Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques

Abstract Objectives Nurses are at a high risk of developing mental health problems due to exposure to work environment risk factors. Previous research in this area has only examined a few factors within nurses’ work environments, and those factors were not conceptualized with the goal of improving w...

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Autores principales:	Farinaz Havaei, Xuejun Ryan Ji, Maura MacPhee, Heather Straight
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Mental health Work environment factors Nursing National standard of psychological health and safety Machine learning RT1-120
Acceso en línea:	https://doaj.org/article/9179915db8c641fd92ec505d8a6c6add
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spelling	oai:doaj.org-article:9179915db8c641fd92ec505d8a6c6add2021-11-07T12:10:33ZIdentifying the most important workplace factors in predicting nurse mental health using machine learning techniques10.1186/s12912-021-00742-91472-6955https://doaj.org/article/9179915db8c641fd92ec505d8a6c6add2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12912-021-00742-9https://doaj.org/toc/1472-6955Abstract Objectives Nurses are at a high risk of developing mental health problems due to exposure to work environment risk factors. Previous research in this area has only examined a few factors within nurses’ work environments, and those factors were not conceptualized with the goal of improving workplace mental health. The purpose of this study is to identify the most important work environment predictors of nurse mental health using a comprehensive and theoretically grounded measure based on the National Standard of Psychological Health and Safety in the Workplace. Methods This is an exploratory cross-sectional survey study of nurses in British Columbia, Canada. For this study, responses from a convenience sample of 4029 actively working direct care nurses were analyzed using random forest regression methods. Key predictors include 13 work environment factors. Study outcomes include depression, anxiety, post-traumatic stress disorder (PTSD), burnout and life satisfaction. Results Overall, healthier reports of work environment conditions were associated with better nurse mental health. More specifically balance, psychological protection and workload management were the most important predictors of depression, anxiety, PTSD and emotional exhaustion. While engagement, workload management, psychological protection and balance were the most important predictors of depersonalization, engagement was the most important predictor of personal accomplishment. Balance, psychological protection and engagement were the most important predictors of life satisfaction. Conclusions Routine assessment with standardized tools of nurses’ work environment conditions and mental health is an important, evidence-based organizational intervention. This study suggests nurses’ mental health is particularly influenced by worklife balance, psychological protection and workload management.Farinaz HavaeiXuejun Ryan JiMaura MacPheeHeather StraightBMCarticleMental healthWork environment factorsNursingNational standard of psychological health and safetyMachine learningNursingRT1-120ENBMC Nursing, Vol 20, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Mental health Work environment factors Nursing National standard of psychological health and safety Machine learning Nursing RT1-120
spellingShingle	Mental health Work environment factors Nursing National standard of psychological health and safety Machine learning Nursing RT1-120 Farinaz Havaei Xuejun Ryan Ji Maura MacPhee Heather Straight Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
description	Abstract Objectives Nurses are at a high risk of developing mental health problems due to exposure to work environment risk factors. Previous research in this area has only examined a few factors within nurses’ work environments, and those factors were not conceptualized with the goal of improving workplace mental health. The purpose of this study is to identify the most important work environment predictors of nurse mental health using a comprehensive and theoretically grounded measure based on the National Standard of Psychological Health and Safety in the Workplace. Methods This is an exploratory cross-sectional survey study of nurses in British Columbia, Canada. For this study, responses from a convenience sample of 4029 actively working direct care nurses were analyzed using random forest regression methods. Key predictors include 13 work environment factors. Study outcomes include depression, anxiety, post-traumatic stress disorder (PTSD), burnout and life satisfaction. Results Overall, healthier reports of work environment conditions were associated with better nurse mental health. More specifically balance, psychological protection and workload management were the most important predictors of depression, anxiety, PTSD and emotional exhaustion. While engagement, workload management, psychological protection and balance were the most important predictors of depersonalization, engagement was the most important predictor of personal accomplishment. Balance, psychological protection and engagement were the most important predictors of life satisfaction. Conclusions Routine assessment with standardized tools of nurses’ work environment conditions and mental health is an important, evidence-based organizational intervention. This study suggests nurses’ mental health is particularly influenced by worklife balance, psychological protection and workload management.
format	article
author	Farinaz Havaei Xuejun Ryan Ji Maura MacPhee Heather Straight
author_facet	Farinaz Havaei Xuejun Ryan Ji Maura MacPhee Heather Straight
author_sort	Farinaz Havaei
title	Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
title_short	Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
title_full	Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
title_fullStr	Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
title_full_unstemmed	Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
title_sort	identifying the most important workplace factors in predicting nurse mental health using machine learning techniques
publisher	BMC
publishDate	2021
url	https://doaj.org/article/9179915db8c641fd92ec505d8a6c6add
work_keys_str_mv	AT farinazhavaei identifyingthemostimportantworkplacefactorsinpredictingnursementalhealthusingmachinelearningtechniques AT xuejunryanji identifyingthemostimportantworkplacefactorsinpredictingnursementalhealthusingmachinelearningtechniques AT mauramacphee identifyingthemostimportantworkplacefactorsinpredictingnursementalhealthusingmachinelearningtechniques AT heatherstraight identifyingthemostimportantworkplacefactorsinpredictingnursementalhealthusingmachinelearningtechniques
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Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques

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