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医生能力研究趋势的数据分析:社会网络分析与主题建模方法

Data Analysis of Physician Competence Research Trend: Social Network Analysis and Topic Modeling Approach.

作者信息

Yune So Jung, Kim Youngjon, Lee Jea Woog

机构信息

Department of Medical Education, Pusan National University, Busan, Republic of Korea.

Department of Medical Education, Wonkwang University School of Medicine, Iksan, Republic of Korea.

出版信息

JMIR Med Inform. 2023 Jul 19;11:e47934. doi: 10.2196/47934.

DOI:10.2196/47934
PMID:37467028
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10398558/
Abstract

BACKGROUND

Studies on competency in medical education often explore the acquisition, performance, and evaluation of particular skills, knowledge, or behaviors that constitute physician competency. As physician competency reflects social demands according to changes in the medical environment, analyzing the research trends of physician competency by period is necessary to derive major research topics for future studies. Therefore, a more macroscopic method is required to analyze the core competencies of physicians in this era.

OBJECTIVE

This study aimed to analyze research trends related to physicians' competency in reflecting social needs according to changes in the medical environment.

METHODS

We used topic modeling to identify potential research topics by analyzing data from studies related to physician competency published between 2011 and 2020. We preprocessed 1354 articles and extracted 272 keywords.

RESULTS

The terms that appeared most frequently in the research related to physician competency since 2010 were knowledge, hospital, family, job, guidelines, management, and communication. The terms that appeared in most studies were education, model, knowledge, and hospital. Topic modeling revealed that the main topics about physician competency included Evidence-based clinical practice, Community-based healthcare, Patient care, Career and self-management, Continuous professional development, and Communication and cooperation. We divided the studies into 4 periods (2011-2013, 2014-2016, 2017-2019, and 2020-2021) and performed a linear regression analysis. The results showed a change in topics by period. The hot topics that have shown increased interest among scholars over time include Community-based healthcare, Career and self-management, and Continuous professional development.

CONCLUSIONS

On the basis of the analysis of research trends, it is predicted that physician professionalism and community-based medicine will continue to be studied in future studies on physician competency.

摘要

背景

医学教育中的能力研究通常探讨构成医生能力的特定技能、知识或行为的获取、表现和评估。由于医生能力会根据医疗环境的变化反映社会需求,因此按时间段分析医生能力的研究趋势对于确定未来研究的主要课题很有必要。因此,需要一种更宏观的方法来分析这个时代医生的核心能力。

目的

本研究旨在分析与医生能力相关的研究趋势,这些趋势反映了医疗环境变化下的社会需求。

方法

我们使用主题建模法,通过分析2011年至2020年期间发表的与医生能力相关的研究数据来识别潜在的研究课题。我们对1354篇文章进行了预处理,并提取了272个关键词。

结果

自2010年以来,在与医生能力相关的研究中出现频率最高的词汇是知识、医院、家庭、工作、指南、管理和沟通。在大多数研究中出现的词汇是教育、模式、知识和医院。主题建模显示,关于医生能力的主要主题包括循证临床实践、社区医疗、患者护理、职业与自我管理、持续专业发展以及沟通与合作。我们将研究分为4个时间段(2011 - 2013年、2014 - 2016年、2017 - 2019年和2020 - 2021年),并进行了线性回归分析。结果显示各时间段的主题有所变化。随着时间推移,学者们兴趣增加的热门主题包括社区医疗、职业与自我管理以及持续专业发展。

结论

基于对研究趋势的分析,预计在未来关于医生能力的研究中,医生职业素养和社区医学将继续受到研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/0a1d2b62d58e/medinform_v11i1e47934_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/1ad41e9eeed2/medinform_v11i1e47934_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/41e1004941c5/medinform_v11i1e47934_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/f174f534b128/medinform_v11i1e47934_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/a41297c1b1c3/medinform_v11i1e47934_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/0a1d2b62d58e/medinform_v11i1e47934_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/1ad41e9eeed2/medinform_v11i1e47934_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/41e1004941c5/medinform_v11i1e47934_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/f174f534b128/medinform_v11i1e47934_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/a41297c1b1c3/medinform_v11i1e47934_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f97/10398558/0a1d2b62d58e/medinform_v11i1e47934_fig5.jpg

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