Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective DOI Creative Commons
Lanjun Luo, Yuze Wu,

Siyuan Li

et al.

Journal of Nursing Management, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Background: Workplace violence, defined as any disruptive behavior or threat to employees, seriously threatens junior nurses. Compared with senior nurses, nurses are more vulnerable workplace violence due inexperience, low professional recognition, and limited mental resilience. However, there is an absence of research discussing the risk in particular, lack analysis critical factors within multiple influences targeted prediction models. Objective: Considering influencing faced by this study aims predict using interpretable machine learning models identify their nonlinear effects. Design: An observational, cross-sectional design. Participants: A total 5663 registered 90 tertiary hospitals Sichuan Province, China. Methods: Data all obtained through a questionnaire survey. framework, including Light Gradient Boosting Machine (LightGBM) model two post hoc methods, Accumulate Local Effect SHapely Additive exPlanations (SHAP), conjoined. Results: The LightGBM accurate than other achieving area under receiver operating characteristic curve 0.761 Brier score 0.198 on task. Among dozens potential input into predictive model, seeing medical complaints, psychological demands, identity, etc., most predictors violence. Conclusions: proposed LightGBM-SHAP-ALE approach dynamically effectively identifies at high providing foundation for timely detection intervention.

Language: Английский

Identifying the Most Critical Predictors of Workplace Violence Experienced by Junior Nurses: An Interpretable Machine Learning Perspective DOI Creative Commons
Lanjun Luo, Yuze Wu,

Siyuan Li

et al.

Journal of Nursing Management, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Background: Workplace violence, defined as any disruptive behavior or threat to employees, seriously threatens junior nurses. Compared with senior nurses, nurses are more vulnerable workplace violence due inexperience, low professional recognition, and limited mental resilience. However, there is an absence of research discussing the risk in particular, lack analysis critical factors within multiple influences targeted prediction models. Objective: Considering influencing faced by this study aims predict using interpretable machine learning models identify their nonlinear effects. Design: An observational, cross-sectional design. Participants: A total 5663 registered 90 tertiary hospitals Sichuan Province, China. Methods: Data all obtained through a questionnaire survey. framework, including Light Gradient Boosting Machine (LightGBM) model two post hoc methods, Accumulate Local Effect SHapely Additive exPlanations (SHAP), conjoined. Results: The LightGBM accurate than other achieving area under receiver operating characteristic curve 0.761 Brier score 0.198 on task. Among dozens potential input into predictive model, seeing medical complaints, psychological demands, identity, etc., most predictors violence. Conclusions: proposed LightGBM-SHAP-ALE approach dynamically effectively identifies at high providing foundation for timely detection intervention.

Language: Английский

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