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Work-family conflict, occupational burnout, occupational stress, nursing practice environment, and sleep disorders are associated with workplace violence among emergency department nurses.

In a study of 1540 emergency department nurses in China, 85.0% reported experiencing workplace violence in the past year. Machine learning models identified work-family conflict, occupational burnout, and occupational stress as significant factors across all models, while sleep disorders and nursing practice environment were significant in random forest and decision tree models. Random forest achieved the highest sensitivity (0.916) and accuracy (0.859), while logistic regression had the highest specificity (0.667).

Last updated: Aug 23, 2026•0 RCTs•📖 Read as article →

Evidence Score

Evidence Score34/100
Human RCT☆☆☆☆☆
Meta-analysis☆☆☆☆☆
Mechanism★★★★★
Safety★★★★☆
Confidencelow

Study Evidence

Study 1. Machine Learning Models for Identifying Factors Associated With Workplace Violence Among Emergency Nurses: A Comparative Study.

observational

Lan L, Dai M, Chen Y, Zhang H, Zhong L, Tang S, Chen X · Emergency medicine international (2026)

Participants: N/A
Duration: Not specified (cross-sectional, data collected December 2023 to January 2024)
Intervention: No intervention; observational study using machine learning models (logistic regression, decision tree, random forest) to identify factors associated with workplace violence.
Outcome: Workplace violence experience (yes/no) in the past year; model performance metrics (sensitivity, specificity, PPV, NPV, F1-score, balanced accuracy, AUC).
Effect Size: N/A (no effect size reported; associations reported with p < 0.05)
Population: 1540 emergency department nurses from various regions of China, surveyed between December 2023 and January 2024.

Result:

PubMed: 42621461DOI: 10.1155/emmi/9275645

Mechanism Graph

Work-family conflict increases stress and burnout, reducing coping resources.
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Occupational stress and burnout may lead to reduced tolerance and increased conflict with patients/visitors.
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Poor nursing practice environment (e.g., staffing, support) may increase exposure to violence.
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Sleep disorders may impair emotional regulation and increase vulnerability to violence.
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Result: These factors collectively increase the likelihood of workplace violence experiences.

Limitations

  • ⚠Cross-sectional design prevents causal inference.
  • ⚠Data were self-reported, which may introduce recall bias.
  • ⚠Study conducted only in China, limiting generalizability to other countries.
  • ⚠Machine learning models may overfit; external validation not performed.

Frequently Asked Questions

What is the prevalence of workplace violence among emergency department nurses in this study?▼

85.0% of the 1540 nurses reported experiencing workplace violence in the past year.

Which factors were most strongly associated with workplace violence?▼

Work-family conflict, occupational burnout, and occupational stress were significant in all three models. Sleep disorders and nursing practice environment were also significant in random forest and decision tree models.

Which machine learning model performed best?▼

Random forest achieved the highest sensitivity (0.916), F1-score (0.915), and accuracy (0.859), while logistic regression had the highest specificity (0.667) and balanced accuracy. Logistic regression and random forest had similar AUC values (0.832 vs 0.834).

Can sleep quality predict workplace violence in nurses?▼

Yes, sleep disorders were identified as a significant associated factor in the random forest and decision tree models, suggesting that poor sleep may be linked to increased risk of workplace violence.

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References

  1. 1.Lan L, Dai M, Chen Y, Zhang H, Zhong L, Tang S, Chen X. "Machine Learning Models for Identifying Factors Associated With Workplace Violence Among Emergency Nurses: A Comparative Study.." Emergency medicine international, 2026. PMID: 42621461 DOI: 10.1155/emmi/9275645
Disclaimer: This content is for educational purposes only and is not medical advice. Evidence scores reflect the quality and quantity of available research, not clinical recommendations. Always consult a healthcare professional before starting any supplement or intervention.