AUTHOR=Zhou Li , Xiong Wei , Hu Mengmeng , Chang Hongjuan 

TITLE=Emotional labor and its influencing factors of clinical nurses: a cross-sectional study based on latent profile analysis

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1496648

DOI=10.3389/fpubh.2025.1496648

ISSN=2296-2565

ABSTRACT=ObjectiveThe present study aimed to investigate latent classes of emotional labor among clinical nurses in China and analyze the influencing factors of emotional labor. With this as a reference, care managers can develop more targeted emotional labor intervention programs.MethodsThis study enrolled 1,320 clinical nurses by using stratified random sampling from August to October 2023. A total of 1,279 nurses completed the following questionnaires: General Information Questionnaire, Emotional Labor Scale, Generalized Anxiety Disorder-7 scale (GAD-7), Patient Health Questionnaire-9 scale (PHQ-9), and Work–Family Conflict Scale. Based on the Emotional Labor Scale, the latent profile analysis (LPA) was used to explore the latent classes of nurses’ emotional labor. Then, univariate analysis and multivariate logistic regression analysis were used to explore the influencing factors of emotional labor.ResultsThree latent classes were identified: generally low-level group (1.5%), high-level deep-acting group (17.2%), and high-level surface-acting group (81.3%). Male nurses were more likely to be in the generally low-level group than female nurses. Nurses with low scores on Work–Family Conflict Scale were more likely to be in the high-level deep-acting group than those with high scores. Compared with the nurses with very poor health, the nurses with very good health were more likely to be in the high-level deep-acting group, and nurses with general health were more likely to be in the high-level surface-acting group.ConclusionThe emotional labor of clinical nurses can be identified into three latent classes. Nursing managers can predict the latent classes of emotional labor based on characteristics such as demographic information and work–family conflict. Therefore, precise intervention can be implemented to reduce the consumption of internal resources and the occurrence of nursing adverse events caused by excessive emotional labor.