Evidence map›Paper›PMID 35615527›Full record

ArticleScientometrics2022

The high scholarly value of grey literature before and during Covid-19.

Kayvan Kousha, Mike Thelwall, Matthew Bickley

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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.

Kayvan KoushaStatistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY UK.ORCID 0000-0003-4827-971X
Mike ThelwallStatistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY UK.ORCID 0000-0001-6065-205X
Matthew BickleyStatistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY UK.ORCID 0000-0002-4585-7919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

New academic knowledge in journal articles is partly built on peer reviewed research already published in journals or books. Academics can also draw from non-academic sources, such as the websites of organisations that publish credible information. This article investigates trends in the academic citing of this type of grey literature for 17 health, media, statistics, and large international organisations, with a focus on Covid-19. The results show substantial and steadily increasing numbers of citations to all 17 sites, with larger increases from 2019 to 2020. In 2020, Covid-19 citations to these websites were particularly common for news organisations, the WHO, and the UK Office for National Statistics, apparently for up-to-date information in the rapidly changing circumstances of the pandemic. Except for the UN, the most cited URLs of each organisation were not traditional report-like grey literature but were other types, such as news stories, data, statistics, and general guidance. The Covid-19 citations to most of these websites originated primarily from medical research, commonly for coronavirus data and statistics. Other fields extensively cited some of the non-health websites, as illustrated by social science (including psychology) studies often citing UNESCO. The results confirm that grey literature from major websites has become even more important within academia during the pandemic, providing up-to-date information from credible sources despite a lack of academic peer review. Researchers, reviewers, and editors should accept that it is reasonable to cite this information, when relevant, and evaluators should value academic work that supports these non-academic outputs. Supplementary Information: The online version of this article (10.1007/s11192-022-04398-3) contains supplementary material, which is available to authorized users.

Indexed as

CoronavirusCovid-19Grey literature impactNon-academic impact

Identifiers

PMID35615527
PMCPMC9122808

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.