同情疲劳
倦怠
逻辑回归
心理学
多元分析
多级模型
同情
临床心理学
比例(比率)
移情
社会支持
护理部
干预(咨询)
重症监护室
医疗保健
潜在类模型
医学
回归分析
多元统计
情绪衰竭
单变量分析
横断面研究
作者
Jian Zhang,Xilin Yu,Lili Wei,Songnan Yao,Xiuyun Fu,Xue Xue
出处
期刊:Nursing Ethics
[SAGE Publishing]
日期:2026-03-13
卷期号:33 (5): 1397-1416
被引量:1
标识
DOI:10.1177/09697330261426430
摘要
Purpose This study aims to explore the latent classes of compassion fatigue among intensive care unit (ICU) nurses and identify the factors that influence their compassion fatigue. Methods Between November 2024 and February 2025, 1029 ICU nurses were selected as study participants using convenience sampling. Data were gathered through general demographic questionnaires, the Chinese version of the Short Scale of Compassion Fatigue (CFSS), the Occupational Stress Scale, the Perceived Social Support Scale, as well as the Professional Identity Scale. A latent profile analysis (LPA) was conducted based on the three dimensions of the CFSS as observed indicators. Additionally, factors influencing outcomes were analyzed using both univariate and multivariate logistic regression methods. Ethical considerations This study was approved by the Institutional Review Board of the Affiliated Hospital of Qingdao University. Results A total of 1029 valid questionnaires were obtained, resulting in an effective response rate of 93.46%. The average score on the ICU Nurse Compassion Fatigue Scale was 60.00 ± 27.36 points. Three distinct profiles were identified: low compassion fatigue–low secondary trauma type (33.04%), moderate compassion fatigue–overall fluctuation type (48.30%), and high compassion fatigue–high burnout type (18.66%). Multivariate logistic regression analysis revealed that health status, sleep quality, highest education level, occupational stress, professional identity, and social support significantly influence the potential compassion fatigue profiles among critical care nurses ( p < .05). Conclusion Compassion fatigue varies significantly among ICU nurses. To effectively manage and reduce compassion fatigue, nursing managers should create tailored intervention plans that consider the unique factors influencing each specific profile.
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