{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T04:05:10Z","timestamp":1761278710205,"version":"build-2065373602"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J of Nursing Practice"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To identify patterns and predictors of nurse turnover intentions based on years of nursing experience using a cluster analysis approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Nurses with varying years of experience have different characteristics. These differences can also lead to distinct patterns and predictors of turnover intentions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      For this descriptive study, 785 nurses from hospitals across different regions of T\u00fcrkiye participated in a survey. Data was collected through online questionnaires between April and May 2022. The\n                      <jats:italic>K<\/jats:italic>\n                      \u2010means 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Cluster analysis grouped nurses into three categories by experience level: low, medium and high. The medium\u2010experience group had the highest turnover intention, whereas the high\u2010experience group had the lowest. Work stress was the only common predictor across all groups. Low income predicted turnover only for the low\u2010experience group, and gender was significant only for the medium\u2010experience group.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ijn.70049","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T07:21:11Z","timestamp":1756711271000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Patterns and Predictors of Nurse Turnover Intentions Based on Years of Nursing Experience: A Cluster Analysis Approach"],"prefix":"10.1111","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5322-4081","authenticated-orcid":false,"given":"Veysel\u00a0Karani","family":"Baris","sequence":"first","affiliation":[{"name":"Nursing Management Department, Faculty of Nursing Dokuz Eylul University  Izmir T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4720-3118","authenticated-orcid":false,"given":"Akgun","family":"Yesiltepe","sequence":"additional","affiliation":[{"name":"Faculty of Health Science Munzur University  Tunceli T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4133-3533","authenticated-orcid":false,"given":"Gulbahar","family":"Celik","sequence":"additional","affiliation":[{"name":"Puclic Health Nursing Department, Faculty of Nursing Dokuz Eylul University  Izmir T\u00fcrkiye"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,9]]},"reference":[{"volume-title":"Machine Learning With R Cookbook: Explore Over 110 Recipes to Analyze Data and Build Predictive Models With the Simple and Easy\u2010to\u2010Use R Code","year":"2017","author":"Bhatia 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