Factors Affecting Intention to Leave Among ICU Healthcare Professionals in China: Insights from a Cross-Sectional Survey and XGBoost Analysis - Wu J; Zhang C; He F; Wang Y; Zeng L; Liu W; Zhao D; Mao J; Gao F (Dove Medical Press, 2023)
Jiangnan Wu,1,* Chao Zhang,2,* Feng He,3,* Yuan Wang,4 Liangnan Zeng,5 Wei Liu,6 Di Zhao,7 Jingkun Mao,1 Fei Gao8 1Department of Artificial Intelligence, Tianjin University of Technology, Tianjin, People's Republic of China; 2Sixth Department of Oncology, Hebei General Hospital, Shijiazhuang, People's Republic of China; 3The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China; 4Department of Neurosurgery, Tangshan Gongren Hospital, Tangshan, People's Republic of China; 5Department of Nursing, Chengdu Fifth People's Hospital, The Fifth People's Hospital Affiliated to Chengdu University of Traditional Chinese Medicine, Chengdu, People's Republic of China; 6Hebei Psychological Counselor Association, Shijiazhuang, People's Republic of China; 7Department of Neurosurgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China; 8Hebei General Hospital, Shijiazhuang, People's Republic of China*These authors contributed equally to this workCorrespondence: Di Zhao; Fei Gao, Tel +86-13363833833 ; +86-13933173693, Email 179381749@qq.com; 27909361@qq.comBackground: 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.Keywords: ICU healthcare professionals, intention to leave, cross-sectional survey, extreme gradient boosting, XGBoost