ArticleRisk management and healthcare policy2023
Factors Affecting Intention to Leave Among ICU Healthcare Professionals in China: Insights from a Cross-Sectional Survey and XGBoost Analysis.
Article in Risk management and healthcare policy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A global prevalence of turnover intention among doctors: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Identifying key influencers of patient satisfaction using an explainable machine learning approach.Scientific reports · 2025Article
- Predictors of Compassion Satisfaction, Compassion Fatigue, and Burnout Among Nursing Professionals in a Medical City in Saudi Arabia.Risk management and healthcare policy · 2023Article
- Predictors of Critical Care Nurses' Intention to Leave Unit and Profession in Public Jordan Hospitals: A Cross-Sectional Study.SAGE open nursingArticle
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Authors and funding
9 authors.
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Abstract
Background: The intention to leave among intensive care unit (ICU) healthcare professionals in China has become a concerning issue. Therefore, understanding the factors influencing the intention to leave and implementing appropriate measures have become urgent needs for maintaining a stable healthcare workforce. Objective: This study aims to investigate the current status of intention to leave among ICU healthcare professionals in China, explore the relevant factors affecting this intention, and provide targeted recommendations to reduce the intention to leave among healthcare professionals. Methods: A cross-sectional survey was conducted, involving ICU healthcare professionals from 3-A hospitals of the 34 provinces in China. The survey encompassed 22 indicators, including demographic information (marital status, children, income), work-related factors (weekly working hours, night shift frequency, hospital environment), and psychological assessment (using Symptom Checklist-90 (SCL-90)). The data from a sample population of 3653 individuals were analyzed using the extreme gradient boosting (XGBoost) method to predict intention to leave. Results: The survey results revealed that 62.09% (2268 individuals) of the surveyed ICU healthcare professionals expressed an intention to leave. The XGBoost model achieved a predictive accuracy of 75.38% and an Area Under the Curve (AUC) of 0.77. Conclusion: Satisfaction with income was found to be the strongest predictor of intention to leave among ICU healthcare professionals. Additionally, factors such as years of experience, night shift frequency, and pride in hospital work were found to play significant roles in influencing the intention to leave.
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