摘要
目的:通过对我国医护人员身份建构的研究,为医护人员身份建构相关领域提供数据参考。方法;通过中国知网系统检索医护人员身份建构研究内容,利用CiteSpace软件进行分析,以关键词共现和关键词聚类形成,以及突现词和时区图对医护人员身份建构主题进行可视化分析。结果:国内2005年出现首篇医护人员身份建构为主题的文献,2021年研究达到最高峰27篇。在研究热点上,医护人员身份建构的研究热点有:身份建构、身份认同、医患关系、医患会话、会话分析、在线医疗问诊和医生身份等。医护人员身份建构研究的最新前沿集中在身份建构、身份认同、医生身份和医患会话等方面。但医护人员研究在作者以及机构合作上研究较少。结论:建议未来拓宽关于医护人员身份建构的研究,如被试医护的范围、影响因素、内容、等进行多视角、多领域、深层次的研究分析。
Objective:To provide data reference for the field of identity construction of medical staff in China through the study of identity construction of medical staff in China.Method:The research content of identity construction of medical staff was retrieved through the CNKI system,and the analysis was carried out by CiteSpace software,and the theme of identity construction of medical staff was visually analyzed by keyword co-occurrence and keyword clustering,as well as emergent words and time zone maps.Results:In 2005,the first literature on the identity construction of medical staff appeared in China,and the research reached a peak of 27 in 2021.In terms of research hotspots,the research hotspots of identity construction of medical staff include:identity construction,identity,doctor-patient relationship,doctor-patient conversation,conversation analysis,online medical consultation,and doctor identity.The latest frontiers in the research on identity construction of healthcare workers focus on identity construction,identity,physician identity,and doctor-patient conversation.However,there are fewer studies of authors and institutional collaborations in healthcare worker studies.Conclusion:It is suggested that the research on the identity construction of medical staff should be broadened in the future,such as the scope,influencing factors,and content of the subject’s medical care,and the research and analysis should be carried out from multiple perspectives,fields,and depths.
作者
刘洒洒
Sasa Liu(School of Management,Shanghai University of Engineering Science,Shanghai)
出处
《运筹与模糊学》
2024年第2期1237-1244,共8页
Operations Research and Fuzziology
关键词
身份建构
医护人员
可视化分析
共现分析
聚类分析
Identity Construction
Healthcare Workers
Visual Analysis
Co-Occurrence Analysis
Cluster Analysis