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Related Experiment Videos

Identifying Key Predictors and Developing a Machine Learning Model for Nurse Burnout in China.

Zirong Li1, Qinghua Fan2, Xiao Gan1

  • 1Department of Nursing, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China, gxmu.edu.cn.

Journal of Nursing Management
|July 13, 2026
PubMed
Summary

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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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Current Trends in Nursing I

Current trends in nursing include:

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A machine learning model effectively predicts nurse burnout, identifying key risk factors like work duration and night shifts. This tool aids early intervention and management strategies for nursing staff.

Area of Science:

  • Nursing
  • Machine Learning
  • Healthcare Management

Background:

  • Work pressure significantly increases burnout risk among Chinese nurses.
  • Identifying burnout predictors is crucial for timely nursing interventions.

Purpose of the Study:

  • To develop a machine learning-based nurse burnout prediction model.
  • To support nursing management with predictive insights for early intervention.

Main Methods:

  • A cross-sectional online survey of 1391 Chinese nurses was conducted.
  • The random forest algorithm was employed for prediction after variable selection.
  • Model performance was evaluated using AUC, PR-AUC, calibration, and DCA.

Main Results:

Keywords:
job burnoutmachine learningrisk factor

Related Experiment Videos

  • The random forest model demonstrated high predictive performance (AUC=0.879, PR-AUC=0.940).
  • Key predictors included colleague relationships, work duration, exercise frequency, and night shift hours.
  • A Shiny web calculator was developed for practical application.

    • Conclusions:

    • The random forest model accurately predicts nurse burnout, offering good discrimination and calibration.
    • The developed web calculator facilitates early identification and stratified interventions for nursing management.
    • Integration into hospital systems can enable proactive burnout management and improve workforce stability.