Work engagement profiles of rural nurses: a multicentre cross-sectional study
You Liu, Huayong Huang, Ershan Xu, Yanhui Zhou, Wen TangBackground
Existing research on work engagement among rural nurses is relatively limited. Based on the job demands-resources (JD-R) model, this study aims to identify profiles of work engagement among rural nurses, explore associated factors and derive actionable policy implications.
Methods
A multicentre cross-sectional study was conducted on 848 rural nurses in Hunan from June to August 2024. The survey used self-administered general information questionnaires and work engagement scales. Latent profile analysis (LPA) was used to identify latent profiles of work engagement. Multinomial logistic regression analysis was used to analyse the associated factors on the different profiles.
Results
Among the respondents, 50.9% had an educational attainment of college degree or below, 16.0% received a monthly income of less than 3000 CNY and 26.7% engaged in more than five night shifts per month. Work engagement of rural nurses could be classified into three profiles: low (n=99, 15.8%), moderate (n=410, 66.6%) and high (n=116, 18.6%). Multinomial logistic regression revealed that marital status, number of children, night shifts and leadership position were significantly associated factors of work engagement across different profiles.
Conclusion
Using latent profile analysis, this study identified three distinct work engagement profiles among rural nurses—low, moderate and high—with the moderate group being the most prevalent. It confirms the applicability of the JD-R model in explaining work engagement among rural nurses while identifying context-specific associated factors—particularly marital status, family structure, leadership role occupancy and night shift intensity. These findings suggest three actionable policies: adjusting night shift allocation based on family structure, offering leadership resilience training for nurses and incorporating marital/family status into retention planning.