Latent profile analysis and influence factor study of well-being among nurses in China: a cross-sectional study - Xiaobing Wu; Hui Hu; Ping-Ping Su; Si Man Chen; Feifei Chang; Haihuan Feng; Xiaoyu Feng; Liyun Wen; Jianting Ouyang (BMJ Open)
Objective 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.Design A cross-sectional study was conducted between November 2023 and March 2024.Setting Data were collected from three Class III Grade A hospitals in China.Participants A total of 2272 nurses were recruited for this study.Outcome measures 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.Results Nurses’ SWB was classified into three profiles: (1) high health concern–low well-being (27.3%), (2) moderate health concern–moderate well-being (41.1%) and (3) low health concern–high well-being (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).Conclusion 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.