Hiring in life sciences? Share your open positions with our professional community. Read more Close

Advertisement

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

Created on 26 Aug 2026

Authors

Yujia Huang, Chaojun Shan, Xueni Xiao, Chuanya Huang

Published in

PloS one. Volume 21. Issue 8. Pages e0354384. Epub Aug 25, 2026.

Abstract

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.

PMID:
42640889
Bibliographic data and abstract were imported from PubMed on 26 Aug 2026.

Read full publication at:
Please sign in to see all details.

Advertisement

Stats

  • Community rating n/a 0 votes
  • Reviewers' rating n/a 0 votes
  • Your rating

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

  • Recommendations n/a n/a positive of 0 vote(s)
  • Views 7
  • Comments 0

Recommended by

  • No recommendations yet.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.

Advertisement