Diagnostic model for preschool workers' unwillingness to continue working: Developed using machine-learning techniques

Abstract

The turnover of kindergarten teachers has drastically increased in the past 10 years. Reducing the turnover rates among preschool workers has become an important issue worldwide. Parents have avoided enrolling children in preschools due to insufficient care, which affects their ability to work. Therefore, this study developed a diagnostic model to understand preschool workers' unwillingness to continue working. A total of 1002 full-time preschool workers were divided into 2 groups. Predictors were drawn from general questionnaires, including those for mental health. We compared 3 algorithms: the least absolute shrinkage and selection operator, eXtreme Gradient Boosting, and logistic regression. Additionally, the SHapley Additive exPlanation was used to visualize the relationship between years of work experience and intention to continue working. The logistic regression model was adopted as the diagnostic model, and the predictors were "not living with children," "human relation problems with boss," "high risk of mental distress," and "work experience." The developed risk score and the optimal cutoff value were 14 points. By using the diagnostic model to determine workers' unwillingness to continue working, supervisors can intervene with workers who are experiencing difficulties at work and can help resolve their problems.