Multidimensional perspectives on nurse burnout in China: a cross-sectional study of subgroups and predictors

Yuecong Wang; Xin Wang; Xuejing Li; Surong Wen

doi:10.1186/s12912-024-02622-4

Multidimensional perspectives on nurse burnout in China: a cross-sectional study of subgroups and predictors

BMC Nurs. 2024 Dec 21;23(1):941. doi: 10.1186/s12912-024-02622-4.

Authors

Yuecong Wang¹, Xin Wang², Xuejing Li³, Surong Wen³

Affiliations

¹ Department of Neurology, Huai'an Second People's Hospital, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, Jiangsu, 223002, China. ab1628118912@163.com.
² Department of Nursing, Huaian Hospital of Huaian City, 19 Shanyang Avenue, Huaian, Jiangsu, 223200, China.
³ Department of Rehabilitation Medicine, Huai'an Second People's Hospital, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, Jiangsu, 223002, China.

PMID: 39709393
DOI: 10.1186/s12912-024-02622-4

Abstract

Background: Burnout is a state of physical and mental exhaustion triggered by long-term work stress, which is manifested mainly as emotional exhaustion, depersonalization, and a decreased sense of accomplishment. Among them, emotional exhaustion is its core feature, which often leads to a significant decrease in an individual's enthusiasm for work. Owing to the high intensity of the work environment and the special requirements of emotional labor, the nursing community is more vulnerable to burnout. This burnout not only affects the quality of care but also significantly increases nurses' willingness to leave their jobs.

Objectives: This study aimed to identify burnout subgroups among Chinese nurses and explore the predictors of each subgroup.

Design: A cross-sectional study.

Methods: A total of 500 nurses were recruited for this study via convenience sampling, of whom 470 completed the survey. Nurses' burnout subgroups were identified through latent profile analysis of 15 items on the Burnout Scale. Relationships between subgroups and sociodemographic variables were subsequently explored via one-way ANOVA, chi-square tests, and multivariate logistic regression analyses.

Results: Three burnout subgroups were identified: low depersonalization with low achievement burnout (26.2%, n = 123), overall moderate burnout (52.1%, n = 245), and high emotional exhaustion with low achievement burnout (21.7%, n = 102). The results of multiple logistic regression analysis revealed that age, years of working experience, marital status, education level, and number of night shifts per month were significant predictors of different burnout subgroups among nurses.

Conclusion: This study applied latent profile analysis to explore the subgroups of burnout among Chinese nurses, and the results revealed the diversity of burnout and provided a new perspective for future nursing research. Continued attention to the multifaceted factors affecting burnout and its dynamic changes is recommended to better understand and address the challenges facing the nursing profession.

Keywords: Burnout; Latent profile analysis; Nurse; Predictors; Subgroups.