Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.

<h4>Background</h4>High turnover among healthcare workers is an increasingly common phenomenon in hospitals worldwide, especially in intensive care units (ICUs). In addition to the serious financial consequences, this is a major concern for patient care (disrupted continuity of care, dec...

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Autores principales:	Oumou Salama Daouda, Mounia N Hocine, Laura Temime
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4dd24cf076f441e19638c9aa3c4acb54
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spelling	oai:doaj.org-article:4dd24cf076f441e19638c9aa3c4acb542021-11-25T06:19:13ZDeterminants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.1932-620310.1371/journal.pone.0251779https://doaj.org/article/4dd24cf076f441e19638c9aa3c4acb542021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251779https://doaj.org/toc/1932-6203<h4>Background</h4>High turnover among healthcare workers is an increasingly common phenomenon in hospitals worldwide, especially in intensive care units (ICUs). In addition to the serious financial consequences, this is a major concern for patient care (disrupted continuity of care, decreased quality and safety of care, increased rates of medication errors, …).<h4>Objective</h4>The goal of this article was to understand how the ICU-level nurse turnover rate may be explained from multiple covariates at individual and ICU-level, using data from 526 French registered and auxiliary nurses (RANs).<h4>Methods</h4>A cross-sectional study was conducted in ICUs of Paris-area hospitals in 2013. First, we developed a small extension of a multi-level modeling method proposed in 2007 by Croon and van Veldhoven and validated its properties using a comprehensive simulation study. Second, we applied this approach to explain RAN turnover in French ICUs.<h4>Results</h4>Based on the simulation study, the approach we proposed allows to estimate the regression coefficients with a relative bias below 7% for group-level factors and below 12% for individual-level factors. In our data, the mean observed RAN turnover rate was 0.19 per year (SD = 0.09). Based on our results, social support from colleagues and supervisors as well as long durations of experience in the profession were negatively associated with turnover. Conversely, number of children and impossibility to skip a break due to workload were significantly associated with higher rates of turnover. At ICU-level, number of beds, presence of intermediate care beds (continuous care unit) in the ICU and staff-to-patient ratio emerged as significant predictors.<h4>Conclusions</h4>The findings of this research may help decision makers within hospitals by highlighting major determinants of turnover among RANs. In addition, the new approach proposed here could prove useful to researchers faced with similar micro-macro data.Oumou Salama DaoudaMounia N HocineLaura TemimePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251779 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Oumou Salama Daouda Mounia N Hocine Laura Temime Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.
description	<h4>Background</h4>High turnover among healthcare workers is an increasingly common phenomenon in hospitals worldwide, especially in intensive care units (ICUs). In addition to the serious financial consequences, this is a major concern for patient care (disrupted continuity of care, decreased quality and safety of care, increased rates of medication errors, …).<h4>Objective</h4>The goal of this article was to understand how the ICU-level nurse turnover rate may be explained from multiple covariates at individual and ICU-level, using data from 526 French registered and auxiliary nurses (RANs).<h4>Methods</h4>A cross-sectional study was conducted in ICUs of Paris-area hospitals in 2013. First, we developed a small extension of a multi-level modeling method proposed in 2007 by Croon and van Veldhoven and validated its properties using a comprehensive simulation study. Second, we applied this approach to explain RAN turnover in French ICUs.<h4>Results</h4>Based on the simulation study, the approach we proposed allows to estimate the regression coefficients with a relative bias below 7% for group-level factors and below 12% for individual-level factors. In our data, the mean observed RAN turnover rate was 0.19 per year (SD = 0.09). Based on our results, social support from colleagues and supervisors as well as long durations of experience in the profession were negatively associated with turnover. Conversely, number of children and impossibility to skip a break due to workload were significantly associated with higher rates of turnover. At ICU-level, number of beds, presence of intermediate care beds (continuous care unit) in the ICU and staff-to-patient ratio emerged as significant predictors.<h4>Conclusions</h4>The findings of this research may help decision makers within hospitals by highlighting major determinants of turnover among RANs. In addition, the new approach proposed here could prove useful to researchers faced with similar micro-macro data.
format	article
author	Oumou Salama Daouda Mounia N Hocine Laura Temime
author_facet	Oumou Salama Daouda Mounia N Hocine Laura Temime
author_sort	Oumou Salama Daouda
title	Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.
title_short	Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.
title_full	Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.
title_fullStr	Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.
title_full_unstemmed	Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.
title_sort	determinants of healthcare worker turnover in intensive care units: a micro-macro multilevel analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4dd24cf076f441e19638c9aa3c4acb54
work_keys_str_mv	AT oumousalamadaouda determinantsofhealthcareworkerturnoverinintensivecareunitsamicromacromultilevelanalysis AT mounianhocine determinantsofhealthcareworkerturnoverinintensivecareunitsamicromacromultilevelanalysis AT lauratemime determinantsofhealthcareworkerturnoverinintensivecareunitsamicromacromultilevelanalysis
_version_	1718413923829415936

Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.

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