Predicting turnover intention among newly graduated nurses in South Korea: a decision tree analysis.

ABSTRACT:

Background

The high turnover intention of newly graduated nurses has a multifaceted impact on the healthcare system. Analyzing large datasets using the machine learning methods can more accurately predict influencing factors of turnover intention in this population. This study aims to identify predictors of turnover intention among newly graduated nurses with less than one year of experience in current workplaces, using a decision tree model by analyzing 2016-2020 Graduate Occupational Mobility Survey conducted in South Korea.

Methods

This is a secondary data analysis using a national large dataset. The data of 492 new nursing graduates were included in the analysis. Predictive variables for modeling were grouped into four categories: personal factors, workplace factors, col

SUBMITTER: Moon M 

PROVIDER: S-EPMC12625515 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature