Latent profile analysis and influence factor study of well-being among nurses in China: a cross-sectional study.

Abstract

The present study employed latent profile analysis (LPA) to identify three distinct profiles of subjective well-being (SWB) among Chinese nurses. It further examined the factors influencing these profiles and aimed to provide a foundation for targeted interventions to enhance nurses'SWB. A cross-sectional study was conducted between November 2023 and March 2024. Data were collected from three Class III Grade A hospitals in China. A total of 2272 nurses were recruited for this study. Data collection used a demographic questionnaire, the SWB Scale, the Nurse Job Satisfaction Scale and the Perceived Social Support Scale. LPA identified distinct SWB characteristics, and influencing factors were analysed using χ² tests and multivariable logistic regression analysis. Nurses' SWB was classified into three profiles: (1) <i>high health concern-low well-being</i> (27.3%), (2) <i>moderate health concern-moderate well-being</i> (41.1%) and (3) <i>low health concern-high well-being</i> (31.6%). Multivariable regression analysis revealed significant associations of gender, age, years of experience, professional title, position, self-perceived health, social support and job satisfaction with these profiles (p<0.05). Given the heterogeneity of nurses' SWB identified through LPA, healthcare institutions may design evidence-based interventions tailored to specific profiles (eg, high health concern-low well-being groups) and key predictors (eg, job satisfaction and social support) to promote sustainable well-being and reduce burnout risks.