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

Farinaz Havaei; Xuejun Ryan Ji; Maura MacPhee; Heather Straight

doi:10.1186/s12912-021-00742-9

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

BMC Nurs. 2021 Nov 1;20(1):216. doi: 10.1186/s12912-021-00742-9.

Authors

Farinaz Havaei¹, Xuejun Ryan Ji², Maura MacPhee³, Heather Straight⁴

Affiliations

¹ University of British Columbia (UBC), School of Nursing, 201-2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada. farinaz.havaei@ubc.ca.
² UBC Department of Educational and Counselling Psychology, and Special Education, 2125 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
³ University of British Columbia (UBC), School of Nursing, 201-2211 Wesbrook Mall, Vancouver, BC, V6T 2B5, Canada.
⁴ British Columbia Nurses Union, 4060 Regent Street, Burnaby, BC, V5C 6P5, Canada.

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.

Keywords: Machine learning; Mental health; National standard of psychological health and safety; Nursing; Work environment factors.