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Peer-reviewedCC BY 4.0PLOS One

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

  • Zifeng Li1
  • Xiaojing Zhou2
  • Luhuan Yang3
  • Zuyang Xi4
  • Huiqin Liu5
  • Yuanzhi Fu6
  • Xiaojuan Zhang
6 affiliations
  1. 1Department of Traditional Chinese Medicine, The First College of Clinical Medical Science, Three Gorges University/Yichang Central People’s Hospital, Yichang City, Hubei Province, China
  2. 2Department of Pediatrics, The First College of Clinical Medical Science, Three Gorges University/Yichang Central People’s Hospital, Yichang City, Hubei Province, China
  3. 3Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
  4. 4Department of Nursing, the First College of Clinical Medical Science, Three Gorges University/Yichang Central People’s Hospital, Yichang City, Hubei Province, China
  5. 5Hubei Key Laboratory of Ischemic Cardiovascular Disease, Yichang City, Hubei Province, China
  6. 6Department of Nursing, Gansu Health Vocational College, Lanzhou City, Gansu Province, China
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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.

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Cite

BibTeXRIS
BibTeX
@article{li2026prediction,
  title = {{Prediction models for compassion fatigue in nurses: A protocol for systematic review and critical appraisal}},
  author = {Zifeng Li and Xiaojing Zhou and Luhuan Yang and Zuyang Xi and Huiqin Liu and Yuanzhi Fu and Xiaojuan Zhang},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0342524},
  url = {https://doi.org/10.1371/journal.pone.0342524},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Prediction models for compassion fatigue in nurses: A protocol for systematic review and critical appraisal
AU  - Zifeng Li
AU  - Xiaojing Zhou
AU  - Luhuan Yang
AU  - Zuyang Xi
AU  - Huiqin Liu
AU  - Yuanzhi Fu
AU  - Xiaojuan Zhang
PY  - 2026
DA  - 2026/09/15
JO  - PLOS One
DO  - 10.1371/journal.pone.0342524
UR  - https://doi.org/10.1371/journal.pone.0342524
AB  - 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.
SN  - 1932-6203
ER  -