Willingness of CDC Staff to Participate in Continuing Education in China: A Bayesian Network Analysis

To investigate the willingness of China Centers for Disease Control and Prevention (C-CDC) staff in Northeast China to participate in continuing education and identify the factors associated with such willingness.

This study adopted a cross-sectional design using stratified cluster sampling. Based on Expectancy-Value-Cost (EVC) and Job Demands-Resources (JD-R) theories, literature evidence, and study objectives, the survey variable system was constructed to cover multiple dimensions. Following multiple rounds of questionnaire revision, an online survey was administered to C-CDC staff at different administrative levels in Northeast China, yielding 11,912 valid responses. After identifying relevant variables through univariate analysis, a Bayesian network model was constructed to explore the association and their interrelationships.

A total of 19 variables were identified by univariate analysis as being significantly associated with willingness to participate in continuing education. Bayesian network analysis showed that family support for job, rationality of career development opportunities, work fatigue, and team cohesion were directly associated with willingness to participate in continuing education. Sensitivity analysis further identified nine key nodes. The conditional probability table indicated that when low work fatigue, more rational career development opportunities, higher team cohesion, and stronger family support were present simultaneously, the conditional probability of high willingness to participate in continuing education among C-CDC staff increased to 94.2%.

Bayesian network analysis identified several professional identity related factors, particularly team cohesion, career development opportunities, and family support, as key factors associated with willingness to participate in continuing education. The analysis also revealed complex interrelationships among these factors.