Evidence map›Paper›PMID 39709393›Full record

ArticleBMC nursing2024

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

Yuecong Wang, Xin Wang, Xuejing Li, Surong Wen

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Article in BMC nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

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13citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yuecong WangDepartment 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.
Xin WangDepartment of Nursing, Huaian Hospital of Huaian City, 19 Shanyang Avenue, Huaian, Jiangsu, 223200, China.
Xuejing LiDepartment of Rehabilitation Medicine, Huai'an Second People's Hospital, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, Jiangsu, 223002, China.
Surong WenDepartment of Rehabilitation Medicine, Huai'an Second People's Hospital, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, Jiangsu, 223002, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBurnout 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.

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

designA cross-sectional study.

methodsA 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.

resultsThree 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.

conclusionThis 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.

Indexed as

BurnoutLatent profile analysisNursePredictorsSubgroups

Identifiers

PMID39709393
PMCPMC11662486

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.