ORIGINAL RESEARCH: EMPIRICAL RESEARCH – QUANTITATIVE
Identifying the key predictors for retention in critical care nurses Jo-Ann V. Sawatzky, Carol L. Enns & Carol Legare Accepted for publication 29 April 2015
Correspondence to J.V. Sawatzky: e-mail: [email protected] Jo-Ann V. Sawatzky PhD RN Associate Professor, Associate Dean, Graduate Programs Faculty of Nursing, University of Manitoba, Winnipeg, Manitoba, Canada Carol L. Enns MN RN Senior Instructor, Director, Clinical Education Programs Faculty of Nursing, University of Manitoba, Winnipeg, Manitoba, Canada Carol Legare MN ENC(C) RN Director of Patient Services Adult Emergency, Health Sciences Centre, Winnipeg, Manitoba, Canada
S A W A T Z K Y J . V . , E N N S C . L . & L E G A R E C . ( 2 0 1 5 ) Identifying the key predictors for retention in critical care nurses. Journal of Advanced Nursing 71(10), 2315– 2325. doi: 10.1111/jan.12701
Abstract Aims. The aim of this study was to explore the key predictors of retention in nurses working in critical care areas. Background. The shortage of critical care nurses is reaching crisis proportions in Canada and throughout the industrialized world. Identifying the key influencing (i.e. person and organizational) factors and intermediary factors (i.e. job satisfaction, engagement, professional quality of life and caring) that affect intent to leave is central to developing optimal retention strategies for critical care nurses. Design. As part of a larger mixed-methods study, we used a quantitative, crosssectional research design. A novel framework: the Conceptual Framework for Predicting Nurse Retention was used to guide this study. Methods. On-line survey data were collected from on a convenience sample of 188 registered nurses working in critical care areas of hospitals in the province of Manitoba, CANADA in 2011. Results. Twenty-four per cent of the respondents reported that they would probably/definitely leave critical care in the next year. Based on bivariate and regression analyses, the key influencing factors that were significantly related to the intermediary factors and intent to leave critical care and nursing included: professional practice, management, physician/nurse collaboration, nurse competence, control/responsibility and autonomy. Of the intermediary factors, all but compassion satisfaction were related to intent to leave both critical care and nursing. Conclusion. This study highlights the importance of exploring multiple organizational and intermediary factors to determine strategies to retain critical care nurses. The findings also support the Conceptual Framework for Predicting Nurse Retention as a theoretical basis for further research. Keywords: critical care, intensive care, intent to leave, job satisfaction, nurses, nursing, retention
© 2015 John Wiley & Sons Ltd
2315
J.V. Sawatzky et al.
Why is this research needed? The availability of critical nurses is threatened by a changing demographical and the physical and emotional stressors associated with this work environment There has been a lack of research that has examined influencing (organizational, personal) and intermediary (job satisfaction, professional quality of life, engagement and caring) factors in this population.
What are the key findings? While several organizational factors influence intent to leave, control/responsibility appears to be a central influencing factor in predicting nurse retention in critical care. The intermediary factors of job satisfaction, engagement, compassion satisfaction and burnout are convincing predictors of intent to leave critical care and nursing overall.
How should the findings be used to influence policy/ practice/research/education? Nursing leaders should develop retention strategies that include addressing management issues, ongoing professional development and organizational structures to empower critical care nurses. The findings support the use of a novel framework: the Conceptual Framework for Predicting Nurse Retention, as the theoretical basis for future research.
Introduction The ever-increasing shortage of nurses is one of the most critical issues in the healthcare sector today. Based on employment projections, the US Bureau of Labor Statistics (2014) anticipates the total number of job vacancies for nurses will increase by 19% or greater than 500,000 vacancies by 2022. In Canada, the Canadian Nurses Association (CNA 2002) predicts a shortage of 113,000 registered nurses (RNs) by 2016. According to a recent systematic review of the literature (Lu et al. 2012), this looming crisis is a global issue, affecting developed and developing countries around the world. Nowhere is this crisis more prominent than in specialty areas, such as critical care (CC; Buerhaus et al. 2000, Odom 2000, Andrews & Dziegielewski 2005, Davis et al. 2007, O-Brien-Pallas et al. 2008, Chan & Lai 2010). Nursing shortages have a negative impact on every aspect of healthcare delivery; research evidence supports the contention that economic, patient and nursing outcomes are affected by high nursing turnover (Strachota et al. 2003, O’Brien-Pallas et al. 2006, Davis et al. 2007, Chan & Lai 2316
2010). Economic costs include such factors as the initial lower productivity in new employees and the decrease in staff morale and productivity caused by the turnover. Patient outcomes may also be compromised as a result of higher patient to nurse ratios (Aiken et al. 2002). For example, based on regression analysis, a recent study in the USA reported a statistically significant (P < 0001) association between understaffing and the risk of nosocomial infections in 67 Neonatal Intensive Care Units (NICUs; Rogowski et al. 2013). Finally, specific to nursing outcomes, poor job satisfaction and high levels of burnout are among the consequences of nursing shortages and turnover rates in critical care (Aiken et al. 2002, Stone et al. 2006). While the need for research on CC nursing staffing has been identified as a priority (Canadian Association of Critical Care Nurses, 2010, Fitzpatrick et al. 2010), there is still a lack of research in this area; Canadian research, in particular is lacking. Studies completed in the USA and abroad may not be relevant to the unique aspects of Canada’s healthcare system. Unlike the USA, Canada’s national health insurance programme, often referred to as ‘Medicare,’ is designed to provide all Canadians with free and reasonable access to medical care. As well, in the USA, nursing unions are generally the exception rather than the norm. While unions do ensure fairness to the masses, they can also impose restrictions related to the development of creative, individualized staffing strategies. Therefore, determining the key predictors of critical care nursing retention and intent to leave will facilitate the development of specific leadership strategies to retain CC nurses and optimize patient outcomes.
Background The shortage of nurses has been an ongoing issue for many decades. In fact, as early as 1954, Roberta Spohn, the Assistant Executive Secretary of the American Nurses’ Association, Research and Statistics Unit, voiced concern regarding nursing’s ‘dubious distinction’ of continually suffering from manpower shortages (Spohn 1954, p. 865). Despite shortlived periods of stability and ongoing efforts to ameliorate the problem, the nursing shortage continues to reign as one of the most statistically significant issues in health care today. Although all areas of nursing are affected by this crisis, critical care areas are especially vulnerable to recruitment and retention issues. Critical care areas and intensive care units (ICUs) in particular, tend to attract younger RNs, with almost 80% of ICU nurses reportedly being under the age of 40 (Buerhaus et al. 2000). However, according to a Canadian Institute of © 2015 John Wiley & Sons Ltd
JAN: ORIGINAL RESEARCH: EMPIRICAL RESEARCH – QUANTITATIVE
Health Information (CIHI 2011) study, in 2010, the average age of RNs in Canada was 454% and 255% of all RNs were over the age of 55. Similarly, the average age of employed RNs in the USA in 2013 was 442, with 24% aged 55 or older (US Bureau of Labor Statistics, 2013). This ageing cohort of nurses will play a major role in the projected overall shortage of 260,000 RNs in the USA by 2025 (Buerhaus et al. 2009). Although the challenge and excitement of critical care areas may attract the younger RN, the unremitting emotional and physical demands in this work environment tend to be a major barrier to retention. Moreover, there is emerging evidence that the younger Generation X and Y nurses, born between 1960–1980 and 1976–2000 respectively, have higher turnover intentions than their older Baby Boomer counterparts (born between 1943–1960; Brunetto et al. 2013). To date, turnover intention, or intent to leave (ITL) research has focused on the general nursing population (Tzeng 2002, Sourdiff 2004, Lynn & Redman 2005, O’Brien-Pallas et al. 2010, Brunetto et al. 2013). However, there is a lack of published research on ITL in critical care nurses (Stone et al. 2006, van Dam et al. 2013). Although Stone and associates explored organizational and personal predictors of ITL in a large sample of ICU nurses (N = 2323), potential intermediary factors were not explored. van Dam et al.’s survey of Dutch ICU nurses (N = 461) elicited insight into the individual and contextual factors related to perceptions of work pressure and turnover; however, organizational factors were not included in their study. Therefore, we explored the key influencing (i.e. organizational and personal) and intermediary (i.e. job satisfaction, engagement, professional quality of life and caring) predictors of retention in critical care nurses in central Canada.
Study framework The organizational framework for this study was an investigator developed Conceptual Framework for Predicting Nurse Retention (CFPNR; see Figure 1). This framework represents the theoretical links between influencing factors, intermediating factors and the outcome of intent to leave (ITL). The CFPNR is based on research based evidence, and several existing empirical models related to predictors of nurse retention (Price & Mueller 1981, O’Brien-Pallas et al. 2001, Tzeng 2002, Larrabee et al. 2003). Influencing factors include the organizational climate, and personal/ demographical considerations. These factors may predict ITL either directly or indirectly by their effect on the intermediary factors. The intermediary factors, including job satisfaction, professional quality of life (i.e. compassion © 2015 John Wiley & Sons Ltd
Retention in critical care nurses
satisfaction, compassion fatigue and burnout) and caring, are influenced by the individual’s organizational climate and personal/demographical factors and as such, may intercede with the influencing factors or directly impact one’s ITL. The CFPNR has been described in further detail elsewhere (Sawatzky & Enns 2012).
The study Aim The primary aim of this study was to explore and describe the factors that predict the retention of nurses working in CC areas, in the context of the CFPNR. Our overall goal was to establish research-based evidence that can be used to develop ongoing strategies for the retention of CC nurses in acute care facilities. The insights gleaned from this research may also be applicable to other areas of nursing and in other parts of Canada and the industrialized world.
Design As part of a larger mix-methods study, a quantitative, cross-sectional survey was used to achieve the aims and goals of this research.
Sample/setting The study sample was drawn from the population of nurses who worked in critical care areas in the province of Manitoba, Canada. According to the provincial regulatory body, the College of Registered Nurses of Manitoba (CRNM), there were 893 self-reported CC nurses residing in this province in 2011; 778 had submitted e-mail addresses to the CRNM. Inclusion criteria were: general duty RNs, Clinical Resource Nurses (CRNs) and educators who were currently employed in a CC area. Exclusion criteria were: senior CC managers and nurses who were employed in critical care on a casual basis. The setting for the study included all hospital based CC areas in the province: two urban tertiary care hospitals; five urban community hospitals and five rural facilities with designated critical care areas/intensive care units.
Data collection Invitations to participate in the study, and the consent form and the questionnaire package, were sent to all potential participants via the web-based SurveyMonkeyâ. This Inter2317
J.V. Sawatzky et al.
Influencing factors
Intermediary factors
Outcomes
Organizational Climate: *Professional practice *Staffing & resources *Nursing management *Nurse/physician collaboration *Nursing competence/expertise *Control/responsibility *Positive scheduling climate
Job Satisfaction Engagement Professional quality of life: *Compassion satisfaction
Intent to leave
*Compassion fatigue *Burnout Caring
Person factors: *Demographics
Figure 1 Conceptual framework for predicting nurse retention. net dissemination process, and sending two reminder letters to non-responders, was completed by the CRNM in March and April of 2011.
Ethical considerations Ethical guidelines, as outlined by the university ethics review board were followed. The return of the completed questionnaire was regarded as consent to participate in the study. The protocol for ensuring informed consent included the provision of written information to each potential participant. Voluntary participation of subjects in the study was established/reinforced on the consent form. There were no risks to participating in this study. Steps were taken to ensure participant confidentiality: The CRNM did not have access to the SurveyMonkeyâ data and the research team did not have access to the identity of potential or actual participants. Participants had the option to receive a summary report of the study findings. 2318
Validity and reliability The Critical Care Nurse Retention Survey was designed to operationalize the concepts in the CFPNR. Accordingly, we used existing tools, with well-established validity and reliability, to measure each of the influencing and intermediary factors, and the primary outcome of intent to leave. Influencing factor measures According to Choi et al. (2004), the 42-item Perceived Nurse Working Environment (PNWE) scale captures key dimensions of the nursing organizational climate including, leadership, communication, structural attributes and quality of work life in the context of each of the seven sub-scales. Likert scale options range from 1 (strongly agree)-5 (strongly disagree) for each statements/item, in the categories of: professional practice (13 items; e.g. Nursing staff is supported in pursuing degrees in nursing), staffing and resource adequacy (five items; e.g. There are enough RNs © 2015 John Wiley & Sons Ltd
JAN: ORIGINAL RESEARCH: EMPIRICAL RESEARCH – QUANTITATIVE
on staff to provide quality patient care), nursing management (five items; e.g. The nurse manager is a good manager and leader), nurse/physician collaboration (four items; e.g. Physicians and nurses have good working relationships), nursing competence (six items; e.g. I work with nurses who are clinically competent), positive scheduling environment (three items; e.g. Flexible or modified work schedules are available). Based on consultation with several critical care experts, the seventh sub-scale, related to the nursing process (six items), was deemed invalid and therefore removed. We were able to remove this subscale because the PNWE scale is designed to use subscale scoring; as well, original pairwise Pearson correlation coefficient analysis supported sufficient independence of the subscales (r < 060; Choi et al. 2004). The PNWE scale was initially tested on a large sample of critical care nurses (N = 2324) and has been used in a variety of settings. According to Choi and associates, this measure exhibits sound psychometric properties. Chronbach’s alphas between 056-091 lend support for internal consistency of the PNWE scale. One subscale of the McCloskey Mueller Satisfaction Scale (MMSS; Mueller & McCloskey 1990) was used to measure control/responsibility. The MMSS is a reliable and valid measure of nurses’ job satisfaction (Mueller & McCloskey 1990). The MMSS is designed to use summative and subscale scoring; therefore, the use of one subscale is appropriate. This five-item subscale uses a five-point Likert scale (1 = very satisfied; 5 = very dissatisfied), with questions related to the degree of satisfaction related to control/ responsibility in the work environment (e.g. How satisfied are you with your control over working conditions?). Based on consultation with critical care experts, an additional, independent question related to autonomy was added to this component of the questionnaire (i.e. How satisfied are you with your autonomy in clinical decision-making?). The Nursing Expertise Self-Report Scale (NESRS; Garland 1996) was used to gain insight into the relationship between nursing expertise and job satisfaction. This 20-item tool operationalizes Benner’s (1984) levels of clinical expertise: novice, advanced beginner, competent, proficient and expert. Adequate face and content validity, and test–retest reliability has been reported on this tool (Garland 1996). Specific findings related to clinical expertise will be reported elsewhere. Demographical questions were gleaned from the research literature and were based on the personal characteristics that may affect influencing and intermediary factors and/or ITL. These person factors included: age, gender, marital status, number of children, education and years of CC/nursing experience. © 2015 John Wiley & Sons Ltd
Retention in critical care nurses
Intermediary factor measures A composite of the following established measures was used to operationalize the intermediary factors. Job satisfaction was quantified by a single five-point Likert scale item (1 = not at all satisfied; 5 = very satisfied). Previous research lends support for the use of a single-item measure of job satisfaction rather than a multi-item questionnaire (Scarpello & Campbell 1983, Nagy 2002). The Engagement Composite Questionnaire is based on the three key behaviours of engaged employees: say, stay and serve (Hewitt Associates, 2008). The six 4-point Likert scale (1 = strongly disagree; 4 = strongly agree) questions captured this conceptualization of engagement, thus supporting the content validity of this measure. Previous engagement research has reportedly found negative correlations with staff turnover and positive relationships with business performance, which supports the concurrent and predictive validity of this scale. The reliability of the composite questionnaire has been established by internal consistency scores of 093 or higher (Hewitt Associates 2008). The Professional Quality of Life (ProQOL) Scale (Stamm 2005) was derived from Figley’s (1995) Compassion Fatigue Self Test. The ProQOL scale includes a total of 30 items, with 10 items for each of the three subscales of compassion fatigue, burnout and compassion satisfaction. The six-point Likert scale options for each statement range from 0 (never)-5 (very often). Stamm reports that each of these subscales has adequate reliability (a > 080). In addition, convergent and discriminant validity testing has verified the independence of the three subscales (Stamm 2005). Caring was operationally defined in the context of the intermediary factors of compassion fatigue and compassion satisfaction; as well, a qualitative component of the study focused on caring (to be reported elsewhere).
Outcome measures On the basis of Price and Mueller’s (1981) research, we used two single item 5-point Likert scale (1 = definitely will not leave; 5 = definitely will leave) questions to determine the participants’ intentions to leave CC and intentions to leave nursing in the coming year. If participants indicated ITL (either CC and/or nursing), they were asked to provide the rationale for the response. The purpose of this additional qualitative question was to identify specific reasons for ITL, especially those factors which may not have been captured in the influencing and intermediary factors, such as retirement or career opportunities (Stone et al. 2006). 2319
J.V. Sawatzky et al.
Data analysis Data were analysed with the Statistical Analysis Software (SAS) version 9 0 (SAS Institute Inc. 2002–2005); a P < 005 level of significance was used for all analyses. Missing data were replaced by group means. Bivariate statistics, including contingency table analyses and analysis of variance, were used to assess the relationships between influencing factors, intermediary factors and ITL; independent variables that emerged as statistically significant in these initial analyses were used to the multivariate models. We used regression models to analyse the effects of influencing factors on intermediary factors in a multivariable context. These multivariate models produced adjusted estimates of the interrelationships, which in turn enabled us to determine the variables that were significantly influential on the outcomes of interest. Specific to ordinal outcomes, such as intent to leave, we used proportional odds logistic regression models. Odds ratios and confidence intervals were reported for all variables included in the final models. Finally, for continuous outcomes such as engagement, ordinary least squares (OLS) regression models were used; here, we have reported regression coefficients and their confidence intervals. Model fit was assessed with the appropriate statistics.
Results Person factors Table 1 provides a summary of the sample’s demographical data. A total of 188 CC nurses completed and returned the questionnaires, for a response rate of 24%. Participants ranged in age from 24-64 years and most were female (92%), which is consistent with CNA (2010) workforce profiles; however, with a median age of 415, our sample was older than the typical profile of critical care nurses. While 59% had a baccalaureate degree, 61% had completed a continuing education certification programme in CC. The average number of years of nursing and CC experience were reportedly 158 and 102 years respectively. Most respondents (70%) worked in tertiary facilities and less than 40% worked full-time. Approximately 50% off all participants worked rotating 12-hour day/night shifts. Typically, participants were married/living common-law (72%) with children (62%).
Influencing factors Based on the bivariate (Chi-square/Fisher’s exact tests; Table 2), there was a statistically significant relationship 2320
Table 1 Sample
description:
person/organizational
factors
(N = 188). Factor
#/mean
Age
Mean = 409; median = 415 [range = 24-64]
Gender Female Male Marital status: married Have children Education Diploma Degree CE education certificate No Yes No. of CC years
No. of nursing years
Employer Tertiary Urban/community/rural Employment status Full time Part time Shiftwork Days only 12 hour rotating Other SD,
%/SD SD
975
145 12 115/159 97/157
92% 8% 72% 62%
64 93
41% 59%
74 114 Mean = 102; median = 70 [range = 05-41 ] Mean = 158; median = 130 [range = 1-45]
39% 61% SD 926
107 55
66% 34%
60 100
37% 63%
22 93 73
12% 49% 39%
SD
1073
standard deviation; CE, continuing education; CC, critical care.
(P < 005) between the following organizational factors and ITL both CC and nursing: professional practice, management, physician/nurse collaboration, nurse competence, control/responsibility and autonomy. Two additional variables (i.e. staffing resources and positive scheduling environment) emerged as significantly related to ITL CC but not ITL nursing. While most additional work-related factors were non-significant, weekends off and shifts worked (days vs. other) emerged as significant for ITL CC and there was a significant relationship between ITL Nursing and years as a nurse/CC nurse and working full vs. part-time. Interestingly, there were no common significant person factors for ITL CC and ITL nursing. While the relationship between ITL CC and family income emerged as significant, there was a significant relationship between ITL nursing and gender. It is, however, important to note that although 24% of respondents reported that they would probably or definitely leave CC in the next year, only 4% reported that they © 2015 John Wiley & Sons Ltd
JAN: ORIGINAL RESEARCH: EMPIRICAL RESEARCH – QUANTITATIVE
would probably or definitely leave nursing in the same time frame.
Intermediary factors Bivariate and ordinal least squares (OLS) regression and ordinal logistic regression (OLR) models were used to assess the relationships between the intermediary factors and ITL outcomes, and between the influencing and intermediary factors (Tables 2 and 3). To address the primary aim and goals of the study, each of the intermediary factors, including job satisfaction, engagement, compassion satisfaction, compassion failure and burnout were classified as outcomes variables (Table 3). In the bivariate analyses (Table 2), there was a significant relationship
Retention in critical care nurses
between all of the intermediary factors and ITL CC; similarly, all of the intermediary factors, except compassion fatigue emerged as significantly related to ITL nursing. Of note was that 65% of respondents reported that they were satisfied (i.e. satisfied or very satisfied) in their current position. The regression models, with each of the intermediary factors as outcome variables, revealed that many of the same influencing factors that emerged as significant in the ITL CC and Nursing analyses were also significantly related to the intermediary factors (Table 3). Most notably, control/ responsibility was a significant factor in all of the intermediary factor regression models; as well, nursing expertise was significant in the job satisfaction, compassion fatigue and burnout models. Management was significant to job
Table 2 Bivariate analyses re influencing/intermediary factors & ITL critical care/nursing. ITL critical care Parameters Influencing factors Organizational factors Professional practice Staffing resources Management Physician/nurse collaboration Nurse competence Nurse expertise Control/responsibility Autonomy Positive scheduling climate Weekly overtime Years as nurse Years as critical care nurse Weekends off Shifts worked Employer [tertiary vs. other] Staff nurse vs. other Full vs. part-time Person factors Age Gender Marital status Family income Children Education [degree vs. diploma] Intermediary factors Job satisfaction Engagement Compassion satisfaction Compassion fatigue Burnout
Chi-square
ITL nursing d.f.
P value
Chi-square
29352 36302 23037 30172 29200
4 4 4 4 4
58240 67925 16248
4 16 4
Identifying the key predictors for retention in critical care nurses Jo-Ann V. Sawatzky, Carol L. Enns & Carol Legare Accepted for publication 29 April 2015
Correspondence to J.V. Sawatzky: e-mail: [email protected] Jo-Ann V. Sawatzky PhD RN Associate Professor, Associate Dean, Graduate Programs Faculty of Nursing, University of Manitoba, Winnipeg, Manitoba, Canada Carol L. Enns MN RN Senior Instructor, Director, Clinical Education Programs Faculty of Nursing, University of Manitoba, Winnipeg, Manitoba, Canada Carol Legare MN ENC(C) RN Director of Patient Services Adult Emergency, Health Sciences Centre, Winnipeg, Manitoba, Canada
S A W A T Z K Y J . V . , E N N S C . L . & L E G A R E C . ( 2 0 1 5 ) Identifying the key predictors for retention in critical care nurses. Journal of Advanced Nursing 71(10), 2315– 2325. doi: 10.1111/jan.12701
Abstract Aims. The aim of this study was to explore the key predictors of retention in nurses working in critical care areas. Background. The shortage of critical care nurses is reaching crisis proportions in Canada and throughout the industrialized world. Identifying the key influencing (i.e. person and organizational) factors and intermediary factors (i.e. job satisfaction, engagement, professional quality of life and caring) that affect intent to leave is central to developing optimal retention strategies for critical care nurses. Design. As part of a larger mixed-methods study, we used a quantitative, crosssectional research design. A novel framework: the Conceptual Framework for Predicting Nurse Retention was used to guide this study. Methods. On-line survey data were collected from on a convenience sample of 188 registered nurses working in critical care areas of hospitals in the province of Manitoba, CANADA in 2011. Results. Twenty-four per cent of the respondents reported that they would probably/definitely leave critical care in the next year. Based on bivariate and regression analyses, the key influencing factors that were significantly related to the intermediary factors and intent to leave critical care and nursing included: professional practice, management, physician/nurse collaboration, nurse competence, control/responsibility and autonomy. Of the intermediary factors, all but compassion satisfaction were related to intent to leave both critical care and nursing. Conclusion. This study highlights the importance of exploring multiple organizational and intermediary factors to determine strategies to retain critical care nurses. The findings also support the Conceptual Framework for Predicting Nurse Retention as a theoretical basis for further research. Keywords: critical care, intensive care, intent to leave, job satisfaction, nurses, nursing, retention
© 2015 John Wiley & Sons Ltd
2315
J.V. Sawatzky et al.
Why is this research needed? The availability of critical nurses is threatened by a changing demographical and the physical and emotional stressors associated with this work environment There has been a lack of research that has examined influencing (organizational, personal) and intermediary (job satisfaction, professional quality of life, engagement and caring) factors in this population.
What are the key findings? While several organizational factors influence intent to leave, control/responsibility appears to be a central influencing factor in predicting nurse retention in critical care. The intermediary factors of job satisfaction, engagement, compassion satisfaction and burnout are convincing predictors of intent to leave critical care and nursing overall.
How should the findings be used to influence policy/ practice/research/education? Nursing leaders should develop retention strategies that include addressing management issues, ongoing professional development and organizational structures to empower critical care nurses. The findings support the use of a novel framework: the Conceptual Framework for Predicting Nurse Retention, as the theoretical basis for future research.
Introduction The ever-increasing shortage of nurses is one of the most critical issues in the healthcare sector today. Based on employment projections, the US Bureau of Labor Statistics (2014) anticipates the total number of job vacancies for nurses will increase by 19% or greater than 500,000 vacancies by 2022. In Canada, the Canadian Nurses Association (CNA 2002) predicts a shortage of 113,000 registered nurses (RNs) by 2016. According to a recent systematic review of the literature (Lu et al. 2012), this looming crisis is a global issue, affecting developed and developing countries around the world. Nowhere is this crisis more prominent than in specialty areas, such as critical care (CC; Buerhaus et al. 2000, Odom 2000, Andrews & Dziegielewski 2005, Davis et al. 2007, O-Brien-Pallas et al. 2008, Chan & Lai 2010). Nursing shortages have a negative impact on every aspect of healthcare delivery; research evidence supports the contention that economic, patient and nursing outcomes are affected by high nursing turnover (Strachota et al. 2003, O’Brien-Pallas et al. 2006, Davis et al. 2007, Chan & Lai 2316
2010). Economic costs include such factors as the initial lower productivity in new employees and the decrease in staff morale and productivity caused by the turnover. Patient outcomes may also be compromised as a result of higher patient to nurse ratios (Aiken et al. 2002). For example, based on regression analysis, a recent study in the USA reported a statistically significant (P < 0001) association between understaffing and the risk of nosocomial infections in 67 Neonatal Intensive Care Units (NICUs; Rogowski et al. 2013). Finally, specific to nursing outcomes, poor job satisfaction and high levels of burnout are among the consequences of nursing shortages and turnover rates in critical care (Aiken et al. 2002, Stone et al. 2006). While the need for research on CC nursing staffing has been identified as a priority (Canadian Association of Critical Care Nurses, 2010, Fitzpatrick et al. 2010), there is still a lack of research in this area; Canadian research, in particular is lacking. Studies completed in the USA and abroad may not be relevant to the unique aspects of Canada’s healthcare system. Unlike the USA, Canada’s national health insurance programme, often referred to as ‘Medicare,’ is designed to provide all Canadians with free and reasonable access to medical care. As well, in the USA, nursing unions are generally the exception rather than the norm. While unions do ensure fairness to the masses, they can also impose restrictions related to the development of creative, individualized staffing strategies. Therefore, determining the key predictors of critical care nursing retention and intent to leave will facilitate the development of specific leadership strategies to retain CC nurses and optimize patient outcomes.
Background The shortage of nurses has been an ongoing issue for many decades. In fact, as early as 1954, Roberta Spohn, the Assistant Executive Secretary of the American Nurses’ Association, Research and Statistics Unit, voiced concern regarding nursing’s ‘dubious distinction’ of continually suffering from manpower shortages (Spohn 1954, p. 865). Despite shortlived periods of stability and ongoing efforts to ameliorate the problem, the nursing shortage continues to reign as one of the most statistically significant issues in health care today. Although all areas of nursing are affected by this crisis, critical care areas are especially vulnerable to recruitment and retention issues. Critical care areas and intensive care units (ICUs) in particular, tend to attract younger RNs, with almost 80% of ICU nurses reportedly being under the age of 40 (Buerhaus et al. 2000). However, according to a Canadian Institute of © 2015 John Wiley & Sons Ltd
JAN: ORIGINAL RESEARCH: EMPIRICAL RESEARCH – QUANTITATIVE
Health Information (CIHI 2011) study, in 2010, the average age of RNs in Canada was 454% and 255% of all RNs were over the age of 55. Similarly, the average age of employed RNs in the USA in 2013 was 442, with 24% aged 55 or older (US Bureau of Labor Statistics, 2013). This ageing cohort of nurses will play a major role in the projected overall shortage of 260,000 RNs in the USA by 2025 (Buerhaus et al. 2009). Although the challenge and excitement of critical care areas may attract the younger RN, the unremitting emotional and physical demands in this work environment tend to be a major barrier to retention. Moreover, there is emerging evidence that the younger Generation X and Y nurses, born between 1960–1980 and 1976–2000 respectively, have higher turnover intentions than their older Baby Boomer counterparts (born between 1943–1960; Brunetto et al. 2013). To date, turnover intention, or intent to leave (ITL) research has focused on the general nursing population (Tzeng 2002, Sourdiff 2004, Lynn & Redman 2005, O’Brien-Pallas et al. 2010, Brunetto et al. 2013). However, there is a lack of published research on ITL in critical care nurses (Stone et al. 2006, van Dam et al. 2013). Although Stone and associates explored organizational and personal predictors of ITL in a large sample of ICU nurses (N = 2323), potential intermediary factors were not explored. van Dam et al.’s survey of Dutch ICU nurses (N = 461) elicited insight into the individual and contextual factors related to perceptions of work pressure and turnover; however, organizational factors were not included in their study. Therefore, we explored the key influencing (i.e. organizational and personal) and intermediary (i.e. job satisfaction, engagement, professional quality of life and caring) predictors of retention in critical care nurses in central Canada.
Study framework The organizational framework for this study was an investigator developed Conceptual Framework for Predicting Nurse Retention (CFPNR; see Figure 1). This framework represents the theoretical links between influencing factors, intermediating factors and the outcome of intent to leave (ITL). The CFPNR is based on research based evidence, and several existing empirical models related to predictors of nurse retention (Price & Mueller 1981, O’Brien-Pallas et al. 2001, Tzeng 2002, Larrabee et al. 2003). Influencing factors include the organizational climate, and personal/ demographical considerations. These factors may predict ITL either directly or indirectly by their effect on the intermediary factors. The intermediary factors, including job satisfaction, professional quality of life (i.e. compassion © 2015 John Wiley & Sons Ltd
Retention in critical care nurses
satisfaction, compassion fatigue and burnout) and caring, are influenced by the individual’s organizational climate and personal/demographical factors and as such, may intercede with the influencing factors or directly impact one’s ITL. The CFPNR has been described in further detail elsewhere (Sawatzky & Enns 2012).
The study Aim The primary aim of this study was to explore and describe the factors that predict the retention of nurses working in CC areas, in the context of the CFPNR. Our overall goal was to establish research-based evidence that can be used to develop ongoing strategies for the retention of CC nurses in acute care facilities. The insights gleaned from this research may also be applicable to other areas of nursing and in other parts of Canada and the industrialized world.
Design As part of a larger mix-methods study, a quantitative, cross-sectional survey was used to achieve the aims and goals of this research.
Sample/setting The study sample was drawn from the population of nurses who worked in critical care areas in the province of Manitoba, Canada. According to the provincial regulatory body, the College of Registered Nurses of Manitoba (CRNM), there were 893 self-reported CC nurses residing in this province in 2011; 778 had submitted e-mail addresses to the CRNM. Inclusion criteria were: general duty RNs, Clinical Resource Nurses (CRNs) and educators who were currently employed in a CC area. Exclusion criteria were: senior CC managers and nurses who were employed in critical care on a casual basis. The setting for the study included all hospital based CC areas in the province: two urban tertiary care hospitals; five urban community hospitals and five rural facilities with designated critical care areas/intensive care units.
Data collection Invitations to participate in the study, and the consent form and the questionnaire package, were sent to all potential participants via the web-based SurveyMonkeyâ. This Inter2317
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Influencing factors
Intermediary factors
Outcomes
Organizational Climate: *Professional practice *Staffing & resources *Nursing management *Nurse/physician collaboration *Nursing competence/expertise *Control/responsibility *Positive scheduling climate
Job Satisfaction Engagement Professional quality of life: *Compassion satisfaction
Intent to leave
*Compassion fatigue *Burnout Caring
Person factors: *Demographics
Figure 1 Conceptual framework for predicting nurse retention. net dissemination process, and sending two reminder letters to non-responders, was completed by the CRNM in March and April of 2011.
Ethical considerations Ethical guidelines, as outlined by the university ethics review board were followed. The return of the completed questionnaire was regarded as consent to participate in the study. The protocol for ensuring informed consent included the provision of written information to each potential participant. Voluntary participation of subjects in the study was established/reinforced on the consent form. There were no risks to participating in this study. Steps were taken to ensure participant confidentiality: The CRNM did not have access to the SurveyMonkeyâ data and the research team did not have access to the identity of potential or actual participants. Participants had the option to receive a summary report of the study findings. 2318
Validity and reliability The Critical Care Nurse Retention Survey was designed to operationalize the concepts in the CFPNR. Accordingly, we used existing tools, with well-established validity and reliability, to measure each of the influencing and intermediary factors, and the primary outcome of intent to leave. Influencing factor measures According to Choi et al. (2004), the 42-item Perceived Nurse Working Environment (PNWE) scale captures key dimensions of the nursing organizational climate including, leadership, communication, structural attributes and quality of work life in the context of each of the seven sub-scales. Likert scale options range from 1 (strongly agree)-5 (strongly disagree) for each statements/item, in the categories of: professional practice (13 items; e.g. Nursing staff is supported in pursuing degrees in nursing), staffing and resource adequacy (five items; e.g. There are enough RNs © 2015 John Wiley & Sons Ltd
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on staff to provide quality patient care), nursing management (five items; e.g. The nurse manager is a good manager and leader), nurse/physician collaboration (four items; e.g. Physicians and nurses have good working relationships), nursing competence (six items; e.g. I work with nurses who are clinically competent), positive scheduling environment (three items; e.g. Flexible or modified work schedules are available). Based on consultation with several critical care experts, the seventh sub-scale, related to the nursing process (six items), was deemed invalid and therefore removed. We were able to remove this subscale because the PNWE scale is designed to use subscale scoring; as well, original pairwise Pearson correlation coefficient analysis supported sufficient independence of the subscales (r < 060; Choi et al. 2004). The PNWE scale was initially tested on a large sample of critical care nurses (N = 2324) and has been used in a variety of settings. According to Choi and associates, this measure exhibits sound psychometric properties. Chronbach’s alphas between 056-091 lend support for internal consistency of the PNWE scale. One subscale of the McCloskey Mueller Satisfaction Scale (MMSS; Mueller & McCloskey 1990) was used to measure control/responsibility. The MMSS is a reliable and valid measure of nurses’ job satisfaction (Mueller & McCloskey 1990). The MMSS is designed to use summative and subscale scoring; therefore, the use of one subscale is appropriate. This five-item subscale uses a five-point Likert scale (1 = very satisfied; 5 = very dissatisfied), with questions related to the degree of satisfaction related to control/ responsibility in the work environment (e.g. How satisfied are you with your control over working conditions?). Based on consultation with critical care experts, an additional, independent question related to autonomy was added to this component of the questionnaire (i.e. How satisfied are you with your autonomy in clinical decision-making?). The Nursing Expertise Self-Report Scale (NESRS; Garland 1996) was used to gain insight into the relationship between nursing expertise and job satisfaction. This 20-item tool operationalizes Benner’s (1984) levels of clinical expertise: novice, advanced beginner, competent, proficient and expert. Adequate face and content validity, and test–retest reliability has been reported on this tool (Garland 1996). Specific findings related to clinical expertise will be reported elsewhere. Demographical questions were gleaned from the research literature and were based on the personal characteristics that may affect influencing and intermediary factors and/or ITL. These person factors included: age, gender, marital status, number of children, education and years of CC/nursing experience. © 2015 John Wiley & Sons Ltd
Retention in critical care nurses
Intermediary factor measures A composite of the following established measures was used to operationalize the intermediary factors. Job satisfaction was quantified by a single five-point Likert scale item (1 = not at all satisfied; 5 = very satisfied). Previous research lends support for the use of a single-item measure of job satisfaction rather than a multi-item questionnaire (Scarpello & Campbell 1983, Nagy 2002). The Engagement Composite Questionnaire is based on the three key behaviours of engaged employees: say, stay and serve (Hewitt Associates, 2008). The six 4-point Likert scale (1 = strongly disagree; 4 = strongly agree) questions captured this conceptualization of engagement, thus supporting the content validity of this measure. Previous engagement research has reportedly found negative correlations with staff turnover and positive relationships with business performance, which supports the concurrent and predictive validity of this scale. The reliability of the composite questionnaire has been established by internal consistency scores of 093 or higher (Hewitt Associates 2008). The Professional Quality of Life (ProQOL) Scale (Stamm 2005) was derived from Figley’s (1995) Compassion Fatigue Self Test. The ProQOL scale includes a total of 30 items, with 10 items for each of the three subscales of compassion fatigue, burnout and compassion satisfaction. The six-point Likert scale options for each statement range from 0 (never)-5 (very often). Stamm reports that each of these subscales has adequate reliability (a > 080). In addition, convergent and discriminant validity testing has verified the independence of the three subscales (Stamm 2005). Caring was operationally defined in the context of the intermediary factors of compassion fatigue and compassion satisfaction; as well, a qualitative component of the study focused on caring (to be reported elsewhere).
Outcome measures On the basis of Price and Mueller’s (1981) research, we used two single item 5-point Likert scale (1 = definitely will not leave; 5 = definitely will leave) questions to determine the participants’ intentions to leave CC and intentions to leave nursing in the coming year. If participants indicated ITL (either CC and/or nursing), they were asked to provide the rationale for the response. The purpose of this additional qualitative question was to identify specific reasons for ITL, especially those factors which may not have been captured in the influencing and intermediary factors, such as retirement or career opportunities (Stone et al. 2006). 2319
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Data analysis Data were analysed with the Statistical Analysis Software (SAS) version 9 0 (SAS Institute Inc. 2002–2005); a P < 005 level of significance was used for all analyses. Missing data were replaced by group means. Bivariate statistics, including contingency table analyses and analysis of variance, were used to assess the relationships between influencing factors, intermediary factors and ITL; independent variables that emerged as statistically significant in these initial analyses were used to the multivariate models. We used regression models to analyse the effects of influencing factors on intermediary factors in a multivariable context. These multivariate models produced adjusted estimates of the interrelationships, which in turn enabled us to determine the variables that were significantly influential on the outcomes of interest. Specific to ordinal outcomes, such as intent to leave, we used proportional odds logistic regression models. Odds ratios and confidence intervals were reported for all variables included in the final models. Finally, for continuous outcomes such as engagement, ordinary least squares (OLS) regression models were used; here, we have reported regression coefficients and their confidence intervals. Model fit was assessed with the appropriate statistics.
Results Person factors Table 1 provides a summary of the sample’s demographical data. A total of 188 CC nurses completed and returned the questionnaires, for a response rate of 24%. Participants ranged in age from 24-64 years and most were female (92%), which is consistent with CNA (2010) workforce profiles; however, with a median age of 415, our sample was older than the typical profile of critical care nurses. While 59% had a baccalaureate degree, 61% had completed a continuing education certification programme in CC. The average number of years of nursing and CC experience were reportedly 158 and 102 years respectively. Most respondents (70%) worked in tertiary facilities and less than 40% worked full-time. Approximately 50% off all participants worked rotating 12-hour day/night shifts. Typically, participants were married/living common-law (72%) with children (62%).
Influencing factors Based on the bivariate (Chi-square/Fisher’s exact tests; Table 2), there was a statistically significant relationship 2320
Table 1 Sample
description:
person/organizational
factors
(N = 188). Factor
#/mean
Age
Mean = 409; median = 415 [range = 24-64]
Gender Female Male Marital status: married Have children Education Diploma Degree CE education certificate No Yes No. of CC years
No. of nursing years
Employer Tertiary Urban/community/rural Employment status Full time Part time Shiftwork Days only 12 hour rotating Other SD,
%/SD SD
975
145 12 115/159 97/157
92% 8% 72% 62%
64 93
41% 59%
74 114 Mean = 102; median = 70 [range = 05-41 ] Mean = 158; median = 130 [range = 1-45]
39% 61% SD 926
107 55
66% 34%
60 100
37% 63%
22 93 73
12% 49% 39%
SD
1073
standard deviation; CE, continuing education; CC, critical care.
(P < 005) between the following organizational factors and ITL both CC and nursing: professional practice, management, physician/nurse collaboration, nurse competence, control/responsibility and autonomy. Two additional variables (i.e. staffing resources and positive scheduling environment) emerged as significantly related to ITL CC but not ITL nursing. While most additional work-related factors were non-significant, weekends off and shifts worked (days vs. other) emerged as significant for ITL CC and there was a significant relationship between ITL Nursing and years as a nurse/CC nurse and working full vs. part-time. Interestingly, there were no common significant person factors for ITL CC and ITL nursing. While the relationship between ITL CC and family income emerged as significant, there was a significant relationship between ITL nursing and gender. It is, however, important to note that although 24% of respondents reported that they would probably or definitely leave CC in the next year, only 4% reported that they © 2015 John Wiley & Sons Ltd
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would probably or definitely leave nursing in the same time frame.
Intermediary factors Bivariate and ordinal least squares (OLS) regression and ordinal logistic regression (OLR) models were used to assess the relationships between the intermediary factors and ITL outcomes, and between the influencing and intermediary factors (Tables 2 and 3). To address the primary aim and goals of the study, each of the intermediary factors, including job satisfaction, engagement, compassion satisfaction, compassion failure and burnout were classified as outcomes variables (Table 3). In the bivariate analyses (Table 2), there was a significant relationship
Retention in critical care nurses
between all of the intermediary factors and ITL CC; similarly, all of the intermediary factors, except compassion fatigue emerged as significantly related to ITL nursing. Of note was that 65% of respondents reported that they were satisfied (i.e. satisfied or very satisfied) in their current position. The regression models, with each of the intermediary factors as outcome variables, revealed that many of the same influencing factors that emerged as significant in the ITL CC and Nursing analyses were also significantly related to the intermediary factors (Table 3). Most notably, control/ responsibility was a significant factor in all of the intermediary factor regression models; as well, nursing expertise was significant in the job satisfaction, compassion fatigue and burnout models. Management was significant to job
Table 2 Bivariate analyses re influencing/intermediary factors & ITL critical care/nursing. ITL critical care Parameters Influencing factors Organizational factors Professional practice Staffing resources Management Physician/nurse collaboration Nurse competence Nurse expertise Control/responsibility Autonomy Positive scheduling climate Weekly overtime Years as nurse Years as critical care nurse Weekends off Shifts worked Employer [tertiary vs. other] Staff nurse vs. other Full vs. part-time Person factors Age Gender Marital status Family income Children Education [degree vs. diploma] Intermediary factors Job satisfaction Engagement Compassion satisfaction Compassion fatigue Burnout
Chi-square
ITL nursing d.f.
P value
Chi-square
29352 36302 23037 30172 29200
4 4 4 4 4
58240 67925 16248
4 16 4
