Factors Associated With Care Workers’ Intention to Leave Employment in Nursing Homes: A Secondary Data Analysis of the Swiss Nursing Homes Human Resources Project

Abstract

The emerging care personnel shortage in Swiss nursing homes is aggravated by high turnover rates. As intention to leave is a predictor of turnover, awareness of its associated factors is essential. This study applied a secondary data analysis to evaluate the prevalence and variability of 3,984 nursing home care workers’ intention to leave. Work environment factors and care worker outcomes were tested via multiple regression analysis. Although 56% of care workers reported intention to leave, prevalences varied widely between facilities. Overall, intention to leave showed strong inverse relationships with supportive leadership and affective organizational commitment and weaker positive relationships with stress due to workload, emotional exhaustion, and care worker health problems. The strong direct relationship of nursing home care workers’ intention to leave with affective organizational commitment and perceptions of leadership quality suggest that multilevel interventions to improve these factors might reduce intention to leave.

Keywords

affective organizational commitment intention to leave leadership nursing homes physical health

Introduction

In European countries, demographic aging increases the demand for care workers in nursing homes (Colombo, Llena-Nozal, Mercier, & Tjadens, 2011; Jaccard Ruedin & Weaver, 2009). Across the Organisation for Economic Co-Operation and Development (OECD) countries, the proportion of the overall population aged 80 years and older is expected to increase from 4% in 2010 to nearly 10% by 2050 (Colombo et al., 2011). In the last years of life, care dependency increases: Older persons become more fragile, often experiencing reduced functional status, multiple co-morbidities, polypharmacy, and cognitive impairment (OECD & World Health Organization [WHO], 2011). Considering the increased burdens their needs place on family members and other caregivers, growing numbers of older persons will eventually need stationary long-term care (Colombo et al., 2011). In 2013, Switzerland’s 1,560 nursing homes housed approximately 92,000 residents, 75% of whom were older than 80 years. The mean length of stay was 942 days (Bundesamt für Statistik, 2015). With a high prevalence of chronic disease, the oldest are often in poor health, leading to a high degree of dependence in everyday activities (Bundesamt für Statistik, 2012). Although the number of nursing home care workers in Switzerland increased by 13.2% between 2010 and 2014, the need for care workers here has already surpassed the output of Swiss nursing educational programs (Schweizerische Konferenz der kantonalen Gesundheitsdirektorinnen und-direktoren [GDK] & OdASanté, 2016).

Faced with widespread staffing shortages, one partial solution is to retain current staff longer in nursing homes (GDK & OdASanté, 2016). Unfortunately, considering the high turnover rates in most countries, this presents a major challenge (Choi & Johantgen, 2012; McGilton, Tourangeau, Kavcic, & Wodchis, 2013). For U.S. nursing homes, Banaszak-Holl, Castle, Lin, Shrivastwa, and Spreitzer (2013) reported a 35.8% annual turnover among nursing aides (NAs), alongside 19.5% among licensed practical nurses (LPNs), and 19.3% among registered nurses (RNs). In contrast, in Denmark, Clausen, Tufte, and Borg (2014) observed only a 6.6% nursing home care staff turnover rate within 1 year; in Sweden, Colombo et al. (2011) found an average annual turnover rate of 5%. The relatively low turnover rate in Sweden might be attributable to excellent working conditions, including fair wages and family-friendly policies such as day care services for children (Colombo et al., 2011). As for Switzerland, the most recent data—from 2002—suggested a 22% annual nursing home staff turnover rate (Künzi & Schär Moser, 2002).

Care worker turnover weakens nursing home care quality (Castle, Engberg, & Men, 2007). On one hand, many residents are cognitively impaired and have reduced social resources. Staff changes can increase their disorientation (Cohen-Mansfield, 1997). On the other hand, higher NA and RN turnover rates are related to increased restraint and urinary catheter use, more pressure ulcers, along with insufficient pain management—all potential indicators of declining quality of care (Castle & Anderson, 2011). Moreover, in addition to increasing the cost of recruiting and training new staff, staff transitions cause workload surges for the remaining care workers (Colombo et al., 2011; Hanh, 2013). In turn, these surges and the stress that accompanies them can increase the intention to leave, leading to a spiraling interaction between turnover and workload (Cohen-Mansfield, 1997).

Intention to Leave

Mobley, Horner, and Hollingsworth (1978) described three steps in the job-leaving process: thinking about leaving, intending to leave, and deciding to seek new employment. This process is further supported by Fishbein and Ajzen’s (1975) Theory of Reasoned Action, which explains the relationship between attitudes and behaviors and can be applied to this study in the sense that stronger intention to leave will increase the likelihood for actual turnover behavior. Various studies have confirmed the reliability of intention to leave as a predictor of turnover in nursing homes, hospitals, and commercial enterprises (Cowden, Cummings, & Profetto-McGrath, 2011; Firth, Mellor, Moore, & Loquet, 2004; Hayes et al., 2012; Zhang, Punnett, & Gore, 2014). For example, in a longitudinal study of certified nursing assistants (CNAs) in U.S. nursing homes, Rosen, Stiehl, Mittal, and Leana (2011) showed that reported intention to leave was a significant predictor of actual 1-year turnover (odds ratio [OR] = 2.06; 95% confidence interval [CI] = [1.59, 2.67]). For CNAs reporting low intention to leave, actual turnover was 65% after 1 year; for those who reported a very high intention to leave it was 92%. Behavioral intentions such as intention to leave have shown stronger predictive power regarding turnover than affective variables such as job satisfaction (Steel & Ovalle, 1984). Accordingly, for this study, we interpreted intention to leave as the first conscious step toward the outcome of not remaining in the organization (Cowden et al., 2011), that is, focusing on leaving the organization as a workplace, and not on leaving the work sector or profession in general. The term intention to leave refers here to voluntary turnover.

Factors Related to Intention to Leave

Factors related to intention to leave are most commonly categorized as organizational, work-related, employee-related, or external factors (Takase, 2010). Our analyses were guided by the turnover model of Cohen-Mansfield (1997), who suggested that organizational/work-related factors and employee-related/personal factors affect the decision to leave via a person’s job fit and individual responses to that fit, which in turn affect employees’ intentions to remain or leave the workplace (cf. Figure 1). For example, a care worker’s decision to leave may be influenced by physiological responses (e.g., health problems), cognitive reactions (e.g., negative thoughts toward the administration), and emotional reactions, which derive from both work-related and personal factors (Cohen-Mansfield, 1997). In addition, the model suggested that external factors such as an organization’s environment (e.g., job market) are also influencing one’s intention to leave. As this study was a secondary data analysis, we followed Cohen-Mansfield’s model, focusing on a combination of work-related factors and individual responses, selecting the variables in accordance with the literature to date, and excluding factors not measured in the main study, for example, external factors.

Figure 1.

Factors associated with care workers’ intention to leave employment in nursing homes.

Literature Review

Several studies examined the relationship between work-related factors, such as leadership, staffing and resources adequacy, collaboration/teamwork or work stressors, and intention to leave. As for the first of these, leadership, studies identified decreased turnover or intention to leave when leaders’ considered work groups’ input into their decision making and development of shared visions, and enabled or encouraged others to act (Chu, Wodchis, & McGilton, 2014; Donoghue & Castle, 2009). Yet published findings are inconsistent, and some studies have failed to confirm these findings. For example, one recent study found no significant relationship between leadership qualities and intention to leave (Tourangeau, Cranley, Spence Laschinger, & Pachis, 2010). As for staffing and resources adequacy, magnet hospitals and dialysis facilities show increased care worker retention when care workers perceive sufficient time and staff to deal with all necessary daily work (Galletta, Portoghese, Penna, Battistelli, & Saiani, 2011; Gardner, Thomas-Hawkins, Fogg, & Latham, 2007). However, while it might be expected that nursing home care workers’ perceptions of staff and resources adequacy are related to their intention to leave, we found no studies that tested that relationship. Concerning collaboration and teamwork, favorable findings have been associated with lower turnover percentages (Chu et al., 2014; Dill, Keefe, & McGrath, 2012; Tummers, Groeneveld, & Lankhaar, 2013), while higher work stress has been linked to higher intention to leave (Kuo, Lin, & Li, 2014; Stewart et al., 2011); yet few studies followed Cohen-Mansfield’s (1995) suggestion to measure how work stress relates to structural-level characteristics, such as on the institutional or unit level (e.g., work climate, interpersonal conflicts) and resident levels (e.g., demanding care situations due to behavioral symptoms of residents).

As for care workers’ health, intention to leave has consistently been related to emotional (e.g., emotional exhaustion) and physical health problems (Clausen et al., 2014; Rosen et al., 2011). Other studies revealed that affective organizational commitment, that is, an emotionally positive connection to the organization (Taylor & Pillemer, 2009) was inversely related to turnover (Choi & Johantgen, 2012; Decker, Harris-Kojetin, & Bercovitz, 2009; Karsh, Booske, & Sainfort, 2005; Taylor & Pillemer, 2009; Tourangeau et al., 2010). Numerous studies found job satisfaction inversely linked with intention to leave (Apkera, Proppa, & Zabava, 2009; Francis-Felsen et al., 1996; Kuo et al., 2014; Parsons, Simmons, Penn, & Furlough, 2003). Finally external factors, for example, unemployment, labor mobility, and travel time to work, are described to be linked to turnover (Banaszak-Holl & Hines, 1996; Francis-Felsen et al., 1996; Rondeau, Williams, & Wagar, 2008).

Although intention to leave is a consistent indicator of staff turnover, recent reviews indicate limited evidence linking intention to leave with possible contributing factors present in the nursing home context but not in acute hospitals; and as most studies so far were conducted in the United States, international evidence is needed. In addition, few studies have simultaneously explored the links between work environment factors, individual responses, and intention to leave in nursing homes (Cowden et al., 2011; Hayes et al., 2012).

Therefore, the aims of this secondary data analysis were (a) to describe the prevalence of care workers’ intention to leave and its variability between facilities in Swiss nursing homes (where care workers included RNs, LPNs, and NAs in direct contact with residents), and (b) to examine care workers’ intention to leave as it relates to work environment factors (leadership, staffing and resources adequacy, collaboration/teamwork, and three subfactors of work stressors, namely, “conflict and lack of recognition,” “workload,” and “lack of preparation”) and individual responses, which are treated here as care worker outcomes (emotional exhaustion, physical health problems, and affective organizational commitment). We expected that intention to leave would be higher with increased work stressors, emotional exhaustion, and physical health problems and lower with better leadership, staffing and resources adequacy, collaboration/teamwork, and higher affective organizational commitment.

Method

Design, Setting, and Sample

This study is a secondary analysis of data from Switzerland’s multicenter cross-sectional Swiss Nursing Homes Human Resources Project (SHURP) study (Schwendimann et al., 2014). Using a random initial selection of 162 Swiss nursing homes, SHURP aimed to explore the relations between various nursing homes’ characteristics and care worker and resident outcomes in Swiss nursing homes. Eligible care workers included RNs (≥3-year education), LPNs (3-year education), CNAs (1- to 2-year education), and NAs (short courses or training on the job). Care worker data (N = 5,323) were obtained via self-reported questionnaire. Unit and facility questionnaires were completed by unit supervisors or directors of nursing. Administrative data on residents were supplied by directors of nursing and/or nursing home administrators.

For study purposes, data from respondents in leadership positions (e.g., nurse mangers, unit supervisors), and those from units that did not report unit-level data were excluded. Full details of the SHURP study design, sampling, methodology, and questionnaire development have been published elsewhere (Schwendimann et al., 2014).

Variables and Measurements

The variables under study are described in detail in Table 1. The dependent variable intention to leave was measured with three items, each using a 5-point Likert-type agreement scale ranging from 1 to 5, with higher numbers indicating stronger agreement. Due to a strongly left-skewed distribution, the variable was dichotomized into two groups: intention to leave versus no intention to leave. Two different cutoffs were used to perform sensitivity analyses and test the robustness of our findings as described below. To estimate the independent variables for leadership, we used data from the Practice Environment Scale–Nursing Work Index (PES-NWI; Lake, 2002), which consisted of five items measured on 4-point Likert-type scale. Collaboration/teamwork was measured with a single item on a 5-point Likert-type scale derived from the Safety Attitudes Questionnaire (SAQ; Sexton et al., 2006). Work stressors were assessed with a 12-item shortened version of the Health Professions Stress Inventory (HPSI; Lapane & Hughes, 2007). The items were grouped into three subscales: “conflict and lack of recognition” (six items), “workload” (three items), and “lack of preparation” (three items). As recommended by Cohen-Mansfield (1995), these address stress sources at both the unit and the resident level. Emotional exhaustion was assessed with a single item from the Maslach Burnout Inventory (Maslach & Jackson, 1981). Physical health problems were numbered on an ordinal scale, using a self-constructed health index based on items chosen from the Swiss Health Survey (Bundesamt für Statistik, 2007); and finally, to assess affective organizational commitment, the five-item continuous scale of the Questionnaire for the Assessment of Affective, Calculatory and Normative Commitment to the Organization, the Profession/Activity and Business (Felfe, Six, Schmook, & Knorz, 2010) was used. Although job satisfaction is indicated as a strong predictor of affective commitment in the SHURP data set (Graf, Cignacco, Zimmermann, & Zúñiga, 2016), it is less stable than affective commitment as it is more prone to immediate reactions to specific and tangible aspects of the work environment (Mowday, Steers, & Porter, 1979); therefore, we selected affective organizational commitment as a factor related to intention to leave.

Table 1.

Description of Variables Used in the Study.

Variables	Instrument used; number of items/number of response categories	Items/questions	Scale calculation
Dependent variable Intention to leave	Two items from the Michigan Organizational Assessment Questionnaire developed by Cammann, Fichman, Jenkins, & Klesh (1979), one item from Mobley, Horner, and Hollingsworth (1978); 3/5 (1 = strongly disagree to 5 = strongly agree)	● I often think about quitting ● I will probably look for a new job the next year I am currently looking for another job (in another organization)	Cronbach’s α = .91. Scale was calculated as sum over all items minus 3 (index starts with 0 for no intention to leave) ranging from 0 to 12. Due to the left-skewed distribution, two groups were built intention to leave (0) vs. no intention to leave (1-12) and a sensitivity analysis was performed with cutoff of 0-2 vs. 3-12
Independent variables Work environment factors
Leadership	Practice Environment Scale–Nursing Work Index (PES-NWI; Lake, 2002); 5/4 (1 = strongly disagree to 4 = strongly agree)	● A supervisory staff that is supportive of the care workers● Supervisors use mistakes as learning opportunities, not criticism● A nurse/unit manager who is a competent leader● Praise and recognition for job well doneA nurse/unit manager who backs up the care worker staff in decision making, even if the conflict is with other professions	Cronbach’s α = .84.The scale was calculated as the mean over all items of the subscales. Higher values indicate more favorable ratings.
Staffing and resources adequacy	PES-NWI (Lake, 2002); 3/4 (1 = strongly disagree to 4 = strongly agree)	● Enough time for discussion of resident problems● Enough qualified personnel to provide quality of careEnough staff to get work done	Cronbach’s α = .74.The scale was calculated as the mean over all items of the subscales. Higher values indicate more favorable ratings.
Collaboration with colleagues	Safety Attitudes Questionnaire (SAQ; Sexton et al., 2006); Single-item question 1/4 (1 = very low to 4 = very high and the option “don’t know”)	With respect to your experiences on this unit describe the quality of collaboration that you have experienced with: team colleagues on your unit	The item was dichotomized into low (very/rather low) vs. high (rather/very high) collaboration due to a high negative skew.
Work stressors:	Health Professions Stress Inventory (HPSI; Lapane & Hughes, 2007); 12/5, (0 = never to 4 = very often) out of the 12 items, three new subscales (see below) were built based on an exploratory factor analysis
Conflict and lack of recognition	HPSI (Lapane & Hughes, 2007); 6/5 (0 = never to 4 = very often)	How often do you feel stressed . . .● because you disagree with other health professionals concerning the treatment of a resident?● because you experience conflicts with supervisors and/or managers?● because you do not have adequate information regarding a resident’s condition? (e.g., information is not available or there is no time to obtain the information)● because no one asks for your opinion when making decisions about your job?● because you are not paid enough?because you are not able to use all of your skills on the job?	Cronbach’s α = .76.The subscale was measured as mean over all items where higher values indicate a higher frequency of work stress.
Workload	HPSI (Lapane & Hughes, 2007); 3/5 (0 = never to 4 = very often)	How often do you feel stressed . . .● dealing with “difficult” residents (for example, behavior problems)?● because you have so much work to do that everything cannot be done well?because there aren’t enough people working to get the work done well?	Cronbach’s α = .74. The subscale was measured as mean over all items where higher values indicate a higher frequency of work stress.
Lack of preparation	HPSI (Lapane & Hughes, 2007); 3/5 (0 = never to 4 = very often)	How often do you feel stressed . . .● because you are not prepared to meet the needs of residents based on your training or experience?● because you are afraid you will make a mistake in the treatment of a resident?How often does caring for terminally ill residents make you overwhelmed?	Cronbach’s α = .64.The subscale was measured as mean over all items where higher values indicate a higher frequency of work stress,
Care worker outcomes
Emotional exhaustion	(Maslach & Jackson, 1981); Single-item question 1/7 (0 = never to 6 = daily)	About care workers’ feelings regarding emotional exhaustion due to their work	The validity of measuring emotional exhaustion with a single item has been described by West, Dyrbye, Sloan, and Shanafelt (2009).
Physical health problems	Health Index defined in Swiss Nursing Homes Human Resources Project (SHURP) based on items from the Swiss Health Survey (Bundesamt für Statistik, 2007); 5/3 (0 = not at all to 2 = strongly)	Did you suffer from any of the following conditions in the last 4 weeks:● Back pain, low-back pain● General weakness, tiredness, lack of energy● Problems with sleeping● Headache, pressure in your head or pain in your facePain in your joints or limbs	Cronbach’s α = .69.The index was calculated as the sum over all items. Higher values indicate more health problems.
Affective organizational commitment	Assessment of Affective, Calculatory and Normative Commitment to the Organization, the Profession/Activity and Employment Commitment (COBB; Felfe, Six, Schmook, & Knorz et al., 2010); 5/5 (1 = strongly disagree to 5 = strongly agree)	● I would be very happy to spend the next years with this organization● I feel a strong sense of “belonging” to my organization● I do not feel “emotionally attached” to this organization● I am proud of working in this organizationI think, my ideals about good care fit with the ideals of this organization	Cronbach’s α = .87.The subscale was measured as mean over all items where higher values indicate a higher affective organizational commitment.

Control Variables

Organizational factors were assessed in the unit and facility questionnaires. They were included as control variables based on prior empirical evidence (Clausen et al., 2014; Decker et al., 2009; Donoghue & Castle, 2009), except for language region (1 = German-, 2 = French-, 3 = Italian-speaking), which was added to control for cultural differences between Switzerland’s three linguistic regions. The control variables included nursing home bed size (20-49 beds = small, 50-99 beds = medium and ≥100 beds = large), profit status (1 = public, 2 = private subsidized, 3 = private), and service area type (rural vs. urban; close to a national border vs. not close). For service area type, proximity to the Swiss border was relevant because Swiss nursing homes close to neighboring countries report fewer recruitment problems. The staffing level was compared using the number of full-time equivalent (FTE) positions per 100 beds. Units’ mean care load data (derived from reports supplied to the Swiss national health care provider reimbursement system) were used as a proxy for their residents’ care dependency levels. Based on these figures, residents were grouped into 12 care levels. Each higher level corresponds to a 20-min increment in daily care needs (e.g., a resident in care group 8 would receive 160 min of direct-care time per 24-hr period; Curaviva.ch., 2010). The nursing homes supplied these data from their administrative systems. As for care worker characteristics, two items from the care worker questionnaire were assessed: age (years) and education (RNs ≥ 3-year education), LPNs (3-year education), CNAs (1- to 2-year education), and NAs (short courses or training on the job).

Data Collection

All SHURP data were collected between May 2012 and April 2013 by the Institute of Nursing Science, University of Basel. Prior to each facility’s inclusion in the study, the responsible administrator supplied written informed consent. For individual respondents, the voluntary return of the anonymous care worker questionnaire was considered informed consent. The study was approved by all Swiss cantonal ethics committees (leading ethics committee: Ethikkommission Nordwest- und Zentralschweiz [EKNZ]; Ref. Nr. EK: 02 / 12). Detailed information concerning the data collection process is provided by Schwendimann et al. (2014).

Data Analysis

To assess the prevalence of care workers’ intention to leave and its variability between facilities, that is, to address Aim 1 of this study, each facility’s ratio of workers reporting intention to leave (as specified below) to its total number of included respondents was calculated. To test the relationship between intention to leave (as a dependent variable) with work environment factors and care worker outcomes (independent variables)—that is, to address Aim 2—a multiple regression model with generalized estimating equations (GEEs) was used. First, bivariate models were applied; next, a multivariate logistic regression was conducted on a model including all variables. Multicollinearity was tested with the variance inflation factor (VIF), expecting values below 5 (Field, Miles, & Field, 2012). The results indicated that all variables could be included in the model. To examine whether the care workers’ responses depended upon unit or facility membership, intraclass correlation coefficients (ICC1) were calculated. The resulting facility and unit-level ICC1s—.08 and .09, respectively—were indicating a need to control for nestedness of care workers on both levels. To compare the models’ fits, Quasi-Likelihoods under the Independence Model Criterion (QICs) were calculated. As the threshold to differentiate no intention to leave from intention to leave was initially set very low, with the cutoff set to 1 (i.e., 0 = no intention to leave; 1-12 = intention to leave), a sensitivity analysis was performed with the cutoff set to 3 (i.e., 0-2 = no intention to leave; 3-12 = intention to leave). The significance level was set at a p value of <.05. Cases with missing data were removed listwise from the analysis. The control variables were used in the adjusted model. Data analysis was performed with IBM SPSS Statistics for Windows, Version 22.0 (Armonk, New York: IBM Corp.).

Results

Sample Description

The final sample consisted of 3,984 care workers from 481 units in 156 nursing homes. The mean response rate across all units was 79.3%. Almost half of all respondents (48%) were employed in medium-sized nursing homes (50-99 beds) and had a mean age of 43 years (SD: 12) with 26% of care workers being RNs. Across all units, the average number of FTEs per 100 beds was 51 (SD: 15). Based on 3,960 valid responses and a scale of 0 to 12, using 0 (cutoff: 1) as no intention to leave, 56% respondents had an intention to leave. When using ratings between 0 and 2 (cutoff: 3) as an indicator of no intention to leave, 38% respondents showed intention to leave. As for variability between nursing homes, in two facilities, no care workers expressed any intention to leave, while in two other, all care workers indicated intention to leave. In 11 of the remaining 152 nursing homes, 80% or more care workers reported intention to leave their facility; and in three, 20% or fewer indicated so. The percentage of respondents who were rather or very satisfied with their collaboration with colleagues was 96%. Overall, leadership was rated as encouraging and positive (mean: 3.1 on a scale of 1-4). More detailed information on the independent variables is shown in Table 2.

Table 2.

Sample Characteristics.

Characteristics/factors	%	M	SD	Missing (%)
Facility characteristics (n = 156)
Facility size
Small (20-49 beds)	37.8			0 (0)
Medium (50-99 beds)	48.1			0 (0)
Large (≥100)	14.1			0 (0)
Ownership status
Public	37.2			0 (0)
Private subsidized	26.3			0 (0)
Private	36.5			0 (0)
Language regions
German	75.6			0 (0)
French	18.6			0 (0)
Italian	5.8			0 (0)
Service area
Urban	37.8			0 (0)
Rural	62.2			0 (0)
Close to the country border (yes)	23.7			0 (0)
Unit characteristics (n = 481 units)
Number of FTE/100 beds		51	15.4	0 (0)
Mean care load (scale from 1 to 12)		5.8	1.6	0 (0)
Care worker characteristics (n = 3,984)
Age (years) (n = 3,875)		43.1	12.3	109 (2.8)
Education (n = 3,949)				35 (0.9)
Registered nurse (3-4 years of education)	25.7
Licensed practical nurse (3 years of education)	21.8
Certified assistant nurse (1-2 years of education)	19.5
Nurse aide (training on the job)	29.8
Other	3.2
Work environment factors (scale range)
PES-NWI: Leadership (1-4) (n = 3,984)		3.1	0.6	0 (0)
PES-NWI: Staffing and resources adequacy (1-4) (n = 3,977)		2.8	0.7	7 (0.2)
Collaboration with colleagues very/rather satisfied (n = 3,932)	96.1			52 (1.3)
HPSI Work stressors: Lack of preparation (0-4) (n = 3,971)		0.7	0.6	13 (0.3)
HPSI Work stressors: Conflict and lack of recognition (0-4) (n = 3,977)		0.9	0.7	7 (0.2)
HPSI Work stressors: Workload (0-4) (n = 3,977)		1.5	0.8	7 (0.2)
Care worker outcomes
Emotional exhaustion (0-6) (n = 3,938)		2.1	1.6	46 (1.2)
Physical health problems (Health Index (0-10)) (n = 3,878)		3.5	2.3	106 (2.7)
COBB: Affective organizational commitment (1-5) (n = 3,978)		3.8	0.8	6 (0.1)

Note. Preferable scores are underlined. FTE = full-time equivalent; PES-NWI = Practice Environment Scale–Nursing Work Index; HPSI = Health Professions Stress Inventory; COBB = Assessment of Affective, Calculatory and Normative Commitment to the Organization, the Profession/Activity and Employment Commitment.

Relationship Between Work Environment Factors and Care Worker Outcomes

On average, respondents with higher overall intention to leave reported lower leadership ratings (OR = 0.62, 95% CI = [0.50, 0.76]) and higher stress due to conflict and lack of recognition (OR = 1.61, 95% CI = [1.32, 1.98]). Higher intention to leave was also related to lower affective organizational commitment (OR = 0.14, 95% CI [0.11, 0.16]). Intention to leave increased with higher emotional exhaustion (OR = 1.18, 95% CI = [1.10, 1.26]) and more physical health problems (OR = 1.09, 95% CI = [1.04, 1.14]). The results of unadjusted and adjusted models were very similar. The sensitivity analysis with a higher cutoff for intention to leave showed no differences. Further details are provided in Table 3.

Table 3.

Relationship of Work Environment Factors and Care Worker Outcomes With Intention to Leave.

Factors	Unadjusted (n = 3,663)			Adjusted^a (n = 3,663)
Factors	OR	95% CI	p value	OR	95% CI	p value
Work environment factors
PES-NWI: Leadership	0.62	[0.51, 0.76]	.00	0.62	[0.50, 0.76]	.00
PES-NWI: Staffing and resources adequacy	1.13	[0.94, 1.36]	.18	1.18	[0.98, 1.42]	.09
Collaboration with colleagues	0.92	[0.52, 1.64]	.79	0.79	[0.43, 1.46]	.46
HPSI Work stressors: Conflict and lack of recognition	1.55	[1.27, 1.89]	.00	1.61	[1.32, 1.98]	.00
HPSI Work stressors: Workload	1.12	[0.96, 1.30]	.16	1.15	[0.98, 1.35]	.08
HPSI Work stressors: Lack of preparation	0.82	[0.68, 0.98]	.03	0.84	[0.70, 1.02]	.07
Care worker outcomes
Emotional exhaustion	1.21	[1.13, 1.29]	.00	1.18	[1.10, 1.26]	.00
Physical health problems (Health Index)	1.09	[1.04, 1.14]	.00	1.09	[1.04, 1.14]	.00
COBBAOC: Affective organizational commitment	0.14	[0.12, 0.16]	.00	0.14	[0.11, 0.16]	.00
QIC		3,256.99			3,209.44

Note. Bold = significant. OR = odds ratio; CI = confidence interval; PES-NWI = Practice Environment Scale–Nursing Work Index; HPSI = Health Professions Stress Inventory; COBB = Assessment of Affective, Calculatory and Normative Commitment to the Organization, the Profession/Activity and Employment Commitment; QIC = Quasi-Likelihoods under the Independence Model Criterion; FTE = full-time equivalent.

The adjusted model was controlled for nursing home level: Size, profit status, language region, service areas; unit level: FTE/100 beds, mean care load; care worker level: Age, education.

Discussion

This study described both the overall prevalence of intention to leave of respondents and the interfacility variability of intention to leave in addition to a number of factors related to care worker intention to leave in Swiss nursing homes. Although prevalences varied widely between facilities (range: 0%-100%), more than half of the surveyed care workers reported intention to leave. When looking at the work environment factors, our results confirm the excepted relationship between nursing home care workers’ intention to leave and their perception of having a supportive and competent leadership as well as their experiences of workplace stress due to “conflict and lack of recognition.” Moreover, for all care worker outcomes (i.e., emotional exhaustion, physical health problems, and affective organizational commitment), our assumptions were confirmed. However, perceptions of staffing and resources adequacy, collaboration/teamwork, and work stress due to “workload” and “lack of preparation” did not show any relationship with intention to leave.

Since definitions for intention to leave differ concerning inclusion criteria and settings between studies of this topic (Takase, 2010), we are unable to compare either our prevalences or the interfacility variability with those of other studies. Our sample’s high prevalence of intention to leave is certainly due to the high sensitivity of the scale used: Arguably, a cutoff of 1 (with 0 as no intention to leave and 1-12 as intention to leave) on a scale from 0 to 12 is a rather rigorous cutoff. Still, our sensitivity analysis showed that even with a cutoff of 3 (with 0-2 as no intention to leave and 3-12 as intention to leave), 38% of the care workers still indicated intention to leave. The high interfacility variability of intention to leave might reflect the importance of the organizational context.

Congruent with previous studies (Chenoweth, Jeon, Merlyn, & Brodaty, 2010), our findings support the claim that individual nursing homes’ leadership practices (e.g., supporting care workers in decision making, using mistakes as learning opportunities) affect their staff turnover. As previous studies (Blomberg, James, & Kihlgren, 2013; McGilton, Boscart, Brown, & Bowers, 2014; Tourangeau et al., 2010) have linked lower intention to leave with more acknowledgment from superiors, this issue might be reduced in facilities where care workers receive praise and other forms of recognition from their supervisors and leaders. Another explanation for this relationship might be the challenging nature of the care work with multimorbid and cognitively impaired nursing home residents (Cohen & Golan, 2007; Moseley, Jeffers, & Paterson, 2008). The most successful leaders empower and support care workers not only to face these challenges but to develop meaningful relationships with residents (McGilton et al., 2014), thereby decreasing the intention to leave of their employees. They also incorporate care workers’ input in decision making and inspire a shared vision (Donoghue & Castle, 2009; Moseley et al., 2008). Furthermore, other studies have found that care workers whose leaders offer learning opportunities, skills training and other professional development report lower intention to leave (Chenoweth et al., 2010; McGilton et al., 2014). In fact, one defining characteristic of supportiveness in leadership is the encouragement of employees to develop new skills and pursue further education (Erenstein & McCaffrey, 2007).

Although one might reasonably expect a relationship between intention to leave and staffing and resources adequacy based on studies in the hospital setting (Galletta et al., 2011; Gardner et al., 2007), our analyses did not confirm such a relationship. To our knowledge, no research has yet specifically examined this relationship in nursing homes. Based on our results, nursing homes care workers might define staffing and resources adequacy differently from their hospital counterparts. Blomberg et al. (2013) explained that, compared with similar employees in hospitals, nursing home care workers consider their work basically less stressful (even when personnel is low), and accept that, while they usually have enough personnel, there will be days when they have to work shorthanded.

Contrary to our expectations—and previous research findings (McGilton et al., 2014; Prentice & Black, 2007; Tummers et al., 2013)—no significant relationship was found between collaboration/teamwork and intention to leave. This difference from previous findings might result partly from differing definitions of collaboration/teamwork. For example, Tummers et al. (2013) referred to it as a “good working atmosphere characterized by a pleasant interaction with colleagues working in the same unit, a good team spirit and collegial behavior” (p. 2829). In contrast, our study addressed the issue using a single-item and a dichotomized 4-point Likert-type scale to assess overall quality of collaboration with team colleagues, which showed little variability.

As for the relationship between work stressors and intention to leave, only work stress due to “conflict and lack of recognition” was related to intention to leave. The other two factors—“workload,” that is, measuring perceived workload and staffing resources, and “lack of preparation,” that is, the management of complex resident situations and care workers’ skills/training—showed no significant relationship. In general, research shows a direct relationship between work stressors and intention to leave (Karantzas et al., 2012; Stewart et al., 2011). It should be noted that work stressor measurement methods differed between studies: Stewart et al. (2011) measured perceived stress with a global four-item measurement. Therefore, our result for this variable is only partially comparable with those of the other studies cited. It is also possible that we found a significant association only in the “conflict and lack of recognition” subfactor because, considering care workers’ strong positive feelings about the care they provide, stress related to residents is simply less severe. That is, they may have assessed the stress they felt in relation to interpersonal problems with coworkers and leaders much more seriously—and associated it much more with intention to leave—than worker-resident issues, which may be comparatively transitory (Cohen-Mansfield, 1989; Schaefer & Moos, 1996).

In line with other studies, both emotional exhaustion and health problems had modestly significant relationships to intention to leave. In a longitudinal study, Rosen et al. (2011) found that care workers’ intention to leave was predicted both by reduced emotional well-being and by increased physical health problems. Care workers exposed to a continuously high level of physical and/or emotional strain suffering from health problems might prefer to leave to protect themselves from further deterioration of their situation (Clausen et al., 2014).

Similar to previous studies in the nursing home setting (Karsh et al., 2005; Takase, 2010), our findings indicate that high affective organizational commitment is significantly linked with decreased intention to leave (Westphal & Gmür, 2009). One possible explanation could be that high affective organizational commitment accompanies trust in the organization, satisfaction with management and superior behavior, leading to increasingly close affiliation with the organization and increasing self-esteem and in turn to less intention to leave (Westphal & Gmür, 2009).

Finally, it should be acknowledged that the work environment factors and/or care worker outcomes researched in this study account only for one set of intention to leave factors that depend on presumably modifiable characteristics within nursing homes. Other linked factors, for example, relocation, career advancement, family/personal issues, and mobility (LeVasseur, Wang, Mathews, & Boland, 2009; Rondeau et al., 2008) were beyond the scope of this study.

Strengths and Limitations

The SHURP study’s high response rate, large sample size, and stratified random selection of participating facilities permit generalization of our findings to all nursing homes with at least 20 beds across Switzerland. However, considering the particular characteristics of Swiss nursing homes (e.g., 31% RNs, 46% medium size nursing homes with 50-99 beds), international comparisons should be addressed with caution. The selection of previously validated instruments is another important strength. Nevertheless, the following potential limitations should be noted.

First, the obtained survey measurements stem from a single study, leaving the possibility of method bias; second, the cross-sectional study design precludes the inference of causal relationships between independent and dependent study variables. Moreover, the secondary data analysis limited the ability to fully evaluate the relationship between intention to leave and other factors found in the literature such as the market situation. Third, we may have lost statistical power by dichotomizing intention to leave, which we did because of its high skewness. However, a sensitivity analysis showed the robustness of our results. Our study showed a high variability of intention to leave between nursing homes. Although the exploration of positive deviant cases with very low rates of intention to leave might contribute to interventions to reduce turnover, such an in-depth examination was beyond the scope of this secondary analysis.

Implications for Education and Research

For future research, to permit national and international comparison, work stressor data should be collected and assessed with standardized measurement tools. Likewise, future studies should consider variables not included in this study, for example, external factors such as the organization’s surrounding environment (e.g., job market, family conditions, and mobility). Longitudinal research is needed to explore and illustrate causal relationships between intention to leave and related factors. In this study, we assumed that intention to leave would be strongly related to turnover, but it is possible that care workers remain in institutions despite their intention to leave (e.g., because they cannot find suitable alternatives). From this perspective, it would be interesting to examine the consequences of such noncommitted behavior (e.g., absenteeism, decrease of care performance quality).

Implication for Practice and Policy

According to our findings, a positive and attractive work environment with supportive leadership is crucial for workforce retention. Our findings indicate that more investment is warranted in leadership training for nursing home managers (Cummings et al., 2014; Jeon et al., 2015; Vogelsmeier, Farrah, Roam, & Ott, 2010). In addition to focusing on the evidence regarding risk factors and correlates of intention to leave, this training should emphasize options for multilevel interventions that can be used by nurse managers to reduce turnover. One concrete goal would be the provision of a positive organizational climate with the possibility to offer high-quality care, ongoing education, and family-friendly services (Blomberg et al., 2013; Chenoweth et al., 2010; Curaviva.ch, 2013). Although emotional exhaustion and physical health problems were only modestly associated with intention to leave, it should be noted that the impact of these factors could be reduced by leadership commitment to empowering and supporting care workers regarding the physical and emotional strains of their daily work (Schaefer & Moos, 1996). Moreover, work incapacitation and absenteeism due to emotional exhaustion and physical symptoms impact not only the individual but also the organization, generating high costs and reducing quality of care and patient safety (Letvak, Ruhm, & Gupta, 2012). Therefore, it is crucial that protective measures for care worker health begin at the organizational level, for example, with a systematic approach to recording and examining reasons for work incapacity, and include action plans to promote care worker health (Keller, 2006). Accordingly, evidence-based nursing home management demands that nurse managers be prepared to take policy initiative, advocating for optimal working conditions for their workforce both in- and outside the nursing home.

At the policy level, it is necessary to formulate strategies and action plans that will allow nursing homes to offer equal pay as other health sectors . Also, the dissemination of best practice examples should support nursing homes in developing and tailoring strategies to improve retention, facilitate open discussions between stakeholders about emerging trends and challenges, and promote the creation of networks to encourage more efficient use of resources (GDK & OdASanté, 2016).

Conclusion

Nursing homes are struggling to maintain high care quality in the face of increased demand, difficulties in staff recruitment and staff retention, while experiencing high turnover rates. As intention to leave is a powerful and potentially useful predictor of turnover, awareness of related factors (work environment factors and care worker outcomes) will allow nursing home administrators to implement multilevel countermeasures, for example, interventions to modify their leadership styles and bolster care workers’ affective organizational commitment.

The study findings indicated that, of the factors explored, leadership and affective organizational commitment were most strongly related to intention to leave. Although the cross-sectional design allows no causal inferences, it can be suggested that supportive leadership fosters a sense of organizational commitment in staff members, thereby reducing their intention to leave. In Swiss nursing homes, leadership development interventions incorporating the findings described above may decrease care workers’ intention to leave.

Footnotes

Acknowledgements

We acknowledge the contributions of all members of the Swiss Nursing Homes Human Resources Project (SHURP) research team and express our thanks to the participating nursing homes and the care workers for their time and collaboration.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Swiss Health Observatory, Neuchâtel, Switzerland, the Nursing Science Foundation Switzerland, Basel, Switzerland, the University of Basel’s Research Fund 2012, Basel, Switzerland, the Swiss Alzheimer Association, Yverdon, Switzerland, and private sponsors.

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