ArticlePloS one2026
Prediction models for nurse turnover: A protocol for a systematic review.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHigh 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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: PROSPERO Registration Number CRD42024576727.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.