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Coevolution of COVID-19 research and China’s policies
BACKGROUND: In the era of evidence-based policy-making (EBPM), scientific outputs and public policy should engage with each other in a more interactive and coherent way. Notably, this is becoming increasingly critical in preparing for public health emergencies. METHODS: To explore the coevolution dy...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419657/ https://www.ncbi.nlm.nih.gov/pubmed/34488797 http://dx.doi.org/10.1186/s12961-021-00770-6 |
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author | Cheng, Xi Tang, Li Zhou, Maotian Wang, Guoyan |
author_facet | Cheng, Xi Tang, Li Zhou, Maotian Wang, Guoyan |
author_sort | Cheng, Xi |
collection | PubMed |
description | BACKGROUND: In the era of evidence-based policy-making (EBPM), scientific outputs and public policy should engage with each other in a more interactive and coherent way. Notably, this is becoming increasingly critical in preparing for public health emergencies. METHODS: To explore the coevolution dynamics between science and policy (SAP), this study explored the changes in, and development of, COVID-19 research in the early period of the COVID-19 outbreak in China, from 30 December 2019 to 26 June 2020. In this study, VOSviewer was adopted to calculate the link strength of items extracted from scientific publications, and machine learning clustering analysis of scientific publications was carried out to explore dynamic trends in scientific research. Trends in relevant policies that corresponded to changing trends in scientific research were then traced. RESULTS: The study observes a salient change in research content as follows: an earlier focus on “children and pregnant patients”, “common symptoms”, “nucleic acid test”, and “non-Chinese medicine” was gradually replaced with a focus on “aged patients”, “pregnant patients”, “severe symptoms and asymptomatic infection”, “antibody assay”, and “Chinese medicine”. “Mental health” is persistent throughout China’s COVID-19 research. Further, our research reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. The average issuance time of relevant COVID-19 policies in China is 8.36 days after the launching of related research. CONCLUSIONS: In the early stage of the outbreak in China, the formulation of research-driven-COVID-19 policies and related scientific research followed a similar dynamic trend, which is clearly a manifestation of a coevolution model (CEM). The results of this study apply more broadly to the formulation of policies during public health emergencies, and provide the foundation for future EBPM research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12961-021-00770-6. |
format | Online Article Text |
id | pubmed-8419657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84196572021-09-07 Coevolution of COVID-19 research and China’s policies Cheng, Xi Tang, Li Zhou, Maotian Wang, Guoyan Health Res Policy Syst Research BACKGROUND: In the era of evidence-based policy-making (EBPM), scientific outputs and public policy should engage with each other in a more interactive and coherent way. Notably, this is becoming increasingly critical in preparing for public health emergencies. METHODS: To explore the coevolution dynamics between science and policy (SAP), this study explored the changes in, and development of, COVID-19 research in the early period of the COVID-19 outbreak in China, from 30 December 2019 to 26 June 2020. In this study, VOSviewer was adopted to calculate the link strength of items extracted from scientific publications, and machine learning clustering analysis of scientific publications was carried out to explore dynamic trends in scientific research. Trends in relevant policies that corresponded to changing trends in scientific research were then traced. RESULTS: The study observes a salient change in research content as follows: an earlier focus on “children and pregnant patients”, “common symptoms”, “nucleic acid test”, and “non-Chinese medicine” was gradually replaced with a focus on “aged patients”, “pregnant patients”, “severe symptoms and asymptomatic infection”, “antibody assay”, and “Chinese medicine”. “Mental health” is persistent throughout China’s COVID-19 research. Further, our research reveals a correlation between the evolution of COVID-19 policies and the dynamic development of COVID-19 research. The average issuance time of relevant COVID-19 policies in China is 8.36 days after the launching of related research. CONCLUSIONS: In the early stage of the outbreak in China, the formulation of research-driven-COVID-19 policies and related scientific research followed a similar dynamic trend, which is clearly a manifestation of a coevolution model (CEM). The results of this study apply more broadly to the formulation of policies during public health emergencies, and provide the foundation for future EBPM research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12961-021-00770-6. BioMed Central 2021-09-06 /pmc/articles/PMC8419657/ /pubmed/34488797 http://dx.doi.org/10.1186/s12961-021-00770-6 Text en © The Author(s) 2021 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 Cheng, Xi Tang, Li Zhou, Maotian Wang, Guoyan Coevolution of COVID-19 research and China’s policies |
title | Coevolution of COVID-19 research and China’s policies |
title_full | Coevolution of COVID-19 research and China’s policies |
title_fullStr | Coevolution of COVID-19 research and China’s policies |
title_full_unstemmed | Coevolution of COVID-19 research and China’s policies |
title_short | Coevolution of COVID-19 research and China’s policies |
title_sort | coevolution of covid-19 research and china’s policies |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8419657/ https://www.ncbi.nlm.nih.gov/pubmed/34488797 http://dx.doi.org/10.1186/s12961-021-00770-6 |
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