Biomedical Knowledge Engineering Laboratory, Seoul National University, Seoul, Republic of Korea.
National Center of Excellence in Software, Chungnam National University, Daejeon, Republic of Korea.
BMC Med Educ. 2018 Sep 24;18(1):222. doi: 10.1186/s12909-018-1323-y.
As studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education.
The authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963-2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software.
Newly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963-1975), the birth phase (1976-1990), the growth phase (1991-1996), the maturity phase (1997-2005), and the expansion phase (2006-2015).
The study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society's needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.
随着分析学术领域研究课题网络和关系结构的研究不断涌现，应用网络和关系分析方法（如社会网络分析（SNA））的研究越来越受到关注。本研究旨在运用复杂系统理论的 SNA 方法探讨医学教育学科的相互作用，并分析医学教育的发展趋势。
作者使用 Medical Subject Headings 术语从 1963 年至 2015 年在 PubMed 上发表的 9379 篇研究文章（162866 个关键词）中提取关键词。他们生成了一个出现频率矩阵，使用加权 Jaccard 相似性计算关联性，并使用 Gephi 软件分析和可视化网络。
按时间段划分的新兴主题被确定为历史趋势，通过网络分析获得了 20 个全球层面的主题群。时间序列分析确定了五个历史时期：觉醒期（1963-1975 年）、诞生期（1976-1990 年）、成长期（1991-1996 年）、成熟期（1997-2005 年）和扩展期（2006-2015 年）。
本研究使用 SNA 分析了医学教育研究的发展趋势，并运用复杂系统理论分析了其意义。在研究的 53 年期间，医学教育研究不断细分，并随着社会需求的变化而扩展、改进和改变。通过运用复杂系统理论的概念框架分析医学教育的发展趋势，研究小组确定医学教育通过与社会研究、哲学等领域的相互作用，正在医学领域形成一种自愿秩序感，并建立了合法性和原创性。
