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Exploring the Predictors of Nurses' Turnover Intentions Through Neural Network Modeling: A National Cross-Sectional
Arūnas Žiedelis1, Jurgita Lazauskaitė-Zabielskė1, Natalja Istomina2
1Institute of Psychology, Vilnius University, 01513 Vilnius, Lithuania.
Healthcare (Basel, Switzerland)
|April 14, 2026
Summary
Younger nurses show higher turnover intentions, influenced by job satisfaction, burnout, and work environment. Machine learning identified key predictors for targeted nurse retention strategies.
Area of Science:
- Healthcare workforce dynamics
- Nursing retention
- Machine learning in healthcare research
Background:
- Nurses' turnover intentions predict actual turnover, impacting healthcare costs and quality.
- Understanding turnover drivers is crucial for workforce stability.
- Advanced analytics can reveal complex relationships in nurse turnover.
Purpose of the Study:
- Identify key predictors of nurses' turnover intentions.
- Explore demographic, well-being, and work environment influences.
- Utilize machine learning for precise prediction and analysis.
Main Methods:
- Cross-sectional study with 2459 nurses.
- Artificial neural network regression with feature selection and SHAP analysis.
- Investigated nonlinear and context-dependent variable relationships.
Main Results:
- Seven predictors explained 49.8% of turnover intention variance.
- Age was the strongest predictor; younger nurses had higher intentions.
- Job satisfaction, burnout, managerial support, conflict, professional development, and role conflict were significant factors.
Conclusions:
- Machine learning offers advanced insights into nurse turnover.
- Age-sensitive retention strategies and well-being monitoring are recommended.
- Organizational interventions should focus on support and development to ensure healthcare stability.
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