中华医学教育杂志 ›› 2022, Vol. 42 ›› Issue (8): 748-752.DOI: 10.3760/cma.j.cn115259-20211214-01527

• 继续医学教育 • 上一篇    下一篇

乡村医生健康管理服务技术培训效果及其影响因素分析

廖康, 冯玫, 王晓旭, 白雪婷, 卫蓉蓉, 李丽琪   

  1. 山西医科大学第三医院 山西白求恩医院 山西医学科学院 同济山西医院全科医疗科,太原 030032
  • 收稿日期:2021-12-14 发布日期:2022-08-01
  • 通讯作者: 李丽琪, Email: liliqi2011@163.com
  • 基金资助:
    山西省软科学研究计划项目(2019042002-3)

The effect of rural doctors' training for technology applied in health management and influencing factors

Liao Kang, Feng Mei, Wang Xiaoxu, Wei Rongrong, Bai Xueting, LI Liqi   

  1. General Medical Department, Third Hospital of Shanxi Medical University & Shanxi Bethune Hospital & Shanxi Academy of Medical Sciences & Tongji Shanxi Hospital, Taiyuan 030032, China
  • Received:2021-12-14 Published:2022-08-01
  • Contact: Li Liqi, Email: liliqi2011@163.com
  • Supported by:
    Shanxi Soft Science Research Project(2019042002-3)

摘要: 目的 了解山西省部分地区乡村医生健康管理服务技术培训效果并对影响因素进行分析,为提高乡村医生健康管理服务技术培训质量提供依据。方法 本研究采用问卷调查方法。2021年5至9月,采用自制调查问卷对山西省太原市和临汾市参加重点疾病健康管理服务技术培训的294名乡村医生展开调查,了解其一般情况及培训效果,采用logistic回归进行调查结果分析。结果 68.7%(202/294)的乡村医生培训效果良好。获得执业(助理)医师资格的乡村医生培训效果良好的比例为74.3%(130/175),完成规范化培训的乡村医生培训效果良好的比例为73.7%(98/133),对以往培训满意的乡村医生培训效果良好的比例为70.7%(195/276)。培训效果良好与欠佳的乡村医生在反应层维度[16(16,19)分比12(10,12)分]、结果层维度[12(9,12)分比9(6,9)分]评分比较,其差异均具有统计学意义(均P<0.05)。logistics多因素回归分析表明,取得执业(助理)医师资格的(OR=0.461,95%CI:0.239~0.888,P<0.05)、完成规范化培训的(OR=0.545,95%CI:0.310~0.956,P<0.05)、对以往培训满意的(OR=0.140,95%CI:0.045~0.435,P<0.05)、结果层维度评分高的(OR=0.124,95%CI:0.020~0.768,P<0.05)乡村医生培训效果更好。结论 山西省部分地区乡村医生健康管理服务技术培训效果尚可。未取得执业(助理)医师资格、未完成规范化培训、对以往培训不满意、反应层维度评分低、结果层维度评分低是影响乡村医生培训效果的因素。应结合不同层次的乡村医生培训需求,科学合理且有针对性地组织培训,从而提高培训效果。

关键词: 乡村医生, 健康管理服务技术, 培训效果, 影响因素, 柯氏模型, 分析

Abstract: Objective To evaluate the effect of rural doctors' training for health management service technology in some areas of Shanxi Province and to analyze the influencing factors, so as to provide basis for improving the quality of training program. Methods From May to September 2021, a self-made questionnaire was used to investigate 294 rural doctors who participated in the technical training for health management services targeting at major diseases in Taiyuan and Linfen, Shanxi Province in order to understand their general conditions and training effects. Logistic regression was used to analyze the survey results. Results There were 68.7% (202/294) of rural doctors' training achieved good results. There were 74.3% (130/175) of rural doctors who had obtained the qualification of practicing (assistant) doctors had a good training effect, 73.7% (98/133) of rural doctors who had completed standardized training had a good training effect, and 70.7% (195/276) of rural doctors satisfied with previous training had a good training effect. There were statistically significant differences in response layer dimension [16(16, 19) vs. 12(10,12) ] and outcome layer dimension [12(9, 12) vs. 9 (6,9)] between rural doctors with good and poor training effect (all P<0.05). Logistic multivariate regression analysis showed that those who had joined the qualification training for practicing (assistant) physician (OR=0.461, 95%CI: 0.239~0.888, P < 0.05), those who had completed standardized training (OR=0.545, 95%CI: 0.310~0.956, P<0.05), those who were satisfied with previous training (OR=0.140, 95%CI: 0.045~0.435, P<0.05), and those who scored high in outcome dimension (OR=0.124, 95%CI: 0.020~0.768, P<0.05) showed a better training effect. Conclusions The training effect of health management service technology for rural doctors in some areas of Shanxi Province is satisfactory. Fail of obtaining the qualifications of practicing (assistant) physicians, of completing standardized training, negative attitude to previous training and getting low scores in the response dimension outcome dimensions are all the factors that affect the training effect of rural doctors. The training should be re-organized in a specific and scientific way to meet the needs of rural doctors of different levels, so as to improve the training effect.

Key words: Rural doctors, Health management service technology, Training effect, Influencing factors, Kirkpatrick Model, Analyze

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