ArticleBMC nursing2021
Identifying the most important workplace factors in predicting nurse mental health using machine learning techniques.
Article in BMC nursing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Healthcare Professionals' Subjective Well-Being: A Systematic Review and Methodological Appraisal of Conceptual Models, Measurement Instruments, and Associated Factors.International journal of environmental research and public health · 2026Pooled it
- Data Analytics and Administrative Decision-Making in Nursing Management: A Systematic Review.Journal of nursing management · 2025Pooled it
- Comparing shared governance and psychological empowerment among nurses in Magnet and non-Magnet hospitals.BMC nursing · 2026Article
- Machine learning and SHAP interpretation to identify high-level compassion fatigue among operating room nurses: a multicenter cross-sectional study.BMC nursing · 2026Article
- Machine learning in the analysis of mental health at work: a scoping review.Journal of occupational health · 2026Article
- Detecting Burnout Among Undergraduate Computing Students with Supervised Machine Learning.Healthcare (Basel, Switzerland) · 2025Article
- Article
- Identifying vulnerable groups of community-dwelling older adults with a strong willingness to receive volunteer services based on machine learning methods.BMC public health · 2025Article
- Predicting sleep disturbance among patients with breast cancer in China through machine learning algorithms-a multi-site survey study.BMC psychiatry · 2025Article
- Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis.BMC nursing · 2025Article
- Decoding machine learning in nursing research: A scoping review of effective algorithms.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- A machine learning model to predict the risk factors causing feelings of burnout and emotional exhaustion amongst nursing staff in South Africa.BMC health services research · 2024Article
- Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis.Heliyon · 2024Article
- Workplace Predictors of Violence against Nurses Using Machine Learning Techniques: A Cross-Sectional Study Utilizing the National Standard of Psychological Workplace Health and Safety.Healthcare (Basel, Switzerland) · 2023Article
- Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study.Journal of nursing management · 2023Article
- Workplace Stress in Portuguese Oncology Nurses Delivering Palliative Care: A Pilot Study.Nursing reports (Pavia, Italy) · 2022Article
- The Association between Mental Health Symptoms and Quality and Safety of Patient Care before and during COVID-19 among Canadian Nurses.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesNurses 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.
methodsThis 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.
resultsOverall, 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.
conclusionsRoutine 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.
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