resource needs (PRN workload methodology) and the worked hours of

care per patient, and how does variance affect patient and nurse outcomes?

3. At what nurse-patient ratio and with what proportion of registered nurse worked hours

are productivity/utilization and patient and nurse outcomes improved, after controlling

for the influence of patient, nurse, organizational, and environmental factors?

4. Which data elements, in addition to those routinely collected within administrative

databases, are critical for routine data collection in Canada? To what extent do policy and

administrative decision makers support the feasibility of routine data collection?

II. Implications

Numerous findings provide important evidence to guide policy and management decisions

related to the deployment and use of nursing personnel. Although hospitals have little control

over patient severity and complexity, organizations can manage nurse characteristics, system

characteristics and behaviours, and environmental factors that influence patient, nurse, and

system outcomes. The implications of this study are directed at those latter factors, which are

amenable to policy and management intervention.


System Implications 1. Results of this study suggest a target of 85 percent (plus or minus five percent) unit

productivity/utilization on a daily basis. Sustained productivity/utilization outside this range

will result in higher costs and poorer quality of care. Rationale: Different levels of unit

productivity/utilization are associated with different outcomes as summarized in Table 1.

Although the goal is to maximize nurse activity, at productivity/utilization levels above 80

percent, negative outcomes emerge because there aren’t enough nurses to meet demands. The

maximum work capacity of any employee is 93 percent, because seven percent is allocated to

paid breaks during which time no work is contractually expected. At 93 percent, nurses are

working flat out with no flexibility to meet unanticipated demands or rapidly changing

patient acuity. This study demonstrates that significant benefits, both fiscal and human, can

be achieved by moderating productivity/utilization levels within a range of 85 percent, plus

or minus five percent. It must be noted however, the suggested range may not be applicable

to specialty units with variable patient flow demands, such as emergency and labour and

delivery departments.

Depending on performance goals, organizations may wish to target specific unit

productivity/utilization values in Table 1. These values are cumulative in nature, such that, if

a unit works at a 92 percent productivity/utilization level, not only will lengths of stay be

longer, but all of the other negative outcomes that occur with productivity/utilization values

below 92 percent will apply.


Table 1. Productivity/Utilization Levels and Associated Outcomes

Productivity/Utilization Levels (%)

Outcomes

> 91 Longer length of stay > 90 Higher costs per resource intensity weight > 88 Less improvement in patient behaviour scores at discharge > 85 Higher nurse autonomy

Deteriorated nurse relationships with physicians > 83 Higher intention to leave among nurses > 80 More nurse absenteeism

Less improvement in patient physical health at discharge Less nurse job satisfaction

Although the Canadian Institute for Health Information defines productivity/utilization as

“workload over worked hours,”9 this neither accounts for the quality and outcomes of care

delivered, nor the impact of length of stay on total cost. This definition is not a measure of

productivity/utilization as an output, but rather a measure of use as a process. “Workload

over worked hours” actually measures use of nursing resources and thus evaluates an

organization’s ability to operate to meet patient care standards and needs.

2. Unit productivity/utilization levels below 90 percent, strategies to address nurse health, and

incentives to retain experienced nurses who are expert in their field should lower resource

intensity weight costs (the cost of providing services to groups of people with different

characteristics). Rationale: Lower costs per resource intensity weight are associated with

higher physical health scores for nurses, expert clinical practice, reduced length of stay, and

unit productivity/utilization levels below 90 percent.

3. Attendance at pre-operative clinics as a routine process for surgical patients, adequate

staffing to prevent medical problems, and unit productivity/utilization levels below 91

percent are recommended. Rationale: Shorter-than-expected length of stay is 185 percent


more likely when patients attend pre-operative clinics and 57 percent less likely when

patients suffer medical problems as a consequence of their treatment.

4. Maintaining unit productivity/utilization levels below 90 percent and recognizing the effect

of complex and numerous nursing diagnoses will optimize the actual worked hours per

patient. Rationale: Increases in actual worked hours per patient are associated with increases

in nursing worked hours and with higher numbers of nursing diagnoses. Actual patient care

hours decline as unit productivity/utilization exceeds 90 percent and with increases in the

proportion of both full-time nurses and average clinical expertise on the unit.

5. Efforts should be made to prevent adverse events to reduce overall costs. Rationale: Patients

who suffer medical consequences are 319 percent more likely to be referred to homecare, and

for each additional hour of care given, the patient is 13 percent more likely to suffer a

medical consequence.

6. Staffing should be sufficient to account for the rapidly changing conditions in hospitalized

patients so that all key nursing interventions can be done. Rationale: Patient interventions are

more likely to be left undone when there are more unanticipated changes in patient acuity or

when nurses experience violence. The likelihood of patient interventions not being completed

increases by 260 percent for nurses at risk of feeling their efforts are not properly rewarded.

7. Providing innovative programs to create aggression-free work environments will enable

nurses to complete key nursing interventions on time. Rationale: Delays in interventions are

more likely when nurses on the unit experience violence, but they are 27 percent less likely

for every 10 percent increase in degree-prepared nurses on the unit.

8. Efforts to improve the job satisfaction of nurses will lead to better ratings of quality of

nursing care. Rationale: Nurse ratings of good/excellent quality of nursing care are 606


percent more likely when nurses rate the quality of patient care over the past year as

improved and 159 percent more likely when nurses are satisfied.

9. Staffing units with degree-prepared nurses and ensuring that nurses can provide the quality

nursing care that they deem appropriate will improve nurse perceptions of patient care

quality over the last year. Rationale: Ratings of improved quality of patient care over the past

year are 915 percent more likely when nurses report good/excellent quality of nursing care

and are 40 percent more likely for every 10 percent increase in degree-prepared nurses on the

unit.

10. Unit productivity/utilization levels should be kept below 80 percent, and work environments

should be assessed to determine why there is higher absenteeism among full-time nurses.

Rationale: Absenteeism is reduced when unit productivity/utilization remains below 80

percent. Full-time nurses are 152 percent more likely to be absent than those who work part-

time or casually. Nurses who are physically healthy are five percent less likely to be absent.

11. Job security and allowing nurses with degrees to work to their full scope of practice will

prevent nurses from leaving. Rationale: Intent to leave is 197 percent more likely among

nurses who are concerned about job security and 101 percent more likely among degree-

prepared nurses. As unit productivity/utilization exceeds 83 percent, intent to leave increases.

However, intent to leave is 97 percent less likely for every 10 percent increase in proportion

of nurse ratings of improved quality of nursing care on unit, 58 percent less likely when

nurses are satisfied, and 51 percent less likely when nurses work full-time.


Patient Implications 12. Reducing overtime hours and unit productivity/utilization levels below 80 percent will

improve patient’s physical status at discharge. Rationale: Improvements in patient SF-12

physical scores at discharge are 45 percent less likely when productivity/utilization exceeds

80 percent and seven percent less likely for each additional hour of nurse overtime.

13. Creating satisfying work environments, offering secure employment, and ensuring unit

productivity/utilization does not exceed 88 percent enhances changes in patient behaviours

related to nursing diagnoses. Rationale: Patient behaviour scores are more likely to decrease

when unit productivity/utilization exceeds 88 percent. Improvements in patient behaviour

scores at discharge are 176 percent more likely when nurses are satisfied but 53 percent less

likely when nurses were forced to change units within the past year or anticipate forced

changes in units in the next year.

14. Employing more nurses in full-time positions, facilitating autonomy, and reducing the

frequency of shift changes improves patients’ knowledge about their conditions at discharge.

Rationale: Improved patient knowledge scores at discharge are 74 percent more likely for

every 10 percent increase in nurses’ worked hours on the unit and 24 percent more likely for

every 10 percent increase in full-time nurses on the unit. Patient knowledge scores are 44

percent less likely to improve for every 10 percent increase in nurses on the unit with more

than one shift change during the past two weeks.

Nursing Implications 15. Ensuring sufficient numbers of nurses who are physically healthy and continuity of care

providers, as well as facilitating autonomy and decision-making will improve nurse-

physician relationships. Rationale: Improved nurse-physician relationships are associated


with higher proportions of physically healthy nurses and increases in nurses’ hours worked

on the unit. Deterioration in nurse-physician relationships is associated with unit

productivity/utilization beyond 85 percent.

16. Finding balance between the number of patients assigned to a nurse, the rate of occupancy on

the unit, and unit productivity/utilization is recommended to enhance autonomy. Rationale:

Lower nurse autonomy is associated with higher unit occupancy rates, nurses experiencing

effort and reward imbalance, more degree-prepared nurses, and greater nurse clinical

expertise. Higher nurse autonomy is associated with unit productivity/utilization greater than

85 percent, nurse satisfaction, and higher nurse-patient ratios.

17. Hiring degree-prepared nurses, increasing average hours per patient, promoting autonomy,

ensuring good quality nursing care, and maintaining unit productivity/utilization levels below

80 percent are recommended to improve nurse job satisfaction. Rationale: Higher nurse job

satisfaction is 301 percent more likely when nurses rate the quality of nursing care as good or

better, and 10 percent more likely for every hour increase in the average worked hours on the

unit. Improved job satisfaction is also 56 percent more likely for every 10 percent increase of

nurses with degree preparation and 24 percent more likely for each one point increase in

ratings of nurse autonomy. Higher job satisfaction is 57 percent less likely when unit

productivity/utilization levels exceed 80 percent.

18. Environmental scanning for factors that cause full-time nurses to be more emotionally

exhausted is recommended. Rationale: Emotional exhaustion is 242 percent more likely

when nurses experience effort and reward imbalance and 179 percent more likely when

nurses work full-time. However, emotional exhaustion is 66 percent less likely when nurses

are satisfied, 10 percent less likely with every one point increase in mental health scores, and


four percent less likely with every one point increase in physical health scores. For every 10

percent increase in satisfied nurses on the unit, nurses are 32 percent less likely to suffer from

emotional exhaustion.

19. Improving nurse-physician relationships at the unit level, balancing the demands placed on

nurses and the rewards they receive for their work, and enhancing job satisfaction will

improve nurses’ physical health. Rationale: Nurses are 49 percent less likely to be physically

healthy when they experience an effort and reward imbalance and 41 percent less likely to be

physically healthy when they are emotionally exhausted. However, as relationships between

nurses and physicians improve, nurses are more likely to be physically healthy.

III. Approach

This study, which comprised cross-sectional and longitudinal components, included the

cardiac and cardiovascular care units of six hospitals in Ontario and New Brunswick. The Patient

Care Delivery System Model10 was adapted for this study (Appendix B). This model emphasizes

that characteristics of patients, nurses, and the system, as well as system behaviours, interact with

communication and co-ordination, environmental complexity, and care delivery activities to

produce system outputs (intermediate outputs include unit productivity/utilization and daily

hours of care per patient; overall outputs include patient, nurse, and system outcomes) and

provide feedback for the entire system.

Ethical approval was received from the University of Toronto and from hospital sites. Patient

and nurse consent was obtained on site. Eight hospitals met the inclusion criteria (high volumes

of patients in the cardiac case mix groups of interest). The first six hospitals approached agreed

to participate. Each hospital’s chief nursing officer or designate joined the study’s advisory


committee and became a local investigator to oversee hospital ethics approval, hiring of project

staff, and data quality at the site.

On participating units, data for study patients, all nurses, and the unit itself were collected on

each patient for each day of stay. Data were collected from patients and nurses directly as well as

from administrative sources. The key variables and data sources are summarized in Table 1

(Appendix C). A detailed summary of each measure and its related psychometric properties

appears in Appendix D, and data collection forms are presented in Appendix E. In addition to

this unit-level data, nurses completed a survey package questionnaire that addressed issues like

burnout, the balance between work efforts and rewards, nurse-physician relationships, autonomy,

and health. Nurses provided input into the PRN workload measurements, identification of

nursing diagnoses, and ratings of patient knowledge, behaviour, and status.

Data were collected between February and December 2002. Data collection periods averaged

six months at each site to maximize the number of patients assessed, but the target of 145

patients for all specified case mix groups was not achieved. Inter-rater reliability on the

application of all measures remained at 90 percent during orientation and throughout the study.

Of 1,107 surveys provided to nurses at all six sites, 727 were returned (66 percent response

rate). In total, 1,230 patients housed in 24 nursing units from the six hospitals were included in

the full study, accounting for 8,113 patient days of data.

Decision makers were involved in developing the proposal and reviewed all data collection

forms and methods prior to implementation. They also reviewed drafts of the descriptive data for

the study’s final report. They made recommendations on additional data elements that should be

routinely collected and assisted in the overall interpretation of the study’s findings.


The findings will be published in peer reviewed and trade journals to target different

audiences. The report, fact sheets, and a video will be sent to hospital executives, non-

government bodies which influence health policy, and each ministry of health in Canada.

Analysis Techniques: Data were analysed using SPSS version 11 and MLwin beta version

2.0. Initially, the distribution and transformation of variables was conducted. Descriptive

statistics were compiled, and subscale scores and alpha reliabilities for the various research tools

used were generated. Basic comparisons between hospitals or units were made using analysis of

variance (ANOVA). Where applicable, the Pearson Product Moment Correlation was used to

explore interrelationships between variables.

Hierarchical linear modeling is useful for understanding relationships in multilevel

structures. Since data in this study were collected at both the hospital unit level and at the

individual nurse and patient level, a multilevel approach to the analysis was proposed as a way to

better account for the possible clustering of effects within hospitals. That is, questionnaire

responses from nurses within hospitals were likely to be affected by things that are “fixed” for all

employees in that organization, such as the size and type of the organization. The advantage of

hierarchical linear modeling methods is that they can account for this clustering or grouping of

variation in scores on questionnaire measures within a given organization. Without accounting

for the possible clustering of effects within hospitals, the conclusions of the study could be

invalid, since other statistical measures assume that no such clustering occurs.

For multilevel modeling, most variables were dichotomized and hierarchical logistic

regressions were completed. Only unit productivity/utilization, worked hours per patient, cost per

resource intensity weight, nurse-physician relationship, violence, and autonomy were kept as

numeric variables. Worked hours per patient and cost per resource intensity weight were


logarithm transformed due to their highly skewed distributions. The order of entry of variables

into the statistical modeling process was consistent with the theoretical framework at two levels.

The first level included individual nurse and patient variables, while the second unit level

included system characteristics and behaviours and throughput factors. Some of the nurse

questionnaire measures were also aggregated to the unit level as a measurement of unit

atmosphere or morale. Multicollinearity among independent variables was examined, but none of

the variables was very strongly associated with any other. To determine whether or not variables

were associated with outcomes, individual variables were sequentially added to statistical models

and the properties of each newly expanded model were compared to the previous one to see if

the new variable was of any importance (see Appendix F).

IV. Results

Descriptives Descriptive results pertaining directly to the implications outlined above are presented

here. More detailed results and tables are presented in Appendix G.

Patient Characteristics: For 1,230 patients in the study, the mean age was 63.5 years,

and 66.7 percent were male. The most common cardiac case mix group was percutaneous

transluminal coronary angioplast. Of the surgical patients, one-third (33 percent) attended a pre-

operative clinic and more than half (57.5 percent) received post-operative education. About one

in 10 (10.9 percent) was referred to homecare. On a scale of 1 to 5, OMAHA knowledge,

behaviour, and status scores regarding nursing diagnoses averaged 3.4, 4, and 3.3 respectively,

upon admission or identification of new nursing diagnoses. At admission, 87 percent and 49.2

percent of patients scored below the standardized American norms for physical and mental

health, respectively.


Nurse Characteristic: Of 727 nurses who completed the survey, most (93.9 percent) were

female, registered nurses (96.6 percent), with a mean age of 40.6 years. More than 42 percent of

nurses held a bachelor or higher degree. On average, 59.8 percent of nurses were employed full-

time, with 97.8 percent indicating permanent employment. Almost 40 percent of nurses rated

their approach to care delivery as expert, rather than novice.

System Characteristics and Behaviour: On an average day, nurses on each nursing unit

admitted 6.1 and discharged 6.1 patients per 24 hour period. Overall, 64.3 percent of nurses

reported significant increases in employer expectations for overtime in the last year and actual

increases in overtime worked per week: zero to one hour (45.1 percent), two to four hours (32.2

percent), and greater than four hours (22.7 percent). Of the overtime reported, 26.7 percent was

unpaid and 22.8 percent was involuntary. Eight percent of nurses experienced a forced change in

their work unit in the previous year, and 15.1 percent anticipated such a change in the upcoming

year. Nurses continue to perform tasks that could be delegated to non-nursing personnel,

including ancillary services (83.5 percent), venipunctures (64.8 percent), housekeeping (55.1

percent), delivering trays (55.1 percent), and starting intravenous sites (51 percent).

Intermediate System Output: Unit productivity/utilization was determined by dividing

unit workload by total worked hours on the unit. The maximum capacity of any employee is 93

percent, because seven percent is allocated to paid breaks when no work is contractually

expected. At 93 percent, nurses are working flat out with no flexibility to meet unanticipated

demands or rapidly changing patient acuity. On 46 percent of the days, units worked beyond the

ceiling value of 93 percent, and on 61.5 percent of the days units worked beyond 85 percent.

Patient Outcomes: Few medical consequences were reported, although variation existed

among hospitals. Medical consequences included falls with injury (0.7 percent), medication


errors with consequences (1.6 percent), death (0.4 percent), and complications such as urinary

tract infections (1.5 percent), pneumonia (1.3 percent), wound infections (1.4 percent), bed sores

(0.4 percent), and thrombosis (0.2 percent). Between admission and discharge, patients’ scores

for SF-12 physical health status improved (41.1 percent) nearly as often as they declined (44.9

percent). A similar pattern was noted for patients’ mental health status (42.3 percent improving

and 44.9 percent deteriorating). For physical and mental health status scores, 12.8 percent of

patients showed no change. Overall, general improvement of patients was evidenced through

mean changes in OMAHA knowledge (0.43), behaviour (0.25), and status (0.79) scores between

admission and discharge (or appearance and resolution of new nursing diagnoses).

Nurse Outcomes: On average, nurses scored 22.7 for emotional exhaustion, six for

depersonalization, and 12.2 for personal accomplishment using Maslach’s Burnout Inventory.

Almost 30 percent of nurses were at risk for emotional burnout. Additionally, 18 percent of

nurses said their work efforts exceeded work rewards. On average, 17.7 percent of nurses were

dissatisfied with work, primarily due to inadequate opportunities to interact with management

(45.5 percent).

Of the nurse survey respondents, 34.8 percent and 49.2 percent scored below the

standardized American norms for physical and mental health, respectively. During the two weeks

preceding the survey, 32.4 percent of nurses changed their shift time more than once. During the

week preceding the survey, nurses experienced emotional abuse (24.9 percent), threat of assault

(13.6 percent), and physical assault (10.2 percent) while at work. The main sources of this

workplace abuse were patients (31.1 percent), other nurses (21.5 percent), physicians (15.8

percent), and families (10.7 percent).


System Outcomes: Nurse ratings of quality of care and omission or delay of patient

interventions comprised the measures of quality of care. Of 714 responses, 13.4 percent of nurses

rated the nursing care quality on the last shift as fair/poor, while 41.9 percent said patient care

quality had deteriorated over the last year. When faced with insufficient time, nurses generally

omitted nursing (as opposed to physician-dependent) interventions. The most frequently omitted

interventions included care planning (48.2 percent), comforting/talking (38.6 percent), back/skin

care (31.4 percent), oral hygiene (28.7 percent), patient/family teaching (23.3 percent), and

documentation (22.6 percent). Delayed interventions included vital signs/medications/dressings

(37.3 percent), mobilization/turns (30.5 percent), call bell response (25.9 percent), and PRN pain

medications (16.6 percent). In total, nurses reported missing 1,768 work episodes in the last year,

with each episode averaging 2.42 shifts. Although 16.4 percent of nurses were never absent,

frequency of missed episodes ranged from one to two (42.9 percent), three to four (25.2 percent),

and greater than four (15.5 percent). Reasons for absenteeism were reported as physical health

(71.4 percent), mental health (5.4 percent), injury (4.8 percent), and other (18.4 percent).Almost

five percent of nurses planned to leave their job in the next year. Only 5.6 percent of nurses

expected to have difficulty in securing a new job if they wanted one.

Research Question 1. To what extent do patient, nurse, system characteristics and behaviours, and environmental

complexity measures explain variation in nursing worked hours and patient, nurse, and system

outcomes, such as length of stay?

Intermediate System Outputs

Unit productivity/utilization: As indicated earlier, at 93 percent productivity/utilization,

nurses are working at maximum capacity, and high rates of productivity/utilization on the unit


directly influence patient outcomes. This analysis identifies the variables associated with higher

and lower productivity/utilization at the unit level. Higher productivity/utilization levels were

more likely when there were more nursing worked hours on the unit, higher nurse-to-patient

ratios, higher nurse autonomy, and when nurses required more time to complete the work as

specified by the patient care plan. Productivity/Utilization was more likely to be lower when

units were specialized (such as units that only service patients with cardiology conditions) and

where a higher proportion of nurses on the unit were emotionally exhausted or mentally healthy.

When nurses are emotionally exhausted they may not be able to work at the same level of

productivity/utilization than when they are not. Nurses who are mentally healthy may be inclined

to say no to unrealistic work expectations.

Actual Worked Hours per Patient: The actual worked hours per patient were likely to

increase with a higher proportion of nursing worked hours on the unit and when patients had

more nursing diagnoses. Increases in worked hours per patient were associated with increases in

unit productivity/utilization up to the cut-off point of 90 percent. Units with more clinical

expertise or with a higher proportion of full-time nurses were more likely to provide fewer hours

of patient care.

Patient Outcomes

Tables 2 to 19 (Appendix C) display the variables modeled in relation to patient health and

safety outcomes.

Medical Consequences: Since there were so few medical consequences of any one type, all

types of consequences were summed into one category. In this analysis, the factors associated

with the presence or absence of any medical consequences during a patient’s stay were

examined. As patients experienced greater numbers of nursing diagnoses, reflecting more


complex nursing needs, they were more likely to suffer medical consequences. Medical

consequences were 53 percent more likely for each additional nursing diagnosis. In contrast,

patients with better mental health at admission were less likely to have medical consequences.

Patients who experienced medical consequences were more likely to require greater actual

worked hours of nursing care during their stay and 319 percent more likely to be referred to

homecare for follow-up after discharge, resulting in additional expense to the health system.

OMAHA Knowledge, Behaviour, and Status at Discharge. Helping patients understand the

cause and course of their conditions is seen to improve the overall health of patients. A ceiling

effect was observed among the OMAHA knowledge, behaviour, and status scores, in that

patients with higher scores at admission were less likely to demonstrate improvements in these

scores at discharge (because there was less room for improvement). Improved patient knowledge

scores at discharge were 74 percent more likely for every 10 percent increase in nursing worked

hours on the unit and 24 percent more likely for every 10 percent increase in full-time nurses on

the unit. When patients were cared for by nurses who reported higher autonomy in their jobs,

they were more likely to show increases in knowledge about their condition at discharge.

However, patient knowledge was 44 percent less likely to improve for every 10 percent increase

in the proportion of nurses who had at least one shift change in the last two weeks.

Helping patients understand which behaviours they need to change in order to improve their

health status is another important role function of the nurse. When cared for by nurses who were

very satisfied with their work, patients were 176 percent more likely to demonstrate

improvements in their behaviour scores at discharge. Conversely, patients cared for by nurses

with concerns about job security were 53 percent less likely to demonstrate improved behaviour


scores at discharge. Productivity/Utilization levels below 88.2 percent were associated with

increased possibility of improvements in patients’ behaviour scores at discharge.

SF-12 Health Status at Discharge: As with the OMAHA scores, patients with higher

physical and mental health scores at admission were less likely to see improvements in these

scores at discharge. Improvement in patients’ physical health status at discharge was less likely

for patients with higher resource intensity weights and for patients with more nursing diagnosis.

These two factors reflect the medical acuity and nursing complexity of patients’ needs for

nursing care. Patient physical health scores were 45 percent less likely to improve when unit

productivity/utilization exceeded 80 percent and were seven percent less likely to improve for

each additional hour of nurse overtime. However, patients who scored higher in physical health

status at admission were more likely to have improvements in mental health status at discharge.

Patients who stayed longer in hospital were less likely to show improvements in mental health

status scores at discharge. More hours of care were likely to be used if patient mental health was

not improved at discharge.

Nurse Outcomes

Although improving patient outcomes and reducing the risk of medical consequences are

goals of healthcare, achievement of these goals may sometimes occur at the expense of nurse

health and safety. In order to retain and recruit nurses — senior and experienced nurses in

particular — understanding which factors influence nurse outcomes is pivotal. Ten nurse

outcome variables derived from the literature were subsequently used in this analysis. Tables 10

to 19 (Appendix C) display the variables modeled in relation to nurse outcomes.

Emotional Exhaustion: Physically and mentally healthy nurses were less likely to experience

emotional exhaustion (burnout). The likelihood of emotional exhaustion increased by 242


percent when nurses were at risk of an effort and reward imbalance and by 179 percent when

nurses worked full-time. Nurses were 32 percent less likely to suffer high emotional exhaustion

for every 10 percent increase in the proportion of satisfied nurses on units.

Autonomy: Nurses reported higher autonomy in practice when they reported stronger

relationships with physicians, were more satisfied with their job, or said the quality of patient

care improved over the last year. Autonomy was also higher when patients had attended a pre-

operative clinic and when the nurse-patient ratio was high. As unit productivity/utilization

exceeded 85 percent, nurses reported more autonomy, possibly since nurses have to make

decisions on their own under such circumstances. However, lower autonomy scores were

reported by degree-prepared nurses and by nurses who rated themselves as expert clinicians,

perhaps due to organizational constraints imposed on their practice. When occupancy is high on

the unit or when nurses were at risk of an effort and reward imbalance, autonomy was likely to

be lower.

Job Satisfaction: Nurses who were at risk for emotional exhaustion were 71 percent less

likely to be satisfied with their jobs, and when unit productivity/utilization levels were higher

than 80 percent, nursing staff were 57 percent less likely to be satisfied. Nurse satisfaction was

301 percent more likely when nurses rated the nursing care given on the last shift as

good/excellent and 56 percent more likely among degree-prepared nurses. As the average hours

available for care on the unit increased and when nurses’ autonomy increased, so did nurses’

satisfaction.

Nurse-Physician Relationships: On units with higher proportions of physically healthy nurses

and of nursing worked hours, nurses were more likely to have better relationships with

physicians. Nurses who perceived their practice to be more autonomous and those who rated the


quality of nursing care on the last shift as good/excellent were also more likely to have better

relationships with physicians. However, nurse-physician relationships tended to deteriorate when

there was a higher proportion of nurses with frequent shift changes on the unit and as nurses took

on more patients in their daily assignment or care for patients with more nursing diagnoses.

Deteriorated relationships were also more likely as unit productivity/utilization levels exceeded

85 percent.

SF-12 Health Status: Higher physical health status scores were 59 percent less likely for

female nurses; 49 percent less likely when nurses were at risk for an effort and reward

imbalance; and 41 percent less likely for nurses at risk of emotional exhaustion. In contrast,

nurses were more likely to be physically healthy when stronger nurse-physician relationships

were reported on the unit and as the average worked hours available for care on the unit

decreased. The likelihood of being physically healthy increased by 58 percent when nurses were

satisfied with their job, and decreased by 28 percent for every 10 percent increase in nursing

worked hours probably because increased nursing hours came from the same nurses worked on

the unit rather than from new hired nurses.

Female nurses were 52 percent less likely to be mentally healthy than male nurses, and older

nurses reported better mental health. Nurses with one point increases in their physical health

scores were four percent less likely to be mentally healthy. Nurses were less likely to be mentally

healthy when they were at risk of emotional exhaustion and as the average worked hours on the

unit increased. The likelihood of being mentally healthy increased by 74 percent when nurses

were satisfied with their current job and decreased by 79 percent when nurses were at risk of

emotional exhaustion.


System Outcomes

Tables 20 to 29 (Appendix C) display the variables modeled in relation to system outcomes.

Length of Stay: Patients in units where the productivity/utilization of the unit exceeded 91

percent were more likely to have longer-than-expected lengths of stay. Patients with more

nursing diagnoses and with higher resource intensity weights, reflecting greater medical acuity,

were also more likely to have longer lengths of stay. Shorter-than-expected lengths of stay were

two percent and 185 percent more likely for patients whose physical health status scores were

one point higher at admission and for those who attended a pre-operative clinic, respectively.

Shorter-than-expected length of stay was 57 percent less likely when patients experienced

medical consequences and 13 percent less likely for each additional nursing diagnosis.

Interventions Not Done or Delayed: Older, experienced nurses were less likely to have

interventions not completed at the end of their shift. The likelihood of interventions not being

completed increased by 260 percent when nurses were at risk for an effort-reward imbalance.

The more often patients had unanticipated changes in acuity, the more often interventions were

left undone. The more frequently violence was experienced by individual nurses and the higher

the medical complexity (as indicated by the resource intensity weight), the more likely

interventions were not completed. The greater the number of nursing diagnoses, the less likely

interventions were not completed. The likelihood of interventions being left undone was reduced

as units hired nurses with more clinical expertise and reduced for units that increased average

overtime. Interventions not completed were 12 percent less likely with every one point increase

in the ratings of nurse autonomy. The more nurses re-sequenced their activities in response to

demands from others, the less often interventions were left undone.


Delayed interventions were 74 percent more likely when nurses worked full-time, 87 percent

more likely when nurses had concerns about job security, and 123 percent more likely when

nurses were at risk of an effort and reward imbalance. Interventions were 27% less likely to be

delayed for every 10% increase in the proportion of degree-prepared nurses on the unit. More

complex patients with increasing numbers of nursing diagnoses were less likely to experience

delays in receiving interventions. However, when individual nurses experienced violence or

where the average level of violence was high on a unit, interventions were more likely to be

delayed. Interventions were 71 percent more likely to be delayed for every 10 percent increase in

absenteeism at the unit level.

Quality of Patient Care Over the Past Year: When nurses rated themselves as expert

clinicians, they were less likely to rate the quality of patient care on the unit as improved.

Likewise, when interventions were delayed, nurses were 46 percent less likely to report

improvements in the quality of patient care. The likelihood of improved nurse ratings of patient

care increased by 915 percent when nurses rated the quality of nursing care given on the unit as

good/excellent (as opposed to fair/poor) and when nurse autonomy was higher. Improved quality

of patient care was 41 percent less likely with every 10 percent increase in nursing worked hours

on the unit but 40 percent more likely with every 10 percent increase in degree-prepared nurses

on the unit.

Quality of Nursing Care on the Last Shift: Good or excellent ratings by nurses of the quality

of nursing care on the last shift were 606 percent more likely when individual nurses rated the

quality of patient care as improved over the last year; 159 percent more likely when nurses were

satisfied; and more likely when nurses rated themselves as clinical experts. Nurses’ reports of

strong nurse-physician relationships were also associated with good/excellent ratings of nursing


care on the last shift. However, nurses who changed shifts at least once during the past two

weeks were 50 percent less likely to rate the quality of nursing care as good/excellent. Likewise

when a 10 percent increase in the proportion of ratings of quality of nursing care at the unit level

were good/excellent, individual nurses on the unit were 93 percent more likely to rate individual

scores of quality of nursing care as good/excellent. However, for units with higher ratings of

nurse-physician relationships on average, individual nurses were less likely to rate nursing care

as good/excellent.

Absenteeism: Full-time nurses were 152 percent more likely than part-time and casual nurses

to miss work. Nurses who scored one point higher in physical health status scores were five

percent less likely to miss work. When unit productivity/utilization was below 79.7 percent,

nurses tended to have fewer days absent.

Intent to Leave: Degree-prepared nurses were 101 percent more likely to leave as compared

to diploma-prepared nurses. Nurses who reported job instability were 197 percent more likely to

report intentions of leaving than those who did not. Satisfied nurses were 58 percent less likely to

intend to leave. Full-time nurses were 51 percent less likely to leave than part-time or casual

nurses. When productivity/utilization was below 82.8 percent on the unit, nurses were less likely

to leave.

Cost Per Resource Intensity Weight: Patients who were admitted with higher mental health

status scores and with a higher number of nursing diagnoses were more likely to have higher

costs per resource intensity weight, as were patients who attended pre-operative and post-

operative education. Lower costs per resource intensity weight were more likely when care was

provided in part in step-down units, when nurses rated themselves as clinical experts, and with

emergency admissions, higher nurse-patient ratios, and higher physical health status scores


among nurses. As length of stay increased and as unit productivity/utilization exceeded 90

percent, so did the cost per resource intensity weight.

Research Question 2

To what extent is there agreement between the estimates generated by a gold standard for

measuring nursing resource needs (PRN workload methodology) and the worked hours per

patient, and how does variance affect the patient and nurse outcomes?

Table 30 (Appendix C) reveals that only two significant variables were found when

examining the PRN estimates and actual worked hours. When actual worked hours were less

than PRN predicted hours, nurses were more likely to leave in the next year and

productivity/utilization was more likely to be high.

Research Question 3

At what nurse-patient ratio and with what proportion of nursing worked hours are

productivity/utilization and patient and nurse outcomes improved, after controlling for the

influence of patient, nurse, organizational, and environmental factors?

As shown in Table 31 (Appendix C), when a nurse was assigned more patients, the

relationship with physicians deteriorated and autonomy increased. When more patients were

assigned to a nurse, unit productivity/utilization increased and cost per resource intensity weight

decreased. For every additional worked hour per patient, the odds of medical consequences

increased by 13 percent, and the odds of improvement in patient mental health at discharge

decreased by six percent. For every additional hour increase in the average worked hours on the

unit, the likelihood of nurses being satisfied with the current job increased by 10 percent, but

their odds of being physically and mentally health declined by 10 percent and seven percent

respectively. For every 10 percent increase in the proportion of nursing worked hours the odds of


patients having improved knowledge scores increased by 74 percent, but nurses were 28 percent

less likely to be physically healthy, were 41 percent less likely to rate the quality of patient care

as improved.

Given that the maximum productivity/utilization for any unit should not exceed 93 percent,

productivity/utilization levels range from 79.7 percent for absenteeism to 91.4 percent for

shorter-than-expected length of stay. These findings highlight the difficulties nurses face in this

study, where almost 50 percent of the nursing units worked over productivity/utilization levels of

93 percent.

Research Question 4

Which data elements, in addition to those routinely collected within administrative databases,

are critical for routine data collection in Canada? To what extent do policy and administrative

decision makers support the feasibility of routine data collection?

Discussion with our policy and practice decision-making partners identified that:

1. nurse SF-12 physical and mental health status, emotional exhaustion, autonomy, effort and

reward imbalance, and quality of nurse-physician relationships should be monitored annually

in the new National Nursing Health Survey;

2. unit workload data should be checked for reliability and validity at least annually, and these

data, in combination with worked hours, should be tracked regularly by nursing unit

managers to determine if actual values exceed those recommended in this study. The

Environmental Complexity Scale should be completed on each shift by nurses.

Productivity/Utilization and environmental complexity should become quality indicators

used by the Canadian Council of Health Services Accreditation to monitor healthy

workplaces; and


3. nursing diagnoses and OMAHA tool ratings should be used daily in practice. Automated care

planning systems that are easy to access and use are recommended. These are important

indicators of patient goal achievement.

V. Additional Resources

The reader is referred to the works of Aiken et al, O’Brien-Pallas et al, and Shamian and

O’Brien-Pallas et al as referenced in Appendix A.

VI. Further Research

1. Develop and validate a shorter version of the effort and reward imbalance scale.

2. Conduct studies to examine the influencing factors and nature of short- and long-term

illnesses among nurses. Evaluate strategies (such as access to fitness centers, improved hot

meals in the hospital, and mandatory breaks) that may enhance the health of nurses.

3. Replicate this study on other patient populations to determine if the productivity/utilization

cut-off points hold.

4. Explore experienced nurses’ perceptions of quality and develop measures of quality that can

be evaluated yearly at the nursing unit level.

VII. References

1. Canadian Nursing Advisory Committee. (2002).Our health, our future: Creating quality workplaces for Canadian nurses. Toronto, ON: Author.

2. O’Brien-Pallas, L. L., Thomson, D., Alksnis, C., Luba, M., Pagniello, A., Ray, K. et al (2003). Stepping to success and sustainability: An analysis of Ontario’s nursing workforce. Toronto, ON: Nursing Effectiveness, Utilization, and Outcomes Research Unit.

3. Canadian Council for Health Service Accreditation (2002). Recognition guidelines for 2003: Specific issues and related criteria. Ottawa, ON: Author.

4. Baumann, A., O'Brien-Pallas, L., Armstrong-Stassen, M., Blythe, J., Bourbonnais, R. Cameron, S. et al. (2001). Commitment and care: The benefits of a healthy workplace for


nurse, their patients and the system – a policy synthesis. Ottawa, ON: Canadian Health Service Research Foundation.

5. O’Brien-Pallas, L. L., Alksnis, C., Wang, S., Birch, S., & Tomblin Murphy, G. (2003). Bring the future into focus: Projecting RN retirement in Canada. Toronto, ON: Canadian Institute for Health Information.

6. Aiken, L., Clarke, S., Sloane, D., Sochalski, J., & Silber, J. (2002). Hospital nurse staffing and patient mortality, nurse burnout, and job satisfaction. JAMA: The Journal of American Medical Association, 288(16), 1987-1993.

7. Shamian, J., O’Brien-Pallas, L., Kerr, M., Koehoorn, M., Thomson, D., & Alksnis, C. (2001). Effects of job strain, hospital organizational factors and individual characteristics on work-related disability among nurses. Toronto, ON: Ontario Workplace Safety and Insurance Board.

8. Grinspun, D. (2003). Part-time and casual nursing work: The perils of healthcare restructuring. International Journal of Sociology and Social Policy, 23(8/9), 54-70.

9. Canadian Institute for Health Information. (1999). MIS guidelines for Canadian healthcare facilities. Ottawa, ON: Author.

10. O’Brien-Pallas, L., Giovannetti, P., Peereboom, E., & Marton, C. (1995). Case costing and nursing workload: Past, present and future [Working Paper 95-1]. Hamilton, ON: Quality of Nursing Worklife Research Unit.


Appendix A. Annotated Bibliography

Table of Contents

1. Patient Characteristics 35

2. Nurse Characteristics 38

3. System Characteristics and Behaviours 40

4. Throughputs 60

5. Patient Outcomes 62

6. Nurse Outcomes 65

7. System Outcomes 74

8. Glossary 76

9. References 76

Evid

ence

-bas

ed S

taffi

ng

35

1.

Pa

tient

Cha

ract

eris

tics

A

utho

rs/Y

ear

Focu

s Sa

mpl

e In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

1. A

lterm

an,

Shek

elle

, V

erno

n,

Bur

au (1

994)

Dec

isio

n la

titud

e,

psyc

holo

gica

l dem

and,

jo

b st

rain

, & c

oron

ary

hear

t dis

ease

.

Ann

ual e

xam

ine

of 1

,683

men

em

ploy

ed a

t H

awth

orne

W

orks

for 2

5 ye

ars.

Occ

upat

ion,

m

edic

al

diag

nosi

s.

Con

trary

to th

e hy

poth

esis

, tho

se w

ith h

ighe

st

deci

sion

latit

ude

had

low

est c

oron

ary

hear

t di

seas

e de

ath

rate

s (ris

k of

6.8

, with

ave

rage

ris

k be

ing

7.8)

. No

asso

ciat

ion

betw

een

coro

nary

hea

rt di

seas

e &

psy

chol

ogic

al

dem

and.

2.

Bul

l, H

anse

n,

Gro

ss (2

000)

D

isch

arge

pla

nnin

g m

odel

fo

r eld

ers h

ospi

taliz

ed

with

hea

rt fa

ilure

.

158

elde

r/ ca

regi

ver d

yads

, be

fore

-and

-afte

r no

n-eq

uiva

lent

co

ntro

l gro

up.

Patie

nt

dem

ogra

phic

s, ed

ucat

ion,

co

ntin

uity

of

care

, pat

ient

te

achi

ng,

med

ical

di

agno

sis.

Pa

tient

’s

perc

eive

d he

alth

st

atus

, cos

ts o

f ca

re

Elde

rs w

ho re

ceiv

ed n

ew m

odel

of d

isch

arge

pl

anni

ng fe

lt m

ore

prep

ared

to m

anag

e ca

re

(t=4.

30),

felt

in b

ette

r hea

lth (t

=2.0

) & sp

ent

few

er d

ays i

n ho

spita

l whe

n re

adm

itted

. Fa

cilit

ated

eld

er &

car

egiv

er p

artic

ipat

ion

in

plan

ning

.

3. C

alvi

n, K

lein

, V

ande

n B

erg,

, M

eyer

, R

amire

z-M

orge

n,

Parr

illo

(199

8)

Pred

ictin

g re

sour

ce

utili

zatio

n in

pat

ient

s with

un

stab

le a

ngin

a.

465

patie

nts

adm

itted

for

unst

able

ang

ina

to a

terti

ary

care

un

iver

sity

-bas

ed

med

ical

cen

tre,

pros

pect

ive

eval

uatio

n.

Patie

nt

dem

ogra

phic

s, m

edic

al

diag

nosi

s.

Le

ngth

of s

tay,

co

sts o

f car

e,

com

plic

atio

ns

Pred

ictiv

e m

odel

of r

isk

of m

ajor

com

plic

atio

ns

can

be u

sed

to a

naly

ze c

ost o

f car

e, re

sour

ce

utili

zatio

n, &

out

com

es. R

esou

rce

utili

zatio

n in

crea

ses a

s the

pro

babi

lity

of ri

sk fo

r car

diac

co

mpl

icat

ions

incr

ease

s (ex

: hig

hest

risk

gro

up

had

75%

hig

her c

osts

than

the

low

est r

isk

grou

p).

4. C

rille

y, F

arre

r (2

001)

Im

pact

of a

firs

t m

yoca

rdia

l inf

arct

ion

on

self-

perc

eive

d he

alth

st

atus

.

165

patie

nts

wer

e su

rvey

ed 2

ye

ars a

fter a

firs

t m

yoca

rdia

l in

farc

tion

Patie

nt

dem

ogra

phic

s, oc

cupa

tion

SF

-12

patie

nt

heal

th st

atus

Pa

tient

s hav

ing

thei

r firs

t myo

card

ial i

nfar

ctio

n ha

ve si

gnifi

cant

ly lo

wer

leve

ls o

f sel

f-pe

rcei

ved

heal

th st

atus

afte

r 2 y

ears

than

con

trol s

ubje

cts.

Hea

lth st

atus

is a

ssoc

iate

d w

ith p

ersi

sten

t ca

rdia

c sy

mpt

oms &

lack

of e

mpl

oym

ent.

5. C

zar,

Engl

er

(199

7)

Perc

eive

d le

arni

ng n

eeds

of

pat

ient

s with

cor

onar

y ar

tery

dis

ease

.

Con

veni

ence

sa

mpl

e of

28

men

adm

itted

w

ith a

ngin

a or

m

yoca

rdia

l

Patie

nt le

arni

ng

need

s, de

mog

raph

ics,

occu

patio

n

No

sign

ifica

nt d

iffer

ence

in le

arni

ng n

eeds

be

twee

n ho

spita

lizat

ion

& su

bseq

uent

clin

ic

visi

ts. M

ost i

mpo

rtant

lear

ning

nee

ds w

ere

sym

ptom

reco

gniti

on, c

ardi

ac

anat

omy/

phys

iolo

gy, &

med

icat

ions

.

Evid

ence

-bas

ed S

taffi

ng

36

Aut

hors

/Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

in

farc

tion

to a

C

alifo

rnia

un

iver

sity

-af

filia

ted

med

ical

cen

tre.

No

corr

elat

ion

betw

een

lear

ning

nee

ds &

age

, oc

cupa

tion,

smok

ing

or m

arita

l sta

tus.

Mos

t im

porta

nt le

arni

ng n

eeds

are

thos

e th

at a

ffec

t su

rviv

al. A

self-

adm

inis

tere

d qu

estio

nnai

re c

an

be u

sed

to d

eter

min

e pa

tient

’s p

erce

ived

le

arni

ng n

eeds

so e

duca

tion

can

focu

s on

area

s m

ost i

mpo

rtant

to th

e pa

tient

. 6.

Hem

ingw

ay,

Mar

mot

(1

999)

Psyc

hoso

cial

fact

ors i

n th

e de

velo

pmen

t &

prog

nosi

s of c

oron

ary

hear

t dis

ease

.

So

cial

supp

orts

, oc

cupa

tion,

m

edic

al

diag

nosi

s.

Stro

ng c

orre

latio

ns b

etw

een

depr

essi

on/ a

nxie

ty

& d

evel

opm

ent o

f cor

onar

y he

art d

isea

se

(11/

11 st

udie

s). T

raits

such

as t

ype

A/ h

ostil

ity

(6/1

4 st

udie

s), w

ork

orga

niza

tion

(6/1

0 st

udie

s)

& so

cial

supp

ort (

5/8

stud

ies)

als

o ha

ve

mod

erat

e co

rrel

atio

ns w

ith c

oron

ary

hear

t di

seas

e.

7. J

ohns

on,

Stew

art,

Hal

l, Fr

edlu

nd,

Theo

rell

(199

6)

Impa

ct o

f wor

k or

gani

zatio

n (p

sych

olog

ical

dem

and,

w

ork

cont

rol,

& so

cial

su

ppor

t) on

ca

rdio

vasc

ular

dis

ease

m

orta

lity.

12,5

17 S

wed

ish

men

25-

74

curr

ently

or

prev

ious

ly

empl

oyed

. R

ando

m sa

mpl

e fr

om e

ntire

Sw

edis

h po

pula

tion,

80%

re

spon

se ra

te.

Patie

nt

occu

patio

n,

soci

al su

ppor

t, pa

tient

de

mog

raph

ics

educ

atio

n.

Pa

tient

mor

talit

y W

orke

rs w

ith lo

w w

ork

cont

rol h

ad a

hig

her

risk

for c

ardi

ovas

cula

r mor

talit

y (a

fter 5

yea

r ex

posu

re, r

elat

ive

risk

of m

orta

lity

is 1

.46

for

low

con

trol v

s. 1

.00

for h

igh

cont

rol).

No

sign

ifica

nt a

ssoc

iatio

ns b

etw

een

phys

ical

job

dem

and,

wor

k so

cial

supp

ort,

job

haza

rds,

&

card

iova

scul

ar m

orta

lity.

8. M

arch

ette

, H

ollo

man

(1

986)

Var

iabl

e af

fect

ing

leng

th

of st

ay.

500

patie

nts

disc

harg

ed fr

om

an a

cute

car

e ho

spita

l. St

ratif

ied

rand

om sa

mpl

ing

of 1

00 p

atie

nts

with

5 m

ost

com

mon

di

agno

stic

ca

tego

ries.

Med

ical

di

agno

sis,

patie

nt

dem

ogra

phic

s, pa

tient

edu

catio

n gi

ven.

Le

ngth

of s

tay

For e

very

are

a of

dis

char

ge p

lann

ing

the

nurs

e ca

rrie

d ou

t (nu

tritio

n, m

edic

atio

n, e

tc.),

ther

e w

as a

dec

reas

e of

0.8

day

s of h

ospi

taliz

atio

n (2

da

ys fo

r CV

A p

atie

nts)

. Stro

ng re

latio

nshi

p be

twee

n tim

ing

of p

lann

ing

& le

ngth

of s

tay

(for

eve

ry d

ay th

at th

e pl

anni

ng w

as p

ostp

oned

, th

ere

was

an

incr

ease

of 0

.8 d

ays o

f ho

spita

lizat

ion)

.

9. S

hi (1

996)

R

elat

ions

hip

betw

een

274,

311

patie

nt

Patie

nt

Le

ngth

of s

tay

Dire

ct re

latio

nshi

ps b

etw

een

long

er le

ngth

of

Evid

ence

-bas

ed S

taffi

ng

37

Aut

hors

/Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

patie

nt &

hos

pita

l ch

arac

teris

tics o

n le

ngth

of

stay

.

reco

rds &

484

ho

spita

ls,

rand

om sa

mpl

ing

of h

ospi

tals

&

disc

harg

es.

dem

ogra

phic

s, ho

spita

l siz

e,

med

ical

di

agno

sis,

soci

al

supp

ort.

stay

& o

lder

age

, non

-mar

ried

stat

us, b

eing

fe

mal

e, b

eing

Afr

ican

Am

eric

an, &

hav

ing

insu

ranc

e. F

ewer

hos

pita

l bed

s cor

rela

ted

with

sh

orte

r len

gth

of st

ay ta

bles

om

itted

).

10.

Shih

, Chu

, Y

u, H

u,

Hua

ng,

(199

7)

Turn

ing

poin

ts o

f re

cove

ry fr

om c

ardi

ac

surg

ery

in a

n in

tens

ive

care

uni

t.

Con

veni

ence

sa

mpl

e of

30

adul

ts w

ho h

ad

unde

rgon

e ca

rdia

c su

rger

y in

1of

3 g

ener

al

hosp

itals

in

north

ern

Taiw

an.

Patie

nt

dem

ogra

phic

s.

Adm

issi

on to

or

disc

harg

e fr

om

inte

nsiv

e ca

re

unit.

, pos

t-op

com

plic

atio

ns.

Turn

ing

poin

ts in

clud

ed e

vent

s, nu

rsin

g ac

tions

, &

tim

e. C

ompo

nent

s of t

urni

ng p

oint

s wer

e pr

eced

ing

cond

ition

s, m

arke

rs, &

co

nseq

uenc

es. T

urni

ng p

oint

exp

erie

nces

in

clud

ed n

one

(7%

), bo

th (5

7%) o

r one

of

posi

tive

(33%

) & n

egat

ive

(3%

) out

com

es. M

ay

sens

itize

nur

ses t

o de

tect

mor

e qu

ickl

y pa

tient

s’

turn

ing

poin

t exp

erie

nces

. Nur

ses m

ay e

duca

te

patie

nts b

efor

e su

rger

y ba

sed

on tu

rnin

g po

int

know

ledg

e.

11.

Sieg

rist

(199

6)

Impa

ct o

f hig

h ef

fort,

low

re

war

d co

nditi

ons i

n th

e w

orkp

lace

on

card

iova

scul

ar h

ealth

.

M

edic

al

diag

nosi

s

Effo

rt-re

war

d im

bala

nce,

he

alth

stat

us.

Var

iabl

es in

dica

ting

high

eff

ort &

low

rew

ard

(mon

ey, e

stee

m, s

tatu

s) p

redi

ct c

ardi

ovas

cula

r ev

ents

. The

se c

ondi

tions

at w

ork

mus

t be

cons

ider

ed a

risk

for c

ardi

ovas

cula

r hea

lth.

12.

Sieg

rist,

Pete

r, Ju

nger

, C

rem

er,

Seid

el (1

990)

Impa

ct o

f stre

ssfu

l wor

k on

isch

emic

hea

rt di

seas

e.

Rec

ruite

d 41

6 m

iddl

e-ag

ed

blue

-col

lar m

en

from

stee

l &

met

al p

lant

s in

Wes

t Ger

man

y fo

r pro

spec

tive

stud

y, fo

llow

ed

over

6.5

yea

rs.

Patie

nt

dem

ogra

phic

s, oc

cupa

tion

Stat

us in

cons

iste

ncy

(reg

ress

ion

coef

ficie

nt=1

.48)

, job

inse

curit

y (1

.23)

, wor

k pr

essu

re (1

.24)

& n

eed

for c

ontro

l (1.

51)

pred

icte

d is

chem

ic h

eart

dise

ase

occu

rren

ce.

Red

ucin

g bu

rden

of h

igh

wor

kloa

d &

in

crea

sing

rew

ard

& se

curit

y co

uld

redu

ce

isch

emic

hea

rt di

seas

e ris

k. In

divi

dual

pr

even

tion

coul

d be

dire

cted

at c

opin

g w

ith

wor

k de

man

ds &

stre

ngth

enin

g re

gene

rativ

e po

tent

ial.

13

. Si

lber

, R

osen

baum

, R

oss (

1995

)

Pred

icto

rs o

f hos

pita

l ou

tcom

es.

73,1

74 p

atie

nt

adm

issi

ons t

o 13

7 ho

spita

ls.

Dat

a fr

om

natio

nal s

urve

ys.

Staf

fing

ratio

s, m

edic

al

diag

nosi

s, pa

tient

de

mog

raph

ics,

hosp

ital s

ize,

A

dver

se

occu

rren

ces.

N

early

all

of p

redi

ctab

le v

aria

tion

in o

utco

mes

is

from

diff

erin

g pa

tient

cha

ract

eris

tics,

not

hosp

ital o

r sta

ffin

g on

es.

14.

Step

toe

Psyc

hoso

cial

fact

ors i

n

Occ

upat

ion,

Effo

rt &

rew

ard

Thos

e w

ith fe

wer

soci

al su

ppor

ts w

ere

at

Evid

ence

-bas

ed S

taffi

ng

38

Aut

hors

/Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

(1

999)

th

e ca

use

of c

oron

ary

hear

t dis

ease

. so

cial

supp

ort,

med

ical

di

agno

sis

imba

lanc

e in

crea

sed

risk

for c

ardi

ovas

cula

r dea

th. O

ther

ris

k fa

ctor

s: st

ress

ful w

ork

cond

ition

s, cy

nica

lly

host

ile a

ttitu

de.

2.

Nur

se C

hara

cter

istic

s

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

15.

Bru

ce, S

ale,

Sh

amia

n,

O'B

rien-

Palla

s, Th

omso

n (2

002)

.

Des

crib

e nu

rses

' he

alth

stat

us,

exam

ine

trend

s in

inju

ry c

ompe

nsat

ion

clai

ms,

& d

eter

min

e fa

ctor

s con

tribu

ting

to h

igh-

inju

ry c

laim

ra

tes.

121

nurs

es fr

om

10 a

cute

car

e ho

spita

ls w

ith

high

& lo

w

nurs

e-in

jury

co

mpe

nsat

ion

clai

m ra

tes.

Inte

rvie

ws w

ith 5

ch

ief e

xecu

tive

offic

ers,

10 c

hief

nu

rsin

g of

ficer

s, &

9

Occ

upat

iona

l H

ealth

&

Secu

rity

Off

icer

s

Nur

se

dem

ogra

phic

s, w

orkl

oad,

st

affin

g.

Org

aniz

atio

nal

wor

k en

viro

nmen

t.

Abs

ente

eism

, nu

rse

inju

ry.

Nur

ses i

n bo

th h

igh-

clai

m &

low

-cla

im

hosp

itals

iden

tifie

d ph

ysic

al w

ork

envi

ronm

ent,

clai

ms p

roce

ss, &

staf

fing

as fa

ctor

s rel

ated

to

diff

eren

t inj

ury

clai

m ra

tes a

mon

g ho

spita

ls.

Wor

kloa

d is

a c

ontri

butin

g fa

ctor

to h

igh-

inju

ry

rate

s am

ong

nurs

es.

16.

Cou

tts (2

001)

Hig

hlig

hts i

ssue

s fr

om C

anad

ian

Hea

lth S

ervi

ces

Res

earc

h Fo

unda

tion’

s rep

ort

on h

ealth

y w

orkp

lace

s for

nu

rses

.

N

urse

ab

sent

eeis

m

Wor

k en

viro

nmen

t N

urse

job

satis

fact

ion,

vi

olen

ce, n

urse

s’

heal

th.

Whe

n nu

rses

are

mor

e in

depe

nden

t & h

ave

mor

e sa

y in

pat

ient

trea

tmen

t, th

ey fi

nd jo

bs

mor

e sa

tisfy

ing.

The

re is

a d

irect

rela

tions

hip

betw

een

hour

s of o

verti

me

& si

ck ti

me.

Fa

tigue

, too

muc

h to

do

& te

mpo

rary

staf

fing

lead

to n

urse

s get

ting

hurt.

Goo

d w

orki

ng

cond

ition

s for

nur

ses s

houl

d be

a st

anda

rd fo

r ho

spita

l acc

redi

tatio

n.

Evid

ence

-bas

ed S

taffi

ng

39

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

17.

Jose

phso

n,

Lage

rstro

m,

Hag

berg

, Hje

lm

(199

7)

Mus

culo

skel

etal

sy

mpt

oms &

job

stra

in in

nur

ses.

285

nurs

es a

t a

coun

ty h

ospi

tal.

Rep

eate

d cr

oss-

sect

iona

l sur

veys

gi

ven

to a

ll pe

rson

nel o

n w

ards

with

pa

tient

s re

quiri

ng d

aily

ca

re (e

.g.

trans

fers

).

Nur

ses’

hea

lth

stat

us.

Job

stra

in is

a ri

sk fa

ctor

for m

uscu

losk

elet

al

sym

ptom

s & th

e ris

k is

hig

her w

hen

com

bine

d w

ith p

erce

ived

phy

sica

l exe

rtion

(RR

=1.5

-2.1

). In

crea

sed

job

stra

in m

ay b

e as

soci

ated

with

st

aff c

uts,

reor

gani

zatio

n, &

new

requ

irem

ents

.

18.

Jose

phso

n,

Vin

gard

, M

USI

C-

Nor

rtalje

Stu

dy

Gro

up (1

998)

Com

paris

on o

f low

-ba

ck p

ain

in fe

mal

e nu

rses

& o

ther

em

ploy

ed w

omen

.

Ran

dom

sam

ple

of 3

33 w

omen

w

ith b

ack

pain

&

733

wom

en in

co

ntro

l gro

up (8

1 an

d 18

8 re

spec

tivel

y w

ere

empl

oyed

in

nur

sing

).

Nur

se

dem

ogra

phic

s C

onfli

ctin

g de

man

ds, j

ob

stra

in.

Low

-bac

k pr

oble

ms

Fem

ale

nurs

ing

pers

onne

l had

no

incr

ease

d ris

k of

low

-bac

k pa

in c

ompa

red

with

oth

er

empl

oyed

wom

en. C

ombi

natio

ns o

f phy

sica

l &

psyc

hoso

cial

fact

ors a

re a

ssoc

iate

d w

ith h

igh

risk.

Phy

sica

l loa

d is

mor

e si

gnifi

cant

in

nurs

ing

than

psy

chos

ocia

l fac

tors

in re

latio

n to

lo

w-b

ack

pain

19.

Toom

inga

s, Th

eore

ll,

Mic

hels

en,

Nor

dem

ar

(199

7)

Ass

ocia

tions

bet

wee

n se

lf-ra

ted

psyc

hoso

cial

co

nditi

ons &

ch

arac

teris

tics o

f m

uscu

losk

elet

al

sym

ptom

s, si

gns,

&

synd

rom

es.

358

men

&

wom

en fr

om

vario

us

occu

patio

ns (8

3 m

ale

furn

iture

m

over

s, 89

fe

mal

e m

edic

al

secr

etar

ies;

96

men

& 9

0 w

omen

of

wor

king

po

pula

tion)

.

Psyc

hoso

cial

w

ork

cond

ition

s (d

eman

ds, s

ocia

l su

ppor

t, de

cisi

on

latit

ude)

Wor

k en

viro

nmen

t. Sy

mpt

oms,

sign

s, &

sy

ndro

mes

of

mus

culo

skel

etal

or

igin

.

Stro

ng a

ssoc

iatio

ns b

etw

een

poor

psy

chos

ocia

l co

nditi

ons (

espe

cial

ly lo

w su

ppor

t & h

igh

dem

ands

) & m

uscu

losk

elet

al d

isor

ders

(e

spec

ially

cen

tral b

ody

regi

on).

Mos

t pr

eval

ence

ratio

s wer

e ab

ove

1.0

mea

ning

m

ostly

pos

itive

ass

ocia

tions

bet

wee

n un

favo

urab

le c

ondi

tions

& p

ain.

20.

Wun

derli

ch,

Sloa

n, D

avis

(1

996)

Sum

mar

y re

port

from

In

stitu

te o

f Med

icin

e ab

out a

dequ

acy

of

nurs

ing

in h

ospi

tals

N

ursi

ng ra

tios,

prof

essi

onal

st

atus

, pro

porti

on

of n

urse

wor

ked

N

urse

s’ h

ealth

, pa

tient

ou

tcom

es,

viol

ence

.

Incr

ease

in a

cuity

in h

ospi

tals

mea

ns th

at R

Ns

may

nee

d m

ore

educ

atio

n. A

ncill

ary

nurs

ing

pers

onne

l sho

uld

have

doc

umen

ted

evid

ence

of

com

pete

ncy.

Nur

ses h

ave

high

rate

s of w

ork-

Evid

ence

-bas

ed S

taffi

ng

40

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

& n

ursi

ng h

omes

. ho

urs,

staf

fing

leve

ls.

rela

ted

inju

ry &

bac

k in

jurie

s wer

e re

late

d to

st

affin

g is

sues

. Vio

lenc

e to

war

ds h

ealth

care

w

orke

rs is

incr

easi

ng.

3.

Sy

stem

Cha

ract

eris

tics a

nd B

ehav

iour

s

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

21.

Aik

en, S

mith

, La

ke (1

994)

M

orta

lity

rate

s in

hosp

itals

with

hig

her

prop

ortio

n of

RN

st

aff t

o to

tal s

taff

39 m

agne

t ho

spita

ls

mat

ched

with

19

5 co

ntro

l ho

spita

ls in

U.S

. U

nit o

f ana

lysi

s is

hos

pita

l

Hos

pita

l siz

e &

or

gani

zatio

n,

staf

fing

ratio

s.

M

orta

lity

rate

s O

bser

ved

mor

talit

y ra

tes f

or m

agne

t hos

pita

ls

are

7.7%

low

er (9

few

er d

eath

s per

1,0

00

Med

icar

e di

scha

rges

; p=0

.011

). A

fter a

djus

ting

for p

redi

cted

mor

talit

y, m

agne

t hos

pita

l rat

es

wer

e 4.

6% lo

wer

(p=0

.026

, CI 9

5%; 0

.9 to

0.4

fe

wer

dea

ths p

er 1

,000

). M

agne

t hos

pita

ls h

ad si

gnifi

cant

ly h

ighe

r RN

: to

tal n

ursi

ng p

erso

nnel

ratio

s & sl

ight

ly h

ighe

r nu

rse:

pat

ient

ratio

s Sk

ill m

ix &

nur

se: p

atie

nt ra

tios d

o no

t exp

lain

th

e m

orta

lity

effe

ct o

r the

var

iabi

lity

in e

ffec

ts

acro

ss h

ospi

tals

. Aut

hors

pro

pose

that

mor

talit

y ef

fect

der

ives

from

gre

ater

stat

us, a

uton

omy

&

cont

rol a

ffor

ded

nurs

es in

mag

net h

ospi

tals

; not

si

mpl

y an

issu

e of

cre

dent

ials

& n

umbe

r of

nurs

es.

22.

Aik

en, C

lark

e,

Sloa

ne, (

2002

).

Exam

ine

effe

cts o

f nu

rse

staf

fing

&

orga

niza

tiona

l su

ppor

t for

nur

sing

ca

re o

n nu

rses

' di

ssat

isfa

ctio

n w

ith

thei

r job

s, nu

rse

burn

out,

& n

urse

re

ports

of q

ualit

y pa

tient

car

e in

10,3

19 n

urse

s w

orki

ng in

m

edic

al &

su

rgic

al u

nits

in

303

inte

rnat

iona

l ho

spita

ls

Wor

kloa

d,

prop

ortio

n of

nur

se

wor

ked

hour

s, or

gani

zatio

nal

supp

ort.

N

urse

bur

nout

, jo

b sa

tisfa

ctio

n,

nurs

es’

perc

eive

d qu

ality

of

car

e.

Org

aniz

atio

nal/m

anag

eria

l sup

port

for n

ursi

ng

had

a pr

onou

nced

eff

ect o

n nu

rse

diss

atis

fact

ion

& b

urno

ut. O

rgan

izat

iona

l sup

port

for n

ursi

ng

& n

urse

staf

fing

wer

e di

rect

ly re

late

d to

nur

se-

asse

ssed

qua

lity

of c

are.

Nur

se re

ports

of l

ow

qual

ity c

are

wer

e th

ree

times

as l

ikel

y in

ho

spita

ls w

ith lo

w st

affin

g &

supp

ort f

or

nurs

es, c

ompa

red

to h

ospi

tals

with

hig

h st

affin

g &

supp

ort.

Evid

ence

-bas

ed S

taffi

ng

41

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

hosp

itals

. 23

. A

iken

, Cla

rke,

Sl

oane

, So

chal

ski,

Silb

er (2

002)

.

Det

erm

ine

asso

ciat

ion

betw

een

patie

nt-to

-nur

se ra

tio

& p

atie

nt m

orta

lity,

fa

ilure

am

ong

surg

ical

pat

ient

s, &

fa

ctor

s rel

ated

to

nurs

e re

tent

ion.

210

adul

t gen

eral

ho

spita

ls; 1

0,18

4 st

aff n

urse

s ra

ndom

ly

surv

eyed

; 23

2,34

2 pa

tient

s di

scha

rged

.

Nur

se

dem

ogra

phic

s, pr

ofes

sion

al st

atus

, m

edic

al d

iagn

osis

, pa

tient

de

mog

raph

ics,

hosp

ital s

ize.

N

urse

job

satis

fact

ion,

bu

rnou

t, pa

tient

m

orta

lity,

cos

ts,

and

com

plic

atio

ns.

In h

ospi

tals

with

hig

h pa

tient

-to-n

urse

ratio

s, su

rgic

al p

atie

nts e

xper

ienc

e hi

gher

risk

-ad

just

ed 3

0-da

y m

orta

lity

& fa

ilure

-to-r

escu

e ra

tes (

odds

ratio

1.0

7), &

nur

ses a

re m

ore

likel

y to

exp

erie

nce

burn

out &

job

diss

atis

fact

ion

(odd

s rat

io fo

r dis

satis

fact

ion

is 1

.15)

.

24.

Arth

ur, J

ames

(1

994)

Li

tera

ture

revi

ew o

f va

rious

met

hods

of

nurs

e st

affin

g le

vel

mea

sure

men

t.

Pa

tient

de

mog

raph

ics,

nurs

ing

inte

rven

tions

, pa

tient

de

pend

ency

, and

pr

opor

tion

of n

urse

w

orke

d ho

urs.

Wor

kloa

d m

easu

rem

ent.

Staf

fing

ratio

s, le

ngth

of s

tay.

V

ario

us a

ppro

ache

s to

nurs

e de

man

d m

etho

ds:

cons

ensu

s (in

tuiti

ve, c

onsu

ltativ

e m

etho

d), t

op-

dow

n m

anag

emen

t (st

affin

g no

rms,

staf

fing

form

ulae

), &

bot

tom

-up

man

agem

ent (

nurs

ing

inte

rven

tions

, pat

ient

dep

ende

ncy)

. Deb

ates

su

rrou

ndin

g th

ese

met

hods

incl

ude:

co

mpa

rabi

lity

vs. l

ocal

suita

bilit

y, c

ontro

l, ef

ficie

ncy,

eff

ectiv

enes

s, &

phi

loso

phy

of c

are.

25

. B

aker

, M

essm

er,

Gyu

rko,

D

omag

ala,

Fr

ankl

in, E

ads,

Har

shm

an,

Layn

e (2

000)

Hos

pita

l ow

ners

hip,

pe

rfor

man

ce, &

ou

tcom

es.

6,09

7 ho

spita

ls

in th

e U

.S.

Hos

pita

l ow

ners

hip

type

(pub

lic,

priv

ate-

for p

rofit

, pr

ivat

e no

n-pr

ofit)

, pe

rfor

man

ce

Org

aniz

atio

nal

wor

k en

viro

nmen

t.

Adv

erse

eve

nts,

mor

bidi

ty,

mor

talit

y, p

atie

nt

satis

fact

ion,

nu

rse

satis

fact

ion,

co

sts,

prod

uctiv

ity.

Hos

pita

l ow

ners

hip

has a

n im

pact

on

hosp

ital

perf

orm

ance

in re

latio

n to

syst

em o

pera

tions

: co

sts,

pric

es, &

fina

ncia

l man

agem

ent

prac

tices

; & p

erso

nal i

ssue

s. A

ssoc

iatio

n be

twee

n ho

spita

l ow

ners

hip

& a

dver

se e

vent

s co

nsis

tent

ly su

ppor

ted.

26.

Ble

gen,

V

augh

n (1

998)

Nur

se st

affin

g &

pa

tient

occ

urre

nces

. 39

uni

ts in

11

hosp

itals

. St

affin

g m

ix, t

ype

of u

nit.

Pa

tient

co

mpl

icat

ions

(m

ed e

rror

s, fa

lls),

card

iopu

lmon

ary

arre

sts.

Hig

her p

ropo

rtion

of R

Ns r

esul

ted

in fe

wer

pa

tient

com

plic

atio

ns (m

ed e

rror

s/do

se R

2 =-0.

576,

falls

R2 =-

0.45

6) b

ut th

e re

latio

nshi

p is

no

t lin

ear.

Uni

ts w

ith R

N p

ropo

rtion

>85

% h

ad

high

er m

ed e

rror

s (m

ay b

e he

ight

ened

vi

gila

nce,

sick

er p

atie

nts w

ith m

ore

med

s).

27.

Ble

gen,

G

oode

, Ree

d,

(199

8).

Rel

atio

nshi

p am

ong

inci

denc

e ra

tes o

f 6

adve

rse

pt. o

utco

mes

, th

e ho

urs o

f car

e

42 in

patie

nt u

nits

in

an

880-

bed

univ

ersi

ty

hosp

ital.

Patie

nt a

cuity

: (u

niqu

e Pa

tient

C

lass

ifica

tion

Syst

em; l

evel

s

Pa

tient

ou

tcom

es:

med

icat

ion

erro

rs p

er 1

0,00

0

Con

trolli

ng fo

r ave

rage

pat

ient

acu

ity a

djus

ted

at u

nit l

evel

, RN

pro

porti

on w

as in

vers

ely

rela

ted

to th

e un

it ra

tes o

f med

icat

ion

erro

rs,

decu

biti,

& p

atie

nt c

ompl

aint

s. To

tal h

ours

of

Evid

ence

-bas

ed S

taffi

ng

42

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

prov

ided

by

all

nurs

ing

pers

onne

l &

the

prop

ortio

n of

th

ose

care

hou

rs

give

n by

RN

s.

21,7

83

disc

harg

es &

19

8,96

2 pa

tient

da

ys o

f car

e pr

ovid

ed b

y 10

74 F

TE

nurs

ing

staf

f m

embe

rs, 8

32 o

f th

ose

FTEs

wer

e R

Ns.

Fisc

al y

ear 1

993.

rang

e be

twee

n 1

and

7 m

ost

acut

e/m

ost c

are)

N

urse

staf

fing:

All

Hou

rs (m

onth

ly

hour

s of c

are

patie

nt p

er d

ay b

y R

Ns,

LPN

s, N

As/

patie

nt d

ays

on u

nit);

RN

Hou

rs

(hou

rs o

f dire

ct R

N

patie

nt c

are/

patie

nt

days

); R

N

prop

ortio

n (R

N

hour

s pat

ient

per

da

y/A

ll H

ours

)

dose

s (nu

rse

self-

repo

rt), f

alls

, de

cubi

ti, u

rinar

y &

resp

irato

ry

infe

ctio

ns,

patie

nt

com

plai

nts p

er

1,00

0 pa

tient

da

ys &

mor

talit

y ra

tes p

er 1

,000

pa

tient

day

s.

care

from

all

nurs

ing

pers

onne

l wer

e as

soci

ated

di

rect

ly w

ith c

ompl

aint

s, de

cubi

ti, &

mor

talit

y;

how

ever

tota

l hou

rs o

f car

e w

ere

high

ly

corr

elat

ed w

ith a

cuity

. A

larg

er p

ropo

rtion

of c

are

deliv

ery

by R

Ns

was

ass

ocia

ted

with

a d

ecre

ase

in a

dver

se p

t ou

tcom

es u

p to

a le

vel o

f 87.

5% o

f RN

staf

fing.

W

hen

the

prop

ortio

n of

RN

s in

the

staf

f mix

w

as g

reat

er th

an 8

7.5%

, adv

erse

out

com

es a

lso

incr

ease

d.

28.

Bro

wn

(200

1)

Syno

psis

of A

NA

st

udy

on re

latio

nshi

p be

twee

n st

affin

g &

pa

tient

out

com

es.

Nea

rly 1

3 m

illio

n pa

tient

s in

150

0 ho

spita

ls

from

9 st

ates

Prop

ortio

n of

nur

se

wor

ked

hour

s, m

edic

al d

iagn

osis

.

St

affin

g ra

tios,

patie

nt

com

plic

atio

ns &

ou

tcom

es, l

engt

h of

stay

Seve

ral p

atie

nt o

utco

mes

wer

e si

gnifi

cant

ly

rela

ted

to st

affin

g (le

ngth

of s

tay,

pne

umon

ia,

post

-op

infe

ctio

ns, u

lcer

s, ur

inar

y ta

ct

infe

ctio

ns).

Cos

t sav

ings

in re

duct

ions

of s

taff

&

mix

may

not

be

real

savi

ngs w

hen

com

plic

atio

ns &

incr

ease

d le

ngth

of s

tay

are

cons

ider

ed.

29.

Bue

rhau

s (1

997)

M

anda

tory

min

imum

nu

rse

staf

fing

leve

ls

in h

ospi

tals

Pr

opor

tion

of n

urse

w

orke

d ho

urs,

nurs

e-to

-pat

ient

ra

tio

C

osts

of c

are,

cl

inic

al

outc

omes

Staf

fing

regu

latio

ns (i

f im

pose

d) w

ould

forc

e em

ploy

ers t

o ig

nore

dyn

amic

inte

ract

ions

of

econ

omic

, tec

hnol

ogy,

cap

ital &

labo

ur su

pply

va

riabl

es. T

here

wou

ld b

e si

gnifi

cant

cos

ts

asso

ciat

ed w

ith e

nfor

cing

the

regu

latio

n w

hich

w

ould

out

wei

gh th

e be

nefit

s.

Evid

ence

-bas

ed S

taffi

ng

43

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

30.

Bur

ke (2

003)

Rel

atio

nshi

p be

twee

n ch

ange

s in

patie

nt-

nurs

e ra

tios r

esul

ting

from

hos

pita

l re

stru

ctur

ing

&

nurs

ing

staf

f sa

tisfa

ctio

n,

psyc

holo

gica

l hea

lth,

& p

erce

ptio

ns o

f ho

spita

l fun

ctio

ning

.

Self-

repo

rt su

rvey

of 7

44

hosp

ital-b

ased

nu

rsin

g su

rviv

ors.

1st

wav

e N

ov.

1996

. 2nd

wav

e N

ov. 1

999.

Patie

nt-n

urse

ratio

: cu

rren

t & c

hang

es

sinc

e re

stru

ctur

ing

bega

n N

urse

: exp

erie

nce,

em

ploy

men

t sta

tus,

educ

atio

n,

dem

ogra

phic

s H

ospi

tal:

size

W

ork

outc

omes

, w

ork

expe

rienc

es:

exte

nt o

f re

stru

ctur

ing,

pe

rcei

ved

wor

kloa

d, jo

b se

curit

y,

psyc

holo

gica

l he

alth

, hos

pita

l ef

fect

iven

ess:

53%

of n

urse

s rep

orte

d an

incr

ease

d pa

tient

-nu

rse

ratio

. Inc

reas

ed ra

tios a

ssoc

iate

d w

ith le

ss

job

satis

fact

ion

& jo

b se

curit

y, g

reat

er in

tent

ion

to q

uit &

mor

e re

stru

ctur

ing

initi

ativ

es, p

oore

r ps

ycho

logi

cal (

but n

ot p

hysi

cal)

heal

th, &

less

ef

fect

ive

hosp

ital f

unct

ioni

ng.

31.

Cal

iforn

ia

Nur

ses

Ass

ocia

tion.

(2

001)

Rea

sons

for t

he C

NA

ba

cked

nur

se-to

-pa

tient

ratio

s.

M

edic

al d

iagn

osis

Staf

fing

ratio

s St

rong

ratio

s will

hel

p re

duce

the

nurs

ing

shor

tage

. The

y m

ust b

e de

term

ined

in

acco

rdan

ce to

indi

vidu

al p

atie

nt c

are

need

s.

32.

Cal

lagh

an,

Car

twrig

ht,

O’R

ourk

e,

Dav

ies (

2003

)

Rel

atio

nshi

p be

twee

n in

fant

to st

aff r

atio

s in

firs

t thr

ee d

ays o

f lif

e on

the

surv

ival

to

hosp

ital d

isch

arge

692

very

low

bi

rth w

eigh

t in

fant

s in

an

Aus

tralia

n in

tens

ive

care

un

it Ja

n. 1

996

– D

ec.

1999

Infa

nt

char

acte

ristic

s:

depe

nden

cy

(infa

nt:n

urse

ratio

s:

inte

nsiv

e 1:

1, h

igh

1:2,

med

ium

1:3

, &

reco

very

1:5

), bi

rth

hist

ory,

adm

issi

on

& p

hysi

olog

ical

da

ta

Staf

fing:

num

ber o

f nu

rses

wor

king

per

sh

ift, m

axim

um

num

ber o

f inf

ants

pe

r shi

ft

Su

rviv

al to

ho

spita

l di

scha

rge,

ad

just

ed fo

r in

itial

risk

(usi

ng

Clin

ical

Ris

k In

dex

for B

abie

s)

& fo

r uni

t w

orkl

oad

(infa

nt

depe

nden

cy

scor

es)

Ove

rall

hosp

ital m

orta

lity

rate

of 1

2% (8

0 ou

t of

692

infa

nts)

. O

dds o

f mor

talit

y, a

djus

ted

for i

nitia

l ris

k &

un

it w

orkl

oad

impr

oved

by

82%

whe

n an

in

fant

:sta

ff ra

tio o

f gre

ater

than

1.7

1 oc

curr

ed.

33.

Cam

pbel

l, Ta

ylor

, C

alla

ghan

, Sh

uldh

am

Usi

ng c

ase

mix

gro

up

to p

redi

ct w

orkl

oad.

79

8 pa

tient

s &

30 n

urse

s fro

m

one

war

d.

Ret

rosp

ectiv

e

Patie

nt

dem

ogra

phic

s, ad

mis

sion

type

, ca

se m

ix g

roup

.

Wor

kloa

d C

osts

of c

are,

le

ngth

of s

tay.

R

egre

ssio

n an

alys

is sh

owed

that

ther

e w

as n

o re

latio

nshi

p be

twee

n re

sour

ces u

sed

& n

ursi

ng

clin

ical

hou

rs; h

owev

er, t

here

was

a g

ood

rela

tions

hip

betw

een

the

num

ber o

f pat

ient

s &

Evid

ence

-bas

ed S

taffi

ng

44

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

(199

7)

data

from

ho

spita

l pt.

adm

inis

tratio

n sy

stem

.

reso

urce

s use

d. A

vera

ge le

ngth

s of s

tay

for c

ase

mix

gro

ups w

ere

grea

ter t

han

pred

icte

d. F

or

cyst

ic fi

bros

is p

atie

nts (

repr

esen

tativ

e of

sp

ecia

list n

ursi

ng),

case

mix

gro

up a

ccou

nted

fo

r onl

y 18

% o

f var

iatio

n in

nur

sing

tim

e re

quire

d. C

ase

mix

gro

up h

as sh

own

to b

e a

poor

pre

dict

or o

f nur

sing

requ

irem

ent.

34.

Can

adia

n La

bour

&

Bus

ines

s Cen

tre

(200

2).

Cos

t ass

ocia

ted

with

ab

sent

eeis

m,

over

time,

&

invo

lunt

ary

part-

time

empl

oym

ent.

201

bed

hosp

ital

in O

ttaw

a.

Nur

se

dem

ogra

phic

s.

Cos

ts o

f car

e,

abse

ntee

ism

, use

of

age

ncy

nurs

es,

turn

over

.

Ove

rtim

e ho

urs h

ave

incr

ease

d dr

amat

ical

ly

over

pas

t 3 y

ears

. Age

ncy

cost

s rep

rese

nt 3

4 %

of

ove

rtim

e co

sts.

Ther

e ar

e es

timat

es th

at 2

5-30

% o

f abs

ente

eism

is re

late

d to

stre

ss &

in

jury

. Add

ition

al fu

ll-tim

e eq

uiva

lent

s wou

ld

redu

ce c

osts

. 35

. C

lark

(200

2)

Effe

ct o

f nur

se

staf

fing

leve

ls o

n ad

vers

e ev

ents

.

Dat

a fr

om 7

99

hosp

itals

in 1

1 st

ates

.

Prop

ortio

n of

nur

se

wor

ked

hour

s.

Patie

nt

com

plic

atio

ns &

ou

tcom

es, l

engt

h of

stay

, sta

ffin

g ra

tios.

Hig

her p

ropo

rtion

of R

Ns r

esul

ted

in b

ette

r ca

re. N

o as

soci

atio

n be

twee

n lo

wer

rate

s of

outc

omes

& n

umbe

r of h

ours

of c

are

by L

PNs

or n

urse

s’ a

ides

.

Evid

ence

-bas

ed S

taffi

ng

45

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

36.

Cla

rke,

R

ocke

tt, S

loan

e,

Aik

en (2

002)

Org

aniz

atio

nal

clim

ate,

staf

fing,

&

safe

ty e

quip

men

t

1998

surv

ey d

ata

for 2

287

med

ical

su

rgic

al u

nit

nurs

es in

22

US

hosp

itals

(20

wer

e m

agne

t ho

spita

ls).

1998

su

rvey

of

man

agem

ent,

infe

ctio

n co

ntro

l, pu

rcha

sing

of

ficia

ls re

: eq

uipm

ent

sele

ctio

n &

pr

ocur

emen

t

Self-

repo

rt co

mpl

ianc

e w

ith

univ

ersa

l pr

ecau

tions

&

perc

eive

d ris

k -

Nur

se

char

acte

ristic

s. Pr

otec

tive

Equi

pmen

t. N

urse

staf

fing:

pa

tient

s car

ed fo

r on

last

shift

w

orke

d; h

ospi

tal-

leve

l mea

sure

av

erag

ing

patie

nt

load

s, or

gani

zatio

nal

clim

ate

(R-N

WI)

- N

eedl

estic

k in

jurie

s & n

ear-

mis

ses:

self-

repo

rt oc

curr

ence

, fr

eque

ncy

in p

ast

mon

th &

pas

t ye

ar,

circ

umst

ance

s, re

porti

ng

- Ave

rage

day

shift

wor

kloa

d ra

nged

from

3.6

to

8.7

pat

ient

s per

nur

se (n

= 2

2)

- In

n=5,

ave

rage

day

shift

wor

kloa

d of

mor

e th

an 6

pat

ient

s (he

avie

st w

orkl

oad)

- n

urse

s with

hea

vies

t wor

kloa

d w

ere

50%

m

ore

likel

y to

repo

rt an

inju

ry &

40%

mor

e lik

ely

to re

port

a ne

ar-m

iss i

n th

e pr

eced

ing

mon

th

37.

Coc

keril

l, O

’Brie

n-Pa

llas,

Bol

ley,

Pin

k (1

993)

Mea

surin

g nu

rsin

g co

sts u

sing

nur

sing

w

orkl

oad.

256

patie

nt

reco

rds f

rom

4

units

in a

larg

e te

achi

ng

hosp

ital.

Cas

e m

ix g

roup

W

orkl

oad

(GR

ASP

, NIS

S,

Med

icus

, PR

N)

Cos

ts o

f car

e.

Ther

e w

ere

signi

fican

t diff

eren

ces a

mon

g sy

stem

s in

estim

ates

of c

are

for p

atie

nts.

Nur

sing

cos

ts p

er c

ase

diff

er si

gnifi

cant

ly

depe

ndin

g so

lely

on

wor

kloa

d m

easu

rem

ent

syst

em u

sed

(as m

uch

as 3

0% d

iffer

ence

in c

ost

for s

ame

patie

nt).

38

. D

oran

, M

cGill

is H

all,

Sida

ni,

O’B

rien-

Palla

s, D

onne

r, B

aker

, Pi

nk (2

001)

Effe

ct o

f nur

se

staf

fing

issu

es o

n nu

rse

com

mun

icat

ion

& p

atie

nt o

utco

mes

.

1116

nur

ses f

rom

19

urb

an

teac

hing

ho

spita

ls.

Stra

tifie

d ra

ndom

sam

ple,

97

% re

spon

se

rate

.

Staf

fing

ratio

s, pr

opor

tion

of n

urse

w

orke

d ho

urs,

patie

nt

dem

ogra

phic

s, ca

se

mix

gro

ups,

nurs

e ed

ucat

ion,

ex

perie

nce.

Pa

tient

hea

lth

stat

us.

Hig

her m

ix o

f RN

s im

prov

ed c

omm

unic

atio

n &

le

d to

ben

efic

ial p

atie

nt o

utco

mes

. Nur

se-

patie

nt ra

tio (r

=0.1

38) &

pro

porti

on o

f re

gula

ted

staf

f (r=

0.15

5) p

ositi

vely

aff

ecte

d nu

rse

com

mun

icat

ion.

Eff

ectiv

e co

mm

unic

atio

n am

ong

nurs

es le

d to

bet

ter p

atie

nt h

ealth

ou

tcom

es (r

=0.1

75).

39. E

isen

berg

, B

owm

an, F

oste

r (2

001)

Impa

ct o

f wor

kpla

ce

heal

th o

n qu

ality

of

care

.

St

affin

g m

ix, r

atio

s W

ork

envi

ronm

ent

Cos

ts o

f car

e,

qual

ity o

f car

e En

viro

nmen

t, or

gani

zatio

n, st

affin

g, &

cul

ture

in

fluen

ce q

ualit

y of

car

e vi

a th

eir e

ffec

t on

the

heal

thy

wor

kpla

ce. A

dequ

ate

num

ber o

f sta

ff,

Evid

ence

-bas

ed S

taffi

ng

46

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

appr

opria

te b

lend

of s

kills

& p

rope

r equ

ipm

ent

enab

le w

ork.

Hea

vy w

orkl

oads

inhi

bit s

taff

fr

om p

artic

ipat

ing

in re

sear

ch. E

nhan

cing

w

orke

rs’ h

ealth

& sa

tisfa

ctio

n m

ay im

prov

e pa

tient

out

com

es.

40.

Gau

ci B

orda

, N

orm

an (1

997)

Fa

ctor

s tha

t inf

luen

ce

turn

over

& a

bsen

ce

of n

urse

s, th

e re

latio

nshi

p be

twee

n ab

senc

e &

turn

over

.

17 st

udie

s on

rela

tions

hip

betw

een

job

satis

fact

ion,

in

tent

to

stay

/leav

e &

ac

tual

turn

over

.

Nur

se jo

b sa

tisfa

ctio

n, sy

stem

or

gani

zatio

nal

fact

ors.

N

urse

ab

sent

eeis

m,

turn

over

.

Job

satis

fact

ion

influ

ence

s abs

ence

& in

tent

to

stay

. Int

ent t

o st

ay in

cur

rent

em

ploy

men

t in

fluen

ces t

urno

ver.

Inte

nt to

stay

is m

ost

stro

ngly

ass

ocia

ted

with

job

satis

fact

ion.

Pay

&

oppo

rtuni

ty fo

r alte

rnat

ive

empl

oym

ent a

lso

influ

ence

inte

nt to

stay

, whi

ch is

supp

orte

d by

tw

o st

udie

s. A

bsen

ce is

pos

itive

ly re

late

d to

tu

rnov

er (a

bsen

ce in

crea

sed

befo

re tu

rnov

er) &

ne

gativ

ely

rela

ted

to in

tent

to st

ay. K

insh

ip

resp

onsi

bilit

y is

dire

ctly

rela

ted

to in

tent

to

stay

. 41

. G

audi

ne

(200

0)

Nur

ses’

vie

ws o

f w

orkl

oad

& w

ork

over

load

.

31 st

aff n

urse

s fr

om 9

diff

eren

t un

its o

f a

hosp

ital i

n ce

ntra

l Can

ada,

vo

lunt

eer

sam

plin

g.

Nur

se

dem

ogra

phic

s, ex

perie

nce,

and

pa

tient

acu

ity.

Sim

ulta

neou

s de

man

ds,

unan

ticip

ated

ev

ents

, in

terr

uptio

ns,

nois

e le

vel.

Nur

ses’

feel

ings

of

wor

kloa

d &

ov

erlo

ad.

Mea

ning

s tha

t nur

ses a

ttrib

uted

to w

orkl

oad

incl

ude

volu

me,

sim

ulta

neou

s dem

ands

, de

man

ds o

n se

lf, q

ualit

ativ

e ov

erlo

ad,

antic

ipat

ion,

resp

onsi

bilit

y, in

terd

epen

denc

e,

non-

wor

k ro

les &

exh

aust

ion.

The

mea

ning

s of

wor

k ov

erlo

ad in

clud

e si

mul

tane

ous d

eman

ds,

qual

itativ

e w

ork

over

load

, hea

vy lo

ad, &

re

spon

sibi

lity.

The

se m

eani

ngs i

nclu

de m

ore

dim

ensi

ons t

han

curr

ent m

easu

res o

f wor

kloa

d.

Evid

ence

-bas

ed S

taffi

ng

47

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

42.

Gre

engl

ass,

Bur

ke (2

001)

Im

pact

of h

ospi

tal

rest

ruct

urin

g on

nu

rses

.

1363

nur

ses

empl

oyed

in

hosp

itals

un

derg

oing

re

stru

ctur

ing.

R

ando

m

sele

ctio

n fr

om

unio

n m

embe

rshi

p,

35%

resp

onse

ra

te.

Nur

se e

duca

tion,

de

mog

raph

ics,

hosp

ital s

ize.

Pe

rcei

ved

qual

ity

of c

are,

job

satis

fact

ion,

w

orki

ng

cond

ition

s.

Res

truct

urin

g ha

d a

nega

tive

effe

ct o

n st

aff

(97.

9% o

f res

pond

ents

agr

ee) &

wor

king

co

nditi

ons (

94%

). It

has c

ompr

omis

ed th

e qu

ality

of c

are

& re

duce

d nu

rses

’ abi

lity

to

prov

ide

serv

ices

for p

atie

nts.

Dur

ing

hosp

ital

rest

ruct

urin

g w

orkl

oad

was

the

mos

t sig

nific

ant

& c

onsi

sten

t pre

dict

or o

f dis

tress

in n

urse

s, as

m

anife

sted

in lo

wer

job

satis

fact

ion,

pr

ofes

sion

al e

ffic

acy,

& jo

b se

curit

y. G

reat

er

wor

kloa

d al

so c

ontri

bute

d to

dep

ress

ion,

cy

nici

sm, &

anx

iety

. 43

. G

rillo

-Pec

k,

Ris

ner (

1995

) Im

plem

enta

tion

of a

nu

rsin

g pa

rtner

ship

m

odel

.

156

patie

nts

from

a

neur

osci

ence

un

it in

an

800-

bed

not-f

or-

prof

it ho

spita

l in

Ohi

o.

Prop

ortio

n of

nur

se

wor

ked

hour

s, ca

re

deliv

ery

syst

em,

med

ical

dia

gnos

is,

patie

nt

dem

ogra

phic

s, an

d co

ntin

uity

of c

are.

Pa

tient

leng

th o

f st

ay,

com

plic

atio

ns

(infe

ctio

ns,

falls

), m

edic

atio

n er

rors

.

A n

ursi

ng p

artn

ersh

ip m

odel

whi

ch in

clud

ed a

de

crea

se in

RN

s & a

prim

ary

nurs

ing

mod

el

was

impl

emen

ted.

RN

s wer

e pa

rtner

ed w

ith a

pa

tient

car

e te

chni

cian

& a

ssis

ted

by se

rvic

e as

soci

ates

. Thi

s allo

wed

for c

ontin

uity

of c

are.

Pa

tient

com

plic

atio

ns sh

owed

a d

ownw

ard

trend

afte

r the

impl

emen

tatio

n &

leng

th o

f sta

y de

crea

sed.

RN

s wer

e ab

le to

spen

d le

ss ti

me

in

non-

prof

essi

onal

task

s. 44

. H

allo

ran

(198

5)

Effe

cts o

f var

iabl

es

on n

ursi

ng w

orkl

oad.

25

60 p

atie

nt

reco

rds &

141

nu

rsin

g st

aff

mem

bers

from

a

279-

bed

acut

e ca

re, c

omm

unity

ho

spita

l. Th

is

incl

uded

all

patie

nts a

dmitt

ed

& d

isch

arge

d ov

er a

4 m

onth

pe

riod.

Nur

sing

dia

gnos

es,

med

ical

dia

gnos

es

(DR

G),

patie

nt

dem

ogra

phic

s.

C

osts

of c

are,

nu

rsin

g w

orkl

oad.

Var

iatio

ns in

nur

sing

wor

kloa

d w

ere

bette

r ex

plai

ned

by n

ursi

ng c

ondi

tion

than

by

med

ical

co

nditi

on o

r pat

ient

dem

ogra

phic

s (75

% o

f the

su

m o

f the

squa

red

regr

essi

on c

oeff

icie

nt is

as

soci

ated

with

nur

sing

dia

gnos

is &

25

% is

as

soci

ated

with

med

ical

dia

gnos

is).

Ther

e w

as a

st

rong

pos

itive

rela

tions

hip

betw

een

wor

kloa

d &

leng

th o

f sta

y in

hos

pita

l (co

rrel

atio

n co

effic

ient

=0.7

74).

Of d

emog

raph

ic

char

acte

ristic

s, on

ly a

ge is

ass

ocia

ted

with

va

riatio

ns in

wor

kloa

d (r

=0.1

98).

45.

Har

tz,

Kra

kaue

r, K

uhn,

You

ng,

Fact

ors t

hat a

ffec

t pa

tient

mor

talit

y ra

tes.

3100

hos

pita

ls in

th

e U

nite

d St

ates

. Dat

a fr

om

Patie

nt

dem

ogra

phic

s, m

edic

al d

iagn

osis

,

Pa

tient

mor

talit

y.

Hig

her m

orta

lity

rate

s wer

e as

soci

ated

with

for-

prof

it (1

21/1

000

patie

nts v

s. av

erag

e of

11

6/10

00) &

pub

lic h

ospi

tals

(120

/100

0) &

Evid

ence

-bas

ed S

taffi

ng

48

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

Jaco

bsen

, Gay

, M

uenz

, Kat

zoff

, B

aile

y, R

imm

(1

989)

the

Hea

lthca

re

Fina

ncin

g A

dmin

istra

tion

& th

e A

mer

ican

H

ospi

tal

Ass

ocia

tion

annu

al su

rvey

.

hosp

ital s

ize,

pr

opor

tion

of n

urse

w

orke

d ho

urs,

nurs

e ra

tios.

oste

opat

hic

hosp

itals

(129

/100

0). T

he

char

acte

ristic

s mos

t clo

sely

link

ed w

ith

mor

talit

y ar

e re

late

d to

trai

ning

of m

edic

al st

aff

(e.g

. hig

her p

erce

ntag

e of

RN

s = lo

wer

m

orta

lity)

. Hig

her o

ccup

ancy

rate

was

as

soci

ated

with

low

er m

orta

lity

rate

.

46.

Hen

drix

, Fo

rem

an (2

001)

Opt

imal

nur

se

staf

fing

leve

ls in

long

te

rm c

are.

Ove

r 12,

000

fede

rally

ce

rtifie

d sk

illed

&

inte

rmed

iate

nu

rsin

g ho

mes

in

the

Uni

ted

Stat

es

(dat

a fr

om

1994

).

Prop

ortio

n of

nur

se

wor

ked

hour

s, nu

rsin

g ra

tios.

C

ost o

f car

e,

cost

s of i

njur

y,

patie

nt o

utco

mes

(d

ecub

itus

ulce

rs),

publ

ic

burd

en.

Ther

e is

an

optim

al le

vel o

f nur

se st

affin

g th

at

min

imiz

es d

ecub

itus u

lcer

s in

nurs

ing

hom

es.

The

pres

ence

of R

Ns (

supe

rior s

kills

; ß=-

1.65

9)

& n

urse

s’ a

ides

(low

er w

age;

ß=-

7.33

4))

redu

ces c

osts

ass

ocia

ted

with

ulc

er c

are

whi

le

the

pres

ence

of L

PNs (

ß=4.

544)

incr

ease

s the

co

st o

f ulc

er c

are.

Nur

sing

hom

es sh

ould

in

crea

se th

e nu

mbe

r of R

Ns &

NA

s. 47

. K

enne

y.

(200

1)

Pilo

t pro

ject

for

impl

emen

ting

LPN

s to

mai

ntai

n qu

ality

du

ring

staf

fing

shor

tage

s.

St

affin

g m

ix,

ratio

s. W

orkl

oad.

N

urse

s’ h

ealth

, co

sts o

f car

e,

patie

nt

satis

fact

ion,

co

mpl

icat

ions

(f

alls

, med

er

rors

), nu

rse

satis

fact

ion

Ther

e w

ere

no c

hang

es in

qua

lity

of c

are

(num

ber o

f tre

atm

ent/p

roce

dure

err

ors &

falls

re

mai

ned

stab

le) o

r sta

ff sa

tisfa

ctio

n w

ith th

e ad

ditio

n of

LPN

s. LP

Ns w

ere

dele

gate

d to

task

s by

RN

s who

wer

e st

ill re

spon

sibl

e fo

r sev

eral

pa

tient

s.

48.

Kna

us, D

rape

r, W

agne

r, Zi

mm

erm

an

(198

6)

Influ

enci

ng fa

ctor

s on

mor

talit

y in

in

tens

ive

care

uni

ts.

5,03

0 pa

tient

s in

inte

nsiv

e ca

re

units

in 1

3 te

rtiar

y ca

re

hosp

itals

. H

ospi

tals

wer

e se

lf se

lect

ed &

pa

tient

s wer

e co

nven

ienc

e sa

mpl

ed.

Patie

nt

dem

ogra

phic

s, ad

mis

sion

type

, m

edic

al d

iagn

osis

.

Pa

tient

co

mpl

icat

ions

&

outc

omes

.

Deg

ree

of c

oord

inat

ion

of in

tens

ive

care

po

sitiv

ely

influ

ence

s its

eff

ectiv

enes

s. In

tera

ctio

n &

com

mun

icat

ion

betw

een

phys

icia

ns &

nur

ses a

ffec

t pat

ient

mor

talit

y.

49.

Kob

s. (1

997)

A

dequ

acy

of n

urse

st

affin

g.

Pr

opor

tion

of n

urse

w

orke

d ho

urs.

Pa

tient

co

mpl

icat

ions

, Th

ere

is a

pos

itive

cor

rela

tion

betw

een

shor

ter

leng

th o

f sta

y &

hig

her s

taff

ing

leve

ls. A

s RN

Evid

ence

-bas

ed S

taffi

ng

49

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

leng

th o

f sta

y,

staf

fing

ratio

s. st

affin

g in

crea

sed,

pat

ient

com

plic

atio

ns

decr

ease

d.

50.

Kov

ner (

2001

) Im

pact

of s

taff

ing

&

wor

k or

gani

zatio

n on

pa

tient

out

com

es &

he

alth

care

wor

kers

.

N

urse

s’ e

duca

tion,

st

affin

g ra

tios.

N

urse

s’ h

ealth

, pa

tient

co

mpl

icat

ions

.

Inve

rse

rela

tions

hip

betw

een

mor

talit

y &

nu

mbe

r of R

Ns.

Inve

rse

rela

tions

hip

betw

een

com

plic

atio

ns &

num

ber o

f RN

s. Th

ere

are

high

rate

s of i

llnes

s & in

jury

am

ong

heal

thca

re

pers

onne

l. 51

. K

ovne

r, G

erge

n (1

998)

Rel

atio

nshi

p be

twee

n nu

rse

staf

fing

&

adve

rse

even

ts.

589

acut

e-ca

re

hosp

itals

in 1

0 st

ates

, dat

a fr

om

a 20

% st

ratif

ied

prob

abili

ty

sam

ple

to

appr

oxim

ate

US

hosp

itals

.

Nur

se st

affin

g (F

TE R

Ns w

orki

ng

patie

nt p

er d

ay).

Pa

tient

mor

talit

y,

med

icat

ion

erro

r ra

tes,

post

-op

erat

ive

infe

ctio

ns.

Inve

rse

rela

tions

hips

bet

wee

n FT

E R

Ns p

er

adju

sted

inpa

tient

day

& u

rinar

y ta

ct in

fect

ions

(-

636.

96, p

<.00

1), p

neum

onia

(-15

9.41

, p<

.001

), th

rom

bosi

s (-3

3.22

, p<.

01),

pulm

onar

y co

mpr

omis

e (-

59.6

9, p

<.05

) afte

r maj

or

surg

ery.

52.

Kra

kaue

r, B

aile

y, S

kella

n,

Stew

art,

Har

tz,

Kuh

n, R

imm

(1

992)

Eval

uatio

n of

a

mod

el fo

r ana

lyzi

ng

hosp

ital m

orta

lity

rate

s.

42,7

73 p

atie

nts

from

84

hosp

itals

. R

ando

m

sam

plin

g of

di

scha

rges

&

hosp

itals

from

st

rata

.

Prop

ortio

n of

nur

se

wor

ked

hour

s, ho

spita

l siz

e.

Pa

tient

co

mpl

icat

ions

. H

ospi

tals

with

hig

her p

ropo

rtion

of R

Ns h

ad

low

er a

djus

ted

mor

talit

y ra

tes (

diff

eren

ce o

f 2.

1-3.

6%).

53.

Kra

mer

, Sc

hmal

enbe

rg.

(198

8) P

art 1

.

Cha

ract

eris

tics o

f m

agne

t hos

pita

ls.

16 m

agne

t ho

spita

ls.

Hos

pita

l siz

e,

staf

fing

ratio

s, nu

rse

educ

atio

n,

expe

rienc

e.

N

urse

turn

over

. M

agne

t hos

pita

ls a

re su

cces

sful

in re

crui

ting

&

reta

inin

g nu

rses

dur

ing

perio

ds o

f sho

rtage

. Fl

uidi

ty &

info

rmal

ity a

llow

s for

co

mm

unic

atio

n &

exc

hang

e of

info

rmat

ion.

St

aff n

urse

s wer

e al

low

ed ti

me

for r

esea

rch,

pu

blic

atio

n &

spec

ial p

roje

cts.

Ther

e is

supp

ort

for c

ontin

uing

edu

catio

n &

enc

oura

gem

ent o

f au

tono

my

& e

ntre

pren

eurs

hip.

54

. K

ram

er,

Schm

alen

berg

(1

988)

Par

t 2.

Cha

ract

eris

tics o

f m

agne

t hos

pita

ls.

16 m

agne

t ho

spita

ls.

Hos

pita

l siz

e,

staf

fing

ratio

s, nu

rse

educ

atio

n,

expe

rienc

e.

N

urse

turn

over

. M

agne

t hos

pita

ls h

ave

char

acte

ristic

s sim

ilar t

o w

ell r

un c

ompa

nies

. The

y de

al w

ith n

ursi

ng

shor

tage

by

alte

ring

orga

niza

tiona

l con

ditio

ns

to e

limin

ate

inte

rnal

shor

tage

. Lea

ders

are

Evid

ence

-bas

ed S

taffi

ng

50

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

high

ly v

isib

le &

acc

essi

ble.

Mag

net h

ospi

tals

va

lue

qual

ity, a

uton

omy,

info

rmal

co

mm

unic

atio

n, in

nova

tion,

edu

catio

n, re

spec

t, ex

celle

nce,

& b

ringi

ng o

ut th

e be

st in

eac

h in

divi

dual

. 55

. K

utso

gian

nis,

Hag

ue, T

riska

, Jo

hnst

on,

Nos

ewor

thy

(200

1)

Org

aniz

atio

n of

in

tens

ive

care

uni

ts &

th

e in

fluen

ce o

n pa

tient

out

com

es.

C

are

deliv

ery

syst

em.

Wor

k en

viro

nmen

t &

orga

niza

tion.

Cos

ts o

f car

e,

patie

nt

com

plic

atio

ns,

leng

th o

f sta

y.

Impo

rtant

fact

ors i

n or

gani

zatio

n in

clud

e co

mm

unic

atio

n, le

ader

ship

, & in

terd

isci

plin

ary

polit

ics.

Bet

ter s

tand

ardi

zed

prac

tices

&

coor

dina

tion

wer

e re

late

d to

bet

ter o

utco

mes

(lo

wer

mor

talit

y &

mor

bidi

ty).

56.

Man

itoba

N

ursi

ng

Stra

tegy

(200

3).

The

Man

itoba

N

ursi

ng S

trate

gy

(MN

S) is

a re

port

rele

ased

by

the

Man

itoba

go

vern

men

t to

addr

ess t

he c

once

rns

rais

ed b

y nu

rses

&

othe

r sta

keho

lder

s w

ithin

the

heal

thca

re

syst

em.

Wor

k en

viro

nmen

t.

MN

S in

clud

es:

1.

Incr

ease

the

supp

ly o

f nur

ses

2.

Impr

ove

acce

ss to

staf

f dev

elop

men

t for

nu

rses

.

3.

3.

Impr

ove

the

utili

zatio

n of

nur

ses.

4.

Impr

ove

wor

king

con

ditio

ns.

5.

Incr

ease

nur

ses'

oppo

rtuni

ties t

o pr

ovid

e in

put i

nto

deci

sion

-mak

ing.

57.

Max

wel

l (2

002)

. Fa

ctor

s nee

ded

to

crea

te h

igh-

qual

ity

care

env

ironm

ents

.

Sy

stem

or

gani

zatio

n,

wor

kloa

d, n

urse

jo

b co

ntro

l.

N

urse

hea

lth, j

ob

satis

fact

ion,

pa

tient

ou

tcom

es.

Job

desi

gn, j

ob re

war

ds, o

rgan

izat

iona

l cha

nge,

&

job

secu

rity

can

have

maj

or e

mpl

oyee

hea

lth

impl

icat

ions

. Rol

e st

ress

ors &

job

inse

curit

y in

fluen

ce th

e w

ork

envi

ronm

ent.

Wor

kloa

d,

wor

k pa

ce, &

wor

k sc

hedu

ling

are

impo

rtant

w

ork

envi

ronm

ent i

ssue

s fac

ing

heal

th-c

are

wor

kers

. Pos

itive

hea

lth o

utco

mes

for n

urse

s ar

e as

soci

ated

with

hig

h jo

b co

ntro

l & a

bal

ance

of

job

dem

ands

with

suff

icie

nt re

sour

ces.

Hos

pita

ls w

ith p

ositi

ve w

ork

envi

ronm

ents

ha

ve b

ette

r sta

ff re

crui

tmen

t & re

tent

ion,

&

patie

nt o

utco

mes

.

Evid

ence

-bas

ed S

taffi

ng

51

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

58.

McG

illis

Hal

l, Ir

vine

Dor

an,

Bak

er, P

ink,

Si

dani

, O'B

rien-

Palla

s, D

onne

r (2

002)

.

Rep

orts

on

staf

f mix

&

wor

k st

atus

of

nurs

es in

adu

lt m

edic

al, s

urgi

cal,

&

obst

etric

al u

nits

in

Ont

ario

's te

achi

ng

hosp

itals

.

19 te

achi

ng

hosp

itals

, 2,0

46

patie

nts,

1,11

6 nu

rses

, 74

unit

man

ager

s. R

ando

m

sam

plin

g w

as

used

.

Nur

se

dem

ogra

phic

s, ex

perie

nce,

em

ploy

men

t sta

tus,

care

del

iver

y sy

stem

, pro

porti

on

of n

urse

wor

ked

hour

s.

Ther

e is

a n

eed

for d

evel

opin

g ap

prop

riate

le

vels

of k

now

ledg

e &

skill

for c

ompl

ex

inpa

tient

s car

ed fo

r in

med

ical

/sur

gica

l &

obst

etric

al u

nits

. The

pro

porti

ons o

f RN

s with

in

the

indi

vidu

al u

nit s

taff

ing

mod

els r

emai

ned

rela

tivel

y hi

gh (6

0-89

%).

Mor

e th

an o

ne th

ird

of th

e nu

rsin

g st

aff w

ere

empl

oyed

on

a pa

rt-tim

e or

cas

ual b

asis

. 59

. M

itche

ll,

Arm

stro

ng,

Sim

pson

, Len

tz

(198

9)

The

Dem

onst

ratio

n C

ritic

al C

are

Uni

t: or

gani

zatio

nal &

cl

inic

al o

utco

mes

.

42 n

urse

s, 68

ph

ysic

ians

, 192

pa

tient

ad

mis

sion

s. Pa

tient

s re

pres

enta

tive

of

unit’

s pop

ulat

ion

exce

pt fo

r dru

g ov

erdo

se o

r sho

rt st

ay.

Hos

pita

l siz

e &

ty

pe, n

urse

de

mog

raph

ics,

adm

issi

on ty

pe,

patie

nt

dem

ogra

phic

s, m

edic

al d

iagn

osis

.

C

osts

of c

are,

nu

rse

job

satis

fact

ion,

&

burn

out,

patie

nt

com

plic

atio

ns,

leng

th o

f sta

y.

Posi

tive

orga

niza

tiona

l & c

linic

al o

utco

mes

ex

ist w

ith v

alue

d as

pect

s of o

rgan

izat

iona

l en

viro

nmen

t (hi

gh n

urse

-phy

sici

an

colla

bora

tion,

hig

hly

rate

d nu

rsin

g pe

rfor

man

ce, p

ositi

ve o

rgan

izat

iona

l clim

ate)

as

com

pare

d w

ith h

isto

rical

com

paris

on

sam

ples

.

60.

Mitc

hell,

Sh

orte

ll, (1

997)

E7ff

ects

of

orga

niza

tiona

l va

riabl

es in

car

e de

liver

y sy

stem

s on

adve

rse

outc

omes

.

81 re

sear

ch

pape

rs.

Org

aniz

atio

nal

varia

bles

in c

are

deliv

ery

syst

ems.

Pa

tient

m

orbi

dity

, m

orta

lity,

&

adve

rse

effe

cts.

Som

e su

ppor

t tha

t nur

sing

surv

eilla

nce,

qua

lity

of w

orki

ng e

nviro

nmen

t & in

tera

ctio

ns w

ith

othe

r pro

fess

iona

ls a

re re

late

d to

low

er

mor

talit

y &

com

plic

atio

ns. P

atie

nt v

aria

bles

ha

ve a

gre

ater

impa

ct th

an o

rgan

izat

iona

l va

riabl

es. A

dver

se e

vent

s are

mor

e cl

osel

y re

late

d to

org

aniz

atio

nal f

acto

rs th

an is

m

orta

lity.

Evid

ence

-bas

ed S

taffi

ng

52

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

61.

Nee

dlem

an,

Bue

rhau

s, M

attk

e, S

tew

art,

Zele

vins

ky

(200

2)

Exam

ine

the

rela

tion

betw

een

the

amou

nt

of c

are

prov

ided

by

nurs

es a

t the

hos

pita

l &

pat

ient

s' ou

tcom

es.

1997

adm

in d

ata

for 7

99 h

ospi

tal

in 1

1 U

S st

ates

(d

isch

arge

s:

5,07

5,96

9 m

edic

al &

1,

104,

659

surg

ical

pat

ient

s;

acco

unte

d fo

r 26

% o

f 199

7 di

scha

rges

from

no

n-fe

dera

l US

hosp

itals

). U

nit

of a

naly

sis w

as

hosp

ital.

Inpa

tient

staf

fing

leve

ls p

atie

nt p

er

day

adju

sted

for

inpa

tient

vs.

outp

atie

nt b

ias;

di

ffer

ence

s be

twee

n ho

spita

ls

leve

l of n

ursi

ng

care

per

DR

G; r

isk

adju

stm

ent f

or

patie

nt

char

acte

ristic

s; &

ho

spita

l ch

arac

teris

tics

(num

ber o

f hos

pita

l be

ds, t

each

ing

stat

us, s

tate

, &

met

ropo

litan

/non

-m

etro

polit

an).

Le

ngth

of s

tay,

po

st-o

pera

tive

com

plic

atio

ns,

adve

rse

even

ts,

mor

talit

y. F

ailu

re

to re

scue

def

ined

as

: “de

ath

from

pn

eum

onia

, sh

ock

or c

ardi

ac

arre

st, u

pper

GI

blee

ding

, sep

sis,

or d

eep

veno

us

thro

mbo

sis”

p.

1715

- Mea

n ho

urs o

f nur

sing

car

e pe

r pat

ient

-day

w

as 1

1.4;

of w

hich

, 7.8

, 1.2

& 2

.4 p

rovi

ded

by

RN

s, LP

Ns,

& n

ursi

ng a

ides

resp

ectiv

ely.

Mea

n pr

opor

tion

of to

tal h

ours

of R

N c

are

was

68%

&

of n

ursi

ng a

ides

car

e w

as 2

1%.

- Am

ong

med

ical

pat

ient

s, a

high

er p

ropo

rtion

of

RN

hou

rs o

f car

e pa

tient

per

day

& g

reat

er

abso

lute

num

ber o

f RN

hou

rs o

f car

e pe

r day

as

soci

ated

with

shor

ter l

engt

h of

stay

(p=0

.01

&

p<0.

001)

, low

er ra

tes o

f urin

ary

tact

infe

ctio

n (

p<0.

001

& p

=0.0

03) &

low

er ra

tes o

f upp

er G

I bl

eedi

ng (p

=0.0

3 &

p=0

.007

). H

ighe

r pr

opor

tion

of R

N h

ours

ass

ocia

ted

with

low

er

rate

s of p

neum

onia

(p=0

.001

), sh

ock

or c

ardi

ac

arre

st (p

=0.0

07),

& fa

ilure

to re

scue

(p=0

.05)

. - A

mon

g su

rgic

al p

atie

nts,

high

er p

ropo

rtion

of

RN

car

e as

soci

ated

with

low

er ra

tes o

f urin

ary

tact

infe

ctio

n (p

=0.0

4). G

reat

er n

umbe

r of

hour

s of R

N c

are

patie

nt p

er d

ay a

ssoc

iate

d w

ith lo

wer

rate

s of f

ailu

re to

resc

ue (p

=0.0

08).

- N

o as

soci

atio

n fo

und

betw

een

RN

staf

fing

leve

ls &

rate

of i

n-ho

spita

l mor

talit

y. N

o as

soci

atio

n fo

und

betw

een

incr

ease

d st

affin

g by

LP

N o

r nur

sing

aid

es &

rate

of a

dver

se

outc

omes

. 62

. O

’Brie

n-Pa

llas,

Coc

keril

l, Le

att

(199

2)

Det

erm

ine

equi

vale

nce

of

wor

kloa

d es

timat

es

of 5

pat

ient

cl

assi

ficat

ion

met

hods

(NIS

S,

GR

ASP

, Med

icus

, PR

N 7

6 &

PR

N 8

0)

206

patie

nts

from

a la

rge

urba

n te

achi

ng

hosp

ital,

purp

osiv

e sa

mpl

ing

in

sele

cted

uni

ts

(crit

ical

car

e un

it, in

tens

ive

care

uni

t, et

c.).

Cas

e m

ix g

roup

cl

assi

ficat

ion

Pr

ogra

m c

ost

fore

cast

ing

Clin

ical

ly si

gnifi

cant

diff

eren

ces i

n ho

urs o

f ca

re e

stim

ates

foun

d by

eac

h sy

stem

but

a h

igh

corr

elat

ion

betw

een

the

syst

ems s

ugge

sts t

hat

calib

ratio

n co

uld

be u

sed

to c

ompa

re d

ata

(alp

has <

0.0

001)

.

63.

O’B

rien-

Pres

ents

a m

eta-

14 n

ursi

ng u

nits

N

ursi

ng

Pres

ence

of n

ew

Am

ount

of d

irect

M

ultif

acto

rial i

nten

sity

& c

ompl

exity

of c

are

Evid

ence

-bas

ed S

taffi

ng

53

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

Palla

s, Ir

vine

, Pe

ereb

oom

, M

urra

y (1

997)

para

digm

for

exam

inin

g nu

rsin

g w

ork

& re

sour

ce u

se.

in a

489

bed

pe

diat

ric te

rtiar

y ca

re, u

rban

, un

iver

sity

-af

filia

ted

hosp

ital,

cros

s-se

ctio

nal s

ampl

e.

com

plex

ity

(NA

ND

A),

med

ical

com

plex

ity

case

mix

gro

ups

(CM

Gs T

M),

med

ical

seve

rity

(leng

th o

f sta

y).

staf

f, re

lief s

taff

, un

antic

ipat

ed

even

ts.

care

requ

ired

(PR

N 8

0), c

osts

of

car

e.

mod

el e

xam

ines

cos

ts, n

ursi

ng w

ork

&

varia

bilit

y in

reso

urce

use

acr

oss p

atie

nts &

en

viro

nmen

ts. R

elat

ions

hips

obs

erve

d be

twee

n 4

key

varia

bles

& w

orkl

oad:

pat

ient

’s n

ursi

ng

cond

ition

(pos

itive

line

ar re

latio

nshi

p w

ith #

of

diag

nose

s), m

edic

al c

ondi

tion

(mos

t hav

e co

effic

ient

s of v

aria

tion

> 0.

5), c

areg

iver

ch

arac

teris

tics &

the

envi

ronm

ent.

64.

O’B

rien-

Palla

s, D

oran

, M

urra

y,

Coc

keril

l, Si

dani

, Lau

rie-

Shaw

, Lo

chha

ss-

Ger

lach

(200

1)

Var

iabl

es th

at a

ffec

t nu

rsin

g ut

iliza

tion

in

a ho

me

visi

ting

nurs

ing

serv

ice.

38 R

Ns,

11

RPN

s; 7

51

clie

nts r

ecei

ving

ho

me

heal

thca

re

(6,8

40 v

isits

or

7% o

f age

ncy

case

load

dur

ing

stud

y pe

riod)

; co

nven

ienc

e sa

mpl

e.

Clie

nts w

ere

unit

of a

naly

sis.

Clie

nt:

dem

ogra

phic

s, nu

rsin

g &

med

ical

di

agno

ses,

OM

AH

A sc

ores

, SF

-36

heal

th st

atus

, tim

e on

pro

gram

N

urse

: ed

ucat

ion,

ex

perie

nce,

pr

ofes

sion

al st

atus

. A

genc

y:

geog

raph

ic

loca

tion,

vis

it ty

pe,

case

load

, pr

opor

tion

of n

urse

w

orke

d ho

urs &

co

ntin

uity

of c

are.

Age

ncy:

vis

it tim

e En

viro

nmen

tal

Com

plex

ity:

com

petin

g de

man

ds/n

urse

sa

fety

, un

antic

ipat

ed

case

com

plex

ity,

form

al

info

rmat

ion

exch

ange

, voi

ce

mai

l, tra

vel,

unan

ticip

ated

ad

mis

sion

s.

Patie

nt h

ealth

st

atus

, OM

AH

A

scor

es

(kno

wle

dge,

be

havi

our,

and

stat

us).

Nur

se: p

erce

ived

ad

equa

cy o

f car

e tim

e.

Age

ncy:

tota

l vi

sits

.

Ove

rall,

Clie

nt C

are

Del

iver

y M

odel

exp

lain

ed

47%

(R2 =

.46)

of t

he v

aria

tion

in a

vera

ge v

isit

time.

Med

ical

& n

ursi

ng d

iagn

oses

exp

lain

ed

14.7

% o

f var

iatio

n in

ave

rage

vis

it le

ngth

. Sp

ecifi

cally

, men

tal h

ealth

dia

gnos

es

cont

ribut

ed to

long

er b

ut n

ot n

eces

saril

y m

ore

visi

ts. U

nant

icip

ated

cas

e co

mpl

exity

&

unan

ticip

ated

adm

issi

ons w

ere

posi

tivel

y as

soci

ated

with

gre

ater

ave

rage

vis

it tim

e,

expl

aini

ng 2

0.5%

of t

he v

aria

tion.

O

vera

ll, C

lient

Car

e D

eliv

ery

Mod

el e

xpla

ined

35

.6%

(R2 =

.33)

of t

he v

aria

tion

in n

umbe

r of

visi

ts. V

isits

per

form

ed b

y de

gree

-pre

pare

d nu

rses

resu

lted

in fe

wer

tota

l vis

its &

impr

oved

R

N p

erce

ptio

ns o

f vis

it ad

equa

cy. G

reat

er ti

me

per v

isit,

hig

her s

core

s for

form

al in

form

atio

n ex

chan

ge &

con

tinui

ty o

f car

e by

the

prim

ary

nurs

e w

ere

asso

ciat

ed w

ith fe

wer

vis

its. A

m

edic

al d

iagn

osis

of n

eopl

asm

, gre

ater

num

ber

of n

urse

s vis

iting

clie

nt, p

allia

tive

& lo

ng

dura

tion

visi

ts ty

pes,

& in

crea

sing

use

of v

oice

m

ail w

ere

asso

ciat

ed w

ith in

crea

sed

num

ber o

f vi

sits

. 65

. O

’Brie

n-Pa

llas,

Thom

son,

A

lksn

is, B

ruce

Econ

omic

impa

ct o

f st

affin

g de

cisi

ons

Ont

ario

acu

te

care

hos

pita

ls

Hos

pita

l C

hara

cter

istic

s:

earn

ed (p

aid)

hou

rs

patie

nt p

er d

ay (f

or

Bet

wee

n 19

94 a

nd 1

998,

inpa

tient

cas

es

drop

ped

by 1

84,7

66 w

hile

out

patie

nt c

ases

in

crea

sed

by 1

44, 6

03. D

ata

sugg

est t

hat a

n in

crea

se in

the

over

all r

esou

rces

use

d by

Evid

ence

-bas

ed S

taffi

ng

54

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

(200

1)

RN

, RPN

, & U

CP

com

bine

d),

RN

su

rvey

of

abse

ntee

ism

&

over

time,

staf

fing,

co

mpe

nsat

ion,

w

orkl

oad

data

&

prod

uctiv

ity

Patie

nt

Cha

ract

eris

tics:

re

lativ

e in

tens

ity

wei

ghts

, co

mpl

exity

of

inpa

tient

hos

pita

l ca

ses (

1994

/95

– 19

98/9

9);

hosp

italiz

ed p

atie

nts i

n re

cent

yea

rs e

ven

thou

gh th

e nu

mbe

r of h

ospi

taliz

ed c

ases

& th

e av

erag

e le

ngth

of s

tay

have

dec

reas

ed.

Com

plex

ity le

vels

hav

e te

nded

to in

crea

se fo

r al

l age

gro

ups i

n ea

ch y

ear b

etw

een

1994

and

19

98, w

hile

the

over

all n

umbe

r of n

urse

s w

orki

ng in

hos

pita

l set

tings

ahs

dec

reas

ed.

In 1

998/

99 a

lone

, $17

1 m

illio

n sp

ent o

n ov

ertim

e ho

urs (

appr

ox. 2

,250

FTE

s); o

f whi

ch,

$57

mill

ion

on o

verti

me

pay

prem

ium

s. $1

9 m

illio

n sp

ent o

n nu

rsin

g ag

ency

per

sonn

el &

$3

9 m

illio

n sp

ent o

n si

ck ti

me

(app

rox.

765

FT

Es).

Ove

rtim

e co

sts a

lmos

t per

fect

ly c

orre

late

d (r

=.92

8, p

>.01

) with

sick

tim

e co

sts.

Hou

rs

patie

nt p

er d

ay w

ere

rela

ted

to b

oth

over

time

cost

s (r=

.439

, p<.

01) &

sick

tim

e co

sts (

r= .4

88,

p<.0

1) su

gges

ting

that

as h

ours

of c

are

patie

nt

per d

ay in

crea

sed,

so d

id th

e ov

ertim

e th

at

nurs

es w

ere

aske

d to

wor

k &

the

inci

denc

e of

m

isse

d sh

ifts d

ue to

illn

ess.

66.

Pink

erto

n,

Riv

ers (

2001

) Fa

ctor

s tha

t aff

ect

staf

fing

need

s.

Nur

se e

duca

tion,

ex

perie

nce,

use

of

relie

f sta

ff,

wor

kloa

d,

prop

ortio

n of

nur

se

wor

ked

hour

s

Freq

uenc

y &

co

mpl

exity

of

chan

ges.

Bur

nout

. Im

porta

nt v

aria

bles

incl

ude

unit

cohe

sive

ness

, ch

aos f

acto

rs, c

omm

unic

atio

n, o

rgan

izat

iona

l sk

ills o

f nur

ses,

supp

ort s

taff

ava

ilabi

lity,

&

num

ber o

f flo

at st

aff.

Evid

ence

-bas

ed S

taffi

ng

55

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

67.

Potte

r, B

arr,

McS

wee

ney,

Sl

edge

(200

3)

Rel

atio

nshi

p be

twee

n R

N st

affin

g le

vels

&

patie

nt o

utco

mes

.

All

acut

e in

patie

nt c

are

units

(n=3

2) o

f on

e ho

spita

l. 20

00.0

2-20

01.0

1.

Uni

t of a

naly

sis

was

inpa

tient

un

it. A

djus

ted

for f

loat

pe

rcen

tage

&

acui

ty.

8 ho

ur d

ay sh

ift

nurs

e st

affin

g co

nver

ted

to d

irect

nu

rsin

g ca

re d

aily

ho

urs p

er p

atie

nt

for a

ll nu

rsin

g pe

rson

nel

per

mon

th; a

vera

ge:

num

ber o

f hou

rs o

f nu

rsin

g ca

re p

er

patie

nt d

aily

on

day

shift

; pe

rcen

tage

of

RN

& U

AP

dire

ct c

are

hour

s;

float

per

cent

age;

to

tal p

atie

nt c

are

hour

s. Pa

tient

ch

arac

teris

tics:

ac

uity

(ven

dor-

base

d pa

tient

cl

assi

ficat

ion

tool

).

Pa

tient

ou

tcom

es: f

alls

pe

r 100

0 pa

tient

da

ys, m

edic

atio

n er

rors

per

1,0

00

patie

nt d

ays,

self-

repo

rted

sym

ptom

m

anag

emen

t (V

AS)

, sel

f-ca

re

& h

ealth

stat

us

(Nat

iona

l Cen

ter

for H

ealth

St

atis

tics H

ealth

In

terv

iew

Su

rvey

) & p

ost-

disc

harg

e pa

tient

sa

tisfa

ctio

n.

Perc

enta

ge o

f RN

hou

rs n

egat

ivel

y co

rrel

ated

w

ith p

atie

nts’

per

cept

ion

of p

ain

& p

ositi

vely

co

rrel

ated

with

pat

ient

s’ p

erce

ptio

ns o

f sel

f-ca

re a

bilit

y &

hea

lth st

atus

, as w

ell a

s sa

tisfa

ctio

n po

st-d

isch

arge

.

68.

Pres

cott

(199

3)

Impa

ct o

f nur

se

staf

fing

leve

ls &

skill

m

ix o

n pa

tient

ou

tcom

es

Pr

opor

tion

of n

urse

w

orke

d ho

urs,

staf

fing

leve

ls.

Pa

tient

mor

talit

y,

qual

ity, a

nd c

osts

of

car

e.

Hig

h pe

rcen

tage

of R

Ns i

s ass

ocia

ted

with

lo

wer

than

exp

ecte

d m

orta

lity

rate

s (13

st

udie

s), l

engt

h of

stay

, cos

ts, c

ompl

icat

ions

. Sa

lary

savi

ngs o

f dec

linin

g sk

ill m

ix m

ay b

e of

fset

by

prod

uctiv

ity d

eclin

es.

69.

Pres

cott

(198

6)

Whe

ther

or

gani

zatio

nal,

adm

inis

trativ

e, &

pr

actic

e fa

ctor

s di

ffer

entia

te a

mon

g ho

spita

ls &

pat

ient

ca

re u

nits

as t

o re

gist

ered

nur

se

1044

staf

f nur

ses

wor

king

on

90

patie

nt c

are

units

in

15

hosp

itals

. D

ata

colle

cted

in

1981

& 1

982

Uni

t vac

ancy

rate

s, vo

lunt

ary

turn

over

, st

abili

ty.

33 p

redi

ctor

va

riabl

es.

Staf

f-pa

tient

ratio

s

Mod

el e

xpla

ined

52%

of v

aria

bilit

y in

vac

ancy

ra

tes,

56%

of v

aria

bilit

y in

stab

ility

rate

s, &

42

% o

f var

iabi

lity

in re

lativ

e tu

rnov

er.

Hig

h va

canc

y ra

tes a

ssoc

iate

d w

ith 7

var

iabl

es

incl

udin

g hi

gh st

aff-

patie

nt ra

tios o

n ev

enin

g sh

ift &

per

ceiv

ed in

adeq

uacy

of w

orki

ng

cond

ition

s.

Evid

ence

-bas

ed S

taffi

ng

56

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

vaca

ncy,

stab

ility

, &

turn

over

rate

s. at

org

aniz

atio

nal

leve

l. H

igh

stab

ility

ass

ocia

ted

with

7 v

aria

bles

in

clud

ing

incr

ease

d st

aff-

patie

nt ra

tios &

pe

rcei

ved

inad

equa

cy o

f wor

king

con

ditio

ns.

Hig

h tu

rnov

er a

ssoc

iate

d w

ith 7

var

iabl

es

incl

udin

g lo

w st

aff-

patie

nt ra

tios o

n ni

ght s

hift

& p

erce

ived

inad

equa

cy o

f wor

king

con

ditio

ns.

70.

Rob

erts

on,

Dow

d, H

assa

n (1

997)

Staf

fing

inte

nsity

&

cost

s of c

are.

19

6 ac

ute

care

ho

spita

ls in

C

alifo

rnia

from

A

HA

ann

ual

surv

ey.

Prop

ortio

n of

N

urse

wor

ked

hour

s

C

osts

of c

are,

St

affin

g ra

tios.

Neg

ativ

e as

soci

atio

n be

twee

n so

me

staf

fing

leve

ls &

cos

ts o

f car

e (r

=-0.

04 to

r=-0

.36)

. H

ighe

r res

pira

tory

car

e te

chni

cian

staf

fing

inte

nsity

is re

late

d to

low

er c

osts

per

epi

sode

of

care

(r=-

0.36

). Po

sitiv

e re

latio

nshi

p be

twee

n R

N st

affin

g in

tens

ity &

cos

ts o

f car

e (m

ay b

e du

e to

use

of h

igh

skill

mix

stra

tegy

) r=0

.08.

Th

ere

are

limits

of s

kill-

spec

ific

staf

fing

inte

nsity

bel

ow w

hich

cos

ts o

f car

e ar

e ac

tual

ly

incr

ease

d. U

nder

staf

fing

incr

ease

s cos

ts &

re

duce

s qua

lity.

71

. Sa

info

rt,

Kar

sh, B

oosk

e,

Smith

(200

1)

Rev

iew

of l

itera

ture

on

cha

ract

eris

tics &

im

pact

of h

ealth

y w

ork

orga

niza

tions

H

ospi

tal s

ize,

car

e de

liver

y sy

stem

, st

affin

g

Wor

k en

viro

nmen

t N

urse

hea

lth

stat

us, p

atie

nt

com

plic

atio

ns,

job

satis

fact

ion,

bu

rnou

t

Cre

ates

a m

odel

& re

sear

ch a

gend

a fo

r he

alth

care

qua

lity

impr

ovem

ent &

pat

ient

sa

fety

. Org

aniz

atio

nal f

acto

rs &

wor

king

co

nditi

ons a

ffec

t em

ploy

ee h

ealth

&

prod

uctiv

ity.

72.

Seag

o, A

sh,

Spet

z, C

offm

an,

Gru

mba

ch

(200

1)

Cha

ract

eris

tics o

f ac

ute

care

hos

pita

ls

that

repo

rt R

N

shor

tage

s whe

n w

ides

prea

d sh

orta

ge

exis

ts &

whe

n w

ides

prea

d sh

orta

ge

is n

o lo

nger

evi

dent

.

All

acut

e-ca

re

hosp

itals

in

Uni

ted

Stat

es,

seco

ndar

y da

ta

from

nat

iona

l su

rvey

.

Staf

fing

leve

ls

(sho

rtage

s), p

atie

nt

dem

ogra

phic

s, nu

rse

educ

atio

n,

care

del

iver

y sy

stem

.

Loca

tion

in S

outh

, hig

h pe

rcen

tage

of n

on-

whi

te c

ount

y re

side

nts,

high

per

cent

age

of

patie

nts w

ith M

edic

are

as p

ayer

, hig

her p

atie

nt

acui

ty &

use

of t

eam

nur

sing

car

e de

liver

y pr

edic

ted

hosp

itals

repo

rting

shor

tage

s bot

h w

hen

ther

e w

as &

whe

n th

ere

was

not

a

wid

espr

ead

shor

tage

. Wag

e is

not

a si

gnifi

cant

pr

edic

tor o

f sho

rtage

s. R

N w

orkf

orce

pol

icy

need

s to

plac

e em

phas

is o

n di

strib

utio

n re

lativ

e to

ove

rall

supp

ly.

73.

Seyb

olt (

1986

) U

nder

stan

ding

the

647

fem

ales

RN

s C

aree

r sta

ge, w

ork-

N

urse

turn

over

Tu

rnov

er in

tent

ions

of e

mpl

oyee

s at d

iffer

ent

Evid

ence

-bas

ed S

taffi

ng

57

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

caus

es o

f pre

mat

ure

nurs

e tu

rnov

er in

or

der t

o re

tain

nur

ses.

at a

larg

e W

est

Coa

st h

ospi

tal

role

des

ign

(job,

in

tera

ctio

ns,

orga

niza

tiona

l po

licie

s)

inte

ntio

ns

care

er st

ages

are

aff

ecte

d by

diff

erin

g w

ork-

role

de

sign

fact

ors.

74.

Silb

er,

Will

iam

s, K

raka

uer,

Schw

artz

(199

2)

Hos

pita

l & p

atie

nt

char

acte

ristic

s tha

t pr

edic

t mor

talit

y af

ter

surg

ery.

2831

pat

ient

s un

derg

oing

ch

olec

yste

ctom

y &

314

1 pa

tient

s un

derg

oing

tra

nsur

ethr

al

pros

tate

ctom

y.

Ran

dom

se

lect

ion,

from

7

stat

es.

Patie

nt

char

acte

ristic

s, nu

mbe

r of h

ospi

tal

beds

, sta

ff ra

tios

A

dver

se p

atie

nt

outc

omes

, m

orta

lity

Adv

erse

occ

urre

nces

ass

ocia

ted

prim

arily

with

pa

tient

cha

ract

eris

tics.

Failu

re to

resc

ue

asso

ciat

ed m

ore

with

hos

pita

l cha

ract

eris

tics

than

pat

ient

cha

ract

eris

tics (

high

er re

lativ

e ris

k). U

nder

stan

ding

reas

ons b

ehin

d va

riatio

ns

in m

orta

lity

shou

ld b

e us

ed to

upg

rade

qua

lity

of c

are.

75.

Soch

alsk

i (2

001)

Qua

lity

of c

are,

nur

se

staf

fing

& p

atie

nt

outc

omes

.

13,2

00 m

edic

al-

surg

ical

RN

s fr

om a

cute

car

e ho

spita

ls in

Pe

nnsy

lvan

ia.

Ran

dom

sam

ple

from

stat

e bo

ard

data

base

, 52%

re

spon

se ra

te.

Nur

se

dem

ogra

phic

s, ed

ucat

ion,

pr

opor

tion

of n

urse

w

orke

d ho

urs

Wor

k en

viro

nmen

t, w

orkl

oad

Job

satis

fact

ion,

bu

rnou

t, pa

tient

co

mpl

icat

ions

Med

ical

/sur

gica

l RN

s had

low

est s

core

s on

qual

ity o

f car

e (c

ompa

red

to o

ther

type

s of

units

), ha

d a

high

er n

umbe

r of t

asks

left

undo

ne

at th

e en

d of

shift

& a

re e

xper

ienc

ing

sign

ifica

nt le

vels

of b

urno

ut. H

ighe

r wor

kloa

ds

wer

e as

soci

ated

with

low

er q

ualit

y (r

=-0.

24).

41%

of n

urse

s wer

e m

oder

atel

y or

ver

y di

ssat

isfie

d w

ith th

eir j

ob.

76.

Sovi

e, Ja

wad

(2

001)

Im

pact

of h

ospi

tal

rest

ruct

urin

g on

pa

tient

out

com

es

1997

& 1

998

fisca

l yea

r dat

a fr

om 2

9 U

.S.

univ

ersi

ty

teac

hing

ho

spita

ls (w

ith >

30

0 ac

ute

oper

atin

g be

ds)

from

8 o

f 9 U

.S.

cens

us re

gion

s; 1

in

patie

nt a

cute

ad

ult m

edic

al

Stru

ctur

e:

MEC

ON

-PEE

Rx

Ope

ratio

ns

Ben

chm

arki

ng

Dat

abas

e R

epor

ts

(FTE

for e

ach

type

of

nur

sing

pe

rson

nel;

prop

ortio

n of

nur

se

wor

ked

hour

s;

hour

s wor

ked

per

patie

nt d

aily

for

Proc

ess:

M

anag

emen

t Pr

actic

es &

O

rgan

izat

iona

l Pr

oces

ses

Que

stio

nnai

re &

Q

ualit

y of

Em

ploy

men

t Su

rvey

subs

cale

on

aut

onom

y &

de

cisi

on-m

akin

g

Out

com

e:

Patie

nt o

utco

mes

(a

nnua

l fal

l rat

es,

noso

com

ial

pres

sure

ulc

ers,

urin

ary

tact

in

fect

ions

, pa

tient

sa

tisfa

ctio

n w

ith

vario

us su

rvey

s)

- RN

s few

er in

num

ber w

ith a

n in

crea

se in

un

licen

sed

assi

stiv

e pe

rson

nel.

- inc

reas

ed R

N h

ours

wor

ked

patie

nt p

er d

ay

asso

ciat

ed w

ith lo

wer

fall

rate

s (F=

11.7

3,

p=0.

002)

& h

ighe

r pat

ient

satis

fact

ion

with

pai

n m

anag

emen

t (F=

15.0

5, p

=0.0

007)

- i

ncre

ased

wor

ked

hour

s pat

ient

per

day

by

all

staf

f ass

ocia

ted

with

low

er ra

tes o

f urin

ary

tract

in

fect

ions

.

Evid

ence

-bas

ed S

taffi

ng

58

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

unit

& su

rgic

al

unit

per h

ospi

tal;

Uni

ts o

f ana

lysi

s:

hosp

ital n

ursi

ng

dept

(inc

l. in

tens

ive

care

un

its);

med

ical

un

its;

surg

ical

uni

ts

RN

, unl

icen

sed

staf

f, LP

N, c

lerk

s, m

anag

ers;

labo

ur

cost

s per

dis

char

ge;

rest

ruct

urin

g as

sess

men

t too

l; &

in

terv

iew

) - n

urse

de

mog

raph

ics &

sa

tisfa

ctio

n (I

ndiv

idua

l Nur

se

Que

stio

nnai

re)

77.

Stric

klan

d,

Nee

ly (1

995)

Im

plem

enta

tion

of a

St

anda

rd S

taff

ing

Inde

x to

allo

cate

nu

rsin

g st

aff.

9000

-bed

ac

adem

ic

med

ical

cen

tre in

Te

xas.

Staf

fing

mix

, pa

tient

acu

ity

C

osts

of c

are,

pr

oduc

tivity

, st

affin

g ra

tios

New

syst

em d

eter

min

ed n

ursi

ng p

rodu

ctiv

ity

quic

kly

& e

ffic

ient

ly &

spec

ific

patie

nt n

eed

wer

e de

term

ined

thro

ugh

regu

lar &

thor

ough

ev

alua

tions

. One

per

son

was

resp

onsi

ble

for

staf

fing

on e

ach

unit.

The

SSI

allo

wed

for a

n ef

ficie

nt &

acc

urat

e ut

iliza

tion

of n

ursi

ng

pers

onne

l. 78

. Ta

rnow

-M

ordi

, Hau

, W

arde

n,

Shea

rer (

2000

)

The

rela

tions

hip

of

nurs

ing

requ

irem

ents

&

wor

kloa

d m

easu

res

& h

ospi

tal m

orta

lity

in th

e in

tens

ive

care

un

it.

One

adu

lt in

tens

ive

care

un

it in

the

UK

. A

ll ad

mis

sion

s (n

=105

0)

betw

een

1992

&

1995

that

met

cr

iteria

for

adju

stm

ent o

f m

orta

lity

risk

by

the

APA

CH

E II

eq

uatio

n (A

cute

Ph

ysio

logy

&

Chr

onic

Hea

lth

Eval

uatio

n)

- Pat

ient

pre

dict

ed

risk

of m

orta

lity

(APA

CH

E II

eq

uatio

n w

hich

us

es in

form

atio

n fr

om th

e 1st

24h

af

ter a

dmis

sion

). in

tens

ive

care

uni

t w

orkl

oad:

oc

cupa

ncy

(hig

hest

nu

mbe

r of b

eds

occu

pied

eac

h sh

ift

& p

eak

occu

panc

y as

the

high

est

occu

panc

y pe

r shi

ft du

ring

patie

nt

stay

), to

tal

M

orta

lity

rate

s “U

nadj

uste

d m

orta

lity

was

gre

ater

for p

atie

nts

expo

sed

to h

igh

vers

us m

oder

ate

over

all

inte

nsiv

e ca

re u

nit w

orkl

oad

(odd

s rat

io 4

-0 [2

-6-

6.2]

” (p

. 187

). Pa

tient

s exp

osed

to h

igh

inte

nsiv

e ca

re u

nit

wor

kloa

d w

ere

mor

e lik

ely

to d

ie (o

dds r

atio

3-

1 [1

.9-5

.0])

than

thos

e ex

pose

d to

low

er

wor

kloa

d, b

oth

befo

re &

afte

r adj

ustm

ent f

or

risk

by th

e A

PAC

HE

II e

quat

ion.

Tw

o m

easu

res o

f int

ensi

ve c

are

unit

wor

kloa

d m

ost s

trong

ly a

ssoc

iate

d w

ith a

djus

ted

mor

talit

y (e

xclu

ding

tota

l uni

t nur

sing

car

e re

quire

men

ts) w

ere

peak

occ

upan

cy &

the

ratio

of

occ

upie

d to

app

ropr

iate

ly st

affe

d be

ds.

Evid

ence

-bas

ed S

taffi

ng

59

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

Inte

nsiv

e ca

re u

nit

nurs

ing

requ

irem

ent-U

K

Inte

nsiv

e C

are

Soci

ety

reco

mm

enda

tion,

ra

tio o

f occ

upie

d to

ap

prop

riate

ly

staf

fed

beds

Use

of a

ggre

gate

dat

a (to

tal)

inte

nsiv

e ca

re u

nit

nurs

ing

care

requ

irem

ents

) may

exp

lain

the

asso

ciat

ion

betw

een

high

inte

nsiv

e ca

re u

nit

nurs

ing

requ

irem

ent &

mor

talit

y, p

oten

tially

be

caus

e m

ore

serio

usly

ill p

ts a

re m

ore

likel

y to

di

e.

79.

Wai

, Bam

e,

Rob

inso

n (1

998)

Lite

ratu

re re

view

of

nurs

ing

turn

over

.

Nur

se

dem

ogra

phic

s, ed

ucat

ion,

pr

ofes

sion

al st

atus

, ho

spita

l siz

e.

Wor

kloa

d C

osts

of c

are,

job

satis

fact

ion.

O

lder

staf

f, m

inor

ities

, tho

se w

ith h

ighe

r in

com

e, e

mot

iona

l sup

port,

or l

onge

r ten

ure

had

low

er tu

rnov

er. F

acto

rs le

adin

g to

turn

over

in

clud

e jo

b sa

tisfa

ctio

n &

tens

ion,

or

gani

zatio

nal c

omm

itmen

t, jo

b po

ssib

ilitie

s &

supe

rvis

or b

ehav

iour

. 80

. W

eism

an,

Ale

xand

er,

Cha

se (1

981)

Rea

sons

for n

ursi

ng

turn

over

. 12

59 fu

ll-tim

e R

Ns i

n tw

o la

rge

univ

ersi

ty-

affil

iate

d ho

spita

ls. E

ntire

po

pula

tion

was

ta

rget

ed, 9

8%

resp

onse

rate

.

Nur

se

dem

ogra

phic

s.

Job

satis

fact

ion

Maj

ority

resi

gned

due

to jo

b di

ssat

isfa

ctio

n (5

7.1%

and

72.

5%).

Pers

onal

fact

ors h

ave

little

ef

fect

on

turn

over

pro

cess

. Rea

sons

for

resi

gnin

g in

clud

e w

ork

pres

sure

s due

to

unde

rsta

ffin

g &

num

ber o

r sch

edul

ing

of w

ork

hour

s. Lo

w p

ay w

as le

ast f

requ

ently

cite

d as

the

reas

on (l

ess t

han

2%).

81.

Whi

tman

, Y

ooky

ang,

D

avid

son,

Wol

f, W

ang

(200

2)

Det

erm

ine

the

rela

tions

hips

bet

wee

n nu

rsin

g st

affin

g &

sp

ecifi

c nu

rse-

sens

itive

out

com

es.

Obs

erva

tiona

l da

ta fr

om 9

5 pa

tient

car

e un

its

acro

ss 1

0 ac

ute

care

hos

pita

ls in

ea

ster

n U

S.

Nur

se p

rofe

ssio

nal

stat

us, s

taff

ing

hour

s, pa

tient

day

s pe

r uni

t, w

orke

d ho

urs p

atie

nt p

er

day

(WH

PPD

)

Stru

ctur

al

hosp

ital o

r uni

t va

riatio

ns.

Med

ical

err

ors,

fall

rate

s, in

fect

ions

, ulc

er

rate

s.

Sign

ifica

nt in

vers

e re

latio

nshi

ps p

rese

nt

betw

een

staf

fing

& fa

lls in

car

diac

inte

nsiv

e ca

re (r

=-0.

53),

med

icat

ion

erro

rs in

car

diac

&

non-

card

iac

inte

nsiv

e ca

re u

nit (

r=-0

.55

and

-0.

65 re

spec

tivel

y) &

rest

rain

t rat

es in

med

ical

-su

rgic

al u

nits

(r=-

0.48

). N

o st

atis

tical

ly

sign

ifica

nt re

latio

nshi

ps w

ere

foun

d be

twee

n th

e ou

tcom

es o

f cen

tral l

ine

infe

ctio

n ra

tes &

pr

essu

re u

lcer

rate

s & W

HPP

D a

cros

s spe

cial

ty

units

. An

inve

rse

rela

tions

hip

betw

een

WH

PPD

&

falls

was

pre

sent

in c

ardi

ac in

term

edia

te c

are

(r=-

0.53

). Th

e im

pact

of s

taff

ing

on o

utco

mes

Evid

ence

-bas

ed S

taffi

ng

60

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

is h

ighl

y va

riabl

e ac

ross

spec

ialty

uni

ts, b

ut

whe

n pr

esen

t, th

e re

latio

nshi

ps a

re in

vers

ely

rela

ted

with

low

er st

affin

g le

vels

resu

lting

in

high

er ra

tes o

f all

outc

omes

.

4.

Thro

ughp

uts

A

utho

rs, Y

ear

Focu

s Sa

mpl

e

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

82

. A

llred

, M

iche

l, A

rfor

d,

Car

ter,

Vei

tch,

D

ring,

Bea

son,

H

iott,

Fin

ch

(199

4)

Stud

y of

en

viro

nmen

tal

unce

rtain

ty.

113

RN

s fro

m a

te

rtiar

y ca

re

med

ical

cen

tre in

so

uth-

east

ern

Uni

ted

Stat

es.

Stra

tifie

d ra

ndom

sa

mpl

ing,

66%

re

spon

se ra

te.

Nur

ses’

ex

perie

nce.

W

ork

envi

ronm

ent,

unan

ticip

ated

ev

ents

, en

viro

nmen

tal

unce

rtain

ty.

N

o di

ffer

ence

bet

wee

n nu

rses

’ wor

k st

atus

or

expe

rienc

e &

resp

onse

pat

tern

s afte

r chi

-squ

are

anal

ysis

. Inc

reas

e in

env

ironm

ents

’ com

plex

ity

(r=0

.49)

, cha

ngea

bilit

y (r

=0.3

4) &

un

pred

icta

bilit

y (r

=0.5

6) le

ad to

incr

ease

d en

viro

nmen

tal u

ncer

tain

ty.

83.

Bro

wn

(200

0)

One

-Sto

p R

ecov

ery:

a

fast

-trac

k pr

ogra

m

for c

ardi

ac su

rgic

al

patie

nts.

N

urse

ratio

s, pa

tient

teac

hing

, an

d m

edic

al

diag

nosi

s.

Le

ngth

of s

tay

in

inte

nsiv

e ca

re

unit

& st

ep-d

own

units

, cos

ts o

f ca

re, p

atie

nt

com

plic

atio

ns

Fast

-trac

k pr

ogra

ms i

mpr

ove

patie

nt c

omfo

rt,

enha

nce

qual

ity o

f car

e, &

redu

ce c

osts

. B

arrie

rs in

clud

e ph

ysic

ian

relu

ctan

ce, l

imite

d re

sour

ces,

lack

of c

omm

unic

atio

n, &

pat

ient

&

fam

ily a

nxie

ty. O

ne-s

top

Rec

over

y of

fers

co

ntin

uity

of s

taff

, lim

its m

ultip

le in

patie

nt

trans

fers

, inc

reas

es fl

exib

ility

in h

uman

&

inst

itutio

nal r

esou

rces

, red

uces

inte

nsiv

e ca

re

unit

read

mis

sion

s, &

enh

ance

s pat

ient

com

fort

& fa

mily

supp

ort.

Patie

nt c

are

is p

rovi

ded

in

one

loca

tion

by c

onsi

sten

t sta

ff th

roug

hout

the

reco

very

. 84

. C

ady,

Mat

tes,

Bur

ton

(199

5)

Impl

emen

tatio

n of

a

step

-dow

n un

it to

de

crea

se in

tens

ive

care

uni

t len

gth

of

Teac

hing

ho

spita

l with

220

ad

ult b

eds,

27%

ar

e in

tens

ive

care

Patie

nt

dem

ogra

phic

s, m

edic

al

diag

nosi

s, pa

tient

Nur

sing

w

orkl

oad

Leng

th o

f sta

y in

in

tens

ive

care

un

it. ,

read

mis

sion

s to

Tim

e sp

ent i

n in

tens

ive

care

uni

t dec

reas

ed

sign

ifica

ntly

for t

hose

adm

itted

to st

ep d

own

unit

1.99

day

s ins

tead

of 3

.35)

. Len

gth

of st

ay

did

not d

iffer

for t

hose

in a

step

dow

n un

it.

Evid

ence

-bas

ed S

taffi

ng

61

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

stay

. un

it. C

ompa

rabl

e in

stitu

tions

pr

ovid

ed

info

rmat

ion

for

com

paris

on.

acui

ty,

prop

ortio

n of

nu

rse

wor

ked

hour

s, nu

rsin

g ra

tios.

inte

nsiv

e ca

re

unit.

, nu

rse

job

satis

fact

ion,

pa

tient

co

mpl

icat

ions

, co

sts o

f car

e.

Qua

lity

of c

are

rem

aine

d ex

celle

nt &

cos

ts

wer

e de

crea

sed

for p

atie

nt in

the

step

dow

n un

it.

85.

Coh

n,

Ros

boro

ugh,

Fe

rnan

dez

(199

7)

Red

ucin

g co

sts &

le

ngth

of s

tay

&

impr

ovin

g ef

ficie

ncy

& q

ualit

y of

car

e in

ca

rdia

c su

rger

y.

Mul

tidis

cipl

inar

y he

alth

care

team

at

Brig

ham

&

Wom

en’s

ho

spita

l.

Med

ical

di

agno

sis.

Le

ngth

of s

tay,

co

sts o

f car

e M

ultid

isci

plin

ary

grou

p m

et w

eekl

y to

dis

cuss

pr

oble

ms w

ith c

ardi

ac su

rgic

al se

rvic

es. C

are

Coo

rdin

atio

n Te

am m

onito

rs c

linic

al p

athw

ays

& re

com

men

ds w

ays t

o im

prov

e se

rvic

es.

Res

ults

incl

ude

high

er v

olum

e of

surg

ery,

de

crea

sed

leng

th o

f sta

y (b

y ab

out 1

5%),

decr

ease

d co

sts,

& in

crea

sed

patie

nt sa

tisfa

ctio

n (to

95%

). 86

. D

renk

ard

(200

1)

Stra

tegi

c pl

anni

ng

met

hodo

logy

for

nurs

ing

care

.

C

are

deliv

ery

syst

em.

The

met

hodo

logy

use

s a tr

ansf

orm

atio

nal

lead

ersh

ip a

sses

smen

t too

l, qu

ality

pla

nnin

g m

etho

ds &

larg

e gr

oup

inte

rven

tions

to e

ngag

e nu

rses

in im

plem

enta

tion

of st

rate

gies

. Six

dr

ivin

g st

rate

gies

for n

ursi

ng w

ere

dete

rmin

ed:

lead

ersh

ip, p

ract

ice,

cul

ture

, lea

rnin

g, a

nd

rese

arch

& ro

le c

larit

y.

87.

Duf

fy,

Lem

ieux

(199

5)

Serv

ice-

line

conc

ept

& p

atie

nt-c

ente

red

care

in a

car

diac

se

tting

.

C

ontin

uity

of

care

.

Cos

ts o

f car

e,

prod

uctiv

ity.

A h

oriz

onta

l, m

ultid

isci

plin

ary

envi

ronm

ent

with

a p

erfo

rman

ce-b

ased

mod

el is

des

crib

ed.

Empl

oyee

role

s are

bro

aden

ed to

focu

s on

the

entir

e pr

oces

s of c

are

with

all

disc

iplin

es

wor

king

toge

ther

. 88

. H

elt,

Jelin

ek

(198

8)

Nur

sing

pro

duct

ivity

&

qua

lity

in th

e w

ake

of c

ost c

uttin

g.

Eigh

t mill

ion

patie

nt d

ays f

rom

M

edic

us

Nat

iona

l D

atab

ase

in U

S.

Hos

pita

l siz

e,

wor

kloa

d,

prop

ortio

n of

nu

rse

wor

ked

hour

s.

Pe

rcei

ved

qual

ity

of c

are,

leng

th o

f st

ay, c

osts

of

care

.

Even

in fa

ce o

f sta

ffin

g re

duct

ions

, pro

duct

ivity

(d

ecre

ase

in ra

tio o

f nur

sing

hou

rs to

wor

kloa

d)

& q

ualit

y (b

ased

on

spec

ific

obje

ctiv

es) h

ave

impr

oved

. Whi

le a

cuity

incr

ease

d, le

ngth

of

stay

had

dec

reas

ed. I

ncre

ased

per

cent

age

of

RN

s lea

ds to

incr

ease

d pr

oduc

tivity

, onl

y ha

lf of

whi

ch is

lost

due

to h

ighe

r cos

ts.

89.

Lam

b, S

tem

pel

(199

4)

Nur

se c

ase

man

agem

ent f

rom

the

16 p

atie

nts w

ho

had

wor

ked

with

C

ontin

uity

of

care

.

Patie

nt

com

plic

atio

ns,

The

stud

y ex

plor

ed p

atie

nts’

per

spec

tive

of

wor

king

with

a n

urse

cas

e m

anag

er. P

atie

nts

Evid

ence

-bas

ed S

taffi

ng

62

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

patie

nt’s

per

spec

tive:

th

e in

side

r-ex

pert.

a

nurs

e ca

se

man

ager

dur

ing

hosp

italiz

atio

n,

rang

ing

in a

ge

from

66-

100.

heal

th st

atus

. de

scrib

ed th

e pr

oces

s of n

urse

s bec

omin

g th

eir

insi

der-

expe

rt. T

his c

onsi

sts o

f thr

ee p

hase

s:

bond

ing,

wor

king

& c

hang

ing.

Rel

atio

nshi

p w

ith a

nur

se in

side

r-ex

pert

enab

led

patie

nts t

o im

prov

e he

alth

out

com

es, h

ave

few

er

hosp

italiz

atio

ns, &

bet

ter q

ualit

y of

life

.

5.

Patie

nt O

utco

mes

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

90.

Bro

oten

, N

aylo

r (19

95)

Nur

ses’

eff

ects

on

patie

nt o

utco

mes

.

Prop

ortio

n of

nu

rse

wor

ked

hour

s.

Pa

tient

co

mpl

icat

ions

&

outc

omes

, st

affin

g ra

tios,

cost

s of c

are.

Mos

t im

porta

nt is

sues

incl

ude

type

s of p

atie

nt

outc

omes

that

shou

ld b

e m

easu

red

& th

e am

ount

& ty

pe o

f nur

sing

nee

ded

in a

giv

en

envi

ronm

ent,

for s

peci

fic p

atie

nt g

roup

s & in

or

der t

o af

fect

out

com

es.

91.

Dan

sky,

B

rann

on,

Wan

gsne

ss,

(199

4)

Staf

fing

char

acte

ristic

s &

patie

nt sa

tisfa

ctio

n in

ho

me

heal

thca

re.

13 n

ot-f

or-p

rofit

ho

me

heal

th

agen

cies

in

Penn

sylv

ania

&

Ohi

o.

Nur

se e

duca

tion

& e

xper

ienc

e,

agen

cy si

ze.

Pa

tient

sa

tisfa

ctio

n H

ighe

r num

bers

of f

ull-t

ime

staf

f or o

f BN

S-pr

epar

ed R

Ns p

redi

cted

hig

her p

atie

nt

satis

fact

ion.

Siz

e of

age

ncy

had

no im

pact

on

satis

fact

ion.

Age

ncie

s with

med

ium

ben

efits

ha

d th

e hi

ghes

t pat

ient

satis

fact

ion.

92

. Fo

rtins

ky,

Mad

igan

(1

997)

.

Rel

atio

nshi

p be

twee

n m

easu

res o

f hom

e ca

re re

sour

ce

cons

umpt

ion

&

patie

nt o

utco

me

mea

sure

s.

N =

201

adu

lt m

edic

al/s

urgi

cal

hom

e ca

re

patie

nts w

ho

bega

n ne

w

epis

odes

of h

ome

care

from

10

med

icar

e-ce

rtifie

d ho

me

care

age

ncie

s in

Ohi

o.

Patie

nt

char

acte

ristic

s:

the

29 it

em

OA

SIS

asse

ssm

ent

(dem

ogra

phic

s, cl

inic

al &

fu

nctio

nal h

ealth

st

atus

, illn

ess &

re

hab

prog

nosi

s &

am

t. of

fam

ily

& o

ther

info

rmal

su

ppor

t rec

’d b

y

Pa

tient

ou

tcom

es:

disc

harg

e st

atus

; ch

ange

in

clin

ical

&

func

tiona

l hea

lth

stat

us m

easu

res

betw

een

adm

issi

on &

di

scha

rge.

Patie

nts w

hose

epi

sode

s end

ed w

ith d

isch

arge

at

hom

e vs

. hos

pita

lizat

ion

had

sim

ilar t

otal

vi

sit n

umbe

rs &

cos

ts b

ut th

ose

disc

harg

ed to

ho

spita

ls u

tiliz

ed th

e ho

me

care

reso

urce

s ove

r le

ss ti

me.

N

o si

gnifi

cant

diff

eren

ces w

ere

note

d in

the

use

of h

ome

care

reso

urce

s bet

wee

n pa

tient

s who

im

prov

ed &

thos

e w

hose

hea

lth st

atus

dec

lined

du

ring

the

epis

ode

of h

ome

care

. Pa

tient

s who

wer

e ad

mitt

ed to

hom

e ca

re fr

om

hosp

itals

& w

ho re

ceiv

ed h

ome

care

for l

onge

r

Evid

ence

-bas

ed S

taffi

ng

63

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

pts a

t hom

e)

Res

ourc

e co

nsum

ptio

n:

tota

l # o

f hom

e vi

sits

by

all

disc

iplin

es;

disc

iplin

e-sp

ecifi

c co

st

info

rmat

ion;

le

ngth

of s

tay;

se

rvic

e in

tens

ity

(# o

f vis

its p

er

day)

than

the

62-d

ay st

udy

perio

d us

ed th

e gr

eate

st

num

ber o

f res

ourc

es.

93.

Lanc

aste

r (1

997)

Effe

cts o

f sta

ffin

g on

in

fect

ion

rate

s.

Patie

nt

com

plic

atio

ns,

staf

fing

ratio

s.

Incr

ease

d pa

tient

-to-n

urse

ratio

s pla

ce se

vere

tim

e co

nstra

ints

on

nurs

es. T

his h

as b

een

show

n to

resu

lt in

incr

ease

d in

fect

ion

rate

s with

cen

tral

veno

us c

athe

ters

. 94

. O

’Brie

n-Pa

llas,

Dor

an,

Mur

ray,

C

ocke

rill,

Sida

ni, L

aurie

-Sh

aw,

Loch

hass

-G

erla

ch (2

002)

Var

iabl

es th

at a

ffec

t cl

ient

out

com

es w

ith

a ho

me

visi

ting

nurs

ing

serv

ice.

38 R

Ns,

11

RPN

s, 75

1 cl

ient

s rec

eivi

ng

hom

e he

alth

care

, co

nven

ienc

e sa

mpl

e.

Clie

nt:

dem

ogra

phic

s, nu

rsin

g &

m

edic

al

diag

nosi

s, O

MA

HA

scor

es,

SF-3

6 he

alth

st

atus

, tim

e on

pr

ogra

m

Nur

se:

dem

ogra

phic

s, ed

ucat

ion,

ex

perie

nce,

pr

ofes

sion

al

stat

us

Age

ncy:

Pa

tient

hea

lth

stat

us, O

MA

HA

(k

now

ledg

e,

beha

viou

r, st

atus

)

Clie

nts w

ith d

egre

e-pr

epar

ed n

urse

s had

bet

ter

OM

AH

A sc

ores

(1.8

tim

es b

ette

r odd

s of

impr

oved

kno

wle

dge

and

2.2

times

bet

ter o

dds

of im

prov

ed b

ehav

iour

). C

linic

al, p

rovi

der,

orga

niza

tiona

l & e

nviro

nmen

tal f

acto

rs a

ffec

t ou

tcom

es. M

edic

al &

nur

sing

dia

gnos

es e

xpla

in

muc

h of

var

iatio

n in

out

com

es. E

nviro

nmen

tal

com

plex

ity w

as n

egat

ivel

y as

soci

ated

with

cl

ient

out

com

es.

Evid

ence

-bas

ed S

taffi

ng

64

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

geog

raph

ic

loca

tion,

vis

it ty

pe, c

asel

oad,

pr

opor

tion

of

nurs

e w

orke

d ho

urs &

co

ntin

uity

of

care

95

. Pr

octo

r, Y

arch

eski

, O

risce

llo (1

996)

Rel

atio

nshi

ps

betw

een

hosp

ital

varia

bles

& p

atie

nt

outc

omes

.

68 p

atie

nts

diag

nose

d w

ith

MI f

rom

a la

rge

urba

n m

edic

al

cent

re.

Patie

nt

dem

ogra

phic

s

Patie

nt ju

dgm

ent

of q

ualit

y of

ca

re.

Sign

ifica

nt c

orre

latio

ns b

etw

een

nurs

ing

care

&

patie

nt o

utco

me

post

-MI (

0.40

) as w

ell a

s ho

spita

l env

ironm

ent &

pt.

outc

ome

post

-MI

(0.2

8). N

ursi

ng c

are

acco

unte

d fo

r 16

% o

f va

rianc

e in

pt.

outc

omes

. Pt.

leve

l of e

duca

tion

was

rela

ted

to o

utco

me

(0.4

1).

96.

Roh

rer,

Mom

any,

C

hang

(199

3).

The

rela

tions

hip

betw

een

natu

re-o

f-th

e-ta

sk a

spec

ts o

f or

gani

zatio

n de

sign

, st

ruct

ural

asp

ects

of

orga

niza

tion

desi

gn

& o

rgan

izat

iona

l ef

fect

iven

ess

(ope

ratio

naliz

ed a

s ou

tcom

e re

side

nt

func

tiona

l abi

lity)

.

872

nurs

ing

hom

e re

side

nts

& 1

0 nu

rsin

g ho

mes

.

Org

aniz

atio

nal

desi

gn in

clud

ed

3 st

ruct

ural

m

easu

res—

job

assi

gnm

ent,

hier

arch

y,

clos

enes

s of

supe

rvis

ion

& 2

na

ture

-of-

task

m

easu

res—

pace

of

ope

ratio

ns &

w

orkl

oad.

R

esid

ent

outc

omes

: The

7-

item

phy

sica

l fu

nctio

n sc

ale

incl

uded

bla

dder

in

cont

inen

ce,

bow

el

inco

ntin

ence

, ba

thin

g, e

atin

g,

mob

ility

(w

alki

ng o

r w

heel

ing)

, dr

essi

ng, &

tra

nsfe

rrin

g.

Onl

y jo

b as

sign

men

t & h

iera

rchy

(stru

ctur

al

varia

bles

) wer

e re

late

d to

impr

oved

phy

sica

l fu

nctio

n. In

gen

eral

, ass

umin

g a

stab

le p

ace

of

oper

atio

ns &

wor

kloa

d, n

on-s

peci

fic jo

b as

sign

men

t & le

ss h

iera

rchy

wer

e re

late

d to

be

tter p

hysi

cal f

unct

ion.

A c

onsi

sten

t wor

kloa

d ef

fect

was

dem

onst

rate

d in

that

few

er h

eavy

ca

re re

side

nts r

esul

ted

in b

ette

r res

iden

t ph

ysic

al fu

nctio

ning

.

Evid

ence

-bas

ed S

taffi

ng

65

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

97.

Ros

eman

, B

ooke

r (19

95)

Wor

kloa

d &

en

viro

nmen

tal f

acto

rs

in m

edic

atio

n er

rors

.

All

med

err

ors i

n a

5 ye

ar p

erio

d fr

om a

140

bed

ho

spita

l in

Ala

ska.

W

ork

envi

ronm

ent,

wor

kloa

d.

Med

err

ors.

Erro

rs w

ere

posi

tivel

y as

soci

ated

with

num

ber

of sh

ifts w

orke

d by

tem

pora

ry st

aff &

with

pa

tient

day

s but

neg

ativ

ely

asso

ciat

ed w

ith

over

time

shift

s (us

e of

exp

erie

nced

nur

ses)

. A

seas

onal

pat

tern

of e

rror

s em

erge

d: e

rror

s co

rres

pond

ed w

ith th

e le

vel o

f dar

knes

s tha

t oc

curr

ed 2

mon

ths e

arlie

r (i.e

. Win

ter d

arkn

ess

corr

elat

ed w

ith in

crea

sed

med

err

ors i

n ea

rly

sprin

g).

98.

Silb

er,

Ros

enba

um,

Schw

artz

, Ros

s, W

illia

ms (

1995

)

Com

plic

atio

n ra

te a

s a

mea

sure

of q

ualit

y of

car

e in

cor

onar

y ar

tery

byp

ass g

raft

surg

ery.

16,6

73 p

atie

nts

who

und

erw

ent

coro

nary

arte

ry

bypa

ss g

raft

surg

ery

(CA

BG

) at

57

hosp

itals

in

1991

, dat

a fr

om

Am

eric

an

Hos

pita

l A

ssoc

iatio

n an

nual

surv

ey.

Patie

nt

dem

ogra

phic

s, ho

spita

l siz

e,

nurs

ing

ratio

s, m

edic

al

diag

nosi

s.

Pa

tient

mor

talit

y,

com

plic

atio

n, &

fa

ilure

to re

scue

ra

tes.

Man

y ho

spita

ls w

ith h

ighe

r qua

lity

of c

are

had

high

er c

ompl

icat

ion

rate

s but

low

er m

orta

lity

rate

s (ex

: fac

ilitie

s with

an

MR

I had

38%

in

crea

se in

com

plic

atio

ns).

Hos

pita

l ran

king

s ba

sed

on c

ompl

icat

ion

rate

s giv

e di

ffer

ent

info

rmat

ion

than

thos

e ba

sed

on m

orta

lity

rate

s. C

ompl

icat

ion

rate

s sho

uld

not b

e us

ed to

judg

e ho

spita

l qua

lity

of c

are

until

mor

e is

kno

wn

abou

t the

diff

eren

ce.

6.

N

urse

Out

com

es

A

utho

rs, Y

ear

Focu

s Sa

mpl

e In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

99.

Ada

ms,

Bon

d (2

000)

Ef

fect

s of i

ndiv

idua

l &

org

aniz

atio

nal

char

acte

ristic

s on

job

satis

fact

ion.

834

nurs

es fr

om

Engl

and.

Dat

a co

llect

ed v

ia

post

al su

rvey

, ho

spita

ls c

hose

n w

ere

stra

tifie

d to

in

clud

e al

l hea

lth

regi

ons,

resp

onse

ra

te 5

7%.

Nur

se

dem

ogra

phic

s. O

rgan

izat

iona

l ch

arac

teris

tics.

Job

satis

fact

ion.

Th

ere

wer

e no

cor

rela

tions

with

job

satis

fact

ion

& n

urse

s’ a

ge o

r lev

el o

f edu

catio

n. S

ome

corr

elat

ions

wer

e fo

und

betw

een

job

satis

fact

ion

& c

ohes

ion

of n

ursi

ng te

am (0

.51)

, st

aff o

rgan

izat

ion

incl

udin

g st

affin

g &

w

orkl

oad

(0.4

6), l

evel

of p

rofe

ssio

nal p

ract

ice

(0.4

6) &

col

labo

ratio

n w

ith m

edic

al st

aff

(0.4

1). M

ost i

mpo

rtant

fact

ors i

n jo

b sa

tisfa

ctio

n w

ere

soci

al &

pro

fess

iona

l

Evid

ence

-bas

ed S

taffi

ng

66

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

re

latio

nshi

ps w

ith n

ursi

ng &

oth

er c

olle

ague

s. 10

0. A

iken

, Slo

ane

(199

7)

Bur

nout

in A

IDS

care

nu

rses

. 82

0 R

Ns &

LP

Ns o

n A

IDS

units

. All

nurs

es

on se

lect

ed u

nits

w

ere

surv

eyed

w

ith a

n 86

%

resp

onse

rate

.

Car

e de

liver

y sy

stem

, nur

se

educ

atio

n &

ex

perie

nce

Wor

k en

viro

nmen

t &

orga

niza

tion.

Bur

nout

, job

sa

tisfa

ctio

n,

nurs

e he

alth

, pa

tient

co

mpl

icat

ions

.

Nur

ses i

n de

dica

ted

AID

S un

its w

ere

less

em

otio

nally

exh

aust

ed th

an in

scat

tere

d be

d un

its. T

he o

rgan

izat

iona

l attr

ibut

es a

ssoc

iate

d w

ith lo

wer

bur

nout

are

als

o re

late

d to

safe

r w

ork

envi

ronm

ent,

grea

ter s

atis

fact

ion

with

ca

re &

low

er m

orta

lity.

Org

aniz

atio

nal s

uppo

rt ac

coun

ts fo

r 5%

var

ianc

e in

em

otio

nal

exha

ustio

n. N

urse

wel

l-bei

ng is

enh

ance

d by

au

tono

my

& c

ontro

l ove

r wor

k.

101.

Aik

en, C

lark

e,

Sloa

ne (2

000)

D

escr

ibe

how

nur

se

staf

fing

chan

ged

rela

tive

to h

ospi

tal

rest

ruct

urin

g (c

ase

mix

of p

atie

nts

rece

ivin

g ca

re),

&

exam

ine

chan

ges i

n nu

rsin

g pr

actic

e en

viro

nmen

ts.

1998

surv

ey o

f m

ore

than

2,0

00

nurs

es in

22

hosp

itals

; 199

6 su

rvey

s fro

m

chie

f exe

cutiv

e of

ficer

s at 6

46

hosp

itals

.

Prop

ortio

n of

nu

rse

wor

ked

hour

s, w

orkl

oad,

co

ntin

uity

of

care

, nur

se w

ork

inde

x.

Org

aniz

atio

nal

rest

ruct

urin

g.

Mor

talit

y, p

atie

nt

satis

fact

ion.

N

urse

con

trol o

ver t

he p

ract

ice

envi

ronm

ent

expl

ains

var

iatio

ns in

pat

ient

satis

fact

ion.

Bet

ter

orga

niza

tiona

l sup

port

was

ass

ocia

ted

with

lo

wer

em

otio

nal e

xhau

stio

n. T

he h

ighe

r the

st

affin

g le

vel,

the

low

er th

e de

ath

rate

(r=-

0.49

).

102.

Bak

er, K

ilmer

, Sy

nerg

ist,

Shuf

fle, (

2000

)

Inve

stig

ates

as

soci

atio

ns b

etw

een

expe

rienc

es o

f wor

k st

ress

am

ong

nurs

es.

204

fem

ale

nurs

es fr

om a

un

iver

sity

ho

spita

l in

Ger

man

y.

Bur

nout

, eff

ort

& re

war

d im

bala

nce,

Effo

rt &

rew

ard

imba

lanc

e w

as p

redi

ctiv

e of

em

otio

nal e

xhau

stio

n (F

=35.

33) &

de

pers

onal

izat

ion

(F=8

.97)

, bot

h di

men

sion

s of

burn

out.

Nur

ses’

feel

ings

of p

erso

nal

acco

mpl

ishm

ent w

ere

low

est a

mon

g th

ose

with

a

mis

mat

ch b

etw

een

dem

ands

and

rew

ards

. B

urno

ut is

obs

erve

d m

ore

ofte

n am

ong

youn

ger

nurs

es.

103.

Bau

man

n,

Gio

vann

etti,

O

’Brie

n-Pa

llas,

Mal

lette

, Deb

er,

Bly

the,

H

ibbe

rd,

DiC

enso

(200

1)

Nur

ses’

per

cept

ions

af

fect

ed b

y jo

b ch

ange

exp

erie

nces

.

1662

nur

ses f

rom

tw

o la

rge

teac

hing

ho

spita

ls. T

he

entir

e po

pula

tion

was

surv

eyed

, 50

.7%

resp

onse

ra

te.

Nur

se

dem

ogra

phic

s. W

ork

envi

ronm

ent,

wor

kloa

d.

Perc

eive

d qu

ality

of

car

e.

All

nurs

es w

ere

nega

tivel

y af

fect

ed b

y ho

spita

l re

stru

ctur

ing.

Nur

ses r

epor

ted

that

wor

k en

viro

nmen

ts h

ad d

eter

iora

ted

in th

e pr

evio

us

year

with

hea

vier

wor

kloa

ds, g

reat

er p

atie

nt

acui

ty &

mor

e w

orkp

lace

inju

ries.

Nur

ses

perc

eive

d a

decl

ine

in q

ualit

y of

car

e. T

hose

w

ho e

xper

ienc

ed jo

b ch

ange

(inv

olun

tary

re

allo

catio

n) w

ere

mor

e di

ssat

isfie

d, le

ss

conf

iden

t, m

ore

conc

erne

d ab

out p

atie

nt

Evid

ence

-bas

ed S

taffi

ng

67

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

w

elfa

re, &

less

com

mitt

ed.

104.

Ble

gen

(199

3)

Var

iabl

es th

at a

ffec

t nu

rses

’ job

sa

tisfa

ctio

n.

Met

a-an

alys

is o

f 48

stud

ies &

15

,048

subj

ects

.

Nur

se

dem

ogra

phic

s, ex

perie

nce.

Wor

k en

viro

nmen

t. N

urse

job

satis

fact

ion,

bu

rnou

t.

Job

satis

fact

ion

mos

t stro

ngly

ass

ocia

ted

with

st

ress

& o

rgan

izat

iona

l com

mitm

ent.

Oth

er

fact

ors i

nclu

ded

com

mun

icat

ion

with

su

perv

isor

, aut

onom

y, re

cogn

ition

, ro

utin

izat

ion,

com

mun

icat

ion

with

pee

rs,

fairn

ess,

& c

ontro

l. Lo

w c

orre

latio

ns w

ith a

ge,

yrs o

f exp

erie

nce,

edu

catio

n, &

pr

ofes

sion

alis

m.

105.

Bou

rbon

nais

, C

omea

u,

Vez

ina,

Dio

n (1

998)

The

psyc

holo

gica

l ef

fect

s of n

urse

s’

wor

k en

viro

nmen

ts.

1891

nur

ses f

rom

si

x ac

ute

care

ho

spita

ls in

Q

uebe

c,

volu

ntar

y re

crui

tmen

t. M

ostly

bed

side

nu

rses

wor

king

fu

ll tim

e.

Nur

se

dem

ogra

phic

s, ex

perie

nce

N

urse

bur

nout

, ps

ycho

logi

cal

dist

ress

, job

st

rain

, soc

ial

supp

ort a

t wor

k.

Hig

h ps

ycho

logi

cal d

eman

ds &

low

dec

isio

n la

titud

e is

ass

ocia

ted

with

psy

chol

ogic

al

dist

ress

(adj

uste

d od

ds ra

tio o

f 2.3

4) &

em

otio

nal e

xhau

stio

n (O

R=5

.77)

. Soc

ial

supp

ort a

t wor

k al

tere

d m

enta

l hea

lth b

ut n

ot

job

stra

in.

106.

Buc

han

(199

9)

A fo

llow

-up

of

mag

net h

ospi

tals

15

year

s afte

r the

ir de

sign

atio

n to

see

how

rest

ruct

urin

g ha

s af

fect

ed th

e st

atus

.

10 m

agne

t ho

spita

ls &

5

AN

CC

hos

pita

ls

Adm

inis

tratio

n,

prof

essi

onal

pr

actic

e &

de

velo

pmen

t fa

ctor

s (st

affin

g,

care

del

iver

y m

odel

s, pr

opor

tion

of

nurs

e w

orke

d ho

urs)

C

ost o

f car

e D

ue to

reor

gani

zatio

n, so

me

hosp

itals

no

long

er

exhi

bit c

hara

cter

istic

s of “

mag

netis

m.”

It is

not

th

e “m

agne

t hos

pita

l” la

bel t

hat i

s im

porta

nt,

but t

he c

once

pts o

f qua

lity

care

, eff

ectiv

e st

aff

depl

oym

ent &

job

satis

fact

ion.

The

re is

a n

eed

for m

onito

ring

& re

-acc

redi

tatio

n to

mai

ntai

n a

“liv

e” re

gist

er o

f mag

net h

ospi

tals

.

107.

Bur

ke,

Gre

engl

ass

(200

0)

Effe

cts o

f hos

pita

l re

stru

ctur

ing

on

nurs

es.

1362

nur

ses i

n O

ntar

io. R

ando

m

sele

ctio

n fr

om a

nu

rses

' uni

on,

35%

resp

onse

ra

te.

Nur

se

dem

ogra

phic

s, ex

perie

nce,

ed

ucat

ion,

&

prof

essi

onal

st

atus

.

Wor

kloa

d, w

ork

envi

ronm

ent.

Nur

ses’

em

otio

nal &

ph

ysic

al h

ealth

, bu

rnou

t.

Full

time

& p

art t

ime

nurs

es e

xper

ienc

ed

hosp

ital r

estru

ctur

ing

& d

owns

izin

g in

sim

ilar

way

s. FT

nur

ses h

ad p

oore

r hea

lth, w

ere

mor

e em

otio

nally

exh

aust

ed (m

ean=

3.6/

6 vs

. PT

mea

n=3.

0/6)

& w

ere

mor

e lik

ely

to b

e ab

sent

(m

ean=

3.2/

4 vs

. PT

mea

n=2.

5/4)

. Res

truct

urin

g w

as a

ssoc

iate

d w

ith le

ss w

ork

satis

fact

ion

&

poor

er w

ell-b

eing

.

Evid

ence

-bas

ed S

taffi

ng

68

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

10

8. C

amer

on,

Hor

sbur

gh,

Arm

stro

ng-

Stas

sen

(199

4)

Job

satis

fact

ion,

pr

open

sity

to le

ave

&

burn

out i

n R

Ns &

R

N a

ssis

tant

s.

623

RN

s & 2

31

RN

ass

ista

nts

from

3

com

mun

ity

hosp

itals

.

Nur

se

dem

ogra

phic

s W

ork

envi

ronm

ent

Nur

ses’

job

satis

fact

ion,

bu

rnou

t.

Nur

ses w

ere

only

mod

erat

ely

satis

fied

with

th

eir j

obs (

mea

n sc

ores

are

low

er th

an o

ther

em

ploy

ees)

. RN

s with

mor

e ex

perie

nce

had

high

er jo

b sa

tisfa

ctio

n &

less

bur

nout

. RN

s in

psyc

hiat

ric se

tting

s wer

e le

ast s

atis

fied.

Gre

ater

sa

tisfa

ctio

n &

less

bur

nout

whe

n a

“fit”

was

de

mon

stra

ted

betw

een

pers

on &

env

ironm

ent

(tabl

es o

mitt

ed)

109.

Car

ey,

Cam

pbel

l (1

994)

Stra

tegi

es fo

r nur

se

rete

ntio

n: p

rece

ptor

s, m

ento

rs, &

spon

sors

.

143

staf

f nur

ses

from

two

larg

e te

achi

ng

hosp

itals

in

Atla

nta.

Ran

dom

sa

mpl

e se

lect

ion

with

44%

and

36

% re

spon

se

rate

from

re

spec

tive

hosp

itals

.

Nur

se e

duca

tion,

ex

perie

nce.

Job

satis

fact

ion

No

caus

al re

latio

nshi

p be

twee

n m

ento

rs &

job

satis

fact

ion

(R2 fo

r fac

tors

= 0

.01-

0.08

). N

urse

s le

ave

b/c

of d

issa

tisfa

ctio

n ra

ther

than

nee

ds fo

r re

cogn

ition

, acc

ompl

ishm

ent,

or se

lf-w

orth

. En

viro

nmen

ts w

here

man

agem

ent s

uppo

rts

inte

rper

sona

l rel

atio

nshi

ps h

ave

high

er le

vels

of

satis

fact

ion

& le

ss tu

rnov

er.

110.

Cla

rke,

La

schi

nger

, G

iova

nnet

ti,

Sham

ian,

Th

omso

n,

Tour

ange

au,

(200

1)

Effe

cts o

f wor

kpla

ce

attri

bute

s on

nurs

es’

satis

fact

ion

& q

ualit

y of

car

e.

17,9

65 R

Ns i

n 39

2 ho

spita

ls

from

Alb

erta

, O

ntar

io &

B

ritis

h C

olum

bia.

R

epre

sent

ativ

e sa

mpl

es w

ere

draw

n fr

om

Ont

ario

&

Brit

ish

Col

umbi

a w

hile

all

RN

s in

Alb

erta

wer

e sa

mpl

ed.

Res

pons

e ra

te

was

49-

57%

.

Nur

se

dem

ogra

phic

s, ex

perie

nce.

Wor

k en

viro

nmen

t. N

urse

bur

nout

, jo

b sa

tisfa

ctio

n,

perc

eive

d qu

ality

of

car

e, p

atie

nt

adve

rse

even

ts

(fal

ls, m

ed

erro

rs).

Stro

nges

t pre

dict

ors o

f nur

ses’

em

otio

nal

exha

ustio

n &

satis

fact

ion

with

jobs

are

hav

ing

cont

rol o

ver w

ork

envi

ronm

ent,

havi

ng

suff

icie

nt re

sour

ces &

eff

ectiv

e nu

rsin

g le

ader

ship

. Nur

se-a

sses

sed

qual

ity w

as

sign

ifica

ntly

cor

rela

ted

with

occ

urre

nce

of

adve

rse

even

ts (r

=-0.

145f

or fa

lls to

-0.4

54 fo

r w

rong

med

). H

ospi

tals

with

goo

d ph

ysic

ian-

nurs

e co

llabo

ratio

n &

stro

ng n

ursi

ng le

ader

ship

ha

ve le

ss b

urno

ut &

low

er tu

rnov

er in

tent

ions

. Le

ader

ship

& n

urse

s’ le

ngth

of e

xper

ienc

e on

th

e un

it w

ere

pred

ictiv

e of

inte

nt to

leav

e cu

rren

t job

.

111.

Dav

ison

, Ef

fect

s of h

ealth

care

Lo

ngitu

dina

l N

urse

Hin

shaw

&

Perc

eive

d hi

gh w

orkl

oad

(Pric

e &

Mue

ller

Evid

ence

-bas

ed S

taffi

ng

69

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

Fo

lcar

elli,

C

raw

ford

, D

upra

t, C

liffo

rd

(199

7)

refo

rms o

n jo

b sa

tisfa

ctio

n &

vo

lunt

ary

turn

over

am

ong

hosp

ital

nurs

es

surv

ey o

f 685

nu

rses

from

one

ho

spita

l bet

wee

n 19

93 a

nd 1

994

dem

ogra

phic

s, w

age,

clin

ical

ad

vanc

emen

t le

vel,

term

inat

ion

stat

us, w

ork

cond

ition

s

Atw

ood’

s Nur

se

Job

Satis

fact

ion

Scal

e; P

rice

&

Mue

ller’

s Mod

el

of T

urno

ver;

Perli

n &

Sc

hool

er’s

Pe

rson

al M

aste

ry

Scal

e

over

load

subs

cale

) was

an

impo

rtant

de

term

inan

t of l

ow jo

b sa

tisfa

ctio

n. In

suff

icie

nt

time

to c

ompl

ete

the

job

pred

icte

d tu

rnov

er.

112.

Dem

erou

ti,

Bak

ker,

Nac

hrei

ner,

Scha

ufel

i (2

000)

Fact

ors c

ontri

butin

g to

bur

nout

& li

fe

satis

fact

ion

in n

urse

s.

109

nurs

es fr

om

one

hosp

ital &

tw

o nu

rsin

g ho

mes

in

Ger

man

y,

resp

onse

rate

59

%.

Nur

sing

de

mog

raph

ics,

expe

rienc

e.

Wor

kloa

d.

Bur

nout

. A

ge &

occ

upat

iona

l ten

ure

show

ed a

pos

itive

re

latio

nshi

p w

ith e

xhau

stio

n. A

ge w

as

sign

ifica

ntly

, neg

ativ

ely

rela

ted

to li

fe

satis

fact

ion.

Job

dem

ands

hav

e a

stro

ng p

ositi

ve

effe

ct o

n ex

haus

tion

whi

le jo

b re

sour

ces h

ave

a st

rong

neg

ativ

e ef

fect

on

dise

ngag

emen

t. Jo

b de

man

ds &

job

reso

urce

s cor

rela

te n

egat

ivel

y w

ith e

ach

othe

r (-0

.61)

. 11

3. Jo

seph

, D

eshp

ande

(1

997)

Hos

pita

ls c

an h

ave

vario

us ty

pes o

f et

hica

l clim

ates

. M

anag

ers m

ay b

e ab

le to

enh

ance

nu

rses

’ sat

isfa

ctio

n by

alte

ring

this

cl

imat

e.

144

nurs

es fr

om

larg

e no

n-pr

ofit

hosp

ital.

Ave

rage

subj

ect

was

40

year

old

m

arrie

d fe

mal

e w

ho h

as w

orke

d at

hos

pita

l for

9

year

s. 50

%

resp

onse

rate

.

Ethi

cal c

limat

e –

shar

ed

perc

eptio

n of

ho

w is

sues

sh

ould

be

addr

esse

d &

w

hat i

s eth

ical

ly

corr

ect

Jo

b sa

tisfa

ctio

n of

nur

ses (

with

pa

y, p

rom

otio

n,

cow

orke

rs,

supe

rvis

ors,

wor

k its

elf)

Prof

essi

onal

, ins

trum

enta

l (pr

otec

t ow

n in

tere

sts)

, ind

epen

denc

e (d

ecid

e fo

r one

self

wha

t’s ri

ght)

clim

ates

had

no

impa

ct o

n jo

b sa

tisfa

ctio

n. C

arin

g (w

hat’s

bes

t for

eve

ryon

e)

clim

ate

influ

ence

pay

, sup

ervi

sor,

& o

vera

ll sa

tisfa

ctio

n. R

ules

(stri

ct w

ith p

olic

ies)

clim

ate

had

posi

tive

impa

ct o

n ov

eral

l sat

isfa

ctio

n.

Effic

ienc

y (m

ust c

ontro

l cos

ts) c

limat

e ha

d ne

gativ

e im

pact

on

satis

fact

ion

with

su

perv

isor

s. 11

4. K

anga

s, K

ee,

McK

ee-W

addl

e (1

999)

Patie

nt &

nur

se

satis

fact

ion

with

in

diff

eren

t car

e de

liver

y m

odel

s &

orga

niza

tiona

l st

ruct

ures

.

102

nurs

es &

10

2 pa

tient

s fr

om 3

diff

eren

t ho

spita

ls (2

tra

ditio

nal,

1 sh

ared

go

vern

ance

). Sy

stem

atic

ra

ndom

sam

plin

g

Prop

ortio

n of

nu

rse

wor

ked

hour

s, ca

re

deliv

ery

mod

els

(prim

ary

nurs

ing,

te

am n

ursi

ng,

case

m

anag

emen

t),

cont

inui

ty o

f

N

urse

job

satis

fact

ion

No

diff

eren

ce in

nur

se jo

b sa

tisfa

ctio

n be

twee

n ty

pes o

f car

e de

liver

y m

odel

s. So

mew

hat

high

er p

atie

nt sa

tisfa

ctio

n fo

r tho

se w

ithin

the

prim

ary

care

del

iver

y m

odel

(not

sign

ifica

nt).

Supp

ortiv

e en

viro

nmen

t (ß=

-0.7

09) &

wor

king

in

a sp

ecia

lized

uni

t (ß=

-0.3

05) i

ncre

ase

nurs

e jo

b sa

tisfa

ctio

n.

Evid

ence

-bas

ed S

taffi

ng

70

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

us

ed. I

nclu

sion

cr

iteria

incl

uded

6m

os e

xper

ienc

e &

type

of u

nit.

care

, pat

ient

de

mog

raph

ics

115.

Kov

ner,

C.,

Hen

dric

kson

, G

., K

nick

man

, J.,

Fin

kler

, S.

(199

4)

Nur

se re

crui

tmen

t &

rete

ntio

n.

37 h

ospi

tals

, 858

R

Ns o

n 68

pilo

t un

its &

335

RN

s on

com

paris

on

units

. Pilo

t uni

ts

wer

e se

lf-se

lect

ed;

com

paris

on u

nits

w

ere

chos

en b

y ev

alua

tors

to b

e si

mila

r to

pilo

t un

its.

Nur

se

satis

fact

ion

Pilo

t pro

ject

to im

prov

e re

crui

tmen

t &

rete

ntio

n. A

ll of

the

inno

vatio

ns e

nhan

ced

satis

fact

ion.

Nur

ses r

anke

d pa

y as

mos

t im

porta

nt fa

ctor

, fol

low

ed b

y au

tono

my

&

prof

essi

onal

stat

us. E

duca

tion

initi

ativ

es,

reor

gani

zatio

n, &

new

tech

nolo

gy e

nhan

ced

satis

fact

ion.

With

eac

h ch

ange

, the

re w

as a

n in

itial

dis

satis

fact

ion.

116.

Kra

mer

&

Schm

alen

berg

(1

990)

Job

satis

fact

ion

&

rete

ntio

n of

nur

ses i

n m

agne

t & n

on-

mag

net h

ospi

tals

.

1800

nur

ses i

n m

agne

t & n

on-

mag

net h

ospi

tals

ac

ross

Uni

ted

Stat

es.

Imag

e &

va

luat

ion

of

nurs

es –

how

th

ey se

e th

emse

lves

&

how

oth

ers s

ee

them

;

Jo

b sa

tisfa

ctio

n,

incl

udin

g or

gani

zatio

nal

stru

ctur

e,

prof

essi

onal

pr

actic

e,

man

agem

ent

styl

e, q

ualit

y of

le

ader

ship

, pr

ofes

sion

al

deve

lopm

ent;

also

ove

rall

job

satis

fact

ion.

Mag

net h

ospi

tals

hav

e hi

gher

deg

ree

of

satis

fact

ion

& b

ette

r sta

ffin

g si

tuat

ions

than

no

n-m

agne

t. Po

sitiv

e co

rrel

atio

n be

twee

n ho

spita

l im

age

of n

ursi

ng &

ade

quac

y of

st

affin

g.

117.

Kra

mer

, H

afne

r (19

89)

Impa

ct o

f val

ues o

n nu

rses

’ sat

isfa

ctio

n &

pe

rcei

ved

prod

uctiv

ity.

2336

staf

f nur

ses

in 2

4 ho

spita

ls.

A 1

/3 sa

mpl

e,

prop

ortio

nate

by

regi

ons o

f the

co

untry

, of t

he

mag

net h

ospi

tals

W

ork

envi

ronm

ent.

Nur

ses’

job

satis

fact

ion,

pe

rcei

ved

qual

ity

of c

are.

Inve

rse

corr

elat

ion

betw

een

valu

e co

ngru

ence

&

nur

se jo

b sa

tisfa

ctio

n, q

ualit

y of

car

e (f

or

staf

f nur

se-to

p m

anag

er d

yad,

cor

rela

tion

betw

een

valu

e co

ngru

ence

& sa

tisfa

ctio

n ra

nged

from

0.0

74-0

.377

). St

aff n

urse

s rep

orte

d fe

wer

fact

ors a

s im

porta

nt to

satis

fact

ion

&

qual

ity o

f car

e th

an d

id o

ther

mem

bers

of

Evid

ence

-bas

ed S

taffi

ng

71

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

w

as d

raw

n,

rand

om sa

mpl

ing

of n

urse

s.

nurs

ing.

Impo

rtant

fact

ors i

nclu

de ro

le c

larit

y,

role

evo

lvem

ent,

role

dis

tanc

e, re

spon

sive

ness

fr

om m

anag

emen

t, au

tono

my.

11

8. K

utzs

cher

, Sa

bist

on,

Lasc

hing

er,

Nis

h (1

997)

.

Effe

cts o

f tea

mw

ork

on st

aff p

erce

ptio

n of

em

pow

erm

ent &

job

satis

fact

ion.

210

staf

f who

pa

rtici

pate

d on

m

ultid

isci

plin

ary

team

s & a

ra

ndom

sam

ple

of 1

85 st

aff

(res

pons

e ra

te

52%

) who

did

no

t.

W

ork

envi

ronm

ent

Job

satis

fact

ion.

Pe

rcep

tions

of w

ork

empo

wer

men

t wer

e hi

gher

fo

r sta

ff w

ho w

ere

on te

ams (

t=5.

04).

The

staf

f on

team

s was

slig

htly

mor

e sa

tisfie

d bu

t the

di

ffer

ence

was

not

sign

ifica

nt.

119.

Lasc

hing

er,

Fine

gan,

Sh

amia

n (2

001)

Impa

ct o

f wor

kpla

ce

empo

wer

men

t &

orga

niza

tiona

l tru

st

on n

urse

s’ w

ork

satis

fact

ion.

412

staf

f nur

ses

from

Ont

ario

. R

ando

m

sele

ctio

n fr

om

prof

essi

onal

re

gist

ry li

st,

equa

l sam

plin

g of

mal

es &

fe

mal

es.

Nur

se

dem

ogra

phic

s, pr

ofes

sion

al

stat

us.

Wor

k en

viro

nmen

t N

urse

s’ jo

b sa

tisfa

ctio

n St

aff n

urse

em

pow

erm

ent i

mpa

cts o

n th

eir t

rust

in

man

agem

ent &

thei

r job

satis

fact

ion.

Fo

ster

ing

envi

ronm

ents

that

enh

ance

em

pow

erm

ent w

ill h

ave

posi

tive

effe

cts o

n m

embe

rs &

eff

ectiv

enes

s. A

cces

s to

info

rmat

ion

(cor

rela

tion=

0.49

) & su

ppor

t (0.

46)

are

stro

ngly

rela

ted

to tr

ust i

n m

anag

emen

t. Fe

edba

ck &

gui

danc

e ar

e al

so re

late

d to

trus

t.

120.

Leite

r, H

arvi

e,

Friz

zell

(199

8)

Impa

ct o

f nur

se

burn

out o

n pa

tient

sa

tisfa

ctio

n.

711

nurs

es &

60

5 pa

tient

s fr

om si

xtee

n ho

spita

l uni

ts.

Vol

unte

ers

com

plet

ed th

e nu

rse

surv

eys &

pa

tient

s wer

e ra

ndom

ly

sam

pled

.

Nur

se &

pat

ient

de

mog

raph

ics.

N

urse

bur

nout

, m

eani

ngfu

lnes

s of

wor

k, p

atie

nt

outc

omes

, &

satis

fact

ion.

Patie

nts’

per

cept

ions

of q

ualit

y w

ere

corr

elat

ed

with

nur

ses’

rela

tions

hips

with

wor

k (m

ore

mea

ning

, les

s exh

aust

ion=

high

er p

erce

ptio

n of

qu

ality

). Pa

tient

s wer

e m

ore

satis

fied

on u

nits

w

here

nur

ses f

ound

the

wor

k m

eani

ngfu

l &

wer

e le

ss sa

tisfie

d on

uni

ts w

here

nur

ses w

ere

exha

uste

d or

rate

d hi

gh o

n cy

nici

sm (S

pear

man

ra

nk o

rder

cor

rela

tions

). N

o co

rrel

atio

ns

betw

een

prof

essi

onal

eff

icac

y &

pat

ient

sa

tisfa

ctio

n.

Evid

ence

-bas

ed S

taffi

ng

72

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

12

1. M

cGill

is H

all,

Dor

an, B

aker

, Pi

nk, S

idan

i, O

’Brie

n-Pa

llas,

Don

ner (

2001

)

Impa

ct o

f nur

sing

st

aff m

ix m

odel

s &

orga

niza

tiona

l cha

nge

stra

tegi

es.

2046

pat

ient

s, 11

16 n

urse

s, 63

un

it m

anag

ers,

50 se

nior

ex

ecut

ives

from

19

teac

hing

ho

spita

ls a

cros

s O

ntar

io.

Prop

ortio

n of

nu

rse

wor

ked

hour

s, m

edic

al

diag

nosi

s (ca

se

mix

gro

up)

M

ed e

rror

s, in

fect

ions

, nur

se

job

satis

fact

ion,

pe

rcei

ved

qual

ity

of c

are.

Nur

sing

lead

ersh

ip h

as p

ositi

ve in

fluen

ce o

n nu

rses

’ job

satis

fact

ion

(t=4.

88).

Low

er

com

plex

ity o

f pat

ient

s cor

resp

onds

with

hig

h jo

b sa

tisfa

ctio

n (t=

-3.1

7). U

nits

with

low

er

prop

ortio

n of

RN

s to

RPN

s had

mor

e m

ed

erro

rs &

wou

nd in

fect

ions

.

122.

McN

eese

-Sm

ith, C

rook

(2

003)

Var

iabl

es in

fluen

cing

nu

rses

’ val

ues.

412

RN

s fro

m 3

Lo

s Ang

eles

ho

spita

ls.

Hos

pita

ls w

ere

sele

cted

for

conv

enie

nce,

nu

rses

wer

e ra

ndom

ly

sam

pled

.

Nur

se

dem

ogra

phic

s, ed

ucat

ion

N

urse

job

satis

fact

ion

Hig

hest

rate

d va

lue

was

goo

d su

perv

isor

y re

latio

ns. I

f man

ager

s sup

port

nurs

es in

at

tain

ing

valu

es in

wor

k se

tting

, ret

entio

n m

ay

be im

prov

ed. C

orre

latio

ns b

etw

een

supe

rvis

ory

rela

tions

& se

curit

y (r

=0.5

9) &

secu

rity

&

achi

evem

ent (

r=0.

60) w

ere

note

d. N

egat

ive

corr

elat

ion

with

eco

nom

ic v

alue

s & jo

b sa

tisfa

ctio

n (r

=-0.

14).

123.

Mos

s, R

owle

s (1

997)

Th

e ef

fect

of n

urse

m

anag

ers’

m

anag

emen

t sty

les

on st

aff n

urse

job

satis

fact

ion.

623

nurs

es in

3

Mid

wes

tern

ho

spita

ls

Man

agem

ent

styl

es

(exp

loiti

ve/

auth

orita

tive,

be

nevo

lent

/ au

thor

itativ

e,

cons

ulta

tive,

pa

rtici

pativ

e).

Jo

b sa

tisfa

ctio

n B

y us

ing

appr

opria

te m

anag

emen

t sty

les,

staf

f nu

rse

job

satis

fact

ion

may

be

impr

oved

. Job

sa

tisfa

ctio

n im

prov

ed a

s the

styl

e ap

proa

ched

pa

rtici

pativ

e m

anag

emen

t.

124.

Mun

ro (1

983)

Jo

b sa

tisfa

ctio

n am

ong

rece

nt

grad

uate

s.

329

rece

nt

nurs

ing

grad

uate

s. D

esig

n w

as a

st

ratif

ied,

two-

stag

e pr

obab

ility

sa

mpl

e of

hig

h sc

hool

gra

ds in

U

S (2

% in

nu

rsin

g).

Nur

se e

duca

tion

Jo

b sa

tisfa

ctio

n Ed

ucat

ion

back

grou

nd d

id n

ot a

ffec

t job

sa

tisfa

ctio

n. A

chie

vem

ent,

resp

onsi

bilit

y (3

3%

of v

aria

nce)

, adv

ance

men

t, gr

owth

(2.2

% o

f va

rianc

e), &

wor

k its

elf (

5.5%

of v

aria

nce)

are

re

late

d to

satis

fact

ion.

Adm

inis

trato

rs n

eed

to

appe

al to

nur

ses’

nee

ds fo

r cha

lleng

es &

op

portu

nitie

s to

grow

.

125.

Nak

ata,

Say

lor

Man

agem

ent s

tyle

&

102

RN

s &

W

ork

Job

satis

fact

ion.

Po

sitiv

e co

rrel

atio

n be

twee

n pe

rcei

ved

Evid

ence

-bas

ed S

taffi

ng

73

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

(1

994)

nu

rse

satis

fact

ion.

LP

Ns f

rom

an

acut

e ca

re

hosp

ital i

n C

alifo

rnia

. All

staf

f in

sele

cted

un

its w

as

surv

eyed

with

a

43%

resp

onse

ra

te.

envi

ronm

ent.

man

agem

ent s

tyle

& st

aff n

urse

job

satis

fact

ion

(r=0

.48)

. The

clo

ser t

he m

anag

emen

t sty

le to

pa

rtici

pativ

e gr

oup

man

agem

ent,

the

high

er th

e sa

tisfa

ctio

n. N

urse

s wou

ld li

ke to

be

mor

e in

volv

ed in

dec

isio

n-m

akin

g &

setti

ng o

f uni

t go

als.

Aut

onom

y &

aut

horit

y ar

e so

urce

s of j

ob

satis

fact

ion

whi

le p

oor c

omm

unic

atio

n le

ads t

o di

ssat

isfa

ctio

n.

126.

RN

AO

(200

2)

To d

eter

min

e th

e ex

tent

to w

hich

RN

s ha

ve se

en sp

ecifi

c ch

ange

s in

thei

r wor

k en

viro

nmen

ts si

nce

the

task

forc

e re

com

men

datio

ns

wer

e re

leas

ed.

Surv

eys g

iven

to

RN

AO

mem

bers

&

non

-mem

ber

RN

s,

RN

s age

23-

67

yrs.

Con

veni

ence

sa

mpl

e, 5

49

resp

onse

s.

Nur

se

dem

ogra

phic

s, em

ploy

men

t st

atus

, sec

tor o

f em

ploy

men

t, po

sitio

n,

wor

kloa

d,

prop

ortio

n of

nu

rse

wor

ked

hour

s

Wor

k en

viro

nmen

t. N

urse

job

satis

fact

ion.

R

espo

nden

ts a

re n

ot e

xper

ienc

ing

a hi

gh d

egre

e of

con

trol t

hrou

gh fl

exib

ility

of t

he w

ork

sche

dule

s. Pr

ofes

sion

al sa

tisfa

ctio

n w

as ra

ted

high

ly. R

Ns i

ndic

ate

no c

hang

e in

opp

ortu

nitie

s to

par

ticip

ate

in d

ecis

ion-

mak

ing

that

in

fluen

ces p

atie

nt c

are.

Whe

n nu

rses

hav

e sa

tisfa

ctor

y w

orkl

oads

& c

ontin

uity

of p

atie

nt

assi

gnm

ent,

thei

r ove

rall

job

satis

fact

ion

impr

oves

. Con

sist

ency

in p

atie

nt a

ssig

nmen

t is

linke

d to

nur

ses'

perc

eptio

ns o

f im

prov

ed

orga

niza

tiona

l com

mitm

ent t

o nu

rsin

g.

127.

Roe

del,

Nys

trom

(199

8)

Fact

ors t

hat a

ffec

t nu

rses

’ job

sa

tisfa

ctio

n.

135

RN

s fro

m a

20

0-be

d co

mm

unity

ho

spita

l, se

lf-se

lect

ion,

all

fem

ale

resp

onde

nts.

Nur

ses’

ed

ucat

ion.

W

ork

envi

ronm

ent.

Nur

se jo

b sa

tisfa

ctio

n.

Nur

ses r

anke

d co

-wor

ker s

atis

fact

ion

as th

e hi

ghes

t sat

isfa

ctio

n sc

ore.

Les

s tas

k id

entit

y,

auto

nom

y or

feed

back

is re

late

d to

low

er jo

b sa

tisfa

ctio

n. S

kill

varie

ty &

task

sign

ifica

nce

tend

to b

e un

rela

ted

to m

ost f

acet

s of j

ob

satis

fact

ion.

128.

Shul

lanb

erge

r (2

000)

Lite

ratu

re re

view

of

cost

-eff

ectiv

e nu

rse

staf

fing

Pr

opor

tion

of

nurs

e w

orke

d ho

urs

Wor

kloa

d

Cos

ts o

f car

e,

nurs

e sa

tisfa

ctio

n.

Nur

se sa

tisfa

ctio

n ha

s pos

itive

rela

tions

hip

with

se

lf-sc

hedu

ling.

Opt

imal

skill

mix

of 8

5% R

Ns.

Nur

ses’

rem

uner

ativ

e va

lue

is m

ore

than

wha

t ca

n be

mea

sure

d by

wor

kloa

d.

Evid

ence

-bas

ed S

taffi

ng

74

Aut

hors

, Yea

r Fo

cus

Sam

ple

Inpu

ts

Thr

ough

puts

O

utpu

ts

Find

ings

12

9. St

orde

ur,

D’h

oore

, V

ande

nber

ghe

(200

1)

Impa

ct o

f lea

ders

hip

beha

viou

rs o

n nu

rses

’ em

otio

nal

exha

ustio

n.

625

war

d nu

rses

fr

om a

uni

vers

ity

hosp

ital.

39.2

%

resp

onse

rate

but

de

mog

raph

ics o

f sa

mpl

e si

mila

r to

nurs

ing

popu

latio

n.

Wor

k st

ress

ors

(phy

sica

l, ps

ycho

logi

cal,

soci

al

envi

ronm

ents

) &

lead

ersh

ip

beha

viou

rs

Wor

k en

viro

nmen

t Em

otio

nal

exha

ustio

n

com

pone

nt o

f bu

rnou

t

Wor

k st

ress

ors e

xpla

ined

22%

of e

mot

iona

l ex

haus

tion

whe

reas

lead

ersh

ip d

imen

sion

s ex

plai

ned

9%. S

tress

from

phy

sica

l (ß=

0.28

) &

soci

al (ß

=0.1

7) e

nviro

nmen

t, ro

le a

mbi

guity

(ß

=0.1

7), a

ctiv

e m

anag

emen

t-by-

exce

ptio

n le

ader

ship

(ß=0

.13)

sign

ifica

ntly

ass

ocia

ted

with

em

otio

nal e

xhau

stio

n.

130.

Tong

es,

Rot

hste

in,

Car

ter (

1998

)

Var

iabl

es th

at a

ffec

t nu

rses

’ job

sa

tisfa

ctio

n.

222

staf

f nur

ses

in a

cute

car

e ho

spita

ls. A

ll nu

rses

mee

ting

the

sele

ctio

n cr

iteria

wer

e su

rvey

ed.

Con

tinui

ty o

f ca

re, n

urse

de

mog

raph

ics,

expe

rienc

e.

N

urse

s’ jo

b sa

tisfa

ctio

n,

burn

out.

Asp

ects

of j

ob im

porta

nt to

satis

fact

ion

incl

ude

cont

inui

ty o

f car

e, a

uton

omy,

indi

vidu

al

acco

unta

bilit

y, &

per

form

ance

feed

back

.

131.

Tzen

g,

Ket

efia

n (2

002)

R

elat

ions

hip

betw

een

nurs

e jo

b sa

tisfa

ctio

n &

inpa

tient

sa

tisfa

ctio

n.

59 p

atie

nts &

10

3 nu

rses

from

si

x un

its in

a

Taiw

an te

achi

ng

hosp

ital.

Clu

ster

sa

mpl

ing

tech

niqu

e.

Patie

nt te

achi

ng,

cont

inui

ty o

f ca

re, p

atie

nt

dem

ogra

phic

s, nu

rse

dem

ogra

phic

s, ex

perie

nce

Wor

k en

viro

nmen

t N

urse

job

satis

fact

ion,

le

ngth

of s

tay

Nur

ses’

job

satis

fact

ion

is c

orre

late

d w

ith

inpa

tient

satis

fact

ion

fact

ors:

exp

lana

tion

of

care

(r=0

.765

), pa

in m

anag

emen

t (r=

0.86

6) a

s w

ell a

s the

nur

se’s

gen

eral

hap

pine

ss (r

=0.8

91).

Nur

ses’

gen

eral

hap

pine

ss p

ositi

vely

co

ntrib

uted

to p

atie

nt sa

tisfa

ctio

n.

132.

Ver

nare

c (2

000)

. O

verti

me

& w

hat

nurs

es c

an d

o w

hen

face

d w

ith m

anda

tory

or

face

d ov

ertim

e.

N

urse

hea

lth,

burn

out.

Nur

ses w

ho w

ork

over

time

are

also

und

er th

e st

ress

of c

ompe

ting

job

& fa

mily

re

spon

sibi

litie

s, th

eir o

wn

heal

th, &

thei

r pa

tient

s' sa

fety

.

7.

Syst

em O

utco

mes

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

Evid

ence

-bas

ed S

taffi

ng

75

Aut

hors

, Yea

r Fo

cus

Sam

ple

In

puts

T

hrou

ghpu

ts

Out

puts

Fi

ndin

gs

133.

And

erso

n,

Hig

gins

, R

ozm

us (1

999)

Leng

th o

f sta

y in

in

tens

ive

care

uni

t af

ter c

oron

ary

arte

ry

bypa

ss g

raft.

152

patie

nts i

n a

larg

e te

achi

ng

hosp

ital i

n Te

nnes

see,

81%

m

ale,

19%

fe

mal

e.

Patie

nt

dem

ogra

phic

s, m

edic

al

diag

nosi

s.

Le

ngth

of s

tay

in

inte

nsiv

e ca

re

unit

& st

ep-d

own

units

, pat

ient

m

orta

lity,

cos

ts

of c

are,

pos

t-op

com

plic

atio

ns.

Inte

nsiv

e ca

re u

nit l

engt

h of

stay

was

shor

ter

whe

n am

bula

tion

was

initi

ated

soon

er (t

=-2.

68).

Shor

ter h

ospi

taliz

atio

n fo

r pat

ient

s who

stay

ed

in in

tens

ive

care

uni

t 1 d

ay th

an th

ose

stay

ing

2 da

ys (t

=-1.

46; n

ot si

gnifi

cant

).

134.

Bou

rbon

nais

, M

ondo

r Myr

to

(200

1)

The

asso

ciat

ion

betw

een

nurs

es’ j

ob

stra

in &

sick

leav

e.

1793

nur

ses f

rom

si

x ac

ute

care

ho

spita

ls in

Q

uebe

c,

volu

ntar

y re

crui

tmen

t.

Nur

se

dem

ogra

phic

s, ex

perie

nce

N

urse

bur

nout

, jo

b st

rain

, soc

ial

supp

ort a

t wor

k,

shor

t ter

m &

ce

rtifie

d si

ck

leav

es.

Shor

t ter

m si

ck le

aves

wer

e as

soci

ated

with

job

stra

in (i

ncid

ence

-den

sity

ratio

= 1

.20)

& lo

w

soci

al su

ppor

t (ID

R=1

.26)

. Cer

tifie

d si

ck le

aves

w

ere

sign

ifica

ntly

ass

ocia

ted

with

low

soci

al

supp

ort (

IDR

=1.2

7 fo

r all

diag

nose

s &

IDR

=1.7

8 fo

r men

tal h

ealth

dia

gnos

es).

135.

Liu,

Su

bram

ania

n,

Cro

mw

ell

(200

1)

Impl

emen

ting

glob

al

bund

led

paym

ents

on

Hos

pita

l cos

ts o

f co

rona

ry a

rtery

by

pass

gra

fting

.

Patie

nts

unde

rgoi

ng

bypa

ss su

rger

y at

th

ree

hosp

itals

(in

Atla

nta,

Ann

A

rbor

and

B

osto

n).

Patie

nt

dem

ogra

phic

s, ad

mis

sion

type

, m

edic

al

diag

nosi

s.

C

osts

of c

are

(dire

ct v

aria

ble

cost

s e.g

. nur

ses’

w

ages

), po

stop

erat

ive

com

plic

atio

ns,

leng

th o

f sta

y.

All

hosp

itals

had

sign

ifica

nt re

duct

ions

in to

tal

dire

ct v

aria

ble

cost

s, m

any

com

ing

from

re

duce

d nu

rsin

g co

sts (

e.g.

dec

reas

ed le

ngth

of

stay

). Pa

ymen

t met

hod

alig

ns p

hysi

cian

&

hosp

ital i

ncen

tives

. Im

prov

emen

ts in

ef

ficie

ncie

s wer

e m

ade

with

out d

imin

ishi

ng

qual

ity o

f car

e.

136.

Pier

ce (1

997)

Li

tera

ture

revi

ew o

f ou

tcom

es re

late

d to

nu

rsin

g.

C

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8. Glossary

• BNS – Bachelor of Nursing Science

• LPN – Licensed Practical Nurse

• RPN – Registered Practical Nurse

• RN – Registered Nurse

• WHPPD – worked hours per patient day

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Appendix B. Patient Care Delivery Model

Patient Characteristics• Demographics• Significant other support• Medical diagnoses• Nursing diagnoses• OMAHA knowledge, behaviour,

status• Admission type• Pre-operative clinic• Education booked post-op/post

discharge• SF-12 health status

Nurse Characteristics• Demographics• Professional status• Employment status• Education• Clinical expertise• Experience

System Characteristics• Geographic location• Hospital size• Unit size, type, patient mix• Occupancy

System Behaviours• Workload• Nurse-to-patient ratios• Proportion of RN worked hours• Continuity of care/shift change• Unit instability• Overtime• Use of agency & relief staff• # of units nurse works on• Non-nursing tasks

Patient Outcomes• Medical consequences• OMAHA knowledge, behaviour,

status• SF-12 health status• Resource intensity weight• Mortality

Nurse Outcomes• Burnout• Effort & reward imbalance• Autonomy & control• Job satisfaction• Relationships with MDs• SF-12 health status• Violence at work

System Outcomes• Length of stay• Cost per resource intensity

weight• Quality of patient care• Quality of nursing care• Interventions delayed• Interventions not done• Absenteeism• Intent to leave

Patient Care

Delivery System in Cardiac &

Cardio-vascular

Units

INTERMEDIATE OUTPUTS

• Worked hours• Productivity/

Utilization

Environmental Complexity Factors• Resequencing of work in

response to others• Unanticipated delays due to

changes in patient acuity• Characteristics & composition

of caregiving team

INPUTS

Feedback

THROUGHPUTS

OUPUTS

Perceived Work Environment

Interventions

Patient Care Delivery Model(O’Brien-Pallas et al., 2003)


Appendix C. Tables

Table 1. Key Variables and Data Sources ......................................................................................................86 Table 2. Estimates for Patient Outcomes from the Hierarchical Linear Models.................................87 Table 3. Odds Ratios for Patient Outcomes from the Hierarchical Linear Models............................88 Table 4: Hierarchical Logistic Regression for Medical Consequences Developed During Hospital

Stay .......................................................................................................................................................89 Table 5: Hierarchical Logistic Regression for Improvement in Patients’ Physical Health..............90 Table 6: Hierarchical Logistic Regression for Improvement in Patients’ Mental Health ................91 Table 7: Hierarchical Logistic Regression for Patients’ Knowledge Improvement at

Discharge/Diagnoses Resolved ....................................................................................................92 Table 8: Hierarchical Logistic Regression for Patients’ Behaviour Improvement at

Discharge/Diagnoses Resolved ....................................................................................................93 Table 9: Hierarchical Logistic Regression for Patients’ Status Improvement at

Discharge/Diagnoses Resolved ....................................................................................................94 Table 10. Estimates for Nurse Outcomes in the Hierarchical Linear Models......................................95 Table 11. Odds Ratios for Nurse Outcomes in the Hierarchical Linear Models.................................96 Table 12: Hierarchical Linear Regression for Nurse-Physician Relationship .....................................97 Table 13: Hierarchical Linear Regression for Autonomy..........................................................................98 Table 14: Hierarchical Logistic Regression for Job Satisfaction.............................................................99 Table 15: Hierarchical Logistic Regression for Emotional Exhaustion...............................................100 Table 16: Hierarchical Linear Regression for Nurses’ Physical Health ..............................................101 Table 17: Hierarchical Linear Regression for Nurses’ Mental Health.................................................102 Table 18. Estimates for System Outcomes from the Hierarchical Linear Models............................103 Table 19. Odds Ratios for System Outcomes in the Hierarchical Linear Models ............................104 Table 20: Hierarchical Logistic Regression for Patients with Shorter Than Expected Length of

Stay .....................................................................................................................................................105 Table 21: Hierarchical Logistic Regression for Interventions Not Done ............................................106 Table 22: Hierarchical Logistic Regression for Interventions Delayed...............................................107 Table 23: Hierarchical Logistic Regression for Quality of Nursing Care...........................................108 Table 24: Hierarchical Logistic Regression for Quality of Patient Care .............................................109 Table 25: Hierarchical Logistic Regression for Absenteeism ................................................................110 Table 26: Hierarchical Logistic Regression for Intent to Leave ............................................................111 Table 27: Hierarchical Linear Regression for Productivity/Utilization...............................................112 Table 28: Hierarchical Linear Regression for Cost per Resource Intensity Weight (Log Scale).113 Table 29: Hierarchical Linear Regression for Worked Hours per Patient (Log Scale) ...114 Table 30: Hierarchical Linear Models for Patient, Nurse, and System Outcomes on Congruence

Between PRN Hours and Actual Worked Hours per Patient.............................................115 Table 31: Summary Table of the Effect of Nursing Hours, Proportion of RN Worked Hours,

Nurse-Patient Ratio, and Productivity/Utilization on Patient, Nurse and System Outcomes, in Odds Ratio, Coefficient, and Cut point .........................................................116


Table 1. Key Variables and Data Sources

Measure When Administered Method/Source SF-12 Health Status Survey (physical and mental health status)

• Admission or in pre-op clinic

• Discharge

• Patient self-report

NANDA Nursing Diagnoses and OMAHA Problem Rating Scale

• Admission

• Discharge

• Daily to identify new or resolved diagnoses

• Data collector from patient chart/kardex and nurse

Patient Data Form • Once over patient stay • Data collector from patient chart/kardex/interview

PRN Workload Tool • Daily • Data collector from patient chart/kardex, unit workload tool, and nurse

Case Mix Group • After discharge • Electronic file submitted by Heath Records Department

Resource Intensity Weight

• After discharge • Electronic file submitted by Heath Records Department

Nurse Survey • Once at beginning of data collection

• Nurse self-report

Daily Unit Staffing Form • Daily • Data collector from unit assignment sheet and ward clerk

Environmental Complexity Scale

• Daily • Nurses


Table 2. Estimates for Patient Outcomes from the Hierarchical Linear Models

Predictor Medical Conseq.

Physical Health

Mental Health

OmahaKnowledge

Omaha Behaviour

OmahaStatus

Patient Level Pre-Operative Clinics Referral for Homecare 1.43 *Medical Consequences Resource Intensity Weight 0.02 -0.10 * -0.01 0.01 0.08 0.05Number of Nursing Diagnoses 0.43 * -0.12 * -0.06 0.05 0.06 0.08Physical Health at Admission -0.01 -0.13 * 0.01 * 0.01 0.01 0.01Mental Health at Admission -0.03 * 0.00 -0.09 * 0.00 0.00 0.00Knowledge at Admission -1.33 * Behaviour at Admission -2.14 *Status at Admission -1.48 *Worked Hours per Patient 0.13 * 0.00 -0.06 * 0.04 -0.02 0.00Length of Stay 0.03 0.01 -0.04 * 0.01 -0.01 0.00

Nurse Level Education (ref: Diploma) 0.12 0.11 0.05 -0.29 -0.08 0.10Overtime Hours -0.02 -0.08 * 0.04 0.02 -0.03 0.02Unit Instability -0.75 *Interventions Not done -0.97 0.08 0.27 -0.47 0.03 -0.27Interventions Delayed 1.08 -0.02 -0.10 0.10 -0.01 -0.42Autonomy 0.17 * Physical Health 0.03 -0.01 0.01 -0.02 -0.01 -0.02Mental Health -0.02 -0.01 0.01 -0.01 -0.01 0.02Satisfaction with Current Job (ref: Dissatisfied) 1.02 *Nurse-Patient Ratio 0.25 0.05 -0.14 0.11 -0.03 0.16

Unit Level Proportion of RN Worked Hours 3.72 -0.94 0.53 5.55 * 0.42 2.06Productivity/Utilization 7.40 * 17.83 * 4.94 *Productivity/Utilization (Quadratic) -10.11 *Productivity/Utilization (beyond 88%) -1.49 * -0.80 *Productivity/Utilization (beyond 80%) -0.60 *Productivity/Utilization (beyond 85%) -0.67 0.30Proportion of Full-time Employment 2.13 * Proportion of Nurses Reporting Shift Changes -5.75 *

* for p-value at 0.05 or less Notes: (1) All patient outcome variables were dichotomized and modeled in hierarchical logistic regressions. For medical consequences, 1 = development of complications, falls with injury, or death; for physical and mental health, 1 = improved at discharge; for Omaha knowledge, behaviour, and status, 1 = improvement at discharge or diagnoses resolved. (2) The productivity/utilization cut point is 88.2% for Omaha behaviour.


Table 3. Odds Ratios for Patient Outcomes from the Hierarchical Linear Models

Predictor Medical Conseq.

Physical Health

Mental Health

OmahaKnowledge

Omaha Behaviour

OmahaStatus

Patient Level Pre-Operative Clinics Referral for Homecare 4.19 *Medical Consequences Resource Intensity Weight 1.02 0.90 * 0.99 1.01 1.08 1.05Number of Nursing Diagnoses 1.53 * 0.89 * 0.94 1.05 1.07 1.08Physical Health at Admission 0.99 0.88 * 1.01 * 1.01 1.01 1.01Mental Health at Admission 0.97 * 1.00 0.92 * 1.00 1.00 1.00Knowledge at Admission 0.26 * Behaviour at Admission 0.12 * Status at Admission 0.23 *Worked Hours per Patient 1.13 * 1.00 0.94 * 1.04 0.98 1.00Length of Stay 1.03 1.01 0.96 * 1.01 0.99 1.00

Nurse Level Education (ref: Diploma) 1.13 1.11 1.05 0.75 0.92 1.10Overtime Hours 0.98 0.93 * 1.04 1.02 0.97 1.02Unit Instability 0.47 * Interventions Not Done 0.38 1.08 1.32 0.62 1.03 0.76Interventions Delayed 2.94 0.98 0.91 1.11 0.99 0.66Autonomy 1.19 * Physical Health 1.03 1.00 1.01 0.98 0.99 0.98Mental Health 0.98 0.99 1.01 0.99 0.99 1.02Satisfaction with Current Job (ref: Dissatisfied) 2.76 * Nurse-Patient Ratio 1.28 1.05 0.87 1.11 0.97 1.17

Unit Level Proportion of RN Worked Hours 1.45 0.91 1.05 1.74 * 1.04 1.23Productivity/Utilization n/a * n/a * n/a *Productivity/Utilization (Quadratic) n/a * Productivity/Utilization (beyond 88%) n/a * n/a *Productivity/Utilization (beyond 80%) 0.55 *Productivity/Utilization (beyond 85%) 0.51 1.35Proportion of Full-time Employment 1.24 * Proportion of Nurses Reporting Shift Changes 0.56 *

* for p-value at 0.05 or less Notes: (1) All patient outcome variables were dichotomized and modeled in hierarchical logistic regressions. For medical consequences, 1 = falls with injury, medication errors, death, or development of complications; for physical and mental health, 1 = Improved at discharge; for Omaha knowledge, behaviour and status, 1 = improvement at discharge or diagnoses resolved. (2) The productivity/utilization cut point is 88.2% for Omaha behaviour. (3) The odds ratios for proportion of RN worked hours, proportion of full-time employment and proportion of nurses reporting shift changes are based on a 10% increase. (4) Odds ratio for quadratic transformation of productivity/utilization is not reported.


Table 4: Hierarchical Logistic Regression for Medical Consequences Developed During Hospital Stay

Predictor Beta SE Odds Ratio

Patient Level Referral for Homecare 1.43 0.36 4.19 * Resource Intensity Weight 0.02 0.06 1.02 Number of Nursing Diagnoses 0.43 0.08 1.53 * Physical Health at Admission -0.01 0.01 0.99 Mental Health at Admission -0.03 0.01 0.97 * Worked Hours per Patient 0.13 0.05 1.13 * Length of Stay 0.03 0.02 1.03 Nurse Level Education (ref: Diploma) 0.12 0.63 1.13 Overtime Hours -0.02 0.07 0.98 Interventions Not Done -0.97 0.70 0.38 Interventions Delayed 1.08 0.64 2.94 Physical Health 0.03 0.04 1.03 Mental Health -0.02 0.03 0.98 Nurse-Patient Ratio 0.25 0.23 1.28 Unit Level Proportion of RN Worked Hours 3.72 2.59 1.45 Productivity/Utilization (beyond 85%) -0.67 0.53 0.51 * p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase.


Table 5: Hierarchical Logistic Regression for Improvement in Patients’ Physical Health


Patient Level Resource Intensity Weight -0.10 0.04 0.90 * Number of Nursing Diagnoses -0.12 0.04 0.89 * Physical Health at Admission -0.13 0.01 0.88 * Mental Health at Admission 0.00 0.01 1.00 Worked Hours per Patient 0.00 0.02 1.00 Length of Stay 0.01 0.01 1.01 Nurse Level Education (ref: Diploma) 0.11 0.25 1.11 Overtime Hours -0.08 0.04 0.93 * Interventions Not Done 0.08 0.40 1.08 Interventions Delayed -0.02 0.33 0.98 Physical Health -0.01 0.02 1.00 Mental Health -0.01 0.01 0.99 Nurse-Patient Ratio 0.05 0.08 1.05 Unit Level Proportion of RN Worked Hours -0.94 1.15 0.91 Productivity/Utilization (beyond 80%) -0.60 0.26 0.55 * * p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase.


Table 6: Hierarchical Logistic Regression for Improvement in Patients’ Mental Health


Patient Level Resource Intensity Weight -0.01 0.04 0.99 Number of Nursing Diagnoses -0.06 0.04 0.94 Physical Health at Admission 0.01 0.01 1.01 * Mental Health at Admission -0.09 0.01 0.92 * Worked Hours per Patient -0.06 0.03 0.94 * Length of Stay -0.04 0.02 0.96 * Nurse Level Education (ref: Diploma) 0.05 0.24 1.05 Overtime Hours 0.04 0.03 1.04 Interventions Not Done 0.27 0.34 1.32 Interventions Delayed -0.10 0.29 0.91 Physical Health 0.01 0.01 1.01 Mental Health 0.01 0.01 1.01 Nurse-Patient Ratio -0.14 0.08 0.87 Unit Level Proportion of RN Worked Hours 0.53 1.08 1.05 Productivity/Utilization (beyond 85%) 0.30 0.27 1.35

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase.


Table 7: Hierarchical Logistic Regression for Patients’ Knowledge Improvement at Discharge/Diagnoses Resolved


Patient Level Resource Intensity Weight 0.01 0.04 1.01 Number of Nursing Diagnoses 0.05 0.04 1.05 Physical Health at Admission 0.01 0.01 1.01 Mental Health at Admission 0.00 0.01 1.00 Knowledge at Admission -1.33 0.13 0.26 * Worked Hours per Patient 0.04 0.02 1.04 Length of Stay 0.01 0.02 1.01 Nurse Level Education (ref: Diploma) -0.29 0.25 0.75 Overtime Hours 0.02 0.03 1.02 Interventions Not Done -0.47 0.35 0.62 Interventions Delayed 0.10 0.30 1.11 Autonomy 0.17 0.04 1.19 * Physical Health -0.02 0.02 0.98 Mental Health -0.01 0.01 0.99 Nurse-Patient Ratio 0.11 0.08 1.11 Unit Level Proportion of RN Worked Hours 5.55 1.36 1.74 * Productivity/Utilization 7.40 1.16 n/a * Productivity/Utilization (beyond 88%) -1.49 0.32 n/a * Proportion of Full-Time Employment 2.13 0.61 1.24 * Proportion of Nurses Reporting Shift Changes -5.75 0.82 0.56 *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours, proportion of full-time employment, and proportion of nurses reporting shift changes are based on a 10% increase.


Table 8: Hierarchical Logistic Regression for Patients’ Behaviour Improvement at Discharge/Diagnoses Resolved


Patient Level Resource Intensity Weight 0.08 0.05 1.08 Number of Nursing Diagnoses 0.06 0.04 1.07 Physical Health at Admission 0.01 0.01 1.01 Mental Health at Admission 0.00 0.01 1.00 Behaviour at Admission -2.14 0.18 0.12 * Worked Hours per Patient -0.02 0.04 0.98 Length of Stay -0.01 0.02 0.99 Nurse Level Education (ref: Diploma) -0.08 0.28 0.92 Overtime Hours -0.03 0.04 0.97 Unit Instability -0.75 0.33 0.47 * Interventions Not Done 0.03 0.36 1.03 Interventions Delayed -0.01 0.35 0.99 Physical Health -0.01 0.02 0.99 Mental Health -0.01 0.01 0.99 Satisfaction with Current Job (ref: Dissatisfied) 1.02 0.27 2.76 * Nurse-Patient Ratio -0.03 0.08 0.97 Unit Level Proportion of RN Worked Hours 0.42 1.53 1.04 Productivity/Utilization 17.83 1.41 n/a * Productivity/Utilization (Quadratic) -10.11 0.99 n/a *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase. (3) productivity/utilization cut point is 88.2%.


Table 9: Hierarchical Logistic Regression for Patients’ Status Improvement at Discharge/Diagnoses Resolved


Patient Level Resource Intensity Weight 0.05 0.05 1.05 Number of Nursing Diagnoses 0.08 0.05 1.08 Physical Health at Admission 0.01 0.01 1.01 Mental Health at Admission 0.00 0.01 1.00 Status at Admission -1.48 0.18 0.23 * Worked Hours per Patient 0.00 0.03 1.00 Length of Stay 0.00 0.02 1.00 Nurse Level Education (ref: Diploma) 0.10 0.29 1.10 Overtime Hours 0.02 0.04 1.02 Interventions Not Done -0.27 0.39 0.76 Interventions Delayed -0.42 0.33 0.66 Physical Health -0.02 0.02 0.98 Mental Health 0.02 0.01 1.02 Nurse-patient Ratio 0.16 0.09 1.17 Unit Level Proportion of RN Worked Hours 2.06 1.26 1.23 Productivity/Utilization 4.94 1.16 n/a * Productivity/Utilization (beyond 88%) -0.80 0.39 n/a *



Table 10. Estimates for Nurse Outcomes in the Hierarchical Linear Models

PredictorN-P

relation-ship

Auto-tonomy

Satis-faction

Emotional Exhaust'n

Physical Health

Mental Health

Nurse LevelGender (ref: Male) -0.90 * -0.74 *Age -0.03 -0.01 0.03 *Education (ref: Diploma) 0.16 -0.44 * 0.44 * 0.11 0.02 -0.21Full-time Employment (ref: PT/Casual) 0.16 0.03 0.12 1.03 * -0.11 -0.03Over Time Hours 0.01 -0.02 0.01 0.02 0.02Clinical Expertise -0.33 * 0.09 0.21 0.14 0.08Unit Instability -0.03 -0.31 -0.22 -0.27Shift Change -0.01 0.01 0.03 -0.26 -0.05Interventions Not Done -0.33 -0.13 -0.12 -0.10 -0.05Interventions Delayed -0.10 0.01 0.37 -0.14Effort and Reward Imbalance -0.89 * -0.49 1.23 * -0.67 * -0.51Emotional Exhaustion -0.07 -1.23 * -0.53 * -1.56 *Autonomy 0.30 * 0.21 * -0.02 0.05 0.04Nurse-physician Relationship 0.62 * 0.06 0.00 -0.10 0.05Absenteeism -0.36Physical Health -0.01 0.02 0.02 -0.04 * -0.04 *Mental Health 0.01 0.02 0.02 -0.11 * -0.02 *Satisfaction with Current Job (ref: Dissatisfied) 1.04 * -1.09 * 0.46 * 0.55 *Improved Quality of Patient Care (ref: Deteriorated) 0.29 0.67 * 0.37 0.28 0.13 0.14Good Quality of Nursing Care (ref: Poor) 0.59 * 0.28 1.39 * -0.57 -0.24 0.00Nurse-patient Ratio -0.37 * 0.67 * 0.31 0.49 -0.38 -0.29

Patient LevelProportion of Patients Attended Pre-operative Clinics 0.83 *Average Resource Intensity Weight 0.02 0.04 0.05 0.05Average Number of Nursing Diagnoses -0.10 * -0.05 -0.10 -0.07

Unit LevelUnit Occupancy -2.07 *Average Worked Hours -0.02 0.02 0.10 * 0.08 -0.10 * -0.07 *Proportion of RN Worked Hours 2.89 * 1.69 0.78 1.63 -3.28 * -1.37Productivity/Utilization (beyond 85%) -0.40 * 1.58 * -0.53 -0.20 -0.16Productivity/Utilization (beyond 80%) -0.85 *Average Age of Nurses -0.15 *Proportion of Nurses Reporting Shift Changes -2.08 *Proportion of Emotionally Exhausted Nurses -0.43Average Nurse-physician Relationship 0.88 *Proportion of Physically Healthy Nurses 4.62 *Proportion of Satisfied Nurses -3.88 *

* for p-value at 0.05 or lessNotes: (1) Except for nurse-physician relationship and autonomy, all nurse outcomes were dichotomized and modelled in hierarchial logistic regression. For satisfaction, 1 = satisfied with current job; for emotional exhaustion, 1 = at risk; for physical and mental health, 1 =healthier than average of female population.


Table 11. Odds Ratios for Nurse Outcomes in the Hierarchical Linear Models

PredictorN-P

relation-ship

Auto-tonomy

Satis-faction

Emotional Exhaust'n

Physical Health

Mental Health

Nurse LevelGender (ref: Male) 0.41 * 0.48 *Age 0.97 0.99 1.03 *Education (ref: Diploma) n/a n/a * 1.56 * 1.12 1.02 0.81Full-time Employment (ref: PT/Casual) n/a n/a 1.12 2.79 * 0.90 0.97Over Time Hours n/a 0.98 1.01 1.02 1.02Clinical Expertise n/a * 1.09 1.24 1.15 1.09Unit Instability n/a 0.73 0.80 0.76Shift Change n/a 1.01 1.03 0.77 0.96Interventions Not Done n/a 0.88 0.89 0.90 0.95Interventions Delayed n/a 1.01 1.45 0.87Effort and Reward Imbalance n/a * 0.61 3.42 * 0.51 * 0.60Emotional Exhaustion n/a 0.29 * 0.59 * 0.21 *Autonomy n/a * 1.24 * 0.98 1.06 1.04Nurse-physician Relationship n/a * 1.06 1.00 0.91 1.06Absenteeism n/aPhysical Health n/a n/a 1.02 0.96 * 0.96 *Mental Health n/a n/a 1.02 0.90 * 0.98 *Satisfaction with Current Job (ref: Dissatisfied) n/a * 0.34 * 1.58 * 1.74 *Improved Quality of Patient Care (ref: Deteriorated) n/a n/a * 1.45 1.32 1.14 1.15Good Quality of Nursing Care (ref: Poor) n/a * n/a 4.01 * 0.56 0.79 1.00Nurse-patient Ratio n/a * n/a * 1.36 1.64 0.69 0.75

Patient LevelProportion of Patients Attended Pre-operative Clinics n/a *Average Resource Intensity Weight n/a n/a 1.05 1.05Average Number of Nursing Diagnoses n/a * n/a 0.90 0.93

Unit LevelUnit Occupancy n/a *Average Worked Hours n/a n/a 1.10 * 1.08 0.90 * 0.93 *Proportion of RN Worked Hours n/a * n/a 1.08 1.18 0.72 * 0.87Productivity/Utilization (beyond 85%) n/a * n/a * 0.59 0.82 0.85Productivity/Utilization (beyond 80%) 0.43 *Average Age of Nurses 0.86 *Proportion of Nurses Reporting Shift Changes n/a *Proportion of Emotionally Exhausted Nurses 0.65Average Nurse-physician Relationship 2.40 *Proportion of Physically Healthy Nurses n/a *Proportion of Satisfied Nurses 0.68 *

* for p-value at 0.05 or lessNotes: (1) Except for nurse-physician relationship and autonomy, all nurse outcomes were dichotomized and modelled in hierarchial logistic regression. For satisfaction, 1 = satisfied with current job; for emotional exhaustion, 1 = at risk; for physical and mental health, 1 =healthier than average of female population. (2) The odds ratio for proportion of RN worked hours, proportion of nurses with BScN or above, proportion of nurses reporting shift changes, proportion of emotionally exhaused nurses, proportion of physically healthy nurses, proportion of satisfied nurses, proportion of nurses rating good nurse care quality are based on a 10% increase.


Table 12: Hierarchical Linear Regression for Nurse-Physician Relationship


Nurse Level Education (ref: Diploma) 0.16 0.14 n/a Full-Time Employment (ref: PT/Casual) 0.16 0.14 n/a Autonomy 0.30 0.03 n/a * Physical Health -0.01 0.01 n/a Mental Health 0.01 0.01 n/a Improved Quality of Patient Care (ref: Deteriorated) 0.29 0.15 n/a Good Quality of Nursing Care (ref: Poor) 0.59 0.22 n/a * Patient Level Average Resource Intensity Weight 0.02 0.02 n/a Average Number of Nursing Diagnoses -0.10 0.05 n/a * Nurse-Patient Ratio -0.37 0.18 n/a * Unit Level Average Worked Hours -0.02 0.03 n/a Proportion of RN Worked Hours 2.89 1.39 n/a * Productivity/Utilization (beyond 85%) -0.40 0.20 n/a * Proportion of Nurses Reporting Shift Changes -2.08 0.78 n/a * Proportion of Physically Healthy Nurses 4.62 1.01 n/a *

* p-value at 0.05 or less Note: For measurements of predictor and outcome variables, see Appendix F.


Table 13: Hierarchical Linear Regression for Autonomy


Nurse Level Education (ref: Diploma) -0.44 0.20 n/a * Full-Time Employment (ref: PT/Casual) 0.03 0.22 n/a Overtime Hours 0.01 0.02 n/a Clinical Expertise -0.33 0.16 n/a * Unit Instability -0.03 0.25 n/a Shift Change -0.01 0.20 n/a Interventions Not Done -0.33 0.26 n/a Interventions Delayed -0.10 0.23 n/a Effort and Reward Imbalance -0.89 0.29 n/a * Emotional Exhaustion -0.07 0.26 n/a Nurse-Physician Relationship 0.62 0.06 n/a * Absenteeism -0.36 0.25 n/a Physical Health 0.02 0.01 n/a Mental Health 0.02 0.01 n/a Satisfaction with Current Job (ref: Dissatisfied) 1.04 0.22 n/a * Improved Quality of Patient Care (ref: Deteriorated) 0.67 0.23 n/a * Good Quality of Nursing Care (ref: Poor) 0.28 0.32 n/a Nurse-Patient Ratio 0.67 0.24 n/a * Patient Level Proportion of Patients Attended Pre-operative Clinics 0.83 0.37 n/a * Average Resource Intensity Weight 0.04 0.03 n/a Average Number of Nursing Diagnoses -0.05 0.07 n/a Unit Level Unit Occupancy -2.07 1.03 n/a * Average Worked Hours 0.02 0.04 n/a Proportion of RN Worked Hours 1.69 2.01 n/a Productivity/Utilization (beyond 85%) 1.58 0.29 n/a *

* p-value at 0.05 or less Note: For measurements of predictor and outcome variables, see Appendix F.


Table 14: Hierarchical Logistic Regression for Job Satisfaction


Nurse Level Education (ref: Diploma) 0.44 0.22 1.56 * Full-Time Employment (ref: PT/Casual) 0.12 0.23 1.12 Overtime Hours -0.02 0.02 0.98 Clinical Expertise 0.09 0.19 1.09 Unit Instability -0.31 0.27 0.73 Shift Change 0.01 0.22 1.01 Interventions Not Done -0.13 0.27 0.88 Interventions Delayed 0.01 0.25 1.01 Effort and Reward Imbalance -0.49 0.39 0.61 Emotional Exhaustion -1.23 0.30 0.29 * Autonomy 0.21 0.05 1.24 * Nurse-Physician Relationship 0.06 0.07 1.06 Physical Health 0.02 0.01 1.02 Mental Health 0.02 0.01 1.02 Improved Quality of Patient Care (ref: Deteriorated) 0.37 0.25 1.45 Good Quality of Nursing Care (ref: Poor) 1.39 0.46 4.01 * Nurse-Patient Ratio 0.31 0.29 1.36 Patient Level Average Resource Intensity Weight 0.05 0.04 1.05 Average Number of Nursing Diagnoses -0.10 0.08 0.90 Unit Level Average Worked Hours 0.10 0.04 1.10 * Proportion of RN Worked Hours 0.78 1.99 1.08 Productivity/Utilization (beyond 80%) -0.85 0.32 0.43 *



Table 15: Hierarchical Logistic Regression for Emotional Exhaustion


Nurse Level Age -0.03 0.02 0.97 Education (ref: Diploma) 0.11 0.26 1.12 Full-Time Employment (ref: PT/Casual) 1.03 0.28 2.79 * Overtime Hours 0.01 0.03 1.01 Clinical Expertise 0.21 0.22 1.24 Shift Change 0.03 0.27 1.03 Interventions Not Done -0.12 0.34 0.89 Effort and Reward Imbalance 1.23 0.33 3.42 * Autonomy -0.02 0.05 0.98 Nurse-Physician Relationship 0.00 0.08 1.00 Physical Health -0.04 0.02 0.96 * Mental Health -0.11 0.01 0.90 * Satisfaction with Current Job (ref: Dissatisfied) -1.09 0.31 0.34 * Improved Quality of Patient Care (ref: Deteriorated) 0.28 0.29 1.32 Good Quality of Nursing Care (ref: Poor) -0.57 0.40 0.56 Nurse-Patient Ratio 0.49 0.33 1.64 Patient Level Average Resource Intensity Weight 0.05 0.05 1.05 Average Number of Nursing Diagnoses -0.07 0.10 0.93 Unit Level Average Worked Hours 0.08 0.06 1.08 Proportion of RN Worked Hours 1.63 2.36 1.18 Productivity/Utilization (beyond 85%) -0.53 0.35 0.59 Proportion of Satisfied Nurses -3.88 1.37 0.68 *

Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours and proportion of satisfied nurses are based on a 10% increase.


Table 16: Hierarchical Linear Regression for Nurses’ Physical Health


Nurse Level Gender (ref: Male) -0.90 0.42 0.41 * Age -0.01 0.01 0.99 Education (ref: Diploma) 0.02 0.18 1.02 Full-Time Employment (ref: PT/Casual) -0.11 0.18 0.90 Overtime Hours 0.02 0.02 1.02 Clinical Expertise 0.14 0.15 1.15 Unit Instability -0.22 0.21 0.80 Shift Change -0.26 0.17 0.77 Interventions Not Done -0.10 0.23 0.90 Interventions Delayed 0.37 0.20 1.45 Effort and Reward Imbalance -0.67 0.25 0.51 * Emotional Exhaustion -0.53 0.23 0.59 * Autonomy 0.05 0.04 1.06 Nurse-Physician Relationship -0.10 0.05 0.91 Mental Health -0.02 0.01 0.98 * Satisfaction with Current Job (ref: Dissatisfied) 0.46 0.20 1.58 * Improved Quality of Patient Care (ref: Deteriorated) 0.13 0.20 1.14 Good Quality of Nursing Care (ref: Poor) -0.24 0.28 0.79 Nurse-Patient Ratio -0.38 0.21 0.69 Unit Level Average Worked Hours -0.10 0.04 0.90 * Proportion of RN Worked Hours -3.28 1.20 0.72 * Productivity/Utilization (beyond 85%) -0.20 0.21 0.82 Average Age of Nurses -0.15 0.04 0.86 * Average Nurse-Physician Relationship 0.88 0.17 2.40 * * p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase.


Table 17: Hierarchical Linear Regression for Nurses’ Mental Health


Nurse Level Gender (ref: Male) -0.74 0.38 0.48 * Age 0.03 0.01 1.03 * Education (ref: Diploma) -0.21 0.18 0.81 Full-Time Employment (ref: PT/Casual) -0.03 0.18 0.97 Overtime Hours 0.02 0.02 1.02 Clinical Expertise 0.08 0.16 1.09 Unit Instability -0.27 0.23 0.76 Shift Change -0.05 0.18 0.96 Interventions Not Done -0.05 0.23 0.95 Interventions Delayed -0.14 0.20 0.87 Effort and Reward Imbalance -0.51 0.28 0.60 Emotional Exhaustion -1.56 0.23 0.21 * Autonomy 0.04 0.04 1.04 Nurse-Physician Relationship 0.05 0.05 1.06 Physical Health -0.04 0.01 0.96 * Satisfaction with Current Job (ref: Dissatisfied) 0.55 0.20 1.74 * Improved Quality of Patient Care (ref: Deteriorated) 0.14 0.20 1.15 Good Quality of Nursing Care (ref: Poor) 0.00 0.28 1.00 Nurse-Patient Ratio -0.29 0.23 0.75 Unit Level Average Worked Hours -0.07 0.03 0.93 * Proportion of RN Worked Hours -1.37 1.25 0.87 Productivity/Utilization (beyond 85%) -0.16 0.23 0.85 Proportion of Emotionally Exhausted Nurses -0.43 0.88 0.65

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours and proportion of emotionally exhausted nurses are based on a 10% increase.


Table 18. Estimates for System Outcomes from the Hierarchical Linear Models

PredictorLOS Not

DoneDelay Quality of

Nursing CareQuality of

Patient CareAbsente-

eismLeave Productivity/

UtilizationCost per

RIWWorked hours

Nurse LevelAge -0.04 * -0.02 -0.02 0.00Dependent Children (ref: No) 0.56 -0.03Education (ref: Diploma) 0.32 -0.06 -0.01 0.00 0.15 -0.06 0.70 * 0.003 0.047 0.015Work on Multiple Units -0.49 0.29Full-time Employment (ref: PT/Casual) 0.48 0.55 * 0.48 -0.17 0.93 * -0.72 *Over Time Hours -0.03 0.09 * 0.03 0.02 -0.02 0.01 -0.01 -0.001 0.004 -0.002Clinical Expertise 0.11 -0.09 0.58 * -0.84 * -0.20 0.13 -0.163 *Unit Instability 0.06 0.63 * -0.04 0.23 0.27 1.09 *Shift Change -0.48 -0.13 -0.69 * 0.33 0.31 0.42Prevalence of Violence 0.82 * 0.43 *Interventions Not Done -0.10 -0.59 -0.25 0.024 0.069 -0.012Interventions Delayed 0.17 -0.11 -0.62 * 0.000 -0.025 0.018Effort and Reward Imbalance 1.28 * 0.80 * 0.33 -0.44 -0.03 -0.35Emotional Exhaustion -0.12 0.01 -0.61 0.30 -0.04 0.37Autonomy -0.13 * -0.07 0.07 0.16 * -0.08 -0.08 0.007 *Nurse-physician Relationship -0.14 0.06 0.22 * 0.03 0.11 0.07Intent to Leave -0.04 0.37Physical Health -0.02 -0.02 0.01 0.00 0.01 -0.05 * 0.02 0.001 -0.011 * -0.001Mental Health 0.02 -0.01 -0.02 0.01 0.01 -0.02 -0.01 0.001 0.000 0.000Satisfaction with Current Job (ref: Dissatisfied) -0.17 -0.21 0.95 * 0.33 0.01 -0.87 *Improved Quality of Patient Care (ref: Deteriorated) 1.95 * -0.50 -0.35Good Quality of Nursing Care (ref: Poor) 2.32 * 0.54 0.09Nurse-patient Ratio -0.17 0.43 0.50 -0.69 -0.54 0.38 -0.08 0.045 * -0.110 * -0.010Re-sequencing of Work -1.44 *Unanticipated Changes in Patient Acuity 2.97 *More Time Needed 0.001 *

Patient LevelProportion of Patients Employed Full-time 1.10 *Pre-operative Clinics 1.05 * 0.241 *Post-operative/-discharge Education 0.128 *Medical Consequences -0.85 *Emergency Admission (ref: Elective) -0.153 *Resource Intensity Weight -0.31 * 0.11 * 0.01 0.000 0.000Number of Nursing Diagnoses -0.14 * -0.22 * -0.23 * -0.003 0.030 * 0.006 *Physical Health at Admission 0.02 * 0.001 0.000Mental Health at Admission 0.01 0.006 * 0.000Worked Hours per Patient 0.01Length of Stay 0.507 * -0.001

Unit LevelPure Cardiology (ref: Mix) -0.196 *Step Down Unit (ref: Other Types of Units) -2.262 *Average Worked Hours -0.01 0.07 -0.12 -0.05 0.06 -0.01Proportion of RN Worked Hours 0.49 0.56 -1.40 -1.66 -5.22 * -0.25 -1.24 0.532 * 0.090 0.597 *Productivity/Utilization 8.72 * -10.32 * -12.71 * -10.557 * 11.872 *Productivity/Utilization (Quadratic) -4.77 * 6.47 * 7.68 * 5.901 * -6.619 *Productivity/Utilization (beyond 85%) -0.38 -0.24 -0.25 -0.11Proportion of Nurses with BScN or Above -3.21 * 3.34 *Proportion of Full-time Employment -0.297 *Average Overtime Hours -0.29 *Average Clinical Expertise -2.77 * -0.260 *Prevalence of Violence at Unit 2.10 *Proportion of Emotionally Exhausted Nurses -0.557 *Average Nurse-physician Relationship -0.75 *Proportion of Nurses Reporting Sick Leave 5.35 *Proportion of Mentally Healthy Nurses -0.445 *Proportion of Nurses Rating Good Nursing Care Quality 6.57 * -3.62 *

* for p-value at 0.05 or lessNotes: (1) Length of stay, tasks not done or delayed, quality of nursing care and quality of patient care were dichotomized and modeled in hierarchical logistic regressions. For length of stay, 1 = hospital stay shorter than expected; for not done, 1 = at least one task not done on last shift; for delay, 1 = at least one task delayed on last shift; quality of nursing care, 1 = excellent/good; for quality of patient care, 1 = improved; for absenteeism, 1 = more than one occasion in past year; for leave, 1 = intent to leave within next year. (2) The productivity/utilization cut points are 91.4% for LOS, 79.7% for absenteeism, 82.8% for intent to leave, 89.5% for cost per RIW and 89.7% for worked hours.


Table 19. Odds Ratios for System Outcomes in the Hierarchical Linear Models

Predictor LOS Not Done

Delay Quality of Nursing Care

Quality of Patient Care

Absente-eism

Leave Productivity/Utilization

Cost per RIW

Worked hours

Nurse LevelAge 0.96 * 0.98 0.98 1.00Dependent Children (ref: No) 1.75 0.97Education (ref: Diploma) 1.37 0.94 0.99 1.00 1.16 0.94 2.01 * n/a n/a n/aWork on Multiple Units 0.61 1.33Full-time Employment (ref: PT/Casual) 1.62 1.74 * 1.62 0.84 2.52 * 0.49 *Over Time Hours 0.97 1.09 * 1.03 1.02 0.98 1.01 1.00 n/a n/a n/aClinical Expertise 1.12 0.92 1.79 * 0.43 * 0.82 1.14 n/a *Unit Instability 1.06 1.87 * 0.97 1.26 1.31 2.97 *Shift Change 0.62 0.88 0.50 * 1.39 1.37 1.52Prevalence of Violence 2.27 * 1.53 *Interventions Not Done 0.91 0.56 0.78 n/a n/a n/aInterventions Delayed 1.18 0.90 0.54 * n/a n/a n/aEffort and Reward Imbalance 3.60 * 2.23 * 1.40 0.64 0.97 0.70Emotional Exhaustion 0.89 1.01 0.54 1.35 0.96 1.45Autonomy 0.88 * 0.93 1.08 1.17 * 0.92 0.92 n/a *Nurse-physician Relationship 0.87 1.06 1.25 * 1.03 1.12 1.08Intent to leave 0.96 1.45Physical Health 0.98 0.98 1.01 1.00 1.01 0.95 * 1.02 n/a n/a * n/aMental Health 1.02 0.99 0.98 1.01 1.01 0.98 0.99 n/a n/a n/aSatisfaction with Current Job (ref: Dissatisfied) 0.85 0.81 2.59 * 1.39 1.01 0.42 *Improved Quality of Patient Care (ref: Deteriorated) 7.06 * 0.60 0.71Good Quality of Nursing Care (ref: Poor) 10.15 * 1.72 1.10Nurse-patient Ratio 0.85 1.53 1.65 0.50 0.59 1.47 0.92 n/a * n/a * n/aRe-sequencing of Work 0.24 *Unanticipated Changes in Patient Acuity 19.47 *More Time Needed n/a *

Patient Level Proportion of Patients Employed Full-time 3.00 *Pre-operative Clinics 2.85 * n/a *Post-operative/-discharge Education n/a *Medical Consequences 0.43 *Emergency Admission (ref: Elective) n/a *Resource Intensity Weight 0.74 * 1.12 * 1.01 n/a n/aNumber of Nursing Diagnoses 0.87 * 0.80 * 0.80 * n/a n/a * n/a *Physical Health at Admission 1.02 * n/a n/aMental Health at Admission 1.01 n/a * n/aWorked Hours per Patient 1.01Length of Stay n/a * n/a

Unit LevelPure Cardiology (ref: Mix) n/a *Step Down Unit (ref: Other Types of Units) n/a *Average Worked Hours 0.99 1.07 0.89 0.95 1.07 0.99Proportion of RN Worked Hours 1.05 1.06 0.87 0.85 0.59 * 0.97 0.88 n/a * n/a n/a *Productivity/Utilization n/a * n/a * n/a * n/a * n/a *Productivity/Utilization (Quadratic) n/a * n/a * n/a * n/a * n/a *Productivity/Utilization (beyond 85%) 0.69 0.78 0.78 0.89Proportion of Nurses with BScN or Above 0.73 * 1.40 *Proportion of Full-time Employment n/a *Average Overtime Hours 0.75 *Average Clinical Expertise 0.06 * n/a *Prevalence of Violence at Unit 8.14 *Proportion of Emotionally Exhausted Nurses n/a *Average Nurse-physician Relationship 0.47 *Proportion of Nurses Reporting Sick Leave 1.71 *Proportion of Mentally Healthy Nurses n/a *Proportion of Nurses Rating Good Nursing Care Quality 1.93 * 0.03 *

* for p-value at 0.05 or lessNotes: (1) Length of stay, tasks not done or delayed, quality of nursing care and quality of patient care were dichotomized and modeled in hierarchical logistic regressions. For length of stay, 1 = hospital stay shorter than expected; for not done, 1 = at least one task not done on last shift; for delay, 1 = at least one task delayed on last shift; for quality of nursing care, 1 = excellent/good; for quality of patient care, 1 = improved; for absenteeism, 1 = more than one occasion in past year; for leave, 1 = intent to leave within next year. (2) The productivity/utilization cut points are 91.4% for LOS, 79.7% for absenteeism, 82.8% for intent to leave, 89.5% for cost per RIW, and 89.7% for worked hours. (3) The odds ratios for proportion of RN worked hours, proportion of nurses with BScN or above, proportion of nurses reporting sick leave and proportion of nurses rating good nursing care quality are based on a 10% increase. (4) Odds ratio for quadratic transformation of productivity/utilization is not reported.


Table 20: Hierarchical Logistic Regression for Patients with Shorter Than Expected Length of Stay


Patient Level Pre-operative Clinics 1.05 0.26 2.85 * Medical Consequences -0.85 0.31 0.43 * Resource Intensity Weight -0.31 0.04 0.74 * Number of Nursing Diagnoses -0.14 0.04 0.87 * Physical Health at Admission 0.02 0.01 1.02 * Mental Health at Admission 0.01 0.01 1.01 Worked Hours per Patient 0.01 0.03 1.01 Nurse Level Education (ref: Diploma) 0.32 0.29 1.37 Overtime Hours -0.03 0.04 0.97 Interventions Not Done -0.10 0.36 0.91 Interventions Delayed 0.17 0.29 1.18 Physical Health -0.02 0.02 0.98 Mental Health 0.02 0.02 1.02 Nurse-Patient Ratio -0.17 0.10 0.85 Unit Level Proportion of RN Worked Hours 0.49 1.78 1.05 Productivity/Utilization 8.72 2.10 n/a * Productivity/Utilization (Quadratic) -4.77 1.34 n/a *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase. (3) Productivity/utilization cut point is 91.4%.


Table 21: Hierarchical Logistic Regression for Interventions Not Done


Nurse Level

Age -0.044 0.017 0.96 *

Education (ref: Diploma) -0.061 0.246 0.94 Full-Time Employment (ref: PT/Casual) 0.480 0.260 1.62

Overtime Hours 0.085 0.025 1.09 *

Clinical Expertise 0.109 0.210 1.12 Unit Instability 0.058 0.318 1.06 Shift Change -0.477 0.254 0.62 Prevalence of Violence 0.821 0.219 2.27 *

Effort and Reward Imbalance 1.281 0.557 3.60 *

Emotional Exhaustion -0.115 0.347 0.89 Autonomy -0.126 0.051 0.88 *

Nurse-Physician Relationship -0.143 0.077 0.87 Intent to Leave -0.040 0.384 0.96 Physical Health -0.020 0.016 0.98 Mental Health -0.009 0.015 0.99 Satisfaction with Current Job (ref: Dissatisfied) -0.168 0.263 0.85 Nurse-Patient Ratio 0.428 0.481 1.53 Re-sequencing of Work -1.443 0.445 0.24 *

Unanticipated Changes in Patient Acuity 2.969 0.574 19.47 *

Patient Level Proportion of Patients Employed Full-Time 1.099 0.399 3.00 *

Average Resource Intensity Weight 0.113 0.038 1.12 *

Average Number of Nursing Diagnoses -0.218 0.085 0.80 *

Unit Level

Average Worked Hours -0.011 0.067 0.99 Proportion of RN Worked Hours 0.557 2.510 1.06 Productivity/Utilization (beyond 85%) -0.376 0.403 0.69 Average Overtime Hours -0.292 0.139 0.75 *

Average Clinical Expertise -2.771 0.937 0.06 *



Table 22: Hierarchical Logistic Regression for Interventions Delayed


Patient Level Average Resource Intensity Weight 0.01 0.03 1.01

Average Number of Nursing Diagnoses -0.23 0.08 0.80 *

Nurse Level

Age -0.02 0.01 0.98

Education (ref: Diploma) -0.01 0.22 0.99 Full-Time Employment (ref: PT/Casual) 0.55 0.22 1.74 *

Overtime Hours 0.03 0.02 1.03

Clinical Expertise -0.09 0.19 0.92 Unit Instability 0.63 0.28 1.87 *

Shift Change -0.13 0.22 0.88 Prevalence of Violence 0.43 0.17 1.53 *

Effort and Reward Imbalance 0.80 0.37 2.23 *

Emotional Exhaustion 0.01 0.29 1.01 Autonomy -0.07 0.04 0.93

Nurse-Physician Relationship 0.06 0.07 1.06 Intent to Leave 0.37 0.32 1.45 Physical Health 0.01 0.01 1.01 Mental Health -0.02 0.01 0.98 Satisfaction with Current Job (ref: Dissatisfied) -0.21 0.23 0.81 Nurse-Patient Ratio 0.50 0.32 1.65 Unit Level Average Worked Hours 0.07 0.05 1.07 Proportion of RN Worked Hours -1.40 2.20 0.87 Productivity/Utilization (beyond 85%) -0.24 0.30 0.78 Proportion of Nurses with BScN or Above -3.21 1.00 0.73 *

Prevalence of Violence at Unit 2.10 0.81 8.14 *

Proportion of Nurses Reporting Sick Leave 5.35 1.63 1.71 *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours, proportion of nurses with BScN or above, and proportion of nurses reporting sick leave are based on a 10% increase.


Table 23: Hierarchical Logistic Regression for Quality of Nursing Care


Nurse Level Education (ref: Diploma) 0.00 0.29 1.00 Work on Multiple Units -0.49 0.30 0.61 Full-Time Employment (ref: PT/Casual) 0.48 0.30 1.62 Overtime Hours 0.02 0.02 1.02 Clinical Expertise 0.58 0.22 1.79 * Unit Instability -0.04 0.35 0.97 Shift Change -0.69 0.30 0.50 * Interventions Not Done -0.59 0.48 0.56 Interventions Delayed -0.11 0.36 0.90 Effort and Reward Imbalance 0.33 0.35 1.40 Emotional Exhaustion -0.61 0.34 0.54 Autonomy 0.07 0.06 1.08 Nurse-Physician Relationship 0.22 0.09 1.25 * Physical Health 0.00 0.02 1.00 Mental Health 0.01 0.02 1.01 Satisfaction with Current Job (ref: Dissatisfied) 0.95 0.40 2.59 * Improved Quality of Patient Care (ref: Deteriorated) 1.95 0.36 7.06 * Nurse-Patient Ratio -0.69 0.45 0.50 Unit Level Average Worked Hours -0.12 0.07 0.89 Proportion of RN Worked Hours -1.66 2.63 0.85 Productivity/Utilization (beyond 85%) -0.25 0.35 0.78 Average Nurse-Physician Relationship -0.75 0.23 0.47 * Proportion of Nurses Rating Good Nursing Care

Quality 6.57 1.62 1.93 *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours and proportion of nurses rating good nursing care quality are based on a 10% increase.


Table 24: Hierarchical Logistic Regression for Quality of Patient Care


Nurse Level Education (ref: Diploma) 0.15 0.20 1.16 Work on Multiple Units 0.29 0.22 1.33 Full-Time Employment (ref: PT/Casual) -0.17 0.21 0.84 Overtime Hours -0.02 0.02 0.98 Clinical Expertise -0.84 0.18 0.43 * Unit Instability 0.23 0.27 1.26 Shift Change 0.33 0.20 1.39 Interventions Not Done -0.25 0.26 0.78 Interventions Delayed -0.62 0.23 0.54 * Effort and Reward Imbalance -0.44 0.29 0.64 Emotional Exhaustion 0.30 0.26 1.35 Autonomy 0.16 0.04 1.17 * Nurse-Physician Relationship 0.03 0.06 1.03 Physical Health 0.01 0.01 1.01 Mental Health 0.01 0.01 1.01 Satisfaction with Current Job (ref: Dissatisfied) 0.33 0.23 1.39 Good Quality of Nursing Care (ref: Deteriorated) 2.32 0.38 10.15 * Nurse-Patient Ratio -0.54 0.41 0.59 Unit Level Average Worked Hours -0.05 0.06 0.95 Proportion of RN Worked Hours -5.22 1.33 0.59 * Productivity/Utilization (beyond 85%) -0.11 0.47 0.89 Proportion of Nurses with BScN or Above 3.34 1.64 1.40 *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours and proportion of nurses with BScN or above are based on a 10% increase.


Table 25: Hierarchical Logistic Regression for Absenteeism


Nurse Level Age -0.02 0.02 0.98

Dependent Children (ref: No) 0.56 0.45 1.75

Education (ref: Diploma) -0.06 0.24 0.94 Full-Time Employment (ref: PT/Casual) 0.93 0.24 2.52 *

Overtime Hours 0.01 0.02 1.01

Clinical Expertise -0.20 0.22 0.82 Unit Instability 0.27 0.30 1.31 Shift Change 0.31 0.24 1.37 Effort and Reward Imbalance -0.03 0.37 0.97

Emotional Exhaustion -0.04 0.33 0.96 Autonomy -0.08 0.05 0.92

Nurse-Physician Relationship 0.11 0.07 1.12 Physical Health -0.05 0.02 0.95 *

Mental Health -0.02 0.01 0.98 Satisfaction with Current Job (ref: Dissatisfied) 0.01 0.27 1.01 Improved Quality of Patient Care (ref: Deteriorated) -0.50 0.28 0.60 Good Quality of Nursing Care (ref: Poor) 0.54 0.38 1.72 Nurse-Patient Ratio 0.38 0.29 1.47 Unit Level Average Worked Hours 0.06 0.05 1.07 Proportion of RN Worked Hours -0.25 1.84 0.97 Productivity/Utilization -10.32 4.96 n/a *

Productivity/Utilization (Quadratic) 6.47 2.87 n/a *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratio for proportion of RN worked hours is based on a 10% increase. (3) Productivity/utilization cut point is 79.7%.


Table 26: Hierarchical Logistic Regression for Intent to Leave


Nurse Level Age 0.00 0.02 1.00

Dependent Children (ref: No) -0.03 0.37 0.97

Education (ref: Diploma) 0.70 0.23 2.01 * Full-Time Employment (ref: PT/Casual) -0.72 0.24 0.49 * Overtime Hours -0.01 0.02 1.00

Clinical Expertise 0.13 0.19 1.14 Unit Instability 1.09 0.25 2.97 * Shift Change 0.42 0.24 1.52 Effort and Reward Imbalance -0.35 0.31 0.70

Emotional Exhaustion 0.37 0.28 1.45 Autonomy -0.08 0.05 0.92

Nurse-Physician Relationship 0.07 0.07 1.08 Physical Health 0.02 0.01 1.02

Mental Health -0.01 0.01 0.99 Satisfaction with Current Job (ref: Dissatisfied) -0.87 0.29 0.42 * Improved Quality of Patient Care (ref: Deteriorated) -0.35 0.26 0.71 Good Quality of Nursing Care (ref: Poor) 0.09 0.34 1.10 Nurse-Patient Ratio -0.08 0.36 0.92 Unit Level Average Worked Hours -0.01 0.06 0.99 Proportion of RN Worked Hours -1.24 1.97 0.88 Productivity/Utilization -12.71 5.00 n/a * Productivity/Utilization (Quadratic) 7.68 3.00 n/a * Proportion of Nurses Rating Good Nursing Care Quality -3.62 1.14 0.03 *

Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Odds ratios for proportion of RN worked hours and proportion of nurses rating good nursing care quality are based on a 10% increase. (3) Productivity/utilization cut point is 82.8%.


Table 27: Hierarchical Linear Regression for Productivity/Utilization


Patient Level Resource Intensity Weight 0.000 0.003 n/a Number of Nursing Diagnoses -0.003 0.005 n/a Nurse Level Education (ref: Diploma) 0.003 0.018 n/a Overtime Hours -0.001 0.001 n/a Interventions Not Done 0.024 0.024 n/a Interventions Delayed 0.000 0.020 n/a Autonomy 0.007 0.003 n/a * Physical Health 0.001 0.001 n/a Mental Health 0.001 0.001 n/a Nurse-Patient Ratio 0.045 0.007 n/a * More Time Needed 0.001 0.000 n/a * Unit Level Pure Cardiology (ref: Mix) -0.196 0.041 n/a * Proportion of RN Worked Hours 0.532 0.123 n/a * Proportion of Emotionally Exhausted Nurses -0.557 0.140 n/a * Proportion of Mentally Healthy Nurses -0.445 0.170 n/a *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F.


Table 28: Hierarchical Linear Regression for Cost per Resource Intensity Weight (Log Scale)


Patient Level Pre-operative Clinics 0.241 0.056 n/a * Post-operative/-discharge Education 0.128 0.043 n/a * Emergency Admission (ref: Elective) -0.153 0.044 n/a * Number of Nursing Diagnoses 0.030 0.011 n/a * Physical Health at Admission 0.001 0.002 n/a Mental Health at Admission 0.006 0.002 n/a * Length of Stay 0.507 0.029 n/a * Nurse Level Education (ref: Diploma) 0.047 0.068 n/a Overtime Hours 0.004 0.009 n/a Clinical Expertise -0.163 0.052 n/a * Interventions Not Done 0.069 0.086 n/a Interventions Delayed -0.025 0.076 n/a Physical Health -0.011 0.004 n/a * Nurse-Patient Ratio -0.110 0.027 n/a * More Time Needed 0.000 0.004 n/a Unit Level Step Down Unit (ref: Other Types of Units) -2.262 0.681 n/a * Proportion of RN Worked Hours 0.090 0.558 n/a Productivity/Utilization -10.557 0.514 n/a * Productivity/Utilization (Quadratic) 5.901 0.290 n/a *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Productivity/utilization cut point is 89.5%.


Table 29: Hierarchical Linear Regression for Worked Hours per Patient (Log Scale)


Patient Level Resource Intensity Weight 0.000 0.002 n/a Number of Nursing Diagnoses 0.006 0.002 n/a * Physical Health at Admission 0.000 0.000 n/a Mental Health at Admission 0.000 0.000 n/a Length of Stay -0.001 0.015 n/a Nurse Level Education (ref: Diploma) 0.015 0.015 n/a Overtime Hours -0.002 0.002 n/a Interventions Not Done -0.012 0.020 n/a Interventions Delayed 0.018 0.017 n/a Physical Health -0.001 0.001 n/a Mental Health 0.000 0.001 n/a Nurse-Patient Ratio -0.010 0.005 n/a Unit Level Proportion of RN Worked Hours 0.597 0.180 n/a * Productivity/Utilization 11.872 0.353 n/a * Productivity/Utilization (Quadratic) -6.619 0.189 n/a * Proportion of Full-Time Employment -0.297 0.075 n/a * Average Clinical Expertise -0.260 0.043 n/a *

* p-value at 0.05 or less Notes: (1) For measurements of predictor and outcome variables, see Appendix F. (2) Productivity/utilization cut point is 89.7%.


Table 30: Hierarchical Linear Models for Patient, Nurse, and System Outcomes on Congruence Between PRN Hours and Actual Worked Hours per Patient

Coefficient SE p-value OR

Patient Outcome Medical Consequences -0.0026 0.0022 0.2209 1.00 Physical Health Improvement -0.0045 0.0043 0.2927 1.00 Mental Health Improvement 0.0006 0.0044 0.8930 1.00 Omaha Knowledge Improvement -0.0055 0.0041 0.1779 0.99 Omaha Behaviour Improvement -0.0007 0.0042 0.8687 1.00 Omaha Status Improvement 0.0024 0.0037 0.5102 1.00

Nurse Outcome Nurse-Physician Relationship 0.1476 0.0108 0.1721 n/a Autonomy 0.0205 0.1747 0.2405 n/a Satisfaction -0.0024 0.0028 0.3908 1.00 Emotional Exhaustion -0.0006 0.0025 0.8247 1.00 Mentally Healthy 0.0008 0.0027 0.7751 1.00 Physically Healthy 0.0022 0.0026 0.4016 1.00

System Outcome LOS Shorter than Expected LOS 0.0018 0.0040 0.6546 1.00 Interventions Not Done 0.0004 0.0024 0.8766 1.00 Interventions Delayed 0.0047 0.0027 0.0847 1.00 Improved Rating for Quality of Patient Care -0.0041 0.0026 0.1247 1.00 Good Rating for Quality of Nursing Care -0.0002 0.0019 0.9076 1.00 Absenteeism -0.0016 0.0023 0.4864 1.00 Intent to Leave 0.0055 0.0020 0.0067 * 1.01 Cost per Resource Intensity Weight -0.0093 0.0080 0.2433 n/a

Productivity/Utilization 0.0117 0.0025 0.0000 * n/a

* p-value at 0.05 or less Note: (1) For measurements of outcome variables, see Appendix F. (2) The predictor is the PRN hours less the worked hours per patient.


Table 31: Summary Table of the Effect of Nursing Hours, Proportion of RN Worked Hours, Nurse-Patient Ratio, and Productivity/Utilization on Patient, Nurse and System Outcomes, in Odds Ratio, Coefficient, and Cut point

Odds ratio Nurse-Patient

Ratio

Worked Hours

Proportion of RN Worked Hours (10% Increase)

Productivity/ Utilization

(Cut point) Patient outcome Medical Consequences ns 1.13a ns ns Physical Health ns ns ns 80.0% Mental Health ns 0.94a ns ns OMAHA Knowledge ns ns 1.74 n/a OMAHA Behaviour ns ns ns 88.2% OMAHA Status ns ns ns n/a Nurse outcome Satisfaction ns 1.10b ns 80.0% Emotional Exhaustion ns ns ns ns Physical Health ns 0.90b 0.72 ns Mental Health ns 0.93b ns ns System outcome Length of Stay ns ns ns 91.4% Interventions Not Done ns ns ns ns Interventions Delayed ns ns ns ns Quality of Patient Care ns ns 0.59 ns Quality of Nursing Care Ns Ns Ns ns Absenteeism ns ns ns 79.7% Intent to Leave ns ns ns 82.8%

Coefficient

Nurse-Patient

Ratio

Average Worked

Hours

Proportion of RN Worked Hours (10% Increase)

Productivity/ Utilization

(Cut point) Nurse outcome

Relationship with Physician -0.37 ns 0.29 85%

Autonomy 0.67 ns ns 85% System outcome Productivity/Utilization 0.05 n/a 0.05 n/a Cost per RIW -0.11 n/a ns 89.5% Worked Hours per Patient ns n/a 0.60 89.7% a. Worked hours per patient. b. Average worked hours on unit. Notes: (1) Only significant predictors are presented for odds ratios and cut points. (2) "ns" stands for not significant. Predictors with ns have no impact on the outcome variables. (3) "n/a" stands for not applicable. (4) Cost per RIW and worked hours were modeled in logarithm scale, therefore by transforming back to the original scale, a 10% increase in proportion of RN worked hours would lead to an exponential increase of 0.60 (or 1.06 times) in the worked hours per patient which is a 6% increase in worked hours per patient.


Appendix D. Instruments, Psychometric Properties, and Variables

at Individual and Unit Levels

Measure Description and Psychometric Properties

Inputs

Patient Characteristics:

Patient Inputs -age, sex, significant other support

These variables were collected from the patient’s kardex and chart. The actual values of these variables were used at the individual level of analysis.

Patient Input NANDA Form: Nursing Diagnoses1

Nursing diagnoses identify the conditions in patients that create the demand for nursing services. Content validity of the taxonomy of nursing diagnoses is inferred from the judgment and agreement of nurse experts meetings held bi-annually for several years1. The number of different nursing diagnoses the patient had over the hospital stay was used as an independent variable at the individual level.

Patient Input Medical Diagnoses

Patient medical condition was measured by the Case Mix Groups (CMGs) TM developed by the Canadian Institute for Health Information). The CMG methodology has been refined a number of times over the last several years to improve the content validity of the measure. The Resource Intensity Weight (RIW) assigned to an individual CMG was used in the analysis at the individual level. The average RIW was used where aggregation was applied.

Patient Input and Output OMAHA Problem Rating Scale2

Each nursing diagnosis selected is evaluated on three dimensions (knowledge, behaviour, and status) on a 5-point Likert scale at two points in time: at admission or when a new health problem is identified (Time 1), and when the health problem is resolved or at discharge (Time 2)2. Knowledge involves what a client knows and understands about a specific health-related problem. Behaviour involves what a client does - the client’s practices, performances, and skills. Status involves what a client is and how the client’s conditions or circumstances improve, remain stable, or deteriorate2. While this rating scale has been used primarily in the community setting, the actual measurement scale is non sectorial in nature and appropriate for use in the hospital environment. In a previous study, the inter-rater reliability for both nursing diagnoses and the OMAHA outcomes rating scale was maintained at 91% among nurse participants. The admission score ratings for knowledge, behaviour, and status were each entered as an independent variable in the analysis. When used as a dependent variable each variable was dichotomized as improved over hospital stay or as having no change or deteriorated over hospital stay.



Patient Input/Output, Outcomes Measure; Nurse Survey Outcome Measure; Medical Outcomes Study Short Form 12 measure of health status3

The Medical Outcomes Study SF-12®3 is a 12-item scale measuring 8 health domains: physical functioning, vitality, role functioning, physical problems, social functioning, bodily pain, mental health, and general health perceptions 4, 5, 6, 7. The SF12® has demonstrated excellent psychometric properties and is currently the most widely used generic measure of health status, having been employed in hundreds of studies across a broad spectrum of disease states5. When treated as a dependent variable, both physical health and mental health scores were dichotomized into healthy and not healthy using the average score for the US population as the cut point. When treated as a unit level independent variable, the proportion of nurses on the unit with physical and mental health scores over US population norm was used.

Nurse Characteristics:

Nurse Survey -age, sex, professional designation, education, years of experience (this unit, this hospital, nursing in general), usual shift rotation, usual number of units worked, etc.

These data were collected in the nurse survey. Each survey was assigned a code number which was known to the investigators only in order to link nurse characteristics to specific patient assignments. The variables created at the individual level were age, gender, number of occasions absent and number of shifts missed, professional designation, level of education, employment status, work on multiple units, clinical expertise, voluntary and involuntary overtime worked, job stability, prevalence of violence, frequency of shift change, planning to leave in the next 12 months intervention not completed on a shift and interventions delayed on a shift. Unit level variables created include proportions of: nurses on unit with a bachelors degree or higher, nurses reporting shift changes, nurses who work on more than one unit, nurses experiencing job insecurity, intending to leave in the next twelve months, nurses with interventions not completed or delayed, nurses absent from the unit daily, and full time positions on the unit. Unit level variables also included the mean age of nurses, mean years of experience, mean ratings of clinical expertise, prevalence of violence on the unit and average overtime hours.

System Characteristics:

System Characteristics Hospital Profile and Unit Profile

Number of beds in the unit, unit type (In Patient Unit, Critical Care Unit, Step Down Unit, and Day Surgery Unit), patient composition (pure cardiac or mix), and care delivery system were collected from the nurse manager for each unit. These data were used as independent variables at the unit level.



System Behaviours:

System behaviours Daily Unit Staffing Form

The nurse/patient ratio, the daily number of nursing personnel, daily patient census, admission and discharges, actual use of agency and relief staff were collected from the nurse manager or charge nurse on a daily basis. As a measure of continuity, average number of nurses per day over a patient’s hospital stay was used as an independent variable in the models. The actual worked hours per patient was estimated by total work hours divided by the number of patients at midnight census at unit on daily basis. The proportion of worked hours contributed by Registered Nurses was calculated. Number of patients per unit bed was computed as a measure of unit occupancy. The daily data were aggregated to either unit or individual level by taking the average in order to model their effect on outcome variables. The difference between PRN workload hours from actual worked hours per patient was computed in order to answer research question 2. When the actual worked hours at patient level was used as a dependent variable, a logarithm transformation was applied to assume normality.

Workload PRN8

This instrument lists 214 indicators or interventions that nurses complete on behalf of patients during a 24-hour period. Each indicator has a standard given point value which reflects the time involved to complete interventions for patients; each point represents five minutes8. A higher point value indicates greater amounts of nursing care required. The PRN methodology has had extensive testing and has gone through several iterations since it was first developed in 1972. Content validity was established by nurse experts during a series of meetings held over this time. In 1978, Chagnon, Audette, Lebrun, and Tilquin9 established the construct and predictive validity of the tool. Work measurement studies demonstrated that the time estimates predicted by the tool corresponded to the degree of work actually done. In this study the PRN estimates served as the gold standard for care required. In this study, every patient was rated every day using the PRN form in order to determine the direct care and time associated with direct care activities. The method proposed by Tilquin in1980, which is still in used today was used to determine total hours of care per patients (Charles Tilquin, personal communications, October, 2003).



Workload GRASP©/Medicus©

GRASP© or MEDICUS© hours were collected daily for study patients and for the unit as a whole, including non-study patients as well. GRASP captures workload using a “standard time” methodology. Each site develops a list of tasks based on the activities they perform, and times are assigned to each of these tasks. The times are based on time and/or frequency or are established by staff nurse consensus. These times reflect the average time to complete the task, by an average nurse, on an average day, for an average patient in the individual facility. This reflects the physical and organizational characteristics of the individual facility. The MEDICUS system captures workload by multiplying a pre-set relative value per level of care by the target hours per unit of workload.

Throughputs

Environmental Complexity10

Environmental complexity measures the push and pull that nurses experience in providing care to individual patients at the standard outlined in the nursing care plan. Factor analysis in a number of preliminary studies has revealed that the Environmental Complexity measure taps three main domains: unanticipated delays and re-sequencing of work in response to others, unanticipated delays due to changes in patient acuity, and characteristics and composition of the caregiver10. Factor analysis was completed for this study and again the same three factors emerged. The alpha reliabilities were 0.81, 0.84, and 0.84 respectively.

Intermediate System Outputs:

Worked hours, productivity/utilization

Worked hours were collected from the retrospective application of the workload measurement system used at the hospital. The daily unit productivity/utilization for each unit was computed by examining the workload of patients on the unit divided by worked hours. In the analysis of productivity/utilization, linear and quadratic terms were first tried to test the bell or U shape of the effect of productivity/utilization. If the bell or U shape was not supported by data, piecewise linear was tried next. If piecewise linear is significant, it means that the direction at a certain cut point will change. If both strategies failed, dichotomized productivity/utilization at various cut points was tried and the one shown significance was used in the final model. If all these failed, dichotomized productivity/utilization at 85% was tested.



Outputs

Patient Outcomes:

Patient Outcome Measure Medical Outcomes Study Short Form 12 measure of health status3

The psychometric properties of these measures are explained above. Patient SF-12® physical and mental health status was measured at admission and discharge. Patients’ individual admission scores were used as independent variables in many of the models. When treated as a dependent variable, improvement at discharge or no improvement at discharge was used for both physical health and mental health scores.

The Patient Data Form collected information about specific patients over their stay.

Patient medical consequences data were gathered from the patient’s chart on an ongoing basis, including deep or shallow post-operative wound infections, fall with injury, medication errors, urinary tract infections, bedsores, pneumonia were tracked on this form as well as death, transfers back to ICU and whether the patient was re-admitted with the same diagnoses within three months. Patient mortality was obtained from medical records. Since there were numerous medical consequences with very small frequencies, medical consequences including falls with injury, medication errors, death, and complications such as urinary tract infections, pneumonia, wound infections, bed sores, and thrombosis were summed for each patient and that value was used in the analysis. Data were collected about type of admission, presence of a family doctor, attendance at pre-operative clinics, bookings for post-operative or post-discharge education, referrals to home care and support in the home. These variables were used as patient-level independent variables in the analysis.

Nurse Outcomes:

Nurse Survey Outcome Measure Maslach Burnout Inventory10

The Maslach10 Burnout Inventory has 25 items and measures 3 dimensions: emotional exhaustion (alpha = 0.90, test-retest reliability = 0.82), depersonalization (alpha = 0.79, test-retest reliability = 0 .60) and personal accomplishment (alpha = 0.71, test retest reliability = 0 .80). All coefficients were significant at p-value < 0.001. This measure has been used in numerous studies and has proven robust over time. In the current study, only the emotional exhaustion scores for nurses at the individual level and for the proportion of nurses with high levels of emotional exhaustion at the unit level were entered.



Nurse Survey Outcome Measure Siegrist’s Effort and Reward Imbalance11

The 17-item Effort and Reward Imbalance scale identifies the imbalance between high effort spent and low reward received at work, and is assumed to be particularly stressful as this imbalance violates core expectations about reciprocity and adequate exchange in a crucial area of social life.11 This measure has been used extensively over the past ten years in many work settings to measure the effect of an imbalance on the physical and mental health of workers and more recently in the nursing population. Combined variable odds ratios are reported as 8.241 and 95% confidence interval. At the individual level we entered the score for nurses, and the proportion of nurses at risk of effort and reward imbalance was used at the unit level.

Nurse Survey Process Variables Revised Nursing Work Index (Autonomy Control over Practice, Nurse-MD Relations)12

The Nursing Work Index, first developed by Kramer and Hafner12 has been used extensively in the US over the past ten years in research related to “magnet” hospitals, which are hospitals that are known to attract and retain nurses13,14,15,16 and to have better patient outcomes17. Work attributes that have been demonstrated as important to nurses include autonomy (7 items), control over the work environment (18 items) and nurse-physician relations (2 items). In the Aiken et al. (1994) mortality study using ANOVA, the mean difference in observed mortality between magnet and non-magnet hospitals was significantly different ( p = .01). Laschinger & colleagues recomputed the factor analysis on the scale and identified a 5 factor solution. The five factors were autonomy, control over practice, nurse physician relations, leadership, and resources. The alpha reliabilities of each of these new subscales were 0.69, 0.74, 0.83, 0.80, and 0.80 respectively. At the individual level, the scores on each of these subscales were entered into the model, while at the unit level the mean unit score for each variable was entered into the models.

System Outcomes:

System Outcomes -length of stay

These data were retrieved from medical records. The expected length of stay was derived from CIHI’s inpatient database. The actual length of stay or its logarithm transformation was used as an independent variable at the individual level. When used as a dependent variable, it was dichotomized as shorter than expected length of stay versus the same as, or longer than, the expected length of stay.

System Outcomes -cost per case

The actual cost per equivalent weighted case by hospital was extracted from the Ontario Case Costing Data Base and multiplying it by the RIW for each patient. The out of province hospital was not used in this analysis. Logarithm transformation was applied to assume normality when it was modeled as a dependent variable.


References 1. Kim, M. J., McFarland, G.K., & McLane, A. M. (1991). Pocket guide to nursing diagnoses

(4th edition). St. Louis, MO: Mosby. 2. Martin, K. S., & Scheet, N. J. (1992). The OMAHA system: Application for community health

nursing. Philadelphia, PA: WB Saunders. 3. Ware, Jr., J. E, Kosinski, M., & Keller, A. D. (2002). SF-12®: How to score the SF-12®

physical and mental health summary scales (4th Ed.). Lincoln, RI: QualityMetric Incorporated.

4. McHorney, C. A., Ware, Jr., J. E., Rogers, W., & Raczek, A. E. (1992). The validity and relative precision of MOS Short and Long Term Status Scales and Dartmouth COOP. Medical Care, 30, 253-265.

5. Ware, Jr., J.E., & Sherbourne, C.D. (1992). The MOS 36-Item short form health survey (SF-36): Conceptual framework and item selection. Medical Care, 30, 473-483.

6. Ware, Jr., J. E., Snow, K., Kosinski, M., & Gandek, B. (1993). SF-36 Health survey manual and interpretation guide. Boston: The Health Institute.

7. Wu, A. W. (1991). A health status questionnaire using 30 items from the medical outcomes study: Preliminary validation in persons with early HIV infection. Medical Care, 29, 786.

8. Tilquin, C., Carle, J., Saulnier, D., Lambert, P., & Collaborators. (1981). PRN 80: Measuring the level of nursing care required. Equipe de Recherche Opérationnelle en Santé, Institut National de Systématique Appliquée, Université de Montréal: Montréal, QC.

9. Chagnon, M., Audette, L. M., Lebrun, L., & Tilquin, C. (1978). Validation of a patient classification through evaluation of the nursing staff degree of occupation. Medical Care, 16(6), 465-475.

10. O’Brien-Pallas, L. L., Irvine, D., Peereboom, E., & Murray, M. (1997). Measuring nursing workload: Understanding variability. Nursing Economics, 15(4), 172-182.

11. Maslach, C., & Jackson, S. E. (1982). Burnout in health professions: A social psychological analysis. In G. S. Snaders & J. Suls (Eds.), Social psychology of health and illness (pp. 227-251). Hillsdale: Lawrence Erlbaum Associates.

12. Siegrist, J. (1996). Adverse health effects of high-effort/low rewards conditions. Journal of Occupational Health Psychology, 1(1), 27-41.

13. Kramer, M., & Hafner, L. P. (1989). Shared values: Impact on staff nurse satisfaction and perceived productivity. Nursing Research, 38, 172-177.

14. McClure, M. L., Poulin, M. A., Sovie, M. D., & Wandelt, M. A. (1982). Magnet hospitals: Attraction and retention of professional nurses. Kansas City: American Academy of Nurses.

15. Kutzscher, L. I. T., Sabiston, J. A., Laschinger-Spence, H. K., & Nish, M. (1997). The effects of teamwork on staff perception and empowerment and job satisfaction. Healthcare Management Forum, 10(2), 12-17.

16. Kramer, M., & Schmalenberg, C. (1988). Magnet hospitals: Institutions of excellence, Parts I & II. Journal of Nursing Administration, 18(1), 13-24.

17. Kramer. M., & Schmalenberg, C. (1990). Job satisfaction and retention: Insights for the 90’s, Parts I and II. Nursing, 21, 2-7 & 9-13.

18. Aiken, L., Smith, H., & Lake, E.T. (1994). Lower Medicare mortality among a set of hospitals known for good nursing care. Medical Care, 32(8), 771-787.


Appendix E. Data Collection Forms

Nursing Diagnoses (NANDA).................................................................................................... 125

OMAHA Problem Rating Scale.................................................................................................. 126

NANDA and OMAHA Summary Sheet..................................................................................... 128

The SF-12® Health Survey ........................................................................................................ 129

SF-12 Health Survey (French Version) ...................................................................................... 131

Nurse Survey............................................................................................................................... 134

Hospital Profile ........................................................................................................................... 140

Unit/Program Profile................................................................................................................... 141

Daily Unit Staffing Form............................................................................................................ 143

Daily Environmental Complexity Scale ..................................................................................... 148

PRN 80........................................................................................................................................ 151

PRN Daily Workload and Grasp Patient Care Hours ................................................................. 153

Patient Data Form ....................................................................................................................... 154

Maslach=s Burnout Inventory...................................................................................................... 156

Effort-Reward Imbalance............................................................................................................ 158

Nursing Work Index ................................................................................................................... 159

Hospital: ______________Unit: ______________Patient: _________________ Hospital Day: __________Date: ______________Data collector: __________


Evidence-based Standards for Measuring Nurse Staffing and Performance

Nursing Diagnoses (NANDA)

Please circle all numbers representing the patient’s problems that require nursing care.

1. Activity intolerance 2. Activity intolerance, high

risk 3. Adjustment, impaired 4. Anxiety 5. Aspiration, high risk for 6. Body image disturbance 7. Body temperature, altered,

high risk for 8. Bowel incontinence 9. Breathing pattern, ineffective 10. Cardiac output, decreased 11. Communication, impaired

verbal 12. Constipation 13. Constipation, colonic 14. Constipation, perceived 15. Coping, defensive 16. Coping, family: potential for

growth 17. Coping, ineffective family:

compromised 18. Coping, ineffective family:

disabling 19. Coping, ineffective

individual 20. Decisional conflict 21. Denial, ineffective 22. Diarrhea 23. Disuse syndrome, high risk

for 24. Diversional activity deficit 25. Dysreflexia

26. Family processes, altered 27. Fatigue 28. Fear 29. Fluid volume, deficit (1) 30. Fluid volume deficit (2) 31. Fluid volume, high risk for 32. Fluid volume excess 33. Gas exchange impaired 34. Health maintenance, altered 35. Health-seeking behaviours 36. Hopelessness 37. Incontinence, functional 38. Incontinence, reflex 39. Incontinence, stress 40. Incontinence, total 41. Incontinence, urge 42. Infection, high risk for 43. Injury, high risk for 44. Knowledge deficit 45. Management of therapeutic

regimen (individuals), ineffective

46. Mobility, impaired physical 47. Non-compliance 48. Nutrition, altered: less than body

requirements 49. Nutrition, altered: more than

body requirements 50. Nutrition altered: high risk for

more than body requirements 51. Oral mucous membrane, altered 52. Pain 53. Pain, chronic

54. Peripheral neurovascular dysfunction, high risk for

55. Post-trauma response 56. Powerlessness 57. Role performance,

altered 58. Self-care deficit,

bathing/hygiene 59. Self-care deficit,

dressing/grooming 60. Self-care deficit, feeding 61. Self-care deficit, toileting 62. Self-esteem disturbance 63. Self-esteem, chronic low 64. Self-esteem, situational

low 65. Sensory/perceptual

alteration 66. Social interaction,

impaired 67. Social isolation 68. Spiritual distress 69. Swallowing, impaired 70. Thought processes,

altered 71. Tissue integrity, impaired 72. Tissue perfusion, altered 73. Trauma, high risk for 74. Unilateral neglect 75. Urinary elimination,

altered 76. Urinary retention 77. Ventilation, inability to

sustain spontaneous 78. Ventilatory, weaning

process, dysfunctional

Hospital: ______________Unit: ______________Patient: _________________


OMAHA Problem Rating Scale

For each patient, complete at time of: < admission (A) < discharge (D) < new nursing diagnoses after admission (N) < resolution of nursing diagnoses during care (R)

Hospital Day

Nursing Diagnosis

Time

Knowledge Ability of the patient to remember and interpret information

Behaviour Observable responses, actions or activities of the patient fitting the occasion or purpose

Status Condition of the patient in relation to objective and subjective defining characteristics

enter: A D N R

1-No knowledge 2-Minimal knowledge 3-Basic knowledge 4-Adequate knowledge 5-Superior knowledge

1-Never appropriate 2-Rarely appropriate 3-Inconsistently appropriate 4-Usually appropriate 5-Consistently appropriate

1-Extreme signs/symptoms 2-Severe signs/symptoms 3-Moderate signs/symptoms 4-Minimal signs/symptoms 5-No signs/symptoms

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5



Hospital Day

Nursing Diagnosis

Time

Knowledge Ability of the patient to remember and interpret information

Behaviour Observable responses, actions or activities of the patient fitting the occasion or purpose

Status Condition of the patient in relation to objective and subjective defining characteristics

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5

1 2 3 4 5



NANDA and OMAHA Summary Sheet

Date Unit NANDA DiagnosesCode

TimeA D N R

OMAHA Ratings Knowledge Behaviour Status

NRU Use Only



The SF-12® Health Survey

Your Health in General 1. In general, would you say your health is:

Excellent Very good Good Fair Poor

1 2 3 4 5 2. The following questions are about activities you might do during a typical day.

Does your health now limit you in these activities? If so, how much? Yes,

limited a lot

Yes, limited a little

No, not limited at all

a) Moderate activities, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf

1 2 3

b) Climbing several flights of stairs 1 2 3

3. During the past week, have you had any of the following problems with your

work or other regular daily activities as a result of your physical health? Yes No

a) Accomplished less than you would like 1 2

b) Were limited in the kind of work or other activities 1 2

4. During the past week, have you had any of the following problems with your

work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?

Yes No

a) Accomplished less than you would like 1 2

b) Did work or other activities less carefully than usual 1 2



5. During the past week, how much did pain interfere with your normal work

(including both work outside the home and housework)?

Not at all A little bit Moderately Quite a bit Extremely

1 2 3 4 5

6. These questions are about how you feel and how things have been with you during

the past week. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the past week...

All of the

time

Most of the time

A good bit of the

time

Some of the time

A little of the time

None of the time

a) have you felt calm and peaceful?

1 2 3 4 5 6

b) did you have a lot of energy?

1 2 3 4 5 6

c) have you felt downhearted and blue?

1 2 3 4 5 6

7. During the past week, how much of the time has your physical health or emotional

problems interfered with your social activities (like visiting friends, relatives, etc.)?

All of the time

Most of the time

Some of the time

A little of thetime

None of the time

1 2 3 4 5

THANK YOU FOR COMPLETING THIS QUESTIONNAIRE!

Copyright © 1994 Health Assessment Lab. All rights reserved. (SF-12 French (Canadian) Standard Version 1.0) Evidence-based Staffing 131

SF-12 Health Survey (French Version)

QUESTIONNAIRE SUR L'ÉTAT DE SANTÉ - SF-12

DIRECTIVES: Les questions qui suivent portent sur votre santé, telle que vous la percevez. Vos réponses permettront de suivre l'évolution de votre état de santé et de savoir dans quelle mesure vous pouvez accomplir vos activités courantes. Veuillez répondre à toutes les questions en cochant une case. En cas de doute, répondez de votre mieux. 1. En général, diriez-vous que votre santé est:

Excellente Très bonne Bonne Passable Mauvaise

2. Les questions suivantes portent sur les activités que vous pourriez avoir à faire au cours d'une journée normale. Votre état de santé actuel vous limite-t-il dans ces activités? Si oui, dans quelle mesure? Mon état

de santé me limite beaucoup

Mon état de santé

me limite un

peu

Mon état de santé

ne me limite pas

du tout a) Dans les activités modérées comme déplacer

une table, passer l'aspirateur, jouer aux quilles ou au golf

b) Pour monter plusieurs étages à pied


3. Au cours de la dernière semaine, avez-vous eu l'une ou l'autre des difficultés suivantes au travail ou dans vos autres activités quotidiennes à cause de votre état de santé physique? OUI NON a) Avez-vous accompli moins de choses que

vous l'auriez voulu?

b) Avez-vous été limité(e) dans la nature de vos tâches ou de vos autres activités?

4. Au cours de la dernière semaine, avez-vous eu l'une ou l'autre des difficultés suivantes au travail ou dans vos autres activités quotidiennes à cause de l'état de votre moral (comme le fait de vous sentir déprimé(e) ou anxieux(se))?

OUI NON

a) Avez-vous accompli moins de choses que vous l'auriez voulu?

b) Avez-vous fait votre travail ou vos autres activités avec moins de soin qu'à l'habitude?

5. Au cours de la dernière semaine, dans quelle mesure la douleur a-t-elle nui à vos

activités habituelles (au travail comme à la maison)?

Pas du tout Un peu Moyennement Beaucoup Énormément


6. Ces questions portent sur de la dernière semaine. Pour chacune des questions suivantes, donnez la réponse qui s'approche le plus de la façon dont vous vous êtes senti(e). Au cours de la dernière semaine, combien de fois:

Tout le temps

La plupart

du temps

Souvent Quel-quefois

Rare-ment

Jamais

a) Vous êtes-vous senti(e) calme et serein(e)?

b) Avez-vous eu beaucoup d'énergie?

c) Vous êtes-vous senti(e) triste et abattu(e)?

7. Au cours de la dernière semaine, combien de fois votre état physique ou

moral a-t-il nui à vos activités sociales (comme visiter des amis, des parents, etc.)?

Tout le temps La plupart du temps

Parfois Rarement Jamais


Nurse Survey Please circle the number of the appropriate response to each question or, where indicated, fill in the blanks.

A.

Questions About Your Work Life

A1

What is your current employment status at this hospital?

Full time................................ Part time............................... Casual....................................

1 2 3

A2

Is your employment:

Permanent............................. Temporary.............................

1 2

A3

Is this the unit you normally work on?

Yes......................................... No.........................................

1 2

A4

What is your job title?

RN......................................... RPN....................................... Other (specify):___________

1 2 3

A5

How many years have you worked: a) as an RN/RPN b) as an RN/RPN at your present hospital c) as an RN/RPN on your current unit d) as a casual worker

_____ years _____ months _____ years _____ months _____ years _____ months _____ years _____ months

A6

Is your immediate supervisor a nurse? If No, please specify the profession of your supervisor.

Yes......................................... No (specify):______________

1 2

A7

In the past year, how many hours per week did you work, on average: a) in this hospital for paid work? b) for any other paid work?

_______ hours per week _______ hours per week

A8

In the past year, how many hours per week, on average, did you work on units other than the one to which you are usually assigned? (that is, the one where you work the most hours):

_______ hours per week

A9

In the past year, how many hours a week, on average, did you work in this hospital the following types of overtime? a) Voluntary Paid b) Voluntary Unpaid c) Involuntary Paid d) Involuntary Unpaid

_______ hours per week _______ hours per week _______ hours per week _______ hours per week


A10

In the past year, has the amount of overtime required of you:

Increased................................ Remained the same............... Decreased.............................. Not applicable........................

1 2 3 4

A11

In the past 2 weeks, how often did you change shifts? (e.g., from days to evenings, evenings to nights, nights to days, etc.)

None........................................ Once........................................ Twice....................................... Other (specify):____________

1 2 3 4

A12

In the past year, have you been required to permanently change nursing units due to restructuring/reorganization?

Yes......................................... No..........................................

1 2

A13

In the next year, do you anticipate having to permanently change nursing units due to restructuring/reorganization?

Yes..................................... No......................................

1 2

B. Questions About Your Job Satisfaction

How satisfied are you with the following aspects of your current job?

Very Very Dissatisfied Satisfied

B1 Opportunities for social contact at work 1 2 3 4 5

B2 Opportunities for social contact with your colleagues after work 1 2 3 4 5

B3 Opportunities to interact with management/administration 1 2 3 4 5

B4 Your amount of responsibility 1 2 3 4 5

B5 On the whole, how satisfied are you with your present job? 1 2 3 4 5 B6 Independent of your present job, how satisfied are you with being 1 2 3 4 5 a nurse?

B7

Thinking about the next 12 months, how likely is it that you will lose your job?

Very Likely................................... Fairly Likely.................................. Not too likely................................ Not at all likely.............................

1234

B8

Do you plan to leave your present nursing job?

Yes, within the next 6 months....... Yes, within the next year............... No plans within the year...............

123

B9

If you were looking for another job, how easy or difficult do you think it would be for you to find an acceptable job in nursing?

Very easy................................ Fairly easy............................... Fairly difficult........................... Very difficult...........................

1234


C.

Questions About You

C1

What is your gender?

Female................................................... Male......................................................

12

C2

What is your age?

_______ years

C3

Do you have any dependent children/others living with you? a) children b) other dependents

Yes, how many children_____________ No......................................................... Yes, how many other dependents______ No.........................................................

1212

C4

What is your highest Nursing educational credential?

RPN Diploma......................................... RN Diploma........................................... BScN.................................................... MScN.................................................... PhD Nursing.......................................... Post RN Certificate Cardiac.................... Post RN Certificate Other (specify): _______________________________

1234567

C5

What is your highest Non-nursing educational credential?

Diploma................................................. Baccalaureate........................................ Masters................................................. PhD....................................................... Other (specify):___________________ Not applicable........................................

123456

C6

In the past year: a) On how many occasions (episodes) have you missed work due to illness/disability? b) How many shifts have been missed due to illness/disability?

_________ # occasions _________ # shifts

C7

In the past year, what is the most common reason you missed work? (Choose one only)

Physical illness....................................... Mental health day................................... Injury (work related)............................... Family illness/crisis/ commitment............. Unable to get requested day off.............. Other (specify):___________________

123456


D.

Questions About Violence

In the last 5 shifts you worked, have you experienced any of the following while carrying out your responsibilities as a nurse:

D1

a) Physical assault b) If yes, indicate source of physical assault

Yes..................................... No....................................... Source of physical assault: Patient................................. Family/visitor........................ Physician............................. Nursing co-worker............... Other, specify:___________

12 12345

C8

How often are you selected to be a preceptor for another nurse?

Never.................................................... Rarely................................................... Occasionally........................................... Frequently..............................................

1234

C9

How often do nurses come to you for clinical judgment on a difficult clinical problem?

Never.................................................... Rarely................................................... Occasionally........................................... Frequently..............................................

1234

C10

The following descriptions are intended to represent levels of skill and ability in nursing roles and functions. Which one of the following would you say best describes the way in which you practice on your unit?

I am a nurse who... (circle only one response) 1) ...relies primarily on standards of care, unit procedures and physicians= and nurses= orders to guide patient care 2) ...has increased clinical understanding, technical and organizational skills and is able to anticipate the likely course of events 3) ...perceives the patient situation as a whole and responds appropriately as conditions change 4) ...is good at recognizing unexpected clinical responses and often provides an early warning of patient changes

1 2 3 4


D2

a) Threat of assault b) If yes, indicate source of threat of assault

Yes..................................... No....................................... Source of threat of assault: Patient................................. Family/visitor........................ Physician............................. Nursing co-worker............... Other, specify:___________

12 12345

D3

a) Emotional abuse b) If yes, indicate source of emotional abuse

Yes..................................... No....................................... Source of emotional abuse: Patient................................. Family/visitor........................ Physician............................. Nursing co-worker............... Other, specify:___________

12 12345

E.

Questions About Your Perceptions of Quality of Care

E1

Overall, in the past year, would you say the quality of patient care in your unit has:

Improved................ Remained the same. Deteriorated...........

123

E2

How would you describe the quality of nursing care delivered on your last shift?

Excellent................ Good...................... Fair........................ Poor.......................

1234

E3

Which of the following tasks did you perform during your last shift? 1) Delivering/retrieving trays 2) Ordering, coordinating or performing ancillary services (e.g., physical therapy, ordering labs) 3) Starting IVs 4) Arranging discharge referrals and arranging transportation (including nursing homes) 5) Performing ECGs 6) Routine phlebotomy (venipunctures) 7) Transporting patients (including to nursing homes) 8) Housekeeping duties (e.g., cleaning patient rooms)

Circle all that apply 1 2 3 4 5 6 7 8


E4

Which of the following situations occurred on your last shift due to time pressures? 1) Routine vital signs, medications or dressings not done 2) Routine vital signs, medications or dressings not on time 3) Routine mobilization or turns not done 4) Routine mobilization or turns not done on time 5) Delay in administering PRN pain medications 6) Delay in responding to patient bell

Circle all that apply 1 2 3 4 5 6

E5

Which of the following tasks were necessary but left undone during your last shift because you lacked the time to complete them? 1) Routine teaching for patients and families 2) Prepare patient and family for discharge 3) Comforting/talking with patients 4) Adequately documenting nursing care 5) Back rubs and skin care 6) Oral hygiene 7) Develop or update nursing care plan

Circle all that apply 1 2 3 4 5 6 7

Hospital _________ Date ________________________



Hospital Profile Interview Questions

1 What is the title of the nurse responsible for Operating Decisions?

2 What is the title of the nurse responsible for Standards of Practice?

3 If the hospital is structured by Programs, is there any central control of nurse staffing decisions?

Yes.......................................................... No............................................................

1 2

4 Who determines the volume and skill mix for nursing?

5 Does your hospital have: a) Student nurses b) Student physicians c) Residents

Circle all that apply: a b c

6 Where are inpatient cardiovascular and cardiology services provided in your hospital? List the names and designations of nursing units.

7 How long do patients routinely stay in ICU?

8 Does your hospital have a formal preoperative process prior to elective cardiovascular surgery?

Yes.......................................................... No............................................................ If yes, please provide a description and indicate the proportion of annual cardiovascular patient surgical volume that attend________________________

1 2

9 Does your hospital have any policies for violence/abuse against nurses?

Yes, please provide copies...................... No............................................................

1 2

10 Does your hospital have advanced practice nurses for cardiovascular and/or

cardiology services?

Yes, provide copies of their role.............

No............................................................

1

2

11 Does your hospital have any policies for sheath removal?

Yes, please provide copies......................

No............................................................

1

2

Hospital _________ Date ________________________



Unit/Program Profile

1

Does your unit use Critical Pathways or Clinical Guidelines for common cardiovascular and cardiology diagnoses?

Yes, please provide copies................ No.......................................................

1 2

2

Does your unit use standard nursing care plans for common cardiovascular and cardiology diagnoses?

Yes, please provide copies................ No.......................................................

1 2

3

In your unit, do your policies and procedures for cardiovascular and cardiology patients allow nurses, during an emergency situation, to: a) Initiate an IV b) Defibrillate c) Initiate thrombolytic agents d) Initiate oxygen

Circle all that apply: a b c d

4

Is there a formal pre-op program for your patients?

Yes...................................................... No....................................................... Provide a description of program goals, length of program, the services provided and the criteria for inclusion

1 2

5

What is the post discharge follow-up procedure for patients?

Describe and provide an estimate of the percentage of patients that would be involved

6

Do you have a nurse educator: a) For the unit: b) For the program:

a)Yes, indicate #FTEs___________ No....................................................... b)Yes, indicate #FTEs___________ No.......................................................

1 2 1 2

7

Do you have any outpatient activity on this unit?

Yes, please provide details............... No.......................................................

1 2

8

What are the average daily hours of housekeeping support for this unit?

Hospital _________ Date ________________________


9

Are there any current issues related to the physical environment that we should be aware of?

10

Determining turnover rate:

a) How many positions have been posted in the past year? ___________ b) How many positions have been budgeted for in the past year? ______

11

Determining vacancy rate:

a) How many days have been vacant in the past year? ______ b) How many budgeted FTEs in the past year? ______

12

Do you have access to the following allied health professionals and are they dedicated to the unit? a) Physiotherapists b) Occupational Therapists c) Social Workers d) Nutritionists/Dieticians e) Other(s):__________________________

Circle only per discipline: a) Access: Yes................................. No................................. Dedicated: Yes................................ No.................................. #FTEs dedicated to unit_________ b) Access: Yes................................. No................................. Dedicated: Yes................................. No................................. #FTEs dedicated to unit_________ c) Access: Yes................................ No................................. Dedicated: Yes................................. No................................. #FTEs dedicated to unit__________ d) Access: Yes.............................. No................................. Dedicated: Yes................................. No................................. #FTEs dedicated to unit__________ e) Access: Yes............................... No............................... Dedicated: Yes................................ No................................. #FTEs dedicated to unit__________

1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2


Daily Unit Staffing Form

Patient Census by Shift Shifts # of Patients # Admissions or

Transfers In # Discharges or Transfers Out

days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Number of Staff Working on Unit Shifts # RNs

FT PT Casual # RPNs/RNAs FT PT Casual

# other (UCPs) FT PT Casual

days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Number of Agency Staff Shifts # of Agency Nurses # of Agency Non-Nurses (e.g., Sitters)

days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Number of Overtime Hours

Hospital _________ Date ________________________


Number of Staff Absent from Unit Due to Illness Shifts # RNs

FT PT Casual # RPNs/RNAs FT PT Casual


days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Number of Staff Absent from Unit Due to Reasons Other than Illness Shifts # RNs

FT PT Casual#RPNs/RNAs FT PT Casual


days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other:

Shifts # RNs FT PT Casual

# RPNs/RNAs FT PT Casual


days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other:

Hospital _________ Date ________________________


Number of Staff who Floated To Unit Shifts # RNs



days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Number of Staff who Floated From Unit Shifts # RNs



days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Number of Staff on Orientation (either formal or on-the-job training) Shifts # RNs



days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other:

Hospital _________ Date ________________________


Number of Patient Falls or Medication Errors Shifts Patient Falls Medication Errors

days: 0730-1530

evenings: 1530-1930

evenings: 1930-2330

nights: 2330-0730

other: Unit Workload Data Patient Care Workload: GRASP Patient Care Hours or Medicus Patient Type ________________________________________ Non-patient Care Workload: GRASP Non-patient Care Hours or Medicus Non-patient care workload ________________________

Hospital _________ Date ________________________


Number of Patients Assigned to RNs at Beginning of Day Shifts RN Code FT PT Casual

Hospital _________ Date ________________________



Daily Environmental Complexity Scale

For the following, please rate how each item influenced your ability to provide required care for patient(s) on this shift. Please reflect on the workload you anticipated prior to starting your shift and decide the nature of the influence of each item. Did the item increase anticipated workload, decrease anticipated workload or have no influence on anticipated workload? (i=increased work, d=decreased work, or s=same as usual/no change). Then, rate the extent of the item’s influence on a scale of 1 to 5 (1=low influence, 3=medium influence, 5=high influence).

Circle your responses. For those items not applicable to this shift please leave blank.

Incr

ease

d w

orkl

oad

Dec

reas

ed

wor

kloa

d

Sam

e as

usu

al/

No

chan

ge

Low

1 2

Med

ium

3 4 H

igh

5

Students:

1 Students on the unit today required supervision and assistance

i d s 1 2 3 4 5

2 Students wanted access to charts, equipment & supplies

i d s 1 2 3 4 5

Staffing:

3 Scheduled unit staff absent this shift (includes UM, RNs, RNAs, LVNs, clerical and assistive staff)

i d s 1 2 3 4 5

Nursing Team Functioning:

4 Staff unable to pull together to complete unit work i d s 1 2 3 4 5

Assignment:

5 Rushing to get work done i d s 1 2 3 4 5

Unanticipated Communication with Doctors:

6 More than the usual calls to doctors this shift i d s 1 2 3 4 5

7 Clarifying doctors' orders i d s 1 2 3 4 5

Unanticipated Delays:

8 Doctors not answering pages i d s 1 2 3 4 5

9 Multiple delays experienced on the unit i d s 1 2 3 4 5

10 Medication, supplies and narcotic keys missing i d s 1 2 3 4 5

Hospital _________ Date ________________________

Copyright L. O’Brien-Pallas Evidence-based Staffing 149

11 Language barrier with family and/or patient i d s 1 2 3 4 5

Unexpected Change in Patient Condition:

12 Agitated, confused, or restless patient(s) i d s 1 2 3 4 5

13 Unanticipated increase in patient acuity i d s 1 2 3 4 5 Unanticipated Time Consuming

Interventions for Patient and Family:

14 Stat blood work i d s 1 2 3 4 5

Incr

ease

d w

orkl

oad

Dec

reas

ed

wor

kloa

d

Sam

e as

usu

al/

No

chan

ge

Low

1 2 M

ediu

m

3 4

Hig

h

5

15 Extra vital signs i d s 1 2 3 4 5

16 Extra charting and paperwork i d s 1 2 3 4 5

17 Greater demand for routine patient teaching i d s 1 2 3 4 5

18 Greater demand for psychosocial support for patient i d s 1 2 3 4 5

19 Greater demand for psychosocial support for family i d s 1 2 3 4 5

Unanticipated and Time Consuming Non-Patient Care Activities:

20 Completing work of others (e.g. dietary, clerical staff, housekeeping, nursing administration)

i d s 1 2 3 4 5

21 Interruptions (e.g., called back to desk, phone) that influences time with patients and family

i d s 1 2 3 4 5

22 Participating in nursing research i d s 1 2 3 4 5

Please add and rate any other information regarding events that significantly influenced your ability to provide required care on this shift:

23

i d s 1 2 3 4 5

24

i d s 1 2 3 4 5

25

i d s 1 2 3 4 5

Hospital _________ Date ________________________

Copyright L. O’Brien-Pallas Evidence-based Staffing 150

Please rate the time available to deliver

care on THIS SHIFT compared to the last five shifts you have worked. CIRCLE ONLY ONE RESPONSE:

26

Less time than usual

About the same amount of time as usual

More time than usual

Approximately how much more time do you feel you need to give the type of care stated in the nursing care plan or your assessment of patient’s needs to day? CIRCLE ONLY ONE RESPONSE:

27

No more time needed

< 15 minutes

15-30 minutes

31-45 minutes

46-60 minutes

>60 minutes

Hospital __________ Unit __________ Patient __________


PRN 80





PRN Daily Workload and Grasp Patient Care Hours Date Unit Nurse

Code Respiration Feeding Elimination Hygiene Communication Treatment Diagnostic GRASP

PCH




Patient Data Form Please circle the number of the appropriate response to each question or, where indicated, fill in the blanks.

1 Date of admission dd/mm/yy _______________________

2 Date of discharge/transfer/death dd/mm/yy _______________________

3 Admission diagnosis

4 Other concurrent diagnosis

5 Sex Male....................................................... Female...................................................

1 2

6 Age __________years

7 Occupation

8 Highest level of education: Less than high school diploma............. High school diploma............................ Trade certificate/college/some university University degree.................................

1 2 3 4

9 Does the patient have a potential caregiver at home?

Yes............................................................. No...............................................................

1 2

10 Does patient have a family physician? Yes............................................................ No..............................................................

1 2

11 Actual length of stay in ICU: _______ hours

12 Actual length of stay in hospital: _______ hours

13 Has the patient been transferred back to ICU?

Yes, number of hours in ICU___________ No...............................................................

1 2

14 Did patient attend pre-operative clinic? Yes............................................................. No...............................................................

1 2



15 Is patient booked for post-operative/post-discharge education?

Yes......................................................... No..........................................................

1 2

16 Did patient spend time in step-down unit? Yes, number of hours in SDU_______

No........................................................

1 2

17 Did patient have any falls: a) Resulting in injury: b) Not resulting in injury:

a)Yes, specify injury_______________ No..................................................... b) Yes..................................................... No......................................................

1 2 1 2

18 Did the patient develop any of the following: a) urinary tract infection b) pneumonia c) superficial incisional surgical site infection d) deep incisional surgical site infectione) bedsores f) thrombosis

Circle all that apply a b c d e f

19 Did patient get a referral for home care? Yes......................................................... No..........................................................

1 2

20 Was this a planned admission? Yes......................................................... No..........................................................

1 2

21 Was the patient hospitalized for the same condition in the past 3 months?

Yes......................................................... No..........................................................

1 2

22 Were there any nurse medication errors with this patient?

Yes, with patient consequences, specify________________________ Yes, without patient consequences No medication errors

1 2 3


Maslach=s Burnout Inventory This section contains statements of JOB-RELATED FEELINGS. If you have never had this feeling, circle the A0@ (zero) after the statement. Otherwise, indicate how often you feel like this by circling the number (from1 to 6) that best describes how frequently you feel that way.

How Often?

Never

A few times a year or

less

Once a month or less

A few times a month

Once

a week

A few times a week

Every day

1

I feel emotionally drained from my work

0

1

2

3

4

5

6

2

I feel used up at the end of the workday.

0

1

2

3

4

5

6

3

I feel fatigued when I get up in the morning and have to face another day on the job.

0

1

2

3

4

5

6

4

I can easily understand how my patients feel about things.

0

1

2

3

4

5

6

5

I feel I treat some patients as if they were impersonal objects.

0

1

2

3

4

5

6

6

Working with people all day is really a strain for me.

0

1

2

3

4

5

6

7

I deal very effectively with the problems of my patients.

0

1

2

3

4

5

6

8

I feel burned-out from my work.

0

1

2

3

4

5

6

9

I feel I=m positively influencing other people=s lives.

0

1

2

3

4

5

6

10

I=ve become more callous toward people since I took this job.

0

1

2

3

4

5

6

11

I worry that this job is hardening me emotionally.

0

1

2

3

4

5

6

12

I feel very energetic.

0

1

2

3

4

5

6

13

I feel frustrated by my job.

0

1

2

3

4

5

6

14

I feel I=m working too hard on my job.

0

1

2

3

4

5

6

15

I don=t really care what happens to some patients.

0

1

2

3

4

5

6

16

Working directly with people puts too much stress on me.

0

1

2

3

4

5

6

17

I can easily create a relaxed atmosphere with my patients.

0

1

2

3

4

5

6

18

I accomplish many worthwhile things in this job.

0

1

2

3

4

5

6


Never

A few times a year or

less

Once a month or less

A few times a month

Once

a week

A few times a week

Every day

19 I feel exhilarated after working closely with my patients.

0 1

2

3

4

5

6

20

I feel like I=m at the end of my rope.

0

1

2

3

4

5

6

21

In my work, I deal with emotional problems very calmly.

0

1

2

3

4

5

6

22

I feel patients blame me for some of their problems.

0

1

2

3

4

5

6


Effort-Reward Imbalance For each of the following statements, please indicate first whether you agree or disagree with it. If there is an arrow ⇒ behind your answer please also indicate how much you are generally distressed by this situation. Thank you for answering all statements.

I am very distressed 4 I am distressed 3 I am somewhat distressed 2 I am not at all distressed 1

1. I have constant time pressure due to a heavy work load. disagree

agree ⇒

1

2

3

4

2. I have many interruptions and disturbances in my job. disagree agree

⇒

1

2

3

4

3. I have a lot of responsibility in my job. disagree agree

⇒

1

2

3

4

4. I am often pressured to work overtime disagree agree

⇒

1

2

3

4

5. My job is physically demanding. disagree agree

⇒

1

2

3

4

6. Over the past few years, my job has become more and more demanding.

disagree agree

⇒

1

2

3

4

7. I receive the respect I deserve from my superiors. disagree agree

⇒

1 2 3 4

8. I receive the respect I deserve from my colleagues. disagree agree

⇒

1 2 3 4

9. I experience adequate support in difficult situations. disagree agree

⇒

1 2 3 4

10. I am treated unfairly at work. disagree agree

⇒

1

2

3

4

11. My job promotion prospects are poor. disagree agree

⇒

1

2

3

4

12. I have experienced or I expect to experience an undesirable change in my work situation.

disagree agree

⇒

1

2

3

4

13. My job security is poor. disagree agree

⇒

1

2

3

4

14. My current occupational position adequately reflects my education and training.

disagree agree

⇒

1 2 3 4

15. Considering all my efforts and achievements, I receive the respect and prestige I deserve at work.

disagree agree

⇒

1 2 3 4

16. Considering all my efforts and achievements, my work prospects are adequate.

disagree agree

⇒

1 2 3 4

17. Considering all my efforts and achievements, my salary /income is adequate.

disagree agree

⇒

1 2 3 4


Nursing Work Index

For each item in this section, please indicate the extent to which you agree that the following items ARE PRESENT IN YOUR CURRENT JOB. Indicate your degree of agreement by circling the appropriate number.

The following are present in your current job . . .

Strongly Agree

Somewhat

Agree

Somewhat Disagree

Strongly Disagree

1

Adequate support services allow me to spend time with my patients.

1

2

3

4

2

Physicians and nurses have good working relationships.

1

2

3

4

3

A good orientation program for newly employed nurses.

1

2

3

4

4

A supervisory staff that is supportive of the nurses.

1

2

3

4

5

A satisfactory salary.

1

2

3

4

6

Nursing controls its own practice.

1

2

3

4

7

Active staff development or continuing education programs for nurses.

1

2

3

4

8

Career development/clinical ladder opportunity.

1

2

3

4

9

Opportunity for staff nurses to participate in policy decisions.

1

2

3

4

10

Support for new and innovative ideas about patient care.

1

2

3

4

11

Enough time and opportunity to discuss patient care problems with other nurses.

1

2

3

4

12

Enough registered nurses on staff to provide quality patient care.

1

2

3

4

13

A nurse manager or immediate supervisor who is a good manager and leader.

1

2

3

4

14

A senior nursing administrator who is highly visible and accessible to staff.

1

2

3

4

15

Flexible or modified work schedules are available.

1

2

3

4

16

Enough staff to get work done.

1

2

3

4

17

Freedom to make important patient care and work decisions.

1

2

3

4

18

Praise and recognition for a job well done.

1

2

3

4


19

The opportunity for staff nurses to consult with clinical nurse specialists or expert nurse clinicians/educators.

1

2

3

4

20

Good working relationships with other hospital departments or programs.

1

2

3

4

21

Not being placed in a position of having to do things that are against my nursing judgement.

1

2

3

4

22

High standards of nursing care are expected by the administration.

1

2

3

4

23

A senior nursing administrator equal in power and authority to other top level hospital executives.

1

2

3

4

24

A lot of team work between nurses and physicians.

1

2

3

4

25

Physicians give high quality medical care.

1

2

3

4

26

Opportunities for advancement.

1

2

3

4

27

Nursing staff are supported in pursuing degrees in nursing.

1

2

3

4

28

A clear philosophy of nursing that pervades the patient care environment.

1

2

3

4

29

Nurses actively participate in efforts to control costs.

1

2

3

4

30

Working with nurses who are clinically competent.

1

2

3

4

31

The nursing staff participates in selecting new equipment.

1

2

3

4

32

A nurse manager or supervisor who backs up the nursing staff in decision-making, even if the conflict is with a physician.

1

2

3

4

33

Administration that listens and responds to employee concerns.

1

2

3

4

34

An active quality assurance program.

1

2

3

4

35

Staff nurses are involved in the internal governance of the hospital (e.g., practice and policy committees).

1

2

3

4

36

Collaboration between nurses and physicians.

1

2

3

4

37

A preceptor program for newly hired RNs.

1

2

3

4

38

Nursing care is based on a nursing rather than a medical model.

1

2

3

4

39

Staff nurses have the opportunity to serve on hospital and nursing committees.

1

2

3

4


40

The contributions that nurses make to patient care are publicly acknowledged.

1

2

3

4

41

Nurse managers consult with staff on daily problems and procedures.

1

2

3

4

42

A work environment that is pleasant, attractive, and comfortable.

1

2

3

4

43

Opportunity to work on a highly specialized patient care unit.

1

2

3

4

44

Written up-to-date nursing care plans for all patients.

1

2

3

4

45

Patient care assignments that foster continuity of care (i.e., the same nurse cares for the patient from one day to the next).

1

2

3

4

46

Staff nurses do not have to float from their designated unit.

1

2

3

4

47

Staff nurse actively participate in developing their own working schedule (i.e., what days they work, days off, etc.).

1

2

3

4

48

Each patient care unit determines its own policies and procedures.

1

2

3

4

49

Working with experienced nurses who Aknow@ the hospital nurses.

1

2

3

4


Appendix F. Methods

Methods

Methods: Multilevel Modeling and MLwin ............................................................................... 163

Multilevel structures ............................................................................................................... 163

Variables ................................................................................................................................. 165

MLwin ..................................................................................................................................... 167 List of Tables

Table 1: Unit Characteristics Aggregated from Individual Nurse Level.................................... 164

Table 2: Dichotomy of Outcome Variables ................................................................................ 165


Methods: Multilevel Modeling and MLwin Health science research often concerns problems that have a hierarchical structure, for example, when patients are nested within units, and units are nested within hospitals. In multilevel analysis, data structure of this nature is viewed as a multistage sample from a hierarchical population. By focusing on the level of hierarchy in the population, multilevel modeling enables researchers to understand where and how effects are occurring. It provides better estimates in answering simple questions for which single-level analyses were once used and in addition, allows more complex questions to be addressed. For example, how the effect of unit characteristics might affect the patient’s outcome? Or more specifically, patients spent more time in a unit with more full-time nurses would more likely result in a sooner discharge than in other units. Failing to recognize the existence of clustering will generally imply standard errors of regression coefficients for higher level variables to be underestimated, which also leads to a higher probability of type I error. For example, if standard errors were underestimated, it might be inferred that there was a real difference between a teaching hospital and a non-teaching hospital, which in fact could not be claimed due to the lack in number of hospitals included in the study.

Multilevel structures

Three sets of outcome variables, patient, nurse and system outcomes, were modeled in this study. In order to model patient and nurse outcomes, a three-level data structure was originally considered. Level 1 is the individual level, i.e., patient level for patient outcome or nurse level for nurse outcomes. Level 2 is unit and level 3 is hospital. Since there were a small number of hospitals (only 6) recruited in the study, any difference among hospitals would not be detected due to the limited sample size at hospital level. Thus, the hospital level was removed, and only unit level, individual patient and nurse levels were modeled. The removal of hospital level meant that the effect of the hospital level variables was not investigated on patient, nurse, and system outcomes, such as teaching hospital status, hospital size, etc. To study the effect of nurse’s characteristics and measures on patient outcomes and because one or more nurses may have cared for a patient during their hospital stay, information from all those nurses was aggregated to patient level as well as attached to each patient as patient level variables. Similarly, for those nurses who may have cared for multiple patients, information from those patients, during the study period, was aggregated as well as attached to each nurse as nurse level variables. Note that averaging or proportion was used in the aggregation of variables. Missing values were imputed using either regression imputation, cell mean imputation, or mean of nearby points (for daily data). Since patients have changed their units during their hospital stay, the proportion of days in the unit out of their total length of stay was assigned as weights to each unit they ever stay. For nurse outcomes, the unit indicated in the nurse survey form was used in the analyses. In total, there are


24 units at unit level for either patient, nurse or system outcomes. There are 1198 patients in patient outcome models and there are 727 nurses for most of the nurse outcome models. For those nurse outcomes where patient characteristics have immediate impact on, only 555 direct care nurses, during the study period, were included in the models. To answer part of research question 1 and 3, daily productivity/utilization measurements were modeled in a multilevel framework as well. Level 1 is date and Level 2 is the unit. Again, hospital was not included as a level as it was excluded for other outcome models. The nurse and patient characteristics and measurements were aggregated by date and unit such that the impact of patient and nurse variables on the productivity/utilization at unit level can be studied. As a measurement of unit atmosphere or morale at unit, some of individual nurse measurements were aggregated to unit level as unit measurements. Most of them were proportions, such as proportion of full time nurses, proportion of satisfied nurses in the unit. Others were based on average within the unit, for example, nurse age on average. The following variables at unit level, Table 1, have been constructed and considered in models, though not necessary included in the final models.

Table 1: Unit Characteristics Aggregated from Individual Nurse Level Unit Characteristics Aggregated from Individual Nurse Level Average Age of Nurses Proportion of Nurses with BScN or Above Proportion of Nurses Work On Multiple Units Proportion of Full-time Employment Average Overtime Hours Average Clinical Expertise Proportion of Nurses Reporting Job Instability Proportion of Nurses Reporting Shift Changes Prevalence of Violence at Unit Proportion of Nurses with Interventions Not Done Proportion of Nurses with Interventions Delayed Proportion of Nurses with Risk at Effort and Reward Imbalance Proportion of Emotionally Exhausted Nurses Average Nurse Autonomy Average Nurse-physician Relationship Proportion of Nurses Reporting Sick Leave Proportion of Nurses Intending to Leave Current Job Proportion of Physically Healthy Nurses Proportion of Mentally Healthy Nurses Proportion of Satisfied Nurses Proportion of Nurses Rating Good Patient Care Quality Proportion of Nurses Rating Good Nursing Care Quality


Variables Most of outcome variables were dichotomized and multilevel logistic regressions were used to model the effects of predictors. How each variable was constructed or dichotomized is shown in Table 2. Only productivity/utilization, averaged nursing hours, cost per RIW, autonomy and nurse-physical relationship were treated as continuous variables. Logarithm transformation was applied to averaged nursing hours and cost per RIW to assume the normality. Length of stay was also logarithm transformed as a control variable when nursing hours and cost were modeled. Only those measurements to answer research questions and predictors relevant conceptually or theoretically to the outcome variables were included in the models. Other predictors will be included in the final models if they are significantly associated with outcome variables.

Table 2: Dichotomy of Outcome Variables Predictor and Dependent Variable

Measurement

Patient variables Medical consequences Yes to any of the following: fall with injury, medication errors, death, or

complications such as UTI, pneumonia, superficial surgical site infection, deep surgical site infection, bedsores, and thrombosis; dichotomized as yes vs. no.

Length of stay Measured by the difference score between length of stay from medical record and expected length of stay from CIHI inpatient data for Ontario; dichotomized as shorter than expected length of stay vs. others

Patient’s physical health Measured with SF-12 scale at admission and discharge; Improved at discharge vs. others

Patient’s mental health Measured with SF-12 scale at admission and discharge; Improved at discharge vs. others

Omaha knowledge Increased at discharge or diagnosis resolved vs. others Omaha behaviour Improved at discharge or diagnosis resolved vs. others Omaha status Improved at discharge or diagnosis resolved vs. others Actual worked hours per

patient Total nursing hours divided by midnight census

Cost per RIW RIW*actual cost per equivalent weighted case Nurse variables Education Highest nursing educational credential; dichotomized as BScN or above vs.

diploma Work on Multiple Units Work on more than one units vs. one Clinical Expertise Average scores on 4-point scale on being a preceptor for another nurse, providing

clinical advice, level on expertise Unit Instability Reporting any of the following: Forced to change unit in past year, anticipate

forced change of units in next year or expect to lose job within the next year; dichotomized as yes vs. no

Shift Change Reporting more than one shift change in the past 2 weeks vs. none Prevalence of Violence Reporting any of the following: physical assault, threat assault, or emotional

abuse; dichotomized as yes vs. no.


Predictor and Dependent Variable

Measurement

Interventions Not Done Reporting interventions not done on the last shift for the following interventions: vital sign/medications/dressings, mobilization/turns, patient/family teaching, discharge prep, comforting/talking with patients, documenting nursing care, back rubs/skin care, oral hygiene, or care plan; dichotomized as one or more interventions not done vs. none

Interventions Delayed Reporting interventions delayed on the last shift for the following: vital signs/medications/dressings, mobilization/turns, response to patient bell, or PRN pain medication; dichotomized as one or more interventions delayed vs. none

Effort and Reward Imbalance

Dichotomized as at risk of effort and reward imbalance (> 1) vs. not at risk (≤ 1)

Emotional Exhaustion Sum score of nine 7-point scale item; dichotomized as at risk (score > 27) vs. not at risk (score ≤ 27)

Autonomy Sum score of six autonomy items from NWI; the higher the score, the more autonomy nurses feel about work.

Nurse-Physician Relationship

Sum score of three nurse-physician relationship items from NWI; the higher the score, the more positive nurses feel about the nurse-physician relationship.

Absenteeism Number of occasions missing work due to illness and disability; dichotomized as one or more sick leaves vs. none

Intent to Leave Plan to leave within the next year vs. no Physical Health Physical health, measured with SF-12 Mental Health Mental health, measured with SF-12 Satisfaction with Current

Job Average score of 5-point scale on social contact at work, social contact after work, opportunities to interact with management, amount of responsibility, satisfaction with present Job, and satisfaction with a being a nurse; dichotomized as satisfied/very satisfied vs. dissatisfied/very dissatisfied.

Improved Quality of Patient Care

Quality of patient care in the unit in the past year; dichotomized as improvement vs. others

Good Quality of Nursing Care

Quality of nursing care in the last shift; dichotomized as excellent/good vs. fair/poor

Environmental Complexity Scale Re-sequencing of Work Re-sequencing of work in response to others Unanticipated Changes in

Patient Acuity Unanticipated changes in patient condition, unanticipated time consuming interventions for patient and family, and so on.

Composition & Characteristics of Care Team

Supervision and assistance of student nurses

More Time Needed Amount of more time needed to give the type of care stated in the nursing care plan

Unit variables Unit Occupancy Measured by midnight census divided by beds on unit Proportion of RN worked

hours Proportion of nursing hours contributed by RNs in the unit

Average worked hours Average worked hours provided to patients on unit Productivity/utilization Unit workload divided by total worked hours on unit


MLwin MLwin beta version 2.0 was used to analyze the data. In MLwin, the hierarchical structure of the data is identified by variables which label the units at each level1. These are known as level or unit identifiers and must be declared when a model is being set up in the equations window or estimate tables. The data were sorted according to the data hierarchy to ensure MLwin functioned properly. The order of entry of variables was consistent with the theoretical framework at two levels. The level 1 variables were first entered and tested, then moved to the second level. RIGLS/IGLS estimation was used to generate coefficients and their standard errors. In the case of estimation failure from RIGLS/IGLS estimation, MCMC methods were used to continue the estimation. The -2 log likelihood value was used to make comparisons among different models. The test of significance for individual variables was conducted by using the “intervals and tests” facility in MLwin. Reference 1. Rasbash, J., Browne, W. Goldstein, H., et al. (2000). A user’s guide to MLWIN. Multilevel

Models Project [Computer software and manual], Institute of Education, University of London.


Appendix G. Descriptive Analyses

List of Tables .............................................................................................................................. 169

Descriptive Analyses .................................................................................................................. 171

1. System Characteristics .....................................................................................................................171

2. Patient Characteristics .....................................................................................................................173 2.1 Patient Demographics ...............................................................................................................................173 2.2 Medical Diagnoses....................................................................................................................................176 2.3 Nursing Diagnoses and OMAHA Scores at Admission............................................................................177 2.4 Health Status at Admission .......................................................................................................................178

3. Nurse Characteristics ....................................................................................................................... 180 3.1 Nurse Demographics.................................................................................................................................180 3.2 Professional and Employment Status........................................................................................................181 3.3 Education and Clinical Expertise ..............................................................................................................181 3.4 Experience ................................................................................................................................................182

4. System Behaviours............................................................................................................................ 184 4.1 Workload ..................................................................................................................................................184 4.2 Workload Variation by Patient Medical Diagnosis...................................................................................186 4.3 Overtime and Continuity of Care/Shift Change and Unit Instability ........................................................188 4.4 Non-Nursing Tasks ...................................................................................................................................190

5. Intermediate System Outputs ............................................................................................................ 190 5.1 Worked Hours...........................................................................................................................................190 5.2 Productivity/Utilization.............................................................................................................................193

6. Environmental Complexity ............................................................................................................... 195

7. Patient Outcomes.............................................................................................................................. 197 7.1 Medical Consequences..............................................................................................................................197 7.2 OMAHA Scores at Discharge and Change from Admission ....................................................................198 7.3 Health Status at Discharge and Change from Admission .........................................................................198

8. Nurse Outcomes................................................................................................................................ 199 8.1 Burnout and Effort & Reward Imbalance .................................................................................................199 8.2 Autonomy and Control .............................................................................................................................201 8.3 Job Satisfaction .........................................................................................................................................201 8.4 Health Status .............................................................................................................................................202 8.5 Violence at Work ......................................................................................................................................203

9. System Outcomes .............................................................................................................................. 204 9.1 Quality of Care..........................................................................................................................................204 9.2 Absenteeism..............................................................................................................................................205 9.3 Intent to Leave ..........................................................................................................................................206 References................................................................................................................................................. 207


List of Tables Table 1: Hospital Characteristics............................................................................................... 171 Table 2: Unit Characteristics ..................................................................................................... 172 Table 3: Aspects of Care Process – Percent of Patients Reporting Yes to Items in the Table, by

Hospital..................................................................................................................... 173 Table 4: Percent of Surgical Patients who Attended Pre-op Clinic and Post-op Education, by

Hospital..................................................................................................................... 173 Table 5: Patient Demographics, by Hospital ............................................................................. 174 Table 6: Patient Age, by Gender and Hospital........................................................................... 174 Table 7: Percent Distribution of Patient Occupation, by Category, by Hospital....................... 175 Table 8: Patient Employment Status – Percent Distribution by Hospital .................................. 175 Table 9: Patient Educational Status – Percent Distribution by Hospital................................... 175 Table 10: Percent Distribution of the Number of CMGs, by Hospital ....................................... 176 Table 11: Mean of Number of Nursing Diagnoses, by Unit Type .............................................. 177 Table 12: OMAHA Scores at Time 1 (Admission and Appearance of New Diagnosis), by Hospital

................................................................................................................................... 178 Table 13: Patient Health Status at Admission, by Hospital........................................................ 179 Table 14: Patient Health Status at Admission, Percent Less than US Norm, by Hospital......... 179 Table 15: Patient Health Status at Discharge, by Hospital........................................................ 180 Table 16: Patient Health Status at Discharge, Percent Less than US Norm, by Hospital......... 180 Table 17: Nurse Demographics, by Hospital ............................................................................. 181 Table 18: Nurse Employment Status, by Hospital ...................................................................... 181 Table 19: Nurse Education and Expertise, by Hospital ............................................................. 182 Table 20: Nurse Experience, N and Percent of Total Respondents, by Hospital ....................... 182 Table 21: Mean (SD) of PRN Workload (in Minutes) by Category, by Hospital ....................... 185 Table 22: PRN Workload Category as Percent of Total PRN Workload, by Hospital .............. 185 Table 23: Comparison of PRN to GRASP/Medicus Workload (in Hours), by Hospital ............ 186 Table 24: Percent Distribution of Work (in Minutes) by Workload Category, by CMG Type... 187 Table 25: Percent of Nurses Reporting Overtime in Average Hours per Week, by Hospital..... 188 Table 26: Percent Change of Nurse Overtime Hours in the Past Year, by Unit Type ............... 188 Table 27: Percent of Overtime Unpaid or Involuntary, if Working Overtime, by Hospital ....... 189 Table 28: Continuity of Care and Amount of Change, by Hospital ........................................... 189 Table 29: Percent of Nurses Reporting Performing Non-Nursing Tasks for Items in the Table, by

Hospital..................................................................................................................... 190 Table 30: Actual Staffing Hours, by Unit, by Day...................................................................... 191 Table 31: Percent of Actual Staffing, by Unit, by Day ............................................................... 192 Table 32: Daily Patient Census, Admissions, and Discharges, by Unit..................................... 193 Table 33: Number of Days When Unit GRASP/Medicus is Greater than 85% and 93% of Total

Nurse Hours, by Unit ................................................................................................ 194 Table 34: Percent of Nurses Reporting Average Hours Worked Per Week in the Past Year, by

Hospital..................................................................................................................... 195 Table 35: Mean of Three Subscales from ECS, by Hospital....................................................... 196 Table 36: Percent of Nurses Reporting Additional Time Needed to Provide Quality of Care, by

Hospital Unit............................................................................................................. 196


Table 37: Medical Consequences – Percent Reporting Yes to the Items in the Table, by Hospital................................................................................................................................... 197

Table 38: OMAHA Scores at Time 2 (Resolution of Diagnosis or at Discharge), by Hospital . 198 Table 39: Differences in OMAHA Scores Between Time 1 and Time 2, by Hospital................. 198 Table 40: Change in Patient Physical Health Status (SF-12) from Admission to Discharge .... 199 Table 41: Change in Patient Mental Health Status (SF-12) from Admission to Discharge....... 199 Table 42: Burnout – Mean Scores of MBI Subscales, by Hospital............................................. 200 Table 43: Burnout – Percent of Nurses at Risk for Emotional Exhaustion and ERI, by Hospital

................................................................................................................................... 200 Table 44: Nurse Work Index Subscales, by Hospital.................................................................. 201 Table 45: Job Satisfaction – Percent of Nurses Dissatisfied, by Hospital ................................. 202 Table 46: Nurse Health Status, by Hospital ............................................................................... 202 Table 47: Nurse Health Status, Percent of SF-12 Scores Less than US Norm for Females, by

Hospital..................................................................................................................... 203 Table 48: Prevalence of Violence – Percent of Nurses Reporting Yes to the Items in the Table, by

Hospital..................................................................................................................... 203 Table 49: Source of Emotional Abuse, by Hospital.................................................................... 204 Table 50: Quality Issues – Percent of Nurses Reporting Yes to Items in the Table, by Hospital205 Table 51: Absenteeism – Percent of Episodes Absent and Mean Shifts per Episode in the Past

Year, by Hospital ...................................................................................................... 206 Table 52: Absenteeism – Most Common Reason to Miss Work in the Past Year, by Hospital .. 206 Table 53: Intent to Leave – Percent of Nurses Reporting Yes to the Items in the Table, by

Hospital..................................................................................................................... 206


Descriptive Analyses The results of descriptive analyses are presented at hospital or unit level. Hospital names are suppressed to ensure confidentiality. All comparisons between hospitals and units are merely crude rate comparisons that do not take into account differences in characteristics of patients, nurses, or organizations. 1. System Characteristics Tables 1 and 2 outline the profiles of six hospitals and 24 nursing cardiac and cardiovascular units. Six characteristics describe the hospitals. The total number of inpatient beds denotes the overall size of individual hospitals. Hospital 6 had the largest number of beds whereas Hospital 5 had the smallest. Four of the six hospitals were teaching hospitals. The survey period varied at each site because the volume of eligible patients in each hospital influenced the number of study days. Each hospital had a target of 200 patients. Due to staffing problems, Hospitals 1 - 5 agreed to extend their data collection period to ensure that a sufficient number of patients were included in the analysis. Hospital 6, however, was not able to participate fully and thus had fewer patients completing the survey form and finished the study in a much shorter period of time than the other hospitals. The ability to capture patient level data was limited in some organizations due to the length of time required each day to collect staff data from non computerized systems.

Table 1: Hospital Characteristics

Hospital 1 2 3 4 5 6 TotalNumber of Beds 567 778 507 777 121 1060 3243Teaching N Y N Y Y Y n/aSurvey Patients 189 243 259 195 285 59 1230Number of Study Days 136 121 114 184 136 64 755Patient Midnight Census 14 23 33 19 18 13 19Number of Units 3 3 2 5 6 5 24Note: Y=Yes, teaching hospital; N=No, non-teaching hospital; n/a=Not applicable Number of Beds=Total inpatient beds Surveyed Patients=Number of patients who completed Patient Data Form Patient Midnight Census=Average number of patients in surveyed units Hospitals in the sample provided cardiac and cardiology nursing care using a variety of organizational structures. Surgical patients generally received a portion of their care in a critical care unit (CCU) but pre- and post-operative care was provided on an inpatient (IP) unit. In some organizations (hospitals 1 and 6), step-down units (SDU) were used in addition to the CCU. Some patients also received care in a CCU or SDU but many patients did not use critical care services. The structure and organization of health delivery can affect patient, nurse, and system outcomes. For example, attendance at pre admission or post operative education may have an effect on the resources required during the hospital stay and on the overall length of stay.


Table 2: Unit Characteristics

Hospital Unit Name Unit Type

Pure Cardiology*

Number of Beds

Number of Study

Patients 1 Coronary care unit

Step-down unit Cardiac post-surgery/pre-op

CCU SDU IP

Y Y Y

12 18 27

3948

1022 Intensive care unit

Coronary care unit Combined Cardiology/Cardiovascular

CCU CCU IP

N Y N

19 11 40

7134102

3 Critical care area Combined Cardiology/Cardiovascular

CCU IP

N Y

15 48

36223

4 6 Eaton South + 6 NU Cardiovascular intensive care units (2) Interventional short stay unit Inpatient cardiology

IP CCU DS IP

Y Y Y Y

56 19 29 26

19406670

5 Interventional cardiology unit Cardiac surgical unit/Recovery Coronary Care unit Cardiovascular Cardiology/surgical Cardiology

DS CCU CCU IP IP IP

Y Y Y Y N Y

17 16

6 28 27 33

3592

3879

1226 Coronary Care units (3)

Cardiology Cardiology Step-down

CCU IP SDU

Y Y Y

38 32 32

5171

Note: CCU = Critical Care Unit DS = Day Surgery IP = Inpatient SDU = Step-Down Unit *Pure cardiology = units that provide care exclusively for cardiac and cardiovascular patients as opposed to patients with other medical or surgical conditions. Tables 3 and 4 show various aspects of the care process in planned admission, pre-op and post-op clinics, referrals to home care, time in SDU, and transfer to ICU. On average, almost half of survey patients reported that their admission was a planned readmission. More than one-fifth (22%) of the patients attended a pre-op clinic and more than half (53%) had post-admission education. About one in ten patients (10.9%) were referred to home care. There were 11.3% of patients who spent time in a SDU. Only 2% of the patients were transferred back to ICU. Hospital 5 had the largest proportion of patients with planned admission (65.4%), which was almost six times that of Hospital 6 (11.9%). Hospital 1 had more surgical patients attending pre-operation clinics than all other hospitals, while Hospital 3 provided post-admission education for more cardiac and cardiovascular patients than any of the other hospitals. Hospital 2 referred 37.7% of patients to home care which was higher than other hospitals (4.3-7.7%). Hospitals 1 and 6 had a relatively larger proportion of patients spending time in SDU. Few if any patients were transferred back to ICU in Hospitals 2, 5, and 6.


Table 3: Aspects of Care Process – Percent of Patients Reporting Yes to Items in the Table, by Hospital

Hospital 1 2 3 4 5 6 Total Number of cases 189 243 259 195 285 59 1230Planned admission 35.4 32.2 38.2 56.3 65.4 11.9 44.5 Referred to home care 4.3 37.7 6.5 6.6 7.1 7.7 10.9Spent time in SDU 28.0 6.2 7.6 10.0 0.5 28.3 11.3Transferred back to ICU 3.7 0.4 3.1 3.6 0.7 0.0 2.0Note: Due to missing values in each category the denominators to generate percentages are slightly different from N.

Table 4: Percent of Surgical Patients who Attended Pre-op Clinic and Post-op Education, by Hospital

Hospital 1 2 3 4 5 6 TotalNumber of cases 66 69 82 78 129 16 440Attended pre-op clinic 59.1 13.0 31.7 48.7 20.9 37.5 33.0Post-op education 65.2 10.1 72.0 70.5 63.6 43.8 57.5Note: Due to missing values in each category the denominators to generate percentages are slightly different from N. 2. Patient Characteristics Patient characteristics were captured from a variety of data sources.

1) Patients provided information about themselves and their care process in a survey. 2) Each hospital’s Health Records Department provided health records data that included

medical diagnosis at discharge, resource intensity weight, length of stay, admission type, etc.

3) Patients completed a SF-12 Health Survey indicating their functional status at the time of admission and discharge.

4) Data collectors collected nursing diagnoses (NANDA) and ratings of patient OMAHA knowledge, behaviour, and status concerning each nursing diagnosis from the chart, Kardex, and in consultation with the nurse.

In total, 1,230 patients were entered into the study.

2.1 Patient Demographics As shown in Tables 5 and 6, the average age of patients was 63.5 years and two-thirds were male (66.7%). Hospital 2 had the largest proportion of females, and Hospitals 1 and 6 had female proportions well below the average. Hospitals 2 and 3 had high proportions (40.2 and 40.0% respectively) of patients over the age of 70, whereas Hospital 4 has the highest proportion (24.6%) of patients under the age of 50. Patients at Hospitals 2 and 3 were less likely to have a caregiver at home. This may be explained by the higher average age of patients at these sites. On average, over 95% of patients had a family doctor.


Table 5: Patient Demographics, by Hospital

Hospital 1 2 3 4 5 6 Total

Number of patients 189 243 259 195 285 59 1230 % Male 74.1 60.1 63.3 70.8 66.7 74.6 66.7 % Female 25.9 39.9 36.7 29.2 33.7 25.4 33.3 % Age >=70 30.7 40.2 40.0 29.2 37.0 33.9 35.9 % Age < 50 14.8 11.9 13.8 24.6 14.2 11.9 15.3 % Caregiver at home 87.3 79.1 75.9 85.1 84.2 83.1 82.0 % Family Physician 94.7 95.9 93.1 93.8 98.6 91.5 95.2 Note: The denominators used to generate percentages for each demographic may be slightly different from the number of patients presented in the table.

Table 6: Patient Age, by Gender and Hospital

Male Female Total Hosp N Mean SD N Mean SD N Mean SD

1 140 62.0 11.64 49 64.8 13.30 189 62.8 12.122 146 63.9 11.71 97 68.1 13.37 243 65.6 12.543 164 63.4 12.66 95 65.8 11.81 259 64.2 12.394 138 60.1 13.68 57 59.0 16.51 195 59.8 14.535 189 62.5 12.58 96 66.6 14.08 285 63.9 13.226 44 61.5 11.24 15 70.1 11.70 59 63.7 11.86

Total 821 62.4 12.44 409 65.6 13.85 1230 63.5 13.01 Patient occupation was originally collected as an open-ended question. Occupations were subsequently classified into 14 categories according to work environments, knowledge, skill, and level of control1 As seen in Table 7, the occupational distribution of patients varied greatly by hospital. Hospitals 4, 5 and 6 had higher proportions of patients reporting a professional occupation, whereas Hospital 1 had mostly service, outdoor physical, and professional occupations. Hospital 3 had a large proportion of housewives and patients with outdoor physical occupations. In contrast, Hospital 2 had an overall even distribution across all occupation categories.


Table 7: Percent Distribution of Patient Occupation, by Category, by Hospital

Hospital 1 2 3 4 5 6 Total Number of Cases 176 152 220 153 276 57 1034 Self-employed 5.7 0.7 6.4 5.2 4.7 3.5 4.6 Professional 10.8 8.6 8.6 26.8 17.8 19.3 14.7 Outdoor Physical* 10.8 16.4 15.0 3.9 4.7 1.8 9.4 Healthcare Provider 5.1 3.9 1.8 2.6 6.5 3.5 4.2 Government 2.8 3.9 2.3 0.7 7.2 0.0 3.6 Housewife 8.0 8.6 18.6 7.8 5.8 8.8 9.8 Clerical 0.6 6.6 0.9 5.2 6.2 3.5 3.9 Management 8.5 6.6 5.9 5.2 13.4 10.5 8.6 Retail/Sales 3.4 5.3 5.5 3.3 4.3 7.0 4.5 Factory 9.1 7.9 6.4 8.5 7.2 14.0 8.0 Trades 5.1 5.3 3.2 3.3 1.8 12.3 4.0 Business 4.5 3.9 6.4 3.9 6.5 3.5 5.2 Service 23.3 11.2 12.7 16.3 12.3 10.5 14.6 Not working 2.3 11.2 6.4 7.2 1.4 1.8 4.9 *Mostly farmers for Hospital 2 and miners for Hospital 3 in the outdoor physical occupation. More than 60% of patients from Hospitals 1 and 5 were employed, but merely one-third of patients in Hospitals 2 and 3 were working at the time of the survey. Hospital 3 also had the largest proportion (20.0%) of patients not employed (Table 8). The not-employed group consists of housewives, disabled persons, and students. Hospital 2 had primarily retired patients (53.1%) as patients in Hospital 2 were much older than patients in other hospitals. In contrast, less than one-third of patients in Hospitals 1 and 5 fell into the retired group.

Table 8: Patient Employment Status – Percent Distribution by Hospital

Hospital 1 2 3 4 5 6 Total Employed 60.8 34.4 34.9 47.2 65.8 52.6 48.9 Not employed 9.5 12.5 20.0 11.4 6.1 7.0 11.7 Retired 29.6 53.1 45.1 41.5 28.1 40.4 39.4 Number of cases 189 224 255 193 278 57 1196 Note: “Not employed” includes not working and housewives categories in Table 7. Table 9 shows the educational status of the patients. The education level was lower in Hospitals 1 and 3, with less than one third reporting more than high school education. This may reflect the higher proportion of service and outdoor workers in Hospital 1 and the high proportion of housewives in Hospital 3.

Table 9: Patient Educational Status – Percent Distribution by Hospital

Hospital 1 2 3 4 5 6 Total More than high school 28.2 53.5 30.6 45.0 51.3 41.4 41.9

Number of cases 177 185 258 188 281 58 1147


2.2 Medical Diagnoses

Table 10 shows the percent distribution of CMGs by hospital and the dominant CMG group in each hospital is bolded. As can be seen, “Percutaneous Transluminal Coronary Angioplasty without Cardiac Catheterization” accounts for the largest proportion for Hospitals 3, 4 and 5 whereas “Cardiac Catheterization without Specified Cardiac Condition” accounts for the largest proportion in Hospital 1, “Percutaneous Transluminal Coronary Angioplasty with Complicated Cardiac Condition” has the largest percentage for Hospital 6, and “Major Cardio Procedure no Pump no Catheterization” is the dominant CMG group for patients in Hospital 2.

Table 10: Percent Distribution of the Number of CMGs, by Hospital Hospital CMG

1 2 3 4 5 6 Total

PTCA WO CARD CATH 15.3 1.6 15.4 27.2 13.0 5.1 13.5 PERM PACE IMPL WO SPEC CARD C 6.3 5.3 11.6 18.5 12.6 8.5 10.7 C BYPASS W PUMP WO CARD CATH 11.1 6.6 9.7 5.1 13.0 0.0 8.9 PTCA W CARD COMP 4.2 9.9 8.9 4.6 8.1 22.0 8.1 C VALVE REP W PUMP WO CARDIAC 10.6 2.9 6.9 4.6 9.1 8.5 6.9 CARD CATH NO SPEC CARD COND 19.6 0.4 0.0 2.1 10.9 0.0 5.9 C BYPASS W PUMP W CARD CATH 5.3 11.1 2.7 3.6 5.6 5.1 5.7 MAJ CARDIO PR NO PUMP NO CATH 0.0 13.6 4.2 4.1 1.8 5.1 4.9 ARRTHYMIA 4.2 4.9 6.6 4.1 3.2 5.1 4.6 HEART FAILURE 2.6 3.7 4.6 1.5 3.2 3.4 3.3 AMI NO CARD CATH NO SPEC COND 2.1 4.9 4.2 1.0 1.4 10.2 3.2 OTHER CIRCULATORY DIAGNOSIS 0.0 4.9 5.0 4.1 1.8 1.7 3.2 CHEST PAIN 1.6 1.2 8.9 0.0 2.1 3.4 3.0 CARDIAC CATH W SECIFIED CARD 2.1 4.5 0.4 2.6 2.1 0.0 2.2 UNS ANGINA W CATH NO SPEC CON 1.1 3.7 1.9 1.0 1.4 1.7 1.9 AMI WITH CARD CATH NO SP COND 0.0 4.1 1.2 2.1 1.1 3.4 1.8 PERM PACEMAKER/SPEC CARD COND 0.5 2.1 0.4 3.1 2.8 1.7 1.8 AMI WO CARD C W CHF 1.1 3.3 1.2 1.0 0.4 3.4 1.5 UNSTABLE ANGINA WO CATH WO SP 0.5 3.7 1.9 0.5 0.4 0.0 1.4 UNS ANGINA W CATH NO CRD COND 2.6 0.0 1.2 0.0 2.1 0.0 1.1 CARDIAC CATH WITH VENT TACH 0.0 1.6 0.8 1.5 0.4 1.7 0.9 CRD VLV REP W PUMP W CRD CATH 1.1 0.4 0.4 0.5 2.1 0.0 0.9 AMI W CARD CATH W & WO ANGINA 0.0 1.6 0.8 0.5 0.4 1.7 0.7 SYNCOPE AND COLLAPSE 0.5 0.8 0.4 0.0 0.4 1.7 0.5 ANGINA PECTORIS 0.0 1.2 0.4 0.0 0.0 0.0 0.3 AMI NO CARD CATH W VENT TACH 0.0 0.8 0.4 0.0 0.0 0.0 0.2 AMI W CAD CATH WITH CHF 0.5 0.4 0.0 0.0 0.0 0.0 0.2 CAR CATH W CHF 0.5 0.0 0.0 0.0 0.4 0.0 0.2 CARD CATH W VENTR TACH 1.6 0.0 0.0 0.0 0.0 0.0 0.2 MAJOR CARDIO THORA PROCE WO P 1.1 0.0 0.0 0.0 0.0 0.0 0.2 OTHER CARDIOTORACIC PROC W PU 0.5 0.0 0.0 0.0 0.4 0.0 0.2 PROCEDURE CANCELLED MNRH 1.6 0.0 0.0 0.0 0.0 0.0 0.2 ATHEROSCLEROSIS MNRH 0.5 0.0 0.0 0.0 0.0 0.0 0.1 EXTNSIVE UNREL OR PROCESUDRE 0.5 0.0 0.0 0.0 0.0 0.0 0.1 PERRIPHERAL VASCULAR DISEASE 0.5 0.0 0.0 0.0 0.0 0.0 0.1 RENAL FAILURE NO DIALYSIS 0.0 0.0 0.0 0.0 0.4 0.0 0.1 UNS ANGINA W CATH W SPEC COND 0.0 0.4 0.0 0.0 0.0 0.0 0.1 Total 189 243 259 182 285 55 1213 Note: Data are sorted by the percent in the Total column.


2.3 Nursing Diagnoses and OMAHA Scores at Admission Nursing diagnosis is a clinical judgment about an individual or family response to an actual or potential health problem or life process. Nursing diagnoses provide the basis for selecting nursing interventions to achieve outcomes for which nurses are responsible. Nursing diagnoses were recorded from admission until discharge. Table 11 shows that the number of nursing diagnoses averaged nearly five (SD=2.6) across all units. The highest mean number of diagnoses was observed in Hospital 5’s CCU (8.44). The lowest average numbers of nursing diagnoses were observed in the DS unit in Hospital 4 (2.58), the DS unit in Hospital 5 (3.42), and IP unit in Hospital 3 (3.47). (Note that only 1,189 patients were included due to missing nursing diagnoses for some patients.) It should be noted that patients could change units during their hospital stay. Because patients may be admitted to one unit and discharged from another, the number of patients admitted to a unit may be different from the number of patients discharged from that unit.

Table 11: Mean of Number of Nursing Diagnoses, by Unit Type

Total Number of Diagnoses Unit at Discharge

# of Patients Grouped by Unit at Discharge Mean SD

Hospital 1 CCU 20 5.55 2.31 IP 110 6.80 3.36 SDU 59 5.19 2.07 Hospital 2 CCU 105 6.48 3.06 IP 132 4.78 2.50 Hospital 3 CCU 18 4.44 1.89 IP 214 3.47 1.51 Hospital 4 CCU 17 5.12 3.55 DS 59 2.58 1.63 IP 115 4.46 1.96 Hospital 5 CCU 9 8.44 3.28 DS 53 3.42 1.71 IP 219 4.34 2.41 Hospital 6 CCU 45 5.27 1.51 IP 11 4.36 .81 SDU 2 6.50 .71 Total 1189 4.68 2.60 OMAHA Problem Scales for Outcomes is an evaluation tool developed by the Omaha Visiting Nurse Association2. The tool measures clinical progress of patients in relation to specific problems or nursing diagnoses at two points in time: at admission or when a new health problem was identified (time 1), and when the health problem was resolved or at discharge (time 2). The three essential dimensions, knowledge, behaviour, and status, are each rated on a 5-point Likert scale ranging from very negative to very positive. A mean score was computed for each person on each dimension and then averaged for the sample. Data about OMAHA at time 1 are presented in Table 12. The data for OMAHA at time 2 and difference scores are presented under section 7.2 in Patient Outcomes.


Table 12 shows that for all survey patients at time 1, the mean scores were 3.4 (SD=0.75) for knowledge, 4.0 (SD=0.58) for behaviour, and 3.3 (SD=0.62) for status. Patients from Hospital 4 had the highest ratings across all three dimensions.

Table 12: OMAHA Scores at Time 1 (Admission and Appearance of New Diagnosis), by Hospital

Knowledge Behaviour Status Hospital N Mean SD Mean SD Mean SD

1 188 2.9 0.41 3.8 0.38 3.0 0.26 2 237 3.5 0.75 4.0 0.46 3.6 0.43 3 232 3.0 0.52 3.6 0.54 3.0 0.63 4 192 3.8 0.54 4.3 0.52 3.8 0.47 5 282 3.8 0.80 4.2 0.63 2.9 0.55 6 58 2.6 0.67 4.3 0.49 3.5 0.57

Total 1189 3.4 0.75 4.0 0.58 3.3 0.62 Note: Measured on a 5-point Likert scale for Knowledge (1=No knowledge and 5=Superior knowledge), Behaviour (1=Never appropriate in behaviour and 5=Consistently appropriate in behaviour), and Status (1=Extreme symptoms and 5=No symptoms).

2.4 Health Status at Admission The SF-12 is a widely used measure of general health. The questions tap eight health concepts that are reported in two categories: physical and mental health. The eight categories are physical functioning, role physical, bodily pain, general health, energy/fatigue, social functioning, role emotional, and mental health. Although the SF-12 is not quite as reliable and comprehensive as the SF-36, the results of the two measurement tools are highly correlated (r=0.95). Confidence intervals are largely determined by sample size; with large samples, the results will be almost the same regardless of the tool used3. Table 13 shows the two SF-12 subscales measured at admission at the interval level. The mean physical health score of 35.2 (SD=11.2) ranged from, 11.0 to 64.8. The mean mental health score of 48.2 (SD=11.0) ranged from 15.6 to 70.3. The least physically and mentally healthy patients were found in Hospitals 5 and 1 respectively.


Table 13: Patient Health Status at Admission, by Hospital

Physical Health Scale Mental Health Scale Hospital N Mean SD Range Mean SD Range

1 186 35.7 10.3 13.7-63.0 46.9 10.7 18.4-67.22 207 35.6 11.9 11.2-63.4 48.1 10.8 20.4-68.63 256 34.1 10.2 14.2-60.1 47.4 12.0 16.9-69.04 191 37.4 12.1 11.9-62.4 49.6 10.5 19.1-70.35 277 33.4 11.1 11.0-64.8 48.8 10.5 15.6-66.96 59 37.6 11.5 16.8-60.6 49.3 11.7 16.8-66.2

Total 1176 35.2 11.2 11.0-64.8 48.2 11.0 15.6-70.3 These two SF-12 subscales can be dichotomized using US norms for the general population as the cut-point. Dichotomized data are presented in Table 14. Nearly nine in ten patients (87.0%) were below the US population norm in physical health upon admission. About half (49.2%) of patients had mental health scores below the US population norm. The percentage of patients that scored below the norm for physical and mental functional status varied across hospitals (χ2=14.8 and 15.2 respectively, df=5, p<0.05). Generally, patients in Hospital 3 were the least physically and mentally healthy, on average.

Table 14: Patient Health Status at Admission, Percent Less than US Norm, by Hospital

Hospital % Not Physically Healthy

% Not Mentally Healthy

N

1 88.7 53.2 1862 82.6 47.3 2073 91.0 50.8 2564 82.2 47.1 1915 89.1 49.3 2766 84.7 42.4 59

Total 87.0 49.2 1176US Norm3 Physically not healthy: < 50.12 Mentally not healthy: < 50.04 Tables 15 and 16 display two SF-12 subscale scores measured at discharge at the interval and dichotomous levels. Patients in Hospitals 2 and 5 tended to be the least physically healthy at discharge while patients in Hospitals 1 and 5 had the lowest average mental health scores at discharge. In contrast, the highest average physical and mental health scores for patients were observed in Hospitals 6 and 4 respectively. The comparison of changes in patient functional status from admission to discharge at the individual level will be presented in section 7.2.


Table 15: Patient Health Status at Discharge, by Hospital


1 163 34.7 9.3 14.0-58.4 46.0 10.0 25.4-66.3 2 185 31.2 8.7 15.2-56.6 48.0 9.6 20.6-65.9 3 250 35.3 9.7 14.2-56.8 48.9 11.7 17.3-69.0 4 155 36.0 10.0 16.4-60.8 50.7 10.7 23.7-68.4 5 249 32.8 9.4 12.1-56.8 47.3 11.0 17.5-69.8 6 39 36.1 11.0 18.2-59.4 48.2 12.9 15.6-65.1

Total 1041 34.0 9.6 12.1-60.8 48.1 10.9 15.6-69.8

Table 16: Patient Health Status at Discharge, Percent Less than US Norm, by Hospital

Hospital % Not Physically Healthy


N

1 94.5 60.7 1632 95.7 54.6 1853 90.0 47.6 2504 89.0 42.6 1555 95.2 55.4 2496 84.6 43.6 39

Total 92.6 51.9 1041US Norm3 Physically not healthy: PHYSICAL HEALTH SCALE < 50.12 Mentally not healthy: Mental Health Scale < 50.04 3. Nurse Characteristics The nursing information was collected from the Nurse Survey, and all the data are based on self report. The Nurse Survey was a very comprehensive survey, covering mental and physical health, job satisfaction, workload and violence during work using many reliable measures such as functional status of health (SF-12), Maslach Burnout Inventory (MBI), Revised Nursing Work Index (R-NWI), and Effort/Reward Imbalance (ERI). A total of 727 nurses participated in the study.

3.1 Nurse Demographics Table 17 demonstrates the gender and age distributions for nurses in each of the participating hospitals. The vast majority of nurses completing the survey were female (93.9%), which is consistent with the female to male ratio of the Canadian nursing workforce4. Only Hospital 1 reported a slightly higher proportion of males than the other sites. This site also reported the highest proportion of nurses less than 30 years of age and the lowest number over the age of 50. Hospitals 3 and 4 had higher proportions of nurses who were less than 30 than the remaining three hospitals but significantly less then Hospital 1. Unlike Ontario’s profile5, some of these organizations have more nurses under 30 than over 50.


Table 17: Nurse Demographics, by Hospital

Hospital 1 2 3 4 5 6 Total Gender % Female 87.9 94.6 93.2 94.2 95.7 92.9 93.9 Number of Cases 58 186 74 86 210 112 726 Age % < 30 23.2 7.7 13.9 14.3 7.8 6.5 10.2 % > 50 5.4 12.1 11.1 11.9 9.8 17.6 11.6 Number of Cases 56 182 72 84 205 108 707

3.2 Professional and Employment Status Table 18 shows the percentages of nurse respondents who were employed full-time, employed on a permanent basis, and were Registered Nurses. The proportion of full-time nurses varied from 54.3% to 70.9%, with three of the hospital under 60% and three over 65%. Most nurses worked in permanent positions (97.8% on average), with the exception of Hospital 1 (89.8%). The percentage of full-time and/or permanent employees may have been inflated because full-time and/or permanent nurses were more likely to participate in the survey. In terms of nursing composition, respondents were comprised of Registered Nurses (96.6%), Registered Practical Nurses (0.8%), and charge nurses and other personal care workers such as orderlies, Registered Nurse Technicians, clinical leaders, and healthcare aids (2.5%). All respondents from Hospital 2 were Registered Nurses and the respondents from Hospital 3 were all in permanent positions.

Table 18: Nurse Employment Status, by Hospital

Hospital 1 2 3 4 5 6 Total Number of Cases 59 186 74 86 210 112 727 % FT 67.8 54.3 67.6 70.9 56.2 58.0 59.8 % Permanent 89.8 98.9 100.0 98.8 97.1 99.1 97.8 % RN 91.4 100.0 93.2 95.1 98.6 93.6 96.6

3.3 Education and Clinical Expertise Table 19 gives a breakdown of the educational background of the nurses and their level of expertise, as measured by how often peers sought their advice, how often they filled the role of preceptor, and to what degree individuals possessed clinical expertise. The percentage of nurses with BScN and higher degree preparation varied across hospitals, ranging from 29.3% to 61.6%. Despite educational levels, a higher proportion of nurses in Hospital 6 acted as preceptors and 45.0% were frequently asked for clinical advice and considered themselves to be expert clinicians. Nurses at Hospital 1 reported the lowest levels of confidence in their clinical ability and fewer nurses were asked by their peers for clinical advice. Some of the variation in preceptor reporting may be due to variations in the number of student nurses at these hospitals or rate of staff nurse turnover. If turnover rates were high, larger number of staff nurses might be preceptors regardless of their knowledge or skill level.


Table 19: Nurse Education and Expertise, by Hospital

Hospital 1 2 3 4 5 6 Total N 59 186 74 86 210 112 727 Education - % with BScN and higher 29.3 34.9 40.5 61.6 50.5 32.1 42.3

Preceptor - % Frequent 7.0 10.2 14.9 14.1 9.1 22.3 12.4

Clinical advice - % Frequent 32.8 40.3 59.5 43.0 46.2 45.0 44.4

Expert - % Recognized expected clinical responses 19.0 39.7 35.7 36.5 45.9 45.0 39.8

3.4 Experience

Table 20: Nurse Experience, N and Percent of Total Respondents, by Hospital

Hospital < 1 yr 1- 5 yrs 6 -10 yrs 11-15 yrs > 15 yrs Total Yrs as a nurse 0

0.0% 13

23.2%12

21.4%14

25.0%17

30.4% 56

100%Yrs in hospital

1

2.2% 19

42.2%8

17.8%9

20.0%8

17.8% 45

100%

1

Yrs on unit

6 14.3%

22 52.4%

7 16.7%

6 14.3%

1 2.4%

42 100%

Yrs as a nurse

0 0.0%

13 7.1%

23 12.5%

40 21.7%

108 58.7%

184 100%

Yrs in hospital

3 2.1%

37 26.2%

13 9.2%

45 31.9%

43 30.5%

141 100%

2

Yrs on unit

7 5.3%

50 38.2%

19 14.5%

34 26.0%

21 16.0%

131 100%

Yrs as a nurse

0 0.0%

8 11.1%

8 11.1%

21 29.2%

35 48.6%

72 100%

Yrs in hospital

1 2.2%

12 26.1%

4 8.7%

18 39.1%

11 23.9%

46 100%

3

Yrs on unit

1 2.3%

24 54.5%

4 9.1%

9 20.5%

6 13.6%

44 100%

Yrs as a nurse

2 2.4%

19 22.9%

14 16.9%

10 12.0%

38 45.8%

83 100%

Yrs in hospital

1 1.6%

25 41.0%

7 11.5%

15 24.6%

13 21.3%

61 100%

4

Yrs on unit

1 1.8%

35 63.6%

4 7.3%

11 20.0%

4 7.3%

55 100%

Yrs as a nurse

0 0.0%

22 10.6%

17 8.2%

49 23.7%

119 57.5%

207 100%

5

Yrs in hospital

2 1.4%

39 27.1%

16 11.1%

41 28.5%

46 31.9%

144 100%


Hospital < 1 yr 1- 5 yrs 6 -10 yrs 11-15 yrs > 15 yrs Total Yrs on unit

6

4.3% 71

50.4%22

15.6%26

18.4%16

11.3% 141

100%Yrs as a nurse

2

1.9% 6

5.7%12

11.3%25

23.6%61

57.5% 106

100%Yrs in hospital

3

4.6% 11

16.9%7

10.8%21

32.3%23

35.4% 65

100%

6

Yrs on unit 5 8.3%

23 38.3%

11 18.3%

14 23.3%

7 11.7%

60 100%

Yrs as a nurse

4 0.6%

81 11.4%

86 12.1%

159 22.5%

378 53.4%

708 100%

Yrs in hospital

11 2.2%

143 28.5%

55 11.0%

149 29.7%

144 28.7%

502 100%

Total

Yrs on unit

26 5.5%

225 47.6%

67 14.2%

100 21.1%

55 11.6%

473 100%

Table 20 compares overall nursing experience of the nurses, their experience within the same institution and within the same unit. Hospital 1 nurses reported the least experience in all three categories. In all six hospitals, 53.4% of nurses reported more than 15 years of nursing experience. Years as a nurse varied more than years in hospital or years on the nursing unit. Nurses with greater than 15 years experience reported less years on unit than nurses with fewer years of experience. This may reflect hospital restructuring activities in recent years. However, Hospital 1 nurses worked the shortest time on unit compared to any of the other hospitals which may explain lower levels of expertise as reported in Table 19. Age and experience were highly correlated. The correlation coefficients between age and years worked as RN/RPN, years worked as RN/RPN at the current hospital and years worked as RN/RPN at current unit were 0.83, 0.61 and 0.45 respectively (p<0.001). Years worked as RN/RPN was highly associated with all three expert statuses: acting as a preceptor (F=5.27, p<0.05), providing clinical advice (F=39.75, p <0.001), and acting as a clinical expert (F=35.73, p <0.001). A nurse with more years experience as RN/RPN at the current hospital was more likely to be asked for clinical advice (F=23.65, p<0.001) or act as a clinical expert (F=8.51, p <0.01). However, greater years of experience in current hospital were not significantly related to acting as a preceptor. A similar association was found between years on current unit and expert status. A nurse with more years as RN/RPN on his/her current unit was more likely to be asked for clinical advice (F=12.94, p<0.001) or act as a clinical expert (F=42.97, p <0.001). However, more years on current unit was not significantly related to role as a preceptor. Higher education was positively associated with expert status. This association was only statistically significant nurses responded that other nurses sought their clinical judgment


(χ2=4.26, p<0.05). The associations between education and the status of providing clinical advice or acting as a clinical expert were significant at the 0.1 level. 4. System Behaviours

4.1 Workload Workload data (measured by PRN 806) were collected for each study patient on each study day by the site data collectors. The PRN tool measures the volume of nursing work in minutes by selecting the tasks that need to be completed for that day. Each task is assigned a value based on studies completed by the PRN system in numerous facilities. Workload values are presented on the measurement tool in five minute increments. This value reflects the average time to complete the task, by an average nurse, on an average day, for an average patient. This methodology is referred to as an “average” time methodology. The PRN tool does not directly capture the workload associated with activities that are not patient specific, however, the PRN value can be adjusted to account for indirect patient care. The PRN 80 values for indirect care time provided by Tilquin were included to determine the total hours of care patients required in the next 24 hour period (Tilquin, personal communications, August, 2003). Patient care workload for each study patient was also recorded from the unit workload tool. Hospital 2 used MEDICUS while all other study hospitals used GRASP. Both workload tools measure nursing hours including direct and indirect services related to patients. These values were compared to the PRN workload value adjusted for indirect patient care. In this study, GRASP or MEDICUS hours were collected daily for study patients and for the unit as a whole, including non-study patients as well. The GRASP methodology captures workload using a “standard time” methodology. Each site develops a list of tasks based on the activities they perform, and times are assigned to each of these tasks. The times are based on time and/or frequency or are established by staff nurse consensus. These times reflect the average time to complete the task, by an average nurse, on an average day, for an average patient in the individual facility. This reflects the physical and organizational characteristics of the individual facility. The MEDICUS system captures workload by multiplying a pre-set relative value per level of care by the target hours per unit of workload. Table 21 shows PRN patient care time (in minutes) by workload category. Total workload for each day is the sum of seven categories of activity including respiration, feeding and hydration, elimination, hygiene and comfort, communication, treatment, and diagnostic procedures. The average total PRN value for six hospitals was 274.5 minutes, or about 4.5 hours, for each patient day, with wide variations (SD=227.1). In descending order, average minutes for PRN activities are as follows: diagnostic procedures (109.0), treatment (48.6), hygiene and comfort (42.6), communication (32.4), respiration (16.6), feeding and hydration (15.1), and elimination (10.2). Hospital 1 averaged the most PRN minutes in total by category, except for diagnostic procedures. Hospital 3 averaged the fewest PRN in total and tended to rank low across categories.


Table 21: Mean (SD) of PRN Workload (in Minutes) by Category, by Hospital Hospital Resp. Feed. &

Hyd. Elim. Hyg. &

Comfort Comm. Treat-

ment Diag. Proc.

Total

1 42.3 (48.4)

21.2 (8.7)

16.6 (12.5)

66.6 (39.5)

66.7 (18.8)

58.5 (40.1)

132.4 (89.2)

404.2 (179.6)

2 15.3 (17.2)

17.5 (12.2)

12.2 (12.2)

62.0 (39.4)

14.1 (11.2)

42.9 (43.2)

154.9 (156.4)

318.9 (238.3)

3 9.6 (15.0)

13.0 (9.1)

6.5 (10.1)

26.3 (28.2)

31.8 (21.9)

46.3 (50.1)

49.9 (95.6)

183.5 (179.8)

4 9.3 (15.8)

14.4 (9.7)

8.6 (11.0)

30.0 (31.0)

38.0 (14.1)

53.4 (37.2)

96.4 (139.7)

249.7 (213.2)

5 15.0 (25.3)

12.2 (7.5)

8.2 (12.0)

31.4 (26.1)

28.9 (11.6)

48.5 (51.8)

90.3 (149.3)

234.6 (239.2)

6 11.8 (24.6)

10.9 (8.6)

10.2 (11.8)

32.5 (26.9)

39.2 (13.8)

43.1 (32.6)

171.1 (143.8)

318.3 (201.2)

Total 16.6 (27.2)

15.1 (10.2)

10.2 (12.1)

42.6 (36.8)

32.4 (21.9)

48.6 (45.4)

109.0 (140.0)

274.5 (227.1)

Note: Overall means based on daily patient data. Table 22 shows the total PRN minutes accounted for by each workload category. Diagnostic procedures comprised almost one-third (31.1%) of the total PRN minutes, followed by treatment and communication as the second and third highest proportions. Activities related to respiration, feeding and hydration, and elimination accounted for the smallest proportion, less than five percent, of total PRN patient care. Large amounts of variation were observed across hospitals. Hospitals 6 and 2 had the highest proportion of diagnostic procedures, whereas Hospital 3 has the smallest proportion. The proportion of treatment time was much higher in Hospitals 3 and 4 (23.9%) than in other hospitals (14.5%-20.7%). Hygiene and comfort in Hospital 2 was much higher (23.0%) than in other hospitals (10.4%-16.4%). The proportion of time spent in communication with patients was extremely low in Hospital 2 (6.7% vs. 17.3%-23.6% for other hospitals).

Table 22: PRN Workload Category as Percent of Total PRN Workload, by Hospital Hospital Resp. Feed. &

Hyd. Elim. Hyg. &

Comfort Comm. Treat-

ment Diag. Proc.

Total

1 8.8 5.7 4.0 15.8 19.4 14.5 31.7 100.0 2 3.9 8.2 4.0 23.0 6.7 13.8 40.4 100.03 4.3 10.4 3.1 15.0 23.6 23.9 19.8 100.04 2.8 7.7 3.5 11.9 21.7 23.9 28.7 100.05 4.7 8.3 3.3 15.0 19.9 20.7 28.2 100.06 2.7 4.6 2.8 10.4 16.7 14.9 48.1 100.0

Total 4.6 8.0 3.6 16.4 17.3 19.0 31.1 100.0Note: Differences by hospital are statistically significant at p<0.000 Table 23 demonstrates the gap between patient care time PRN and patient workload measured in GRASP or MEDICUS across hospitals. The PRN scores in the last column of Table 21 were adjusted for indirect care time using the method developed by Charles Tilquin. Workload measured by GRASP or MEDICUS was on average 1.6 hours greater than workload measured by PRN. Hospital 2, which used MEDICUS, showed the largest workload value (10.77 hours) among all hospitals. With a mean PRN value of 6.78 hours, Hospital 2 had the largest discrepancy (3.99 hours) with PRN. The discrepancy is also large for Hospital 4 (2.09 hours).


Hospital 1 was high in both GRASP and PRN scores, with only a small discrepancy (0.63 hours). The GRASP values, which were generally higher than PRN, were lower for Hospital 3 (-0.56 hours).

Table 23: Comparison of PRN to GRASP/Medicus Workload (in Hours), by Hospital

WL(GM) PRN WL(GM)-PRN Hospital N Mean SD N Mean SD N Mean SD

1 1094 9.29 5.49 1038 8.65 3.50 1038 0.63 4.83 2 2029 10.77 7.06 2018 6.78 4.68 2018 3.99 4.33 3 1422 3.50 3.12 1421 4.06 3.49 1421 -0.56 2.43 4 1295 7.52 5.40 1280 5.44 4.19 1280 2.09 3.85 5 1939 6.20 5.47 1932 5.09 4.70 1932 1.12 3.31 6 334 7.13 3.94 308 6.99 4.11 308 0.23 3.90

Total 8113 7.54 6.08 7997 5.92 4.48 7997 1.60 4.12 Note: (1) WL(GM) stands for workload measured by GRASP (for Hospitals 1, 3, 4, 5, 6) or MEDICUS (for Hospital 2). (2) Based on overall means using daily patient data.

4.2 Workload Variation by Patient Medical Diagnosis The variation in the distribution of work across various sites may be due to variations in the mix of patients at each site. Table 24 presents the distribution of work by Case Mix Groups (CMG) in descending order of average PRN workload in minutes. This analysis shows that PRN workload varies significantly by CMG. The workload for CMG “Extensive Unrelated Operating Room Procedure” and CMG group “Major Cardio-thoracic Procedures Without Pump” was nearly 10 hours (>588.8 minutes), the highest among all CMGs, and six times the size of workload for “Cardiac Catheterization with Congestive Heart Failure” (1.5 hours or 90 minutes). Table 24 also showed that not all CMGs had the same percentage distribution of workload category as total workload. For example, diagnosis procedures had a much higher percentage for CMG “Acute Myocardial Infarction with Cardiac Catheterization with Congestive Heart Failure” than for others CMG subgroups.

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4.3 Overtime and Continuity of Care/Shift Change and Unit Instability Table 25 shows the worked overtime hours in three categories by hospital (on average, 7% of the nurses worked more than 40 hours per week in their own hospital; see Table 34). Close to half of the nurses (45.1%) in all the hospitals reported either no overtime or less than one hour overtime per week. Another 32.2% worked 1-4 hours of overtime per week. The rest of the nurses (22.7%) worked greater than four hours overtime per week. Within the latter category, the highest and lowest proportions were achieved by Hospitals 6 (38.5%) and 4 (16.7%).

Table 25: Percent of Nurses Reporting Overtime in Average Hours per Week, by Hospital

Hospital N 0-1 1-4 > 41 52 36.5 32.7 30.8 2 153 47.7 33.3 19.0 3 64 37.5 45.3 17.2 4 72 51.4 31.9 16.7 5 184 47.8 32.6 19.6 6 96 40.6 20.8 38.5

Total 621 45.1 32.2 22.7

Table 26 presents the percent change in overtime in the past year by unit type. Nurses were asked whether the amount of overtime required had increased, remained the same, or decreased in the past year. For most nurses (64.3%), overtime work increased. The CCU in Hospital 4 reported the highest proportion of no change in overtime hours. Nurses in Hospitals 4 and 5 more frequently reported decreases in overtime in the past year than the nurses in the other institutions.

Table 26: Percent Change of Nurse Overtime Hours in the Past Year, by Unit Type Unit Type N Increased Remained Same Decreased Hospital 1 CCU 28 64.3 32.1 3.6 IP 18 72.2 22.2 5.6 SDU 3 100.0 0.0 0.0 Hospital 2 CCU 97 62.9 36.1 1.0 IP 37 89.2 10.8 0.0 Hospital 3 CCU 31 54.8 38.7 6.5 IP 35 68.6 28.6 2.9 Hospital 4 CCU 27 18.5 63.0 18.5 DS 5 80.0 20.0 0.0 IP 23 39.1 47.8 13.0 Hospital 5 CCU 52 38.5 40.4 21.2 DS 29 69.0 27.6 3.4 IP 70 54.3 41.4 4.3 Hospital 6 CCU 58 75.9 24.1 0.0 IP 11 63.6 27.3 9.1 SDU 15 60.0 40.0 0.0 Total 539 64.3 32.1 3.6 Note: Significance test is not available because of small N in some of the cells. The DS unit in Hospital 4 also had a very low number of nurses reporting an increase in levels of overtime while the other units in the same hospital had high numbers reporting a decrease (Table


26). These nurses were more likely (51.4%) to report an average of less than one hour of overtime as compared to other hospitals (Table 25). Table 27 contrasts the prevalence of unpaid overtime and involuntary overtime. Unpaid overtime includes both voluntary and involuntary; involuntary includes both paid and unpaid overtime. Hospital 4 reported the highest percentage of unpaid overtime but below average involuntary overtime. Overtime may not by itself be a contributing factor to high stress levels, but rather the involuntary nature of overtime in some organizations may lead to high levels of stress. Some nurses choose to work overtime and therefore overtime may not be a source of stress. However, we hypothesize that when overtime is involuntary in nature it may serve as a source of stress.

Table 27: Percent of Overtime Unpaid or Involuntary, if Working Overtime, by Hospital

Hospital N % Unpaid % Involuntary1 37 13.0 27.7 2 84 28.6 25.6 3 46 36.1 29.3 4 37 42.1 21.3 5 114 25.7 18.4 6 65 18.6 20.5

Total 383 26.7 22.8 Continuity of care was operationalized as the proportion of shift changes more frequent than once in the last two weeks as well as the proportion of nurses forced to change units in the past year and of those who anticipated forced changes in their unit in the coming year (Table 28). Nurses in Hospital 5 experienced higher levels of forced change in the last year, but Hospital 1 had the highest percentage anticipating a change in the next year. The nurses in Hospital 5 also reported the highest number of shift changes per week. Nurses who were forced to change unit in the past year had a higher nurse-patient ratio than nurses not experiencing a forced unit change (F=12.7, p<0.001). The proportion of shift changes and anticipation of forced changes in unit was associated with emotional exhaustion (measured by Maslach’s Burnout Inventory). Those who anticipated forced change of unit were more likely to rank high on the emotional exhaustion index (thus not healthy) than those who did not anticipated forced change of unit (F=8.7, p<0.01).

Table 28: Continuity of Care and Amount of Change, by Hospital

Hospital 1 2 3 4 5 6 Total % More than 1 shift change in the past 2 weeks 36.2 36.3 17.6 30.2 41.3 18.5 32.4N 58 182 74 86 206 108 714% Forced to change nursing units in the past year 10.3 8.7 0.0 2.4 13.8 4.5 8.0N 58 184 74 85 210 111 722% Anticipating forced change of unit 24.6 14.1 6.8 7.1 20.8 17.0 15.7N 57 184 73 85 207 112 718


4.4 Non-Nursing Tasks Despite the nurse’s reports of poor quality nursing care and the high percentage of nurses reporting that essential nursing tasks are not being completed or are delayed, nurses continue to report a high volume of tasks that could be delegated to non-nursing personnel. Table 29 shows how likely a nurse is to perform these tasks. Among eight non-nursing tasks listed in Table 29, nurses more likely reported “ancillary service,” “venipunctures,” “housekeeping”, “delivering trays,” and “starting IVs.” Each hospital varied in which tasks were more likely to be performed by the nurse. Hospital 1 had the largest proportion of nurses who reported “delivering trays”, “venipunctures,” and “performing ECGs.” Hospital 2 was more likely to mention “transporting patients” than the others. Hospital 3 had the largest proportion reporting “housekeeping” and “arranging transportation.” Hospital 5 was most likely to report “starting IVs.” Hospital 6 had the largest proportion reporting “ancillary service.”

Table 29: Percent of Nurses Reporting Performing Non-Nursing Tasks for Items in the Table, by Hospital

Non nursing tasks 1 2 3 4 5 6 Total Number of Cases 58 184 73 86 210 111 722 Ancillary service 87.9 82.6 90.4 89.5 74.3 91.0 83.5 Venipunctures 82.8 72.3 11.0 64.0 73.8 62.2 64.8 Delivering trays 72.4 34.2 54.8 51.2 65.2 64.9 55.1 Housekeeping 63.8 44.6 87.7 47.7 64.3 35.1 55.1 Starting IVs 67.2 17.4 63.0 62.8 71.0 43.2 51.0 Performing ECGs 51.7 15.8 9.6 37.2 50.0 48.6 35.6 Transporting patients 22.4 42.4 24.7 20.9 26.2 36.0 30.7 Arranging transportation 32.8 27.2 38.4 30.2 23.3 31.5 28.7 Note: Non-nursing tasks are sorted by percent in the Total column 5. Intermediate System Outputs Staffing information was collected by the site data collectors for each day of the study and for each nursing unit. Data were collected by shift, by skill level, and by employment status. Data about unit staffing, patient census, and unit daily workload are displayed in Tables 30-34.

5.1 Worked Hours Table 30 shows that the actual staffing hours varied from hospital to hospital and from unit to unit. In general, CCUs had a higher average daily worked hours and workload with the highest for CCU in Hospital 3 (329.3) and lowest (88.0) for the DS unit in Hospital 4. The IP unit in Hospital 4 averaged the highest number of agency worked hours (23.9). In contrast, Hospital 3 used no agency worked hours. The SDU in Hospital 6 averaged the highest number of overtime hours (6.7) and absent hours (27.4).


Table 30: Actual Staffing Hours, by Unit, by Day

Worked Hours All

Staff

Agency Worked Hours

Overtime Hours

Absent Hours

All GRASP Workload

Unit Type

N Mean (A)

N Mean (B)

N Mean (C)

N Mean (D)

N Mean (E)

Hospital 1 CCU 87 146.4 87 3.2 87 4.4 87 14.7 79 148.1 IP 117 160.2 117 3.5 117 1.4 117 8.4 103 149.6 SDU 87 104.9 87 0.0 87 1.3 87 5.4 81 101.1 Hospital 2 CCU 227 298.4 227 0.9 227 0.3 228 12.7 228 300.8 IP 122 206.0 122 5.2 122 0.8 122 9.1 122 213.5 Hospital 3 CCU 108 329.3 108 0.0 108 0.4 108 9.9 107 299.1 IP 112 264.5 112 0.0 112 5.4 112 5.6 112 200.7 Hospital 4 CCU 219 284.4 219 0.1 219 0.4 219 9.4 217 252.4 DS 109 88.0 109 0.1 110 0.1 109 4.8 107 77.2 IP 220 249.1 220 23.9 220 0.6 220 9.2 217 272.3 Hospital 5 CCU 200 203.5 200 0.1 200 2.7 200 7.4 200 171.9 DS 101 134.5 101 0.1 101 1.4 101 6.9 100 115.1 IP 229 143.4 228 2.3 228 0.7 229 6.3 226 141.0 Hospital 6 CCU 142 141.9 142 1.5 142 4.7 142 13.9 140 90.1 IP 62 136.2 62 1.7 62 3.6 62 13.2 60 88.6 SDU 19 222.9 18 7.6 18 6.7 18 27.4 19 107.1 Total 2161 206.1 2159 3.6 2160 1.6 2161 9.2 2118 190.9 Note: (1) N is the number of unit days. (2) Letters A, B, C, D, and E will be used in the following table. (3) GRASP hours are MEDICUS hours for Hospital 2. (3) Agency worked hours includes hours worked by agency nurses and/or agency non-nursing staff (e.g., sitters). The hospital workload measure, agency worked hours, overtime worked hours, and absent hours as a percent (or ratio) of total worked hours are displayed in Table 31. The hospital workload measure averaged 92.6% of total worked hours across all units and hospitals. Of 16 hospital units, four had ratios over 100% and three had ratios over 93%. Hospital 6 has the lowest ratio for all unit types (less than 65%). Units in Hospital 6, on the other hand, reported more overtime hours and absent hours than other units. Agency hours comprised 9.6% of worked hours on Hospital 4’s IP unit as compared to 3.4% or less for other units.


Table 31: Percent of Actual Staffing, by Unit, by Day

WL(GM)/WH Agency/WH Overtime/WH Absent/WH Unit Type (E/A) (B/A) (C/A) (D/A)

Hospital 1 CCU 101.2 2.2 3.0 10.0 IP 93.4 2.2 0.9 5.2 SDU 96.4 0.0 1.2 5.1 Hospital 2 CCU 100.8 0.3 0.1 4.3 IP 103.6 2.5 0.4 4.4 Hospital 3 CCU 90.8 0.0 0.1 3.0 IP 75.9 0.0 2.0 2.1 Hospital 4 CCU 88.7 0.0 0.1 3.3 DS 87.7 0.1 0.1 5.5 IP 109.3 9.6 0.2 3.7 Hospital 5 CCU 84.5 0.0 1.3 3.6 DS 85.6 0.1 1.0 5.1 IP 98.3 1.6 0.5 4.4 Hospital 6 CCU 63.5 1.1 3.3 9.8 IP 65.1 1.2 2.6 9.7 SDU 48.0 3.4 3.0 12.3 Total 92.6 1.7 0.8 4.5

Notes: (1) WH denotes worked hours. (2) See Table 30 for designations of A-E. (3) Agency worked hours includes hours worked by agency nurses and/or agency non-nursing staff (e.g., sitters). As shown in Table 32, the overall daily averages per unit were 19.8 patients on census, 6.1 admissions, and 6.1 discharges. Patient census, daily admission, and daily discharge, however, varied greatly by unit type and by hospital. For all hospitals, IP units averaged the most patients, ranging from 15.5 to 49.7 patients per day, and the most admissions and discharges, except in the case of DS units. The IP unit in Hospital 3 averaged the highest number of patients, admissions, and discharges per day. The IP unit for Hospital 2 also tended to average a higher number of patients. The lowest and highest mean censuses were found in Hospitals 1 and 3 respectively.


Table 32: Daily Patient Census, Admissions, and Discharges, by Unit

Midnight Census Daily Admissions Daily DischargesUnit Type N Mean N Mean N Mean

Hospital 1 CCU 87 10.0 87 2.7 87 2.7 IP 117 17.0 117 6.3 117 6.4 SDU 87 15.1 87 3.7 87 3.6 Hospital 2 CCU 228 15.5 228 4.5 228 4.6 IP 122 37.8 122 9.3 122 9.0 Hospital 3 CCU 108 16.2 108 6.6 108 6.6 IP 112 49.7 111 15.6 112 15.2 Hospital 4 CCU 219 10.4 219 3.4 219 3.4 DS 117 8.3 109 7.0 117 7.8 IP 221 34.5 221 6.5 221 6.7 Hospital 5 CCU 200 9.0 200 2.8 200 2.7 DS 101 12.6 101 7.6 101 7.4 IP 228 28.1 228 6.5 229 6.4 Hospital 6 CCU 142 10.3 142 4.0 142 4.0 IP 62 15.5 62 13.0 62 12.9 SDU 19 29.5 19 5.3 19 5.3 Total 2170 19.8 2161 6.1 2171 6.1

5.2 Productivity/Utilization Nursing unit productivity/utilization is measured as the ratio of GRASP/Medicus workload hours to worked hours. The maximum work capacity of any employee is 93%. Seven percent of worked hours are allocated to paid breaks during which time no workload is contractually expected. At 93% nurses are working flat out with no flexibility to meet unanticipated demands or rapidly changing patient acuity. This study hypothesized that a value of 85% is an appropriate productivity/utilization level to ensure high quality cost effective care. As shown in Table 33, in a large proportion of units, nurses were working beyond 93% productivity/utilization levels. On 61.5% of the study days, productivity/utilization levels were higher than 85%. Generally, IP units had higher productivity/utilization levels than CCUs, SDUs, and DS units. This finding is not unexpected since these units must staff differently to monitor patients. The productivity/utilization levels were lowest in Hospital 6. On 46.5% of the study days, productivity/utilization levels were higher than 93%. CCUs in Hospitals 1, 3, and 6 as well as IP units in Hospitals 2, 4, and 5 were most frequently above 93% productivity/utilization levels within their respective organizations.


Table 33: Number of Days When Unit GRASP/Medicus is Greater than 85% and 93% of Total Nurse Hours, by Unit Unit Type Number of Days

Reported % Days with WL(GM)

> 85% of Total Nursing Hours

% Days with WL(GM) > 93% of

Total Nursing Hours Hospital 1 CCU 79 79.7 62.0 IP 103 63.1 49.5 SDU 81 67.9 58.0 Hospital 2 CCU 225 60.0 51.1 IP 121 99.2 90.1 Hospital 3 CCU 107 60.7 48.6 IP 112 29.5 11.6 Hospital 4 CCU 216 67.1 35.2 DS 99 48.5 34.3 IP 216 96.8 88.0 Hospital 5 CCU 199 41.2 21.6 DS 100 51.0 37.0 IP 226 86.7 63.7 Hospital 6 CCU 140 13.6 9.3 IP 60 10.0 8.3 SDU 19 5.3 0.0 Total 2103 61.5 46.5 Note: (1) WL(GM) stands for workload measured as GRASP (in Hospital 1, 3, 4, 5, 6) or Medicus (in Hospital 2). (2) Differences by unit are statistically significant (χ2=744.9, df=23, p<0.01) Table 34 displays worked hours of nurses in the study hospital and in other employment. Nearly two thirds of surveyed nurses worked between 31 to 40 hours a week in the past year, which is typical of full-time employee work time. One quarter of surveyed nurses reported working hours between 21 and 30 hours. On average, only 3.8% of nurses worked less than 20 hours a week. In addition, 7% of nurses averaged over 40 worked hours per week. Nearly one in four nurses (22.5%) reported paid work outside the study hospital. Hospital 1 had the highest proportion of nurses working full-time (71.9%), whereas the proportion of nurses working part-time (i.e., less than 30 work hours per week) was highest in Hospital 2 (35.8%) and 5 (38.0%). Hospital 2 also had a relatively high proportion of nurses (8.4%) working less than 20 hours weekly in the study hospital. Nurses in Hospitals 3 and 4 are vulnerable to strenuous work conditions: nearly one in nine (11.8%) nurses in Hospital 3, and one in six (15.9%) nurses in Hospital 4 worked the most overtime. In addition, nurses in Hospital 4 were most likely to hold paid employment outside the study hospital. Some of this variation may be due to the available supply of employment opportunities in individual locations.


Table 34: Percent of Nurses Reporting Average Hours Worked Per Week in the Past Year, by Hospital

Hospital In this Hospital Other Employment N < 20

hrs 21-30 hrs 31-40 hrs > 40

hrs N > 10 hrs

1 57 1.8 19.3 71.9 7.0 15 6.7 2 179 8.4 27.4 59.8 4.5 60 21.6 3 69 1.4 24.6 58.0 15.9 21 28.5 4 85 2.4 16.5 69.4 11.8 20 35.0 5 208 4.8 33.2 57.7 4.3 44 27.2 6 107 1.9 24.3 67.3 6.5 26 11.4

Total 705 3.8 26.4 62.3 7.0 186 22.5 6. Environmental Complexity The Environmental Complexity Scale (ECS) captures nurses’ ratings of how daily unit factors influence their ability to provide required care for patients. Twenty-two ECS items were administered daily to all nurses working day shift on participating study units. Missing data were imputed using individual and unit means. Three subscales were constructed to capture different dimensions of nurses’ work complexity: (1) Re-Sequencing of Work in Response to Others; (2) Unanticipated Changes in Patient Acuity; and, (3) Composition and Characteristics of the Care Team which considers students, staffing, and nurse team functioning. The values range between 0 and 10. As shown in Table 35, the means for the three subscales (resequence, change, team) were 6.1 (SD=0.88), 6.5 (SD=1.04), and 5.7 (SD=1.06), respectively, for all study hospitals. The differences by unit for all subscales are statistically significant at p<0.001, but there are no clear patterns by hospital or by unit type.


Table 35: Mean of Three Subscales from ECS, by Hospital

Resequence Change Team Unit Type N* Mean SD Mean SD Mean SD

Hospital 1 CCU 519 5.9 0.75 6.6 1.17 5.8 1.14 IP 765 6.0 1.32 6.5 1.46 6.1 1.64 SDU 355 5.5 0.65 6.3 1.13 5.5 0.93 Hospital 2 CCU 2574 5.6 0.72 6.2 0.98 5.5 0.99 IP 1035 5.8 0.74 6.2 0.98 5.3 0.76 Hospital 3 CCU 438 6.1 0.97 6.7 1.26 5.3 0.81 IP 627 6.3 0.92 6.8 1.17 5.8 1.28 Hospital 4 CCU 531 5.7 0.92 6.7 1.18 5.6 1.58 DS 102 5.9 1.04 6.0 0.98 5.3 0.49 IP 242 6.1 0.81 6.9 1.05 6.3 1.02 Hospital 5 CCU 1673 6.8 0.52 6.7 0.77 5.7 0.46 DS 621 6.6 0.74 6.6 1.01 5.9 0.38 IP 1732 6.4 0.76 6.5 0.94 5.3 0.73 Hospital 6 CCU 827 6.3 0.66 6.7 0.80 6.6 1.09 IP 384 6.3 0.49 6.8 0.68 6.7 1.01 SDU 202 6.1 0.73 6.6 1.05 6.4 1.42 Total 12627 6.1 0.88 6.5 1.04 5.7 1.06 *N=Number of day entries by nurses. On average, 42.9% nurses on study units needed more time on their shift to provide the level of patient care specified in the nursing care plan. These results are presented by hospital unit in Table 36.

Table 36: Percent of Nurses Reporting Additional Time Needed to Provide Quality of Care, by Hospital Unit Unit Type

N*

no more time

needed % <15

min. % 15-30

min. % 31-45

min. % 46-60

min. % >60

min. Hospital 1 CCU 475 31.6 11.2 18.5 13.9 13.9 10.9 IP 702 28.5 15.8 27.4 14.1 7.0 7.3 SDU 304 30.3 5.3 28.6 13.8 9.2 12.8 Hospital 2 CCU 1976 60.7 5.1 12.9 7.9 6.1 7.3 IP 889 46.2 8.8 20.4 11.9 5.6 7.1 Hospital 3 CCU 393 49.6 7.9 16.0 8.9 9.2 8.4 IP 553 25.7 14.1 27.5 14.6 9.2 8.9 Hospital 4 CCU 478 55.9 5.0 12.8 8.4 9.0 9.0 DS 96 33.3 11.5 34.4 7.3 5.2 8.3 IP 217 27.6 3.7 21.2 17.1 14.7 15.7 Hospital 5 CCU 1655 85.1 0.8 6.3 3.3 3.3 1.2 DS 612 78.4 1.1 8.3 5.9 3.9 2.3 IP 1677 71.8 2.1 11.6 4.9 5.2 4.4 Hospital 6 CCU 576 51.9 3.3 16.3 13.0 8.3 7.1 IP 250 51.6 6.8 20.0 12.8 5.6 3.2 SDU 175 13.7 11.4 27.4 21.7 12.6 13.1 Total 11028 57.1 5.6 15.4 8.9 6.6 6.3 *N=Number of day entries by nurses.


7. Patient Outcomes Patient outcomes in this section include medical consequences (from the Patient Data Form) and patient health status change (from the Patient SF-12 Health Status Survey).

7.1 Medical Consequences Data on medical consequences were collected by data collection staff via health records, who recorded the number of negative events and medical complications patients experienced during their hospital stay. Negative events included an unexpected return to the Intensive Care Unit (ICU), a fall with injury, a readmission within three months, and a recorded complication in the health record. Medical complications tracked included urinary tract infection, pneumonia, deep or superficial surgical wound infection, bedsore, and thrombosis. As shown in Table 37, medical consequences were rare for all six hospitals, although these tended to be somewhat higher for Hospitals 4 and 2 than for other hospitals. Hospital 4 had the highest incidences of medical complications. No hospitals reported medical errors with patient consequences except for Hospital 1 (one case reported). Nearly one-third (32%) of patients have been hospitalized for the same condition in the past 3 months. The readmission rate is the highest in Hospital 1 (39%) and lowest in Hospital 6 (12%), where the nurses had low workload to worked hours ratio. In addition, Hospital 1 reported higher incidences in “returned to ICU post-op” than other hospitals.

Table 37: Medical Consequences – Percent Reporting Yes to the Items in the Table, by Hospital

Hospital 1 2 3 4 5 6 TotalNumber of cases 189 243 259 195 285 59 1230

Returned to ICU post-op 3.7 0.4 3.1 3.6 0.7 0.0 2.0

Falls with Injury 0.0 0.8 0.8 0.0 1.4 0.0 0.7

Urinary tract Infection 1.1 2.5 0.0 3.6 1.1 0.0 1.5

Pneumonia

2.1 2.1 0.4 2.6 0.4 0.0 1.3

Wound Infection 0.0 3.3 0.8 2.1 0.7 1.7 1.4

Bedsores

0.0 0.4 0.4 1.5 0.0 0.0 0.4

Thrombosis

0.0 0.0 0.0 1.0 0.0 0.0 0.2

Medication Errors with consequences 0.5 0.0 0.0 0.0 0.0 0.0 0.0

Admitted in past week with same diagnosis 1.6 1.6 1.2 6.0 0.4 1.9 1.6

Hospitalized for the same condition in the past 3 months

38.6

36.7

29.3

31.7

30.6

12.3

32.0


7.2 OMAHA Scores at Discharge and Change from Admission Patient OMAHA scores at admission (or appearance of new diagnosis) were presented earlier in Table 12. Table 38 shows the mean OMAHA scores at time 2. For all study patients, the means were 3.8 (SD=0.67) for knowledge, 4.2 (SD=0.52) for behaviour, and 4.1 (SD=0.62) for status. Patients from Hospital 2 had the highest scores across all three dimensions. Patients from Hospital 1 ranked low for all dimensions at time 2, probably due to low scores at admission.

Table 38: OMAHA Scores at Time 2 (Resolution of Diagnosis or at Discharge), by Hospital


1 188 3.6 0.55 3.9 0.37 3.8 0.57 2 237 4.1 0.72 4.5 0.47 4.3 0.53 3 232 3.8 0.39 4.1 0.39 4.2 0.56 4 192 4.0 0.45 4.4 0.49 4.3 0.48 5 282 3.9 0.75 4.3 0.61 3.9 0.69 6 58 2.8 0.63 4.3 0.45 3.6 0.63

Total 1189 3.8 0.67 4.2 0.52 4.1 0.62 Table 39 shows the mean change scores for each hospital for knowledge, behaviour, and status between time 1 (at admission or appearance of new diagnosis) and time 2 (at discharge or resolution of diagnosis).

Table 39: Differences in OMAHA Scores Between Time 1 and Time 2, by Hospital


1 188 0.63 0.59 0.16 0.35 0.77 0.59 2 237 0.58 0.57 0.45 0.42 0.68 0.53 3 232 0.79 0.55 0.48 0.50 1.16 0.67 4 192 0.25 0.41 0.13 0.46 0.42 0.52 5 282 0.07 0.29 0.08 0.34 0.98 0.77 6 58 0.14 0.26 0.07 0.23 0.12 0.33

Total 1189 0.43 0.55 0.25 0.44 0.79 0.68

7.3 Health Status at Discharge and Change from Admission Patient physical and mental functional status at admission was presented earlier in Tables 13 and 14. Tables 40 and 41 show the changes in patient functional status from admission to discharge. As can be seen, more than 40% of patients had improved physical and mental health at discharge (41% for physical health and 42% for mental health) and about 45% of patients had physical and mental scores at discharge lower than at admission. For patients who reported an improvement in health, Hospital 1 had the highest change score among all hospitals (9.39 for physical health and 10.81 for mental health). For patients who had a decrease in the score, Hospital 3 had the smallest change (-7.37 for physical health and -4.53 for mental health).


Table 40: Change in Patient Physical Health Status (SF-12) from Admission to Discharge

Increased Decreased No Change Hospital

N Mean (SD) N Mean (SD) N

1 64 9.39 (7.72) 83 -9.57 (7.99) 15 2 53 6.19 (5.72) 96 -10.69 (9.00) 12 3 116 7.06 (5.71) 71 -7.37 (7.22) 62 4 64 8.41 (7.48) 81 -10.05 (8.14) 6 5 102 7.63 (6.87) 116 -8.77 (7.17) 28 6 15 7.13 (5.32) 18 -7.44 (6.24) 6

All hospitals 414 7.66 (6.65) 465 -9.27 (7.91) 129 Percent 41.1% 46.1% 12.8%

Table 41: Change in Patient Mental Health Status (SF-12) from Admission to Discharge

Increased Decreased No Change Hospital

N Mean (SD) N Mean (SD) N

1 61 10.81 (9.82) 86 -8.81 (6.78) 15 2 68 8.78 (8.77) 81 -8.27 (6.75) 12 3 112 6.31 (7.09) 75 -4.53 (5.41) 62 4 77 8.70 (5.90) 68 -7.66 (6.33) 6 5 90 7.20 (7.01) 128 -8.93 (7.65) 28 6 18 5.66 (3.72) 15 -10.60 (8.25) 6

All hospitals 426 7.94 (7.64) 453 -7.93 (6.97) 129 Percent 42.3% 44.9% 12.8% 8. Nurse Outcomes This section presents results related to nurse burnout, job satisfaction, absenteeism, and intent to leave.

8.1 Burnout and Effort & Reward Imbalance Maslach’s Burnout Inventory (MBI) and the Effort-Reward Imbalance (ERI) questionnaire were included in the Nurse Survey to measure burnout. The MBI is a 22-item scale with three subscales: emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). A seven point categorical scale ranging from “never,” “a few times a year or less” to “everyday” is used to examine nurse’s feelings about their work and patients. Items are split amongst the EE (9 items), DP (5 items), and PA (8 items) subscales. Table 42 displays the means and standard deviations. The highest MBI subscale scores amongst Hospitals 3, 1, and 5 were associated with the three highest proportions of nurses who ranked below the US norms for mental health for females (Table 48). Table 42 shows nurses who worked at Hospital 3 averaged the worst job-related feelings of emotional exhaustion, while those at Hospital 1 scored highest on depersonalization aspects. The


nurses who worked at Hospital 5 were least likely to claim that they had personal accomplishments.

Table 42: Burnout – Mean Scores of MBI Subscales, by Hospital

EE DP PA Hospital N Mean SD N Mean SD N Mean SD

1 58 25.0 12.1 58 7.6 6.4 56 10.9 7.02 171 20.8 9.6 173 5.5 5.3 163 12.2 7.13 71 26.3 10.8 74 7.1 5.6 70 12.5 5.94 82 20.6 10.2 79 4.6 4.4 82 10.8 5.85 199 23.1 10.2 205 5.9 5.1 205 13.3 7.06 108 23.3 10.5 108 6.0 4.9 101 11.6 6.8

Total 689 22.7 10.5 697 6.0 5.2 677 12.2 6.8Note: EE=Emotional Exhaustion Index DP=Depersonalization Index PA=Personal Accomplishment Index Difference by hospital is statistically significant at p<0.05 for all three subscales. The Effort-Reward Imbalance (ERI) scale, a 17-item scale that measures the balance between nurses’ efforts and their rewards, was also used to assess burnout. The ERI uses a four point scale to measure the extent of stress, from “I am not at all distressed” to “I am very distressed.” The greater the ERI score, the greater the individual’s distress. To determine which proportions of nurses were at risk for ERI or MBI emotional exhaustion, the ERI and MBI (EE) variables were dichotomized. The ERI already has values of 0 or 1, while the MBI (EE) was dichotomized using values greater than 27 as per instrument guidelines. The proportion of nurses at risk is displayed in Table 43. Nurses in Hospital 3 were most at risk for emotional exhaustion (43.7% vs. 22.2%-36.2%). These results are consistent with the MBI EE scores in Table 42, as well as the mental health data in Table 48 where Hospital 3 had the largest proportion of nurses whose mental health was below the US population norm. Nurses in Hospital 3 were also more likely to feel imbalances in effort and reward than nurses in other hospitals (25.8% vs. 10%-22%).

Table 43: Burnout – Percent of Nurses at Risk for Emotional Exhaustion and ERI, by Hospital

Hospital N % at Risk for MBI_EE

N % at Risk for ERI

1 58 36.2 50 22.0 2 171 22.2 141 17.7 3 71 43.7 62 25.8 4 82 25.6 70 10.0 5 199 29.6 166 16.3 6 108 33.3 88 20.5

Total 689 29.9 577 18 Note: ERI=Effort-Reward Imbalance (>1, at risk) MBI_EE=Maslach’s Burnout Inventory-Emotional Exhaustion (>27, at risk) Difference by hospital is not significant (χ2=6.8, df=5, p>.05)


8.2 Autonomy and Control Table 44 shows the subscale response from the Revised Nursing Work Index (R-NWI). The NWI measures nurses’ job satisfaction and perception of quality of care. For each of the 41 items in the survey, the nurses responded on a 4-point forced choice Likert scale, with choice ranging from 1=strongly disagree to 4=strongly agree. No neutral response options are provided. Five subscales were created and listed by hospital. Higher scores reflect better working conditions and job satisfaction. Hospital 4 showed the best scores for all five subscales: autonomy, control over patient care, nurse-physician (NP) relationship, leadership, and resources available.

Table 44: Nurse Work Index Subscales, by Hospital

Autonomy Control NP Relation Leadership Resource Hospital N Mean

(SD) N Mean

(SD) N Mean

(SD) N Mean

(SD) N Mean

(SD) 1 55 16.2

(3.28) 58 17.0

(3.31) 59 7.4

(2.15) 52 28.2

(5.54) 58 9.7

(1.44) 2 173 16.4

(2.83) 181 18.5

(3.71) 182 8.6

(1.76) 167 30.1

(5.72) 184 10.2

(1.37) 3 69 15.2

(3.23) 73 18.1

(3.80) 73 8.1

(2.01) 69 28.7

(5.32) 73 9.5

(1.57) 4 81 17.6

(3.06) 83 20.6

(4.22) 85 9.2

(1.86) 79 32.7

(6.26) 84 10.4

(1.74) 5 203 15.6

(3.11) 204 19.0

(3.98) 208 8.1

(1.99) 201 27.5

(5.65) 206 10.0

(1.51) 6 101 14.7

(3.18) 108 17.5

(3.42) 110 8.4

(1.92) 104 26.2

(6.26) 108 9.9

(1.34) Total 682 15.9

(3.18) 707 18.6

(3.81) 717 8.4

(1.97) 672 28.8

(6.09) 713 10.0

(1.49) Note: Difference by hospital is statistically different at p<0.002 for all nurse work indices.

8.3 Job Satisfaction Table 45 shows the percent of dissatisfied nurses for each hospital. On average, 17.0% of the nurses were dissatisfied with their current jobs. Variation in dissatisfaction in present job across hospitals was minimal with the exceptions of Hospitals 1 (28.9%) and 4 (9.3%). Satisfaction with current job was only moderately correlated with satisfaction of being a nurse (r=0.474, p<0.001). In five hospitals, 37.6-49.0% of the nurses were dissatisfied with their opportunities to interact with management. The one notable exception was Hospital 6 with over 63% of nurses reporting this issue. In four of the six hospitals only 15.3-22.7% of nurses were dissatisfied with the amount of responsibility. Hospitals 1 and 3 reported notably higher scores at 43.9% and 42.3% respectively. In five hospitals, 11.0-15.1% of nurses were dissatisfied with opportunities for social contact at work and 19.2-27.9% with opportunities for social contact after work. The one exception was Hospital 2 where nurses appeared more satisfied with social opportunities.


Table 45: Job Satisfaction – Percent of Nurses Dissatisfied, by Hospital

Hospital 1 2 3 4 5 6 Total Opportunities to interact w/ Mgmt 40.4 37.9 40.8 37.6 49.0 63.6 45.5

Amount of Responsibility 43.9 18.7 42.3 15.3 19.8 22.7 23.6

Social Contact - after work 26.3 16.6 19.2 27.9 20.2 22.0 20.9

Satisfaction - present job 28.6 14.1 19.7 9.3 18.2 18.2 17.0

Satisfaction - nurse 21.1 9.2 21.9 10.5 20.2 13.6 15.4

Social Contact - work 12.3 4.9 13.9 15.1 11.0 13.6 10.7

Number of Cases 57 181 73 86 208 109 714

Note: (1) Items are sorted by percent in the Total column. (2) Number of valid responses varied slightly from item to item.

8.4 Health Status Table 47 shows the physical and mental health levels for nurses in each of the hospitals as measured by the SF-12. The average physical health score for nurses was 50.1, slightly above the US norm for females in the general population (49.11). However, the average mental health score (47.3) was below the US female norm (49.42). Hospital 4 had the highest mean physical health scores (53.0), and Hospital 6 had a slightly better mean mental health score than those of others hospitals (48.2). Since the SF-12 questionnaire was completed by both patients and nurses, comparisons are possible. The overall average mental health score for nurses (47.3) was lower than the average scores for patients, either at admission (48.2) or discharge (48.1) as noted in Tables 13 and 15.

Table 46: Nurse Health Status, by Hospital


1 58 50.7 8.9 15.3-62.2 45.1 12.2 23.7-63.92 178 50.3 8.7 17.6-65.2 48.1 10.0 16.3-64.13 73 48.9 10.2 21.6-64.7 45.9 10.5 18.9-63.84 80 53.0 5.3 36.9-56.0 47.9 9.9 18.7-62.15 204 49.1 8.6 22.6-65.4 47.1 10.5 17.4-62.66 106 50.2 8.1 18.9-64.6 48.2 10.4 20.7-63.8

Total 699 50.1 8.5 15.3-65.4 47.3 10.4 16.3-64.1 Table 48 gives the proportion of nurses in each hospital that scored below the norm for females in the general US population, and thus was not considered healthy. Only 34.8% of nurses scored below the population norm, indicating that, in general, nurses are physically healthier than the general US female population. Nearly half of nurses (49.2%) were categorized as not mentally


healthy, which was comparable to the categorization of patients at admission (49.2%) and discharge (51.9%) as noted in Tables 14 and 16.

Table 47: Nurse Health Status, Percent of SF-12 Scores Less than US Norm for Females, by Hospital

Hospital N % Not Physically Healthy


1 58 31.0 53.4 2 178 34.3 46.1 3 73 38.4 54.8 4 80 20.0 47.5 5 204 39.7 52.0 6 106 36.8 44.3

Total 699 34.8 49.2 Norm for Female3 Physically not healthy: Physical Health Scale < 49.11 Mentally not healthy: Mental Health Scale < 49.42

8.5 Violence at Work Table 49 presents the prevalence of violence and abuse towards nurses. Among 720 nurses completing the survey, 24.9% experienced emotional abuse, 13.6% experienced the threat of assault, and 10.2% experienced physical assault. Hospital 6 reported the highest proportion of emotional abuse and threats of assault. Hospital 1 reported the highest percentage of physical assaults. The smallest proportion of nurses who reported emotional abuse and threats of assault were in Hospital 4. More than 97% of physical assault or threats of assault were from patients (data not shown).

Table 48: Prevalence of Violence – Percent of Nurses Reporting Yes to the Items in the Table, by Hospital

Hospital 1 2 3 4 5 6 TotalPhysical Assault 3.4 10.8 11.0 2.4 13.8 11.8 10.2Threats of Assault 8.5 15.7 13.9 5.9 13.9 18.2 13.6Emotional Abuse 25.4 17.4 27.4 25.9 26.8 30.9 24.9Number of cases 59 184 73 85 209 110 720Note: Statistically significant for physical assault (χ2=12.1, df=5, p<0.05), but not for threat assault or emotional abuse. The sources of emotional abuse varied as evidenced in Table 50. The major source of emotional abuse was from patients (31.1%), followed by abuse from other nurses (21.5%), and then from physicians or patients’ families.


Table 49: Source of Emotional Abuse, by Hospital

Hospital 1 2 3 4 5 6 TotalPatient 33.3 28.1 26.3 22.7 33.9 36.4 31.1Family 0.0 12.5 15.8 13.6 10.7 9.1 10.7MD 0.0 12.5 26.3 13.6 19.6 15.2 15.8Nurse 26.7 28.1 10.5 36.4 17.7 15.2 21.5Any two combined 33.3 12.5 15.8 13.6 7.1 18.2 14.1Any three combined 6.7 3.1 5.3 0.0 7.1 3.0 4.5Any four combined 0.0 3.1 0.0 0.0 3.6 0.0 1.7Number of cases 15 32 19 22 56 32 176 9. System Outcomes

9.1 Quality of Care Table 53 presents the quality of care and likelihood that tasks are delayed or accomplished. Quality of nursing care is shown as the percent of nurses reporting “fair/poor care delivered” to patients; quality of patient care is shown as the percent of nurses reporting “quality of care deteriorated.” Hospital 1 reports an overall high level of tasks not done or delayed whereas Hospital 4 tends to report low levels in almost all categories. When faced with a shortage of time, different decisions appear to be made about which tasks can be left undone or delayed. Nurses are more likely to complete tasks that are ordered by physicians such as vital signs, medication, and dressings. We hypothesize that delayed actions can also have negative consequences in terms of patient clinical outcomes, patient satisfaction, and system costs.


Table 50: Quality Issues – Percent of Nurses Reporting Yes to Items in the Table, by Hospital

Hospital 1 2 3 4 5 6 Total Quality of Care Number of Cases 56 182 73 85 209 109 714Nursing Care: Fair/Poor Care Delivered 31.0 10.9 20.5 10.6 8.1 15.5 13.4Patient Care: Quality of Care Deteriorated 58.9 40.1 61.6 15.3 39.7 47.7 41.9Not Done Number of Cases 58 184 73 86 210 111 722Care Plan* 84.6 64.9 57.6 37.6 42.9 51.8 48.2Comforting/Talking 62.1 34.8 45.2 25.6 35.7 44.1 38.6Back/Skin Care 43.1 27.2 31.5 23.3 32.9 36.0 31.4Oral Hygiene 60.3 41.8 31.5 12.8 17.1 22.5 28.7Pt/Family Teaching 46.6 18.6 26.0 18.6 20.0 27.0 23.3Documentation 42.1 31.5 28.8 15.1 11.4 20.7 22.6Mobilization/Turns 19.0 13.0 13.7 9.3 6.2 27.0 13.3Discharge Prep 25.9 13.7 12.3 7.0 10.5 4.5 11.4VS/Meds/Dressings 6.9 5.4 2.7 3.5 1.0 7.2 4.0Mean # Tasks Not Done 3.19 1.97 2.35 1.52 1.78 2.38 2.06Delayed Number of Cases 58 184 73 86 210 111 722VS/Meds/Dressings 51.7 40.8 47.9 24.4 28.6 43.2 37.3Mobilization/Turns 17.2 40.2 35.6 20.9 27.1 31.5 30.5Call bell Response 53.4 20.7 30.1 19.8 25.2 23.4 25.9PRN pain meds 37.9 13.9 31.5 14.0 9.0 17.1 16.6# Tasks Delayed 1.58 1.14 1.43 .79 .90 1.14 1.09*For care plan, the valid number of cases is low for Hospitals 1 (N=13), 2 (N=37) and 3 (N=59). Note: Items are sorted by percent in the Total column

9.1 Absenteeism The Nurse Survey asked: “In the past year: a) On how many occasions (episodes) have you missed work due to illness/disability? b) How many shifts have been missed due to illness/disability?” Table 51 shows that the total number of missed work episodes from the six survey hospitals was 1,768. Individual nurses missed from 1 to 56 episodes (data not shown). Of the 683 nurses who reported missed occasions in the past year, 42.9% missed 1-2 episodes, 25.2% missed 3-4 episodes, and 15.5% missed more than four episodes. Another 16% indicated nil work episodes missed. Once the number of episodes was grouped into four categories, the pattern of distribution varied amongst hospitals. Hospitals 6, 2, 3, and 1 had the largest proportions in order of ascending categories from “0 episodes” to “>4 episodes”. On average, Hospital 6 averaged the highest missed shifts per episode (3.71; SD=7.7) shifts.


Table 51: Absenteeism – Percent of Episodes Absent and Mean Shifts per Episode in the Past Year, by Hospital

Hospital Total # episodes

N % 0episodes

% 1-2 episodes

% 3-4 episodes

% > 4 episodes

N Mean (SD) shifts/episode

1 226 57 8.8 31.6 24.6 35.1 49 1.61 (0.90)2 382 174 14.4 53.4 24.1 8.0 145 2.47 (4.58)3 172 71 19.7 32.4 35.2 12.7 56 2.31 (2.77)4 252 81 18.5 45.7 22.2 13.6 57 1.39 (0.61)5 513 193 14.5 41.5 22.8 21.2 162 2.41 (5.32)6 225 107 23.4 39.3 27.1 10.3 78 3.71 (7.70)

Total 1768 683 16.4 42.9 25.2 15.5 547 2.42 (4.85) Table 52 presents the common reasons for nurse absenteeism. The primary reason was physical illness (71.4%). Other reasons were mental health days and injury. Nurses at Hospitals 2 and 3 indicated physical illness most frequently, while Hospital 1 nurses tended to report mental health day, injury, and other.

Table 52: Absenteeism – Most Common Reason to Miss Work in the Past Year, by Hospital

Hospital N % Physical illness

% Mental health day

% Injury % Other

1 56 50.0 12.5 8.9 28.6 2 167 77.8 5.4 4.8 12.0 3 63 77.8 4.8 3.2 14.3 4 73 72.6 4.1 2.7 20.5 5 194 73.2 4.1 4.1 18.6 6 94 63.8 5.3 6.4 24.5

Total 647 71.4 5.4 4.8 18.4

9.2 Intent to Leave Table 46 shows the percent of nurses who expected to lose their job or intended to leave within the next 6 months. Hospital 1 nurses not only reported the highest levels of dissatisfaction with their current job (Table 45), but also the highest levels of intent to leave. Surprisingly, nurses in this hospital were most likely to expect difficulties in finding a new job. Hospital 5 had the highest percent of nurses expecting to lose their jobs, while not a single nurse in Hospital 1 expected to lose theirs in the next 12 months.

Table 53: Intent to Leave – Percent of Nurses Reporting Yes to the Items in the Table, by Hospital

Hospital 1 2 3 4 5 6 Total Number of Cases 58 186 74 86 207 110 721 Expect to lose job 0 1.6 2.7 3.5 12.1 5.5 5.4 Plan to Leave – 6 months 10.3 3.8 6.8 3.5 5.3 3.6 5.0 Difficult finding job 14.0 1.6 2.8 2.3 10.6 3.6 5.7


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6. Chagnon, M., Audette, L. M., Lebrun, L., & Tilquin, C. (1978). Validation of a patient classification through evaluation of the nursing staff degree of occupation. Medical Care, 16(6), 465-475.

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Evidence Based Standards for Measuring Nurse Staffing and

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