{"title":"基于多年护理经验的护士离职意向模式及预测因素：聚类分析方法","authors":"Veysel Karani Baris, Akgun Yesiltepe, Gulbahar Celik","doi":"10.1111/ijn.70049","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Aim</h3>\n \n <p>To identify patterns and predictors of nurse turnover intentions based on years of nursing experience using a cluster analysis approach.</p>\n </section>\n \n <section>\n \n <h3> Background</h3>\n \n <p>Nurses with varying years of experience have different characteristics. These differences can also lead to distinct patterns and predictors of turnover intentions.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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 <i>K</i>-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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":14223,"journal":{"name":"International Journal of Nursing Practice","volume":"31 5","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Patterns and Predictors of Nurse Turnover Intentions Based on Years of Nursing Experience: A Cluster Analysis Approach\",\"authors\":\"Veysel Karani Baris, Akgun Yesiltepe, Gulbahar Celik\",\"doi\":\"10.1111/ijn.70049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Aim</h3>\\n \\n <p>To identify patterns and predictors of nurse turnover intentions based on years of nursing experience using a cluster analysis approach.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Nurses with varying years of experience have different characteristics. These differences can also lead to distinct patterns and predictors of turnover intentions.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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 <i>K</i>-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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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. 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Patterns and Predictors of Nurse Turnover Intentions Based on Years of Nursing Experience: A Cluster Analysis Approach
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.
期刊介绍：
International Journal of Nursing Practice is a fully refereed journal that publishes original scholarly work that advances the international understanding and development of nursing, both as a profession and as an academic discipline. The Journal focuses on research papers and professional discussion papers that have a sound scientific, theoretical or philosophical base. Preference is given to high-quality papers written in a way that renders them accessible to a wide audience without compromising quality. The primary criteria for acceptance are excellence, relevance and clarity. All articles are peer-reviewed by at least two researchers expert in the field of the submitted paper.
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