Machine learning for predicting burnout among healthcare workers: a systematic review and meta-analysis
ML models show promise for predicting burnout in HCWs but are limited by methodological weaknesses, heterogeneity, and lack of external validation. Advancing this field requires rigorous design, transparent reporting, multimodal data integration, and ethical safeguards to enable trustworthy clinical use.
Authors
- Huijing Shi (ORCID: https://orcid.org/0009-0004-7101-1214)
- Yanli Zeng (ORCID: https://orcid.org/0000-0002-6015-6926)
- Jinyang Liu (ORCID: https://orcid.org/0000-0001-6170-435X)
- Jingxian Shang (ORCID: https://orcid.org/0009-0005-8174-9519)
- Chaochao Yang (ORCID: https://orcid.org/0009-0006-8128-8836)
Institutions
- Xiyuan Hospital (CN)
- Chengdu University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- Contemporary Nurse
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1080/10376178.2025.2593294
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00