Evidence map›Paper›PMID 34840358›Full record

ArticleScientometrics2022

The state of social science research on COVID-19.

Yan-Li Liu, Wen-Juan Yuan, Shao-Hong Zhu

Abstract read
In one paragraph

Article in Scientometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

3 authors.

Yan-Li LiuLibrary of Hangzhou Medical College, 8 Yikang Street, Lin'an District, Hangzhou, 311399 People's Republic of China.ORCID 0000-0002-4272-4053
Wen-Juan YuanLibrary of Hangzhou Medical College, 8 Yikang Street, Lin'an District, Hangzhou, 311399 People's Republic of China.
Shao-Hong ZhuLibrary of Hangzhou Medical College, 8 Yikang Street, Lin'an District, Hangzhou, 311399 People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Research on COVID-19 has proliferated rapidly since the outbreak of the pandemic at the end of 2019. Many articles have aimed to provide insight into this fast-growing theme. The social sciences have also put effort into research on problems related to COVID-19, with numerous documents having been published. Some studies have evaluated the growth of scientific literature on COVID-19 based on scientometric analysis, but most of these analyses focused on medical research while ignoring social science research on COVID-19. This is the first scientometric study of the performance of social science research on COVID-19. It provides insight into the landscape, the research fields, and international collaboration in this domain. Data obtained from SSCI on the Web of Science platform was analyzed using VOSviewer. The overall performance of the documents was described, and then keyword co-occurrence and co-authorship networks were visualized. The six main research fields with highly active topics were confirmed by analysis and visualization. Mental health and psychology were clearly shown to be the focus of most social science research related to COVID-19. The USA made the most contributions, with the most extensive collaborations globally, with Harvard University as the leading institution. Collaborations throughout the world were strongly related to geographical location. Considering the social impact of the COVID-19 pandemic, this scientometric study is significant for identifying the growth of literature in the social sciences and can help researchers within this field gain quantitative insights into the development of research on COVID-19. The results are useful for finding potential collaborators and for identifying the frontier and gaps in social science research on COVID-19 to shape future studies.

Indexed as

COVID-19ScientometricSocial scienceVisualizationVOSviewer

Identifiers

PMID34840358
PMCPMC8605936

What OpenQuestion holds

Textmetadata
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.