2026-10-10T17:32:59Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33810034 2026-09-15T17:27:36Z category_7 category_931 category_811 category_61 category_64 category_132 portal_5 item_type_3 month_year_09_2026

<p>Full search string for the database “Pubmed”.</p> Zifeng Li (1432006) Xiaojing Zhou (521545) Luhuan Yang (18155914) Zuyang Xi (19565620) Huiqin Liu (2506906) Yuanzhi Fu (5640230) Xiaojuan Zhang (235854) Medicine Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Developmental Biology Cancer Infectious Diseases prisma 2020 statement erodes emotional resources quality assessment according prespecified search strategy compare existing models nurses &# 8217 data extraction form xlink "> using xlink "> nurses predicting compassion fatigue developed prediction models xlink "> prediction models data extraction prespecified inclusion methodological quality included models assessed using compassion fatigue two reviewers several studies routinely exposed registration number probast tool mental health key characteristics exclusion criteria cochrane library charms checklist <div> <p>Introduction</p><p>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.</p> <p>Aims</p><p>This systematic review will comprehensively summarize published prediction models for nurses’ compassion fatigue and describe their key characteristics, predictors, performance, and risk of bias.</p> <p>Methods</p><p>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).</p> <p>Conclusion</p><p>This review will identify and compare existing models for predicting compassion fatigue in nurses and will inform future model development, validation, and application.</p></div> 2026-09-15T17:27:26Z Dataset Dataset 10.1371/journal.pone.0342524.t002 https://figshare.com/articles/dataset/_p_Full_search_string_for_the_database_Pubmed_p_/33810034 CC BY 4.0