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Characterizing Sleep Disturbance Subgroups and Identifying Associated Factors in Traditional Chinese Medicine Nurses:

Chong Liu1, Nieran Lian2, Kristin K Sznajder3

  • 1Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China, cmu.edu.cn.

Journal of Nursing Management
|February 26, 2026
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Summary

Nurses in traditional Chinese medicine (TCM) experience significant sleep disturbances, with latent profile analysis identifying three distinct subgroups. Machine learning models effectively differentiated these groups, highlighting factors like income and resilience for targeted interventions.

Keywords:
explainable machine learningfemale nurseslatent profile analysissleep disturbance

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Area of Science:

  • Nursing
  • Sleep Science
  • Computational Health

Background:

  • Nurses in traditional Chinese medicine (TCM) face considerable occupational stressors impacting sleep.
  • Limited person-centered research exists on classifying sleep disturbance patterns in this population.

Purpose of the Study:

  • To identify distinct sleep disturbance subgroups among TCM nurses using latent profile analysis (LPA).
  • To evaluate the efficacy of explainable machine learning models in discriminating these subgroups based on demographic and occupational factors.

Main Methods:

  • A cross-sectional survey of 7721 TCM nurses was conducted.
  • Latent profile analysis (LPA) categorized sleep disturbance patterns.
  • Explainable machine learning models, including XGBoost with SHAP analysis, were used for subgroup discrimination.

Main Results:

  • Three sleep disturbance subgroups were identified: mild-stable (29.8%), moderate-fluctuating (60%), and severe-persistent (10.2%).
  • The XGBoost model achieved the highest discriminatory performance (AUC = 0.84).
  • Key features for classification included monthly income, organizational support, hospital level, self-compassion, and resilience.

Conclusions:

  • The study successfully characterized three sleep disturbance profiles in TCM nurses, with most falling into the moderate category.
  • Explainable machine learning effectively distinguished these subgroups.
  • Identifying correlates like income and resilience can inform targeted interventions for nurses at risk of severe sleep disturbances.