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The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis
BACKGROUND: This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. METHOD: From Web of Science, we searched for articles publis...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067015/ https://www.ncbi.nlm.nih.gov/pubmed/37005634 http://dx.doi.org/10.1186/s12909-023-04193-5 |
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author | Deng, Huijiao Jiang, Yi Han, Qinrong Zhou, Xingyu Zhong, Siyang Hu, Kai Yang, Lin |
author_facet | Deng, Huijiao Jiang, Yi Han, Qinrong Zhou, Xingyu Zhong, Siyang Hu, Kai Yang, Lin |
author_sort | Deng, Huijiao |
collection | PubMed |
description | BACKGROUND: This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. METHOD: From Web of Science, we searched for articles published between 2020 and 2022 using the terms online education, medical education and COVID-19, ended up with 2555 eligible papers, and the articles published between 2010 and 2019 using the terms online education, medical education and COVID-19, and we ended up with 4313 eligible papers. RESULTS: Before the COVID-19 outbreak, Medical students and care were the most frequent keywords and the most cited author was BRENT THOMA with 18 times. The United States is the country with the greatest involvement and research impact in the field of online medical education. The most cited journal is ACAD MED with 1326 citations. After the COVID-19 outbreak, a surge in the number of research results in related fields, and ANXIETY and four secondary keywords were identified. In addition, the concentration of authors of these publications in the USA and China is a strong indication that local epidemics and communication technologies have influenced the development of online medical education research. Regarding the centrality of research institutions, the most influential co-author network is Harvard Medical School in the United States; and regarding the centrality of references, the most representative journal to which it belongs is VACCINE. CONCLUSION: This study found that hey information such as keywords, major institutions and authors, and countries differ in the papers before and after the COVID-19 outbreak. The novel coronavirus outbreak had a significant impact on the online education aspect. For non-medical and medical students, the pandemic has led to home isolation, making it difficult to offer face-to-face classes such as laboratory operations. Students have lost urgency and control over the specifics of face-to-face instruction, which has reduced the quality of teaching. Therefore, we should improve our education model according to the actual situation to ensure the quality of teaching while taking into account the physical and psychological health of students. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04193-5. |
format | Online Article Text |
id | pubmed-10067015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100670152023-04-03 The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis Deng, Huijiao Jiang, Yi Han, Qinrong Zhou, Xingyu Zhong, Siyang Hu, Kai Yang, Lin BMC Med Educ Research BACKGROUND: This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. METHOD: From Web of Science, we searched for articles published between 2020 and 2022 using the terms online education, medical education and COVID-19, ended up with 2555 eligible papers, and the articles published between 2010 and 2019 using the terms online education, medical education and COVID-19, and we ended up with 4313 eligible papers. RESULTS: Before the COVID-19 outbreak, Medical students and care were the most frequent keywords and the most cited author was BRENT THOMA with 18 times. The United States is the country with the greatest involvement and research impact in the field of online medical education. The most cited journal is ACAD MED with 1326 citations. After the COVID-19 outbreak, a surge in the number of research results in related fields, and ANXIETY and four secondary keywords were identified. In addition, the concentration of authors of these publications in the USA and China is a strong indication that local epidemics and communication technologies have influenced the development of online medical education research. Regarding the centrality of research institutions, the most influential co-author network is Harvard Medical School in the United States; and regarding the centrality of references, the most representative journal to which it belongs is VACCINE. CONCLUSION: This study found that hey information such as keywords, major institutions and authors, and countries differ in the papers before and after the COVID-19 outbreak. The novel coronavirus outbreak had a significant impact on the online education aspect. For non-medical and medical students, the pandemic has led to home isolation, making it difficult to offer face-to-face classes such as laboratory operations. Students have lost urgency and control over the specifics of face-to-face instruction, which has reduced the quality of teaching. Therefore, we should improve our education model according to the actual situation to ensure the quality of teaching while taking into account the physical and psychological health of students. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12909-023-04193-5. BioMed Central 2023-04-01 /pmc/articles/PMC10067015/ /pubmed/37005634 http://dx.doi.org/10.1186/s12909-023-04193-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Deng, Huijiao Jiang, Yi Han, Qinrong Zhou, Xingyu Zhong, Siyang Hu, Kai Yang, Lin The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis |
title | The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis |
title_full | The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis |
title_fullStr | The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis |
title_full_unstemmed | The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis |
title_short | The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis |
title_sort | impact of covid-19 on online medical education: a knowledge graph analysis based on co-term analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067015/ https://www.ncbi.nlm.nih.gov/pubmed/37005634 http://dx.doi.org/10.1186/s12909-023-04193-5 |
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