Prediction models for compassion fatigue in nurses: A protocol for systematic review and critical appraisal

Zifeng Li, Xiaojing Zhou, Luhuan Yang, Zuyang Xi, Huiqin Liu, Yuanzhi Fu and Xiaojuan Zhang

PLOS ONE, 2026, vol. 21, issue 9, 1-100

Abstract: Introduction: Nurses are routinely exposed to high emotional demands associated with illness, disability, and death. Prolonged exposure may deplete nurses’ capacity for compassion, contributing to compassion fatigue. Compassion fatigue not only erodes emotional resources but may also adversely affect professional functioning and mental health. To date, several studies have developed prediction models for nurses’ compassion fatigue, but the methodological quality of these studies remains uncertain. Aims: This systematic review will comprehensively summarize published prediction models for nurses’ compassion fatigue and describe their key characteristics, predictors, performance, and risk of bias. Methods: Using a prespecified search strategy, we will systematically search seven databases: PubMed, Web of Science, Cochrane Library, Embase, CINAHL, PsycINFO, and CNKI. Two reviewers will independently conduct study selection, data extraction, and quality assessment according to prespecified inclusion and exclusion criteria. The data extraction form will be developed in accordance with the CHARMS checklist, and the risk of bias of the included models will be assessed using the PROBAST tool. Results will be presented in tables to facilitate qualitative comparisons across models. The systematic review will be reported in accordance with the PRISMA 2020 statement. The protocol has been registered in INPLASY (registration number: INPLASY202530051). Conclusion: This review will identify and compare existing models for predicting compassion fatigue in nurses and will inform future model development, validation, and application.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0342524

DOI: 10.1371/journal.pone.0342524

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