Development and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: a cross-sectional study utilizing latent profile analysis.

Journal: BMC medical education
PMID: 39702113

Abstract

BACKGROUND: Compassion fatigue is a significant issue in nursing, affecting both registered nurses and nursing students, potentially leading to burnout and reduced quality of care. During internships, compassion fatigue can shape nursing students' career trajectories and intent to stay in the profession. Identifying those at high risk is crucial for timely interventions, yet existing tools often fail to account for within-group variability, limiting their ability to accurately predict compassion fatigue risk.

Authors

  • Lijuan Yi
    Department of Nursing, Hunan Traditional Chinese Medical College, Zhuzhou, China.
  • Ting Shuai
    Second Clinical Division, Peking University School and Hospital of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing, 100081, China.
  • Jingjing Zhou
    School of Life Sciences, Shanghai University, 333 Nanchen Road, Shanghai 200444, China.
  • Liang Cheng
    College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150001, China. liangcheng@hrbmu.edu.cn.
  • María F Jiménez-Herrera
    Nursing Department, Universitat Rovira I Virgili, 43002, Tarragona, Spain.
  • Xu Tian
    Department of Otorhinolaryngology Head and Neck Surgery, the Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

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