The purpose of this research was to examine the hospital job preferences of registered nurses (RNs) in the U.S. using California as a study site. Rural areas face ongoing challenges related to recruitment and retention of qualified health professionals (World Health Organisation, 2012). In the U.S., rural areas have lower nurse-to-population ratios (Fields, Bigbee, & Bell, 2016), an older, poorer and sicker population than the national average (Singh & Siahpush, 2014) and are disproportionally affected by nursing shortages. The most recent projections of registered nurse (RN) workforce growth in the U.S. through 2030 suggest considerable variation between regions, ranging from almost zero growth in the number of RNs per capita in the Pacific and New England regions, to 40% growth in the East and West South Central regions of the U.S. (Auerbach, Buerhaus, & Staiger, 2017).
There is limited information available regarding influences on nurses’ job choice in the U.S., and little understanding of how nurses make trade-offs between desired and less desirable job characteristics when choosing between jobs. International studies have used discrete choice experiments to understand the determinants of health workers’ job preferences, including financial and non-financial factors that influence choice of job, workplace, or specialization (Mandeville, Lagarde, & Hanson, 2014; Scott, et al, 2013), but to our knowledge, none of these have been conducted in the U.S.
The specific aims of the study were: 1) To identify job attributes important to RNs in the U.S. and levels within these attributes feasible for use in recruiting RNs for rural jobs, 2) To identify the relative importance of attributes on job choice and variations between sub-groups of RNs, and 3) To predict the impact of changes in the levels of the attributes on the probability of RNs choosing one job over another.
Methods:
We conducted a discrete choice experiment to compare how California registered nurses (RNs) from urban, large-, small- and isolated-rural communities valued factors identified as important to job choice. Aim 1 was accomplished through a literature review and semi-structured interviews with 11 nurse experts. Data were refined into eight attributes, each with two to four levels. Experimental design was used to create the hypothetical choice sets using Ngene software, and to block the survey into three versions, each with 12 choice sets. The survey was pilot tested in an iterative manner with a convenience sample of 12 urban and rural registered nurses to refine the language, test construct and content validity. The final survey was mailed in 2015 to 1,000 RNs with active California licenses, randomly selected from the California Board of Registered Nursing database.
Aim 2 was achieved using a mixed logit model to estimate RNs preferences for different levels of the attributes for all respondents and for sub-groups. The mixed logit model allows for preference heterogeneity across the sample by treating coefficients as random. It also allows for multiple observations from each respondent, appropriate in our study where each respondent was presented with 12 choice sets. All models included main effects, without interaction terms. To assess potential differences for subgroups with different professional or demographic characteristics, we estimated a main effects mixed logit model for each subgroup.
Willingness to pay estimates and simulations of uptake rates were calculated to address Aim 3. Willingness to pay was estimated as the ratio of the value of a specific attribute level to the negative of the earnings attribute, using preferences estimated in the main effects mixed logit models. The Stata post-estimation command nlcom, which assumes nonlinear combinations of estimated parameters, was used. Uptake rates, or changes in the probability of an RN accepting a job when the level of one attribute was changed compared to the baseline job were also simulated using the nlcom command. Confidence intervals were calculated for willingness to pay and for uptake rates using the delta method. All analyses were conducted using Stata version 14.1.
Results:
Eight factors that were modifiable, feasible for implementation by employers and important to job choice by RNs in the U.S. were identified: earnings, nursing voice in management, tuition reimbursement, scheduling, patient care team, leadership, location and nursing sensitive patient care outcomes. Of the 1,000 RNs surveyed, 238 responses were received, reflecting a 23.8% response rate. Forty-five respondents who were not currently working in nursing and had no plans to return were excluded from the choice experiment.
Respondents consistently valued a cohesive inter-professional patient care team and a strong nursing voice in management highest. Overall, working with a cohesive inter-professional patient care team that interacted frequently, versus a fragmented team with limited interaction, was the most valued attribute (coefficient 1.75 [SE, 0.18]). A strong nursing voice in management (coefficient 1.44 [SE, 0.17]), readily available and responsive nursing leadership (coefficient 1.40 [SE, 0.17]), and flexible scheduling (coefficient 1.32 [SE, 0.18]) were also highly valued. A job in a small city or urban coastal location was not significantly preferred over one in a rural town, but a large urban inland location was negatively valued (coefficient -0.70 [SE, 0.23]). While earnings were important, findings suggest RNs were willing to sacrifice earnings to secure other valued job characteristics.
Conclusion:
Our study findings expand what is known about RN job choice in the U.S. They provide specific, actionable information that California hospitals can use to evaluate their work environment to support increased recruitment and retention of RNs, particularly for rural underserved areas. There is an opportunity for future studies with larger and more geographically diverse samples to explore national and regional variations in preferences, and further contribute to the international literature around how nurses’ choose between jobs.
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