The systemic roots of Chinese nurse burnout: A mixed-methods study using natural language processing of social media data.

BACKGROUND: Burnout, a global occupational health challenge, is particularly prevalent among Chinese nurses. Traditional research methods have limitations in capturing nurses' unfiltered burnout experiences. OBJECTIVES: To identify the core themes of burnout expressed by Chinese nurses on social media, analyze its systemic causes, and provide evidence to inform intervention strategies. DESIGN: An explanatory sequential mixed-methods design. METHODS: We collected 10,164 public posts and comments regarding nurse burnout from four leading Chinese social media platforms (Weibo, REDnote, Zhihu, and DXY.cn). The dataset spans from January 1, 2015, to May 1, 2025, with varied data collection start times across platforms. Latent Dirichlet Allocation topic modeling was used to identify latent themes, followed by qualitative thematic analysis for in-depth interpretation and the development of an explanatory systemic model. RESULTS: Latent Dirichlet Allocation topic modeling identified 11 distinct burnout themes. Subsequent qualitative analysis integrated these findings to develop the systemic-pressure model of nurse burnout, a theoretical framework organizing these pressures into three functional domains: Early-career challenges, organizational & daily pressures, and career & well-being outcomes. CONCLUSION: Burnout among Chinese nurses is a systemic problem rooted in the healthcare system, not an issue of individual adaptation. Findings indicate interventions must target systemic reforms in hospital management, work design, and incentive mechanisms, rather than focusing solely on individual support.