The relationship between emotional labor and job burnout among Chinese medical staff: The mediating role of organizational identification.

This study investigates the relationships and underlying mechanisms among emotional labor, organizational identification, and job burnout in medical staff. A convenience sampling method was employed to conduct a questionnaire survey among 419 medical staff members. The Emotional Labor Scale, Organizational Identification Scale, and Maslach Burnout Inventory-General Survey were utilized for data collection. Statistical software SPSS 26.0 and AMOS 25.0 were used for data analysis, and a structural equation model with organizational identification as a mediator was ultimately constructed. The results revealed that the scores for surface acting, deep acting, organizational identification, and job burnout among medical staff were (16.15 ± 4.55), (10.94 ± 2.26), (25.80 ± 3.55), and (28.76 ± 9.12), respectively. Surface acting was negatively correlated with organizational identification (r = -0.43, P < .01) and positively correlated with job burnout (R = 0.49, P < .01). Deep acting was positively correlated with organizational identification (R = 0.38, P < .01) and negatively correlated with job burnout (r = -0.38, P < .01). Organizational identification was negatively correlated with job burnout (r = -0.67, P < .01). Organizational identification partially mediated the relationships between both surface acting and deep acting with job burnout by accounting for 40.82% and 52.20% of the total effects, respectively. Hospital managers can enhance the quality of medical services by developing effective strategies to reduce job burnout among medical staff through promoting their engagement in deep acting behaviors as well as fostering their sense of organizational identification. The study is constrained by its cross-sectional design, self-reported data bias, and regional sample limitations. Therefore, the generalizability of the findings requires further validation through multi-dimensional data.