Evidence map›Paper›PMID 30249248›Full record

ArticleBMC medical education2018

Research topics and trends in medical education by social network analysis.

Young A Ji, Se Jin Nam, Hong Gee Kim, Jaeil Lee, Soo-Kyoung Lee

Open access · goldAbstract read
In one paragraph

Article in BMC medical education, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 43 citations in OpenAlex.

  1. Article
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  4. Review
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  6. Bibliometric Analysis for Medical Research.Indian journal of psychological medicine · 2023
    Article
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  9. Review
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  11. A Network Analysis of Research Topics and Trends in End-of-Life Care and Nursing.International journal of environmental research and public health · 2021
    Article
  12. Article
  13. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Young A JiBiomedical Knowledge Engineering Laboratory, Seoul National University, Seoul, Republic of Korea.
Se Jin NamNational Center of Excellence in Software, Chungnam National University, Daejeon, Republic of Korea.
Hong Gee KimBiomedical Knowledge Engineering Laboratory, Seoul National University, Seoul, Republic of Korea.
Jaeil LeeCenter for Innovative in Dental Education, Seoul National University, Seoul, Republic of Korea.
Soo-Kyoung LeeCollege of Nursing, Keimyung University, 1095 Dalgubeol-daero, Dalseo-Gu, Daegu, 42601, Republic of Korea. soo1005s@gmail.com.ORCID http://orcid.org/0000-0003-1439-1607
Seoul National University · KRChungnam National University · KRKeimyung University · KR

Funding

National Research Foundation of Korea (KR) 2015R1C1A1A01055753Research Affairs of Seoul National University 860-20140058
6 · The paper itself

Abstract

backgroundAs 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.

methodsThe 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.

resultsNewly 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).

conclusionsThe 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.

Indexed as

Education, MedicalSocial NetworkingBiomedical ResearchFemaleHumansMaleMedical Subject HeadingsComplex systems theoryMedical educationResearch topicsResearch trendsSocial network analysis

Identifiers

PMID30249248
PMCPMC6154904
OpenAlexW2891506050

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.