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Abstract Background To ensure workforce stability in China’s healthcare system and maintain high-quality care, it is essential to comprehensively understand the interplay of factors contributing to turnover intention of healthcare workers. This study aims to examine the associations between potential factors and turnover intention in healthcare workers and explore the mediating effect of job satisfaction and work engagement in the association between them. Methods In this cross-sectional study, a random sample of 1060 healthcare workers working in 98 public medical institutions were recruited to rate their turnover intention in 2018 in Shenzhen, China. Information on socio-demographic characteristics, job-related factors, turnover intention, job satisfaction, work engagement, work stress and doctor-patient relationship of participants were collected. Pearson’s chi-squared tests and binary logistic regression analyses were performed to explore the association between these factor and turnover intention. Mediation analysis was used to explore the roles of potential mediators and moderators. Results The results showed that age (OR: 0.35, 95%CI: 0.16 to 0.72), tenure (OR: 0.98, 95%CI: 0.96 to 0.99), administrative positions (OR: 0.33, 95%CI: 0.16 to 0.63), and night shift frequency (OR: 1.84, 95%CI: 1.26 to 2.67) were significantly associated with turnover intention. We identified the mediating effect of job satisfaction and work engagement in the relationship between administrative positions and turnover intention, while the suppressing effect in the relationship between professional titles and turnover intention. Additionally, we found that monthly income plays a moderating role in the relationship between work engagement and turnover intention, and in the association between professional titles and turnover intention. Conclusions Greater job satisfaction and engagement, along with reasonable remuneration, were found to be associated with lower turnover intention among healthcare workers. Employers should proactively monitor the dynamic interactions among these factors and then develop more tailored interventions in order to alleviate the ongoing loss of healthcare workers.