Predicting New Graduate Nurses' Retention during Transition Using Decision Tree Methods: A Longitudinal Study.

Background: Although retaining new nurses is imperative for the future of the nursing profession, it remains a challenging task in the healthcare industry. Understanding the career journey of new graduates as they transition from students to nurses is vital. However, longitudinal studies investigating the factors influencing retention during this period are lacking. Aim: The aim of this study is to identify the influencing factors and develop a longitudinal prediction model for new graduate nurse retention. Methods: A secondary data analysis was conducted using the New Nurse e-Cohort Study dataset from two survey periods, November-December 2020 and February-March 2022. The participants were categorized into either retention or turnover groups based on their turnover experiences. A decision tree based on classification and regression tree (CART) analysis was utilized. Results: Of the total 586 participants, 463 (79%) were in the retention group. The CART model highlighted that new nurses' retention was significantly associated with younger age, higher readiness for practice (clinical problem-solving) during the nursing program, lower transition shock (such as confusion in professional values, loss of social support, and conflicts between theory and practice), and a higher person-environment fit (person-job fit). The predictive accuracy of the CART model was 79.7%. Conclusion: To retain new nurses, nursing educators and hospital managers should collaborate to prepare nursing students for actual practice, offer support during organizational socialization, and foster healthy professional values for competence in the workplace.