Prediction models for nurse turnover: A protocol for a systematic review

High nurse turnover rates pose significant challenges to healthcare systems, affecting patient care quality, workforce stability, and healthcare institutions' financial performance. While several prediction models for nurse turnover have been developed, their quality and performance have not been systematically evaluated, limiting their application in practice and making policy. This study aims to systematically review these nurse turnover prediction models and provide valuable insights for healthcare administrators and policymakers to improve nurse retention and optimize workforce management.

This study is a systematic review protocol. We will conduct a comprehensive search of PubMed, Embase, Web of Science, SinoMed, CINAHL, Cochrane Library, CNKI, Wanfang Data, and VIP databases, covering all relevant articles published from the inception of databases through May 31, 2026.Studies that developed prediction models for nurse turnover (with or without external validation) will be eligible for inclusion. Two independent reviewers will search, select, extract, assess, and analyze potentially relevant studies. The Prediction Model Risk of Bias Assessment Tool (PROBAST) will be used to evaluate the quality and risk of bias in the included studies. Disagreements between the two reviewers will be resolved through discussion or consultation with a third reviewer. A narrative synthesis will be conducted to present the characteristics and performance of the models in the included studies.

Ethical considerations are not applicable as this is a systematic review of published studies. The results will be published in a peer-reviewed journal.

PROSPERO Registration Number CRD42024576727.