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Latent profiles of workplace violence exposure and predictive modeling of sleep disorders among emergency department nurses

Year: 2026

Type: article

Abstract: Workplace violence and sleep disorders are common among emergency department (ED) nurses, yet heterogeneity in violence exposure and its relationship with sleep disorders remains poorly understood. This nationwide cross-sectional study included 1540 Chinese ED nurses. Latent profile analysis identified violence exposure patterns, while LASSO, 7 machine learning algorithms, and SHAP were used for sleep disorder prediction and model interpretation. Three violence profiles were identified: low-level (87.0%), moderate multi-form (10.3%), and high multi-form exposure (2.7%). Sleep disorders affected 59.3% of participants. Random forest showed the highest predictive performance (ROC AUC = 0.624). SHAP analysis showed greater predictive contributions from emotional exhaustion, cynicism, and occupational characteristics than from violence exposure profiles. Workplace violence exposure among ED nurses was heterogeneous. Burnout-related and occupational characteristics provided stronger predictive information for sleep disorder status than violence profiles. External validation and longitudinal studies are needed to assess generalizability and temporal relationships.

Source: Medicine

DOI: 10.1097/md.0000000000050549

Authors: Qingli Chen, Mei Yuan, Wei Zhang, Le Tong

Institutions: Sichuan University, West China Hospital of Sichuan University

Language: English

Topic: Workplace Violence and Bullying

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: Good health and well-being, Decent work and economic growth, Peace, justice, and strong institutions

Open Access status: gold

Estimated APC paid: $1,950