International journal of nursing practice · 2025
Patterns and Predictors of Nurse Turnover Intentions Based on Years of Nursing Experience: A Cluster Analysis Approach.
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Baris Veysel Karani, Yesiltepe Akgun, Celik Gulbahar
Аннотация
AIM: To identify patterns and predictors of nurse turnover intentions based on years of nursing experience using a cluster analysis approach.
BACKGROUND: Nurses with varying years of experience have different characteristics. These differences can also lead to distinct patterns and predictors of turnover intentions.
METHODS: For this descriptive study, 785 nurses from hospitals across different regions of Türkiye participated in a survey. Data was collected through online questionnaires between April and May 2022. The K-means unsupervised machine learning algorithm was employed to classify nurses into distinct clusters based on their experience. Multiple linear regression analyses were conducted to identify the predictors of turnover intention specific to each cluster. The STROBE guideline was followed for reporting.
RESULTS: Cluster analysis grouped nurses into three categories by experience level: low, medium and high. The medium-experience group had the highest turnover intention, whereas the high-experience group had the lowest. Work stress was the only common predictor across all groups. Low income predicted turnover only for the low-experience group, and gender was significant only for the medium-experience group.
CONCLUSION: This study revealed that turnover intention and its predictors vary by experience level, indicating a need for retention strategies tailored to nurses' years of experience. By considering subgroup characteristics, policymakers can develop targeted interventions to enhance nurse retention.
BACKGROUND: Nurses with varying years of experience have different characteristics. These differences can also lead to distinct patterns and predictors of turnover intentions.
METHODS: For this descriptive study, 785 nurses from hospitals across different regions of Türkiye participated in a survey. Data was collected through online questionnaires between April and May 2022. The K-means unsupervised machine learning algorithm was employed to classify nurses into distinct clusters based on their experience. Multiple linear regression analyses were conducted to identify the predictors of turnover intention specific to each cluster. The STROBE guideline was followed for reporting.
RESULTS: Cluster analysis grouped nurses into three categories by experience level: low, medium and high. The medium-experience group had the highest turnover intention, whereas the high-experience group had the lowest. Work stress was the only common predictor across all groups. Low income predicted turnover only for the low-experience group, and gender was significant only for the medium-experience group.
CONCLUSION: This study revealed that turnover intention and its predictors vary by experience level, indicating a need for retention strategies tailored to nurses' years of experience. By considering subgroup characteristics, policymakers can develop targeted interventions to enhance nurse retention.