兵团医学

目的：分析新疆生产建设兵团（以下简称“兵团”）全科住院医师规范化培训学员的就业现状及全科职业选择意愿影响因素。方法：采用横断面调查设计，对2014-2024年兵团全科住培结业的312名学员进行问卷调查，运用二元logistic回归和结构方程模型分析影响因素。结果：本研究有效回收问卷267份（85.58%），已就业学员中44.64%从事全科医学工作，16.74%在基层执业。logistic回归分析显示，职业兴趣与自我效能、政策落实与环境改善及职业预期与外部支持为主要影响因素（P <0.05）。结构方程模型证实：职业兴趣与自我效能对全科职业选择意愿的直接效应最强，政策落实与环境改善通过直接和间接路径发挥作用，职业预期与外部支持则通过职业兴趣与自我效能产生间接影响。结论：兵团全科住培学员基层执业率偏低，其全科职业选择意愿受内在动机与外部环境影响。建议通过强化全科教育体系、完善基层职业发展平台、优化激励政策等措施提升岗位吸引力。

Objective To analyze the employment status of general practice residents from the Xinjiang Production and Construction Corps and the influencing factors on their willingness to choose a general practice career. Methods A crosssectional survey design was adopted, involving 312 residents who completed standardized general practice training in the Corps from 2014 to 2024. Binary logistic regression and structural equation modeling were utilized to analyze the influencing factors.Results A total of 267 valid questionnaires were recovered(85.58%). Among the employed residents, 44.64% were engaged in general practice, and 16.74% were primary care settings. Logistic regression analysis indicated that career interest and self-efficacy,policy implementation and environmental improvement, and career expectations and external support were the main influencing factors(P < 0.05). The structural equation model confirmed that career interest and self-efficacy had the strongest direct effects on the willingness to choose a general practice career; Policy implementation and environmental improvement exerted effects through both direct and indirect pathways; while career expectations and external support had an indirect influence through career interest and self-efficacy. Conclusion The grassroots practice rate among general practice residents from the Corps is relatively low,and their willingness to choose a general practice career is affected by both intrinsic motivation and the external environment. It is recommended to enhance the general practice education system, improve grassroots career development platforms, and optimize incentive policies to increase job attractiveness.