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The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis

PURPOSE: To perform a bibliometric analysis of the 100 most-cited articles (T100 articles) on COVID-19 vaccine hesitancy to characterize current trends. METHODS: The data of the bibliometric analysis were retrieved from the Web of Science Core Collection (WoSCC) database on January 29, 2023, and the...

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Detalles Bibliográficos
Autores principales: Liu, Bo, You, Junjie, Huang, Lingyi, Chen, Mengling, Shen, Yushan, Xiong, Longyu, Zheng, Silin, Huang, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163891/
https://www.ncbi.nlm.nih.gov/pubmed/37159828
http://dx.doi.org/10.2147/IDR.S408377
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author Liu, Bo
You, Junjie
Huang, Lingyi
Chen, Mengling
Shen, Yushan
Xiong, Longyu
Zheng, Silin
Huang, Min
author_facet Liu, Bo
You, Junjie
Huang, Lingyi
Chen, Mengling
Shen, Yushan
Xiong, Longyu
Zheng, Silin
Huang, Min
author_sort Liu, Bo
collection PubMed
description PURPOSE: To perform a bibliometric analysis of the 100 most-cited articles (T100 articles) on COVID-19 vaccine hesitancy to characterize current trends. METHODS: The data of the bibliometric analysis were retrieved from the Web of Science Core Collection (WoSCC) database on January 29, 2023, and the results were sorted in descending order by citations. Two researchers independently extracted the characteristics of the top 100 cited articles, including title, author, citations, publication year, institution, country, author keywords, Journal Cited Rank, and impact factor. Excel and VOSviewer were used to analyze the data. RESULTS: The T100 articles ranged from 79 to 1125 citations, with a mean of 208.75. The T100 articles were contributed by 29 countries worldwide, of which the USA ranked first with 28 articles and 5417 citations. The T100 articles were published in 61 journals; the top three citations were VACCINES, NATURE MEDICINE, and EUROPEAN JOURNAL OF EPIDEMIOLOGY, and the number of citations was 2690, 1712, and 1644, respectively. Professor Sallam, M(n=4) from Jordan, is the author who participated in the most published articles. Catholic University of the Sacred Heart (n=8) had the most T100 articles. CONCLUSION: It is the first bibliometric analysis of the T100 articles in the field of COVID-19 vaccine hesitancy. We carefully analyzed and described the characteristics of these T100 articles, which provide ideas for further strengthening COVID-19 vaccination and fighting against the epidemic in the future.
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spelling pubmed-101638912023-05-07 The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis Liu, Bo You, Junjie Huang, Lingyi Chen, Mengling Shen, Yushan Xiong, Longyu Zheng, Silin Huang, Min Infect Drug Resist Review PURPOSE: To perform a bibliometric analysis of the 100 most-cited articles (T100 articles) on COVID-19 vaccine hesitancy to characterize current trends. METHODS: The data of the bibliometric analysis were retrieved from the Web of Science Core Collection (WoSCC) database on January 29, 2023, and the results were sorted in descending order by citations. Two researchers independently extracted the characteristics of the top 100 cited articles, including title, author, citations, publication year, institution, country, author keywords, Journal Cited Rank, and impact factor. Excel and VOSviewer were used to analyze the data. RESULTS: The T100 articles ranged from 79 to 1125 citations, with a mean of 208.75. The T100 articles were contributed by 29 countries worldwide, of which the USA ranked first with 28 articles and 5417 citations. The T100 articles were published in 61 journals; the top three citations were VACCINES, NATURE MEDICINE, and EUROPEAN JOURNAL OF EPIDEMIOLOGY, and the number of citations was 2690, 1712, and 1644, respectively. Professor Sallam, M(n=4) from Jordan, is the author who participated in the most published articles. Catholic University of the Sacred Heart (n=8) had the most T100 articles. CONCLUSION: It is the first bibliometric analysis of the T100 articles in the field of COVID-19 vaccine hesitancy. We carefully analyzed and described the characteristics of these T100 articles, which provide ideas for further strengthening COVID-19 vaccination and fighting against the epidemic in the future. Dove 2023-05-02 /pmc/articles/PMC10163891/ /pubmed/37159828 http://dx.doi.org/10.2147/IDR.S408377 Text en © 2023 Liu et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Review
Liu, Bo
You, Junjie
Huang, Lingyi
Chen, Mengling
Shen, Yushan
Xiong, Longyu
Zheng, Silin
Huang, Min
The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis
title The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis
title_full The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis
title_fullStr The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis
title_full_unstemmed The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis
title_short The 100 Most-Cited Articles in COVID-19 Vaccine Hesitancy Based on Web of Science: A Bibliometric Analysis
title_sort 100 most-cited articles in covid-19 vaccine hesitancy based on web of science: a bibliometric analysis
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10163891/
https://www.ncbi.nlm.nih.gov/pubmed/37159828
http://dx.doi.org/10.2147/IDR.S408377
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