Impact of Work Goals on Quiet Quitting Among Chinese Primary Health Professionals Based on Goal Setting Theory: A...

Background: Goal setting has always been a crucial management factor for workforce motivation and is quite complex due to multiple goal characteristics. Considering that the emergence of Quiet Quitting (QQ) has inflicted harm on employees' mental well-being in the healthcare field, urgent attention needs to be paid to the impact of goal setting on QQ. This study aimed to assess the current state of work goal setting and QQ among primary health professionals and to explore the effect of goal characteristics on QQ. Methods: A cross-sectional study was performed among 520 primary health professionals from 11 primary health centers. The Modified Goal Setting Scale and Quiet Quitting Scale were utilized to measure goal characteristics and QQ. Descriptive analysis, cluster analysis, and multiple regression were used for statistical analysis. Results: The mean score of QQ was 2.12. The eight goal characteristics were clustered into five categories. Among them, two categories demonstrated significant negative effects on QQ: Goal Specificity and Identity (Category 1; β = -0.096, p < 0.05) and Goal Fulfillment and Organizational Support (Category 2; β = -0.466, p < 0.001). Conversely, three categories showed significant positive effects: Goal Difficulty (Category 3; β = 0.112, p < 0.05), Goal Attainability (Category 4; β = 0.142, p < 0.01), and Goal Conflict (Category 5; β = 0.185, p < 0.001). Conclusions: The phenomenon of QQ requires attention among Chinese primary health professionals. Setting work goals scientifically may prove to be beneficial in curbing its spread. From a practical perspective, goal setting should be specific, moderately challenging, yet attainable, recognized and accepted by employees, and strongly supported by the organization. This approach is valuable for reducing QQ and fostering supportive work environments in primary healthcare. It should be noted, however, that while this study identifies significant associations, its cross-sectional design precludes causal inference, and the findings are context-specific to Chinese primary healthcare institutions.