Evidence map›Paper›PMID 33678941›Full record

ArticleInformation processing & management2021

Characterizing the patterns of China's policies against COVID-19: A bibliometric study.

Jiang Wu, Kaili Wang, Chaocheng He, Xiao Huang, Ke Dong

Abstract read
In one paragraph

Article in Information processing & management, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. PITS: An Intelligent Transportation System in pandemic times.Engineering applications of artificial intelligence · 2022
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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.

Jiang WuSchool of Information Management, Wuhan University, 299 Bayi Road, Wuhan 430072, China.
Kaili WangSchool of Information Management, Wuhan University, 299 Bayi Road, Wuhan 430072, China.
Chaocheng HeSchool of Information Management, Wuhan University, 299 Bayi Road, Wuhan 430072, China.
Xiao HuangSchool of Information Management, Wuhan University, 299 Bayi Road, Wuhan 430072, China.
Ke DongSchool of Information Management, Wuhan University, 299 Bayi Road, Wuhan 430072, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the beginning of 2020, the Chinese government has implemented substantial policies to prevent and control the COVID-19 epidemic. This research attempts to reveal and characterize the patterns of China's policy against COVID-19. Bibliometric methods are applied for studying policy evolution, with the aim of discovering the transitions of the policies over time, the collaborations among policy makers, and the effects of the policies. A total of 366 policies of epidemic prevention are collected. Policy topic shifting, the cooperation of policy-issuing agencies, and the policy content of agencies are analyzed. According to the results, China's policies are implemented in four stages. Moreover, the policy's foci against COVID-19 shifted from medical support in the early stage to economic development in the late stage. Agencies involved in the policymaking can be categorized into three types: leading agencies, key agencies, and auxiliary agencies, with their corresponding administrative influence ranked in this order. Especially, the Chinese government adopted a multi-agency, joint epidemic prevention and control mechanism to ensure the efficiency of the policymaking cooperation. Furthermore, aside from ensuring cooperation among the policy-issuing agencies, they each had their own primary focus of policies in the early stage, but their foci were gradually shared as the epidemic situation changed. This research reveals how China responded to the public health emergency of COVID-19 from the perspective of policy making.

Indexed as

BibliometricsChinaCOVID-19Epidemic prevention and controlPolicy analysis

Identifiers

PMID33678941
PMCPMC7923916

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.