Psychological resilience and job satisfaction are protective factors, while nursing stress, night shift frequency, and poor sleep quality are risk factors for burnout among ICU nurses.
A machine learning model using nine predictors identified burnout in ICU nurses with an AUC of 0.983 and Brier score of 0.054. Psychological resilience (SHAP value 0.197) and job satisfaction (0.152) were primary protective factors, while nursing stress (0.059), night shift frequency (0.016), and poor sleep quality (0.015) were key risk factors.
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Psychological resilience and job satisfaction are protective factors, while nursing stress, night shift frequency, and poor sleep quality are risk factors for burnout among ICU nurses. The current body of evidence comprises 1 study. EvidenceHub rates the overall confidence at 34/100 (low).
The Claim
Psychological resilience and job satisfaction are protective factors, while nursing stress, night shift frequency, and poor sleep quality are risk factors for burnout among ICU nurses.
This conclusion is most relevant to: 318 ICU nurses from four tertiary hospitals in three provinces of China.
What the Research Shows
The conclusion draws on 1 linked study. Highlights from the cited literature:
- ▸Development and Internal Validation of Interpretable Machine Learning Models for Identifying Burnout Syndrome Among Intensive Care Unit Nurses. (Journal of nursing management, 2026) —
How It Works
The proposed biological pathway:
- ▸Psychological resilience and job satisfaction reduce burnout risk
- ▸Nursing stress, night shift frequency, and poor sleep quality increase burnout risk
- ▸Model identifies key correlates for targeted interventions
- ▸Result: Burnout can be predicted with high accuracy
Who Might Benefit
Evidence fit by population:
- ▸318 ICU nurses from four tertiary hospitals in three provinces of China
Recommended Dose
N/A
Limitations & Caveats
Important context when interpreting this evidence:
- ▸Internal validation only; no external validation in diverse healthcare settings
- ▸Cross-sectional design limits causal inference
- ▸Sample limited to Chinese tertiary hospitals, may not generalize to other regions or settings
Frequently Asked Questions
What is the most important protective factor against burnout in ICU nurses?▼
Psychological resilience, with a SHAP value of 0.197, was the strongest protective factor identified.
How accurate is the machine learning model for predicting burnout?▼
The random forest model achieved an AUC of 0.983 and a Brier score of 0.054, indicating excellent discrimination and calibration.
What are the key risk factors for burnout in this study?▼
Nursing stress (SHAP 0.059), night shift frequency (0.016), and poor sleep quality (0.015) were the main risk factors.
Can this model be used in other hospitals?▼
The model was internally validated but requires further external validation in diverse healthcare settings before widespread use.
References
- 1.Hu W, Ji Y, Chai F, Xu D, Wang Y, Xu C, Li X. “Development and Internal Validation of Interpretable Machine Learning Models for Identifying Burnout Syndrome Among Intensive Care Unit Nurses..” Journal of nursing management, 2026. PMID: 42473329 DOI: 10.1155/jonm/6835251