Nurse turnover: A longitudinal survival analysis of the Korea Nurses' Health Study

Abstract

To identify factors influencing turnover among Korean female nurses from a longitudinal perspective. A national cohort study called the Korea Nurses' Health Study (2013-2020) was used. A national sample of female nurses from module 1 (N=20,613, 2013-2014), module 5 (N=11,527, 2016-2017), module 7 (N=8,658, 2018-2019) and module 8 (N=10,253, 2019-2020) was used. Based on a nurse turnover model, individual, health-related, social work environment and work organizational factors were considered explainable variables for nurse turnover. Kaplan-Meier survival analysis and multivariate Cox regression analysis were used to identify the factors influencing female nurse turnover in South Korea. Female nurses who had less education, were unmarried, were pregnant, and had higher stress levels and an increased probability of experiencing turnover as they aged. Those who perceived moderate health rather than good/very good health, had depressive symptoms, had a higher salary, were charge nurses/unit managers/supervisors or advanced practice nurses, were advanced practice nurses rather than registered nurses, worked shifts, worked in special care units or outpatient wards/administration as opposed to general wards, and worked in larger hospitals had a decreased probability of experiencing turnover as they aged. A two-way interaction analysis revealed that those who had depressive symptoms and increased perceived stress were more likely to experience turnover as they aged. Multiple factors influenced female nurse turnover, including individual, health-related, social work environment and work organizational factors. A multidimensional approach is needed to reduce nurse turnover. Various factors predict nurse turnover as nurses age, implying that a multifaceted approach is needed to manage nurse turnover. The influence of depressive symptoms on turnover should be evaluated by considering the perceived stress level. Nursing managers and policy makers could use our results to develop programs/policies to reduce nurse turnover.