Literature DB >> 22092452

Predictors of actual turnover in a national sample of newly licensed registered nurses employed in hospitals.

Carol S Brewer1, Christine T Kovner, William Greene, Magdalene Tukov-Shuser, Maja Djukic.   

Abstract

AIM: This paper is a report of a study of factors that affect turnover of newly licensed registered nurses in United States hospitals.
BACKGROUND: There is a large body of research related to nursing retention; however, there is little information specific to newly licensed registered nurse turnover. Incidence rates of turnover among new nurses are unknown because most turnover data are not from nationally representative samples of nurses.
METHOD: This study used a longitudinal panel design to obtain data from 1653 registered nurses who were recently licensed by examination for the first time. We mailed surveys to a nationally representative sample of hospital registered nurses 1 year apart. The analytic sample consisted of 1653 nurses who responded to both survey mailings in January of 2006 and 2007.
RESULTS: Full-time employment and more sprains and strains (including back injuries) result in more turnover. Higher intent to stay and hours of voluntary overtime and more than one job for pay reduces turnover. When we omitted intent to stay from the probit model, less job satisfaction and organizational commitment led to more turnover, confirming their importance to turnover. Magnet Recognition Award(®) hospitals and several other work attributes had no effect on turnover.
CONCLUSION: Turnover problems are complex, which means that there is no one solution to decreasing turnover. Multiple points of intervention exist. One specific approach that may improve turnover rates is hospital policies that reduce strains and sprains.
© 2011 The Authors. Journal of Advanced Nursing © 2011 Blackwell Publishing Ltd.

Entities:  

Mesh:

Year:  2011        PMID: 22092452     DOI: 10.1111/j.1365-2648.2011.05753.x

Source DB:  PubMed          Journal:  J Adv Nurs        ISSN: 0309-2402            Impact factor:   3.187


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