This study aims to examine the impact of professional development of exercise rehabilitation specialists on customer satisfaction in China and to identify key predictors of customer satisfaction using a Random Forest machine learning model. Despite the critical role of professional expertise in exercise rehabilitation services, prior research has largely relied on linear statistical models, which are limited in capturing complex and nonlinear relationships. Professional development was conceptualized through five dimensions: educational background, clinical experience, participation in continuous education, certification awareness, and self-directed professional development. Customer satisfaction was measured across reliability, effectiveness, interpersonal interaction, and overall satisfaction. Survey data were collected from users of exercise rehabilitation services in major Chinese cities and analyzed using both Random Forest regression and multiple regression analysis. The results indicate that the Random Forest model outperformed multiple regression in predictive accuracy. Continuous education participation and perceived clinical experience emerged as the most influential predictors of customer satisfaction. Furthermore, the findings reveal nonlinear effects and interaction patterns among professional development factors. This study provides empirical evidence supporting the use of machine learning approaches in rehabilitation service research and offers practical and policy implications for enhancing the quality and sustainability of exercise rehabilitation services in China.
Choi et al. (Thu,) studied this question.
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