Factors Associated with Primary Care Provider’s Job Satisfaction and Organizational Commitment in China: A Machine Learning-Based Random Forest Analysis

Abstract

The objective of the study is to explore the factors that influence the job satisfaction and organizational commitment of primary care providers in China, with a focus on the impact of the COVID-19 pandemic and the rescission of restriction policies. We utilized the 20-item Minnesota Satisfaction Questionnaire (MSQ) and the 25-item organizational commitment survey to assess job satisfaction and organizational commitment. In total, 435 valid responses were included in our analysis. The average scores for job satisfaction and organizational commitment were 80.6 and 90.8. After a two-step tuning process, we built random forest models by machine learning. The results show income change, working years, working years in the current institute, and age were the four most important features associated with job satisfaction, organizational commitment, and most of their dimensions. The number of professional fields engaged, gender, job status, and types of endowment insurance were least associated. During pandemic time, income-related factors remain a core concern for primary care providers, whereas job security may lose its importance. These findings suggest that financial bonuses may be an effective way to boost morale, and age-specific motivation plans may be necessary.