A structural equation model of turnover for a longitudinal survey among early career registered nurses. Issue 11 (November 2015)

• Record Type: Journal Article
• Authors: Brewer, Carol S.; Chao, Ying-Yu; Colder, Craig R.; Kovner, Christine T.; Chacko, Thomas P.
• Is Part Of: International journal of nursing studies. Volume 52:Issue 11(2015:Nov.)
• Page Start: 1735
• Page End: 1745
• Publication Date: 2015-11
• Subjects: Nursing -- Periodicals; Nursing -- Periodicals; Soins infirmiers -- Périodiques; Nursing; Periodicals; 610.73
• DOI: 10.1016/j.ijnurstu.2015.06.017
• Languages: English
• ISSNs: 0020-7489

Abstract

<abstract abstract-type="author" id="abs0005"> <title id="sect0005">Abstract</title> <sec> <title id="sect0010">Background</title> <p id="spar0005">Key predictors of early career nurses' turnover are job satisfaction, organizational commitment, job search, intent to stay, and shock (back injuries) based on the literature review and our previous research. Existing research has often omitted one of these key predictors.</p> </sec> <sec> <title id="sect0015">Objectives</title> <p id="spar0010">The purpose of this study in a sample of early career nurses was to compare predictors of turnover to nurses' actual turnover at two time points in their careers.</p> </sec> <sec> <title id="sect0020">Design</title> <p id="spar0015">A multi-state longitudinal panel survey of early career nurses was used to compare a turnover model across two time periods. The sample has been surveyed five times.</p> <p id="spar0020">Participants: The sample was selected using a two-stage sample of registered nurses nested in 51 metropolitan areas and nine non-metropolitan, rural areas in 34 states and the District of Columbia.</p> </sec> <sec> <title id="sect0025">Methods</title> <p id="spar0025">The associations between key predictors of turnover were tested using structural equation modeling and data from the earliest and latest panels in our study. We used predictors from the respondents who replied to the Wave-1 survey in 2006 and their turnover status from Wave 2 in 2007 (<italic>N</italic> = 2386). We compared these results to the remaining respondents' predictors from Wave 4 in 2011 and their turnover status in Wave 5 in 2013 (<italic>N</italic> = 1073). We tested and found no effect for missingness from Wave 1–5 and little evidence of attrition bias.</p> </sec> <sec> <title id="sect0030">Results</title> <p id="spar0030">Strong support was found for the relationships hypothesized among job satisfaction, organizational commitment, intent to stay, and turnover, with some support for shock and search in the Wave 1–2 sample. However, for Wave 4–5 sample (<italic>n</italic> = 1073), none of the paths through search were significant, nor was the path from shock to turnover.</p> </sec> <sec> <title id="sect0035">Conclusions</title> <p id="spar0035">Nurses in the second analysis who had matured longer in their career did not have a significant response to search or shock (back injuries), which may indicate how easily experienced registered nurses find new jobs and/or accommodation to jobs requiring significant physicality. Nurse turnover is a major concern for healthcare organizations because of its costs and related outcomes. The relevant strength and relationships of these key turnover predictors will be informative to employers for prioritizing strategies to retain their registered nurse workforce. We need more research on programs that implement changes in the work environment that impact these two outcomes, as well as research that focuses on the relevant strength or impact to help administrators prioritize translation of results.</p> </sec> </abstract>