ArticleScientometrics2022
The high scholarly value of grey literature before and during Covid-19.
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.
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.
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.
Who cites it
4 citing papers in PubMed.
- Mapping evidence of spinal manipulation therapy for headaches in South Africa: a scoping review of grey literature.BMC complementary medicine and therapies · 2025Article
- Advancing occupational therapy scoping reviews: Recommendations to enhance quality and methodological rigour.Australian occupational therapy journal · 2025Article
- Financing for pandemic preparedness and response measures: a systematic scoping review.Bulletin of the World Health Organization · 2024Article
- Methodological quality and reporting quality of COVID-19 living systematic review: a cross-sectional study.BMC medical research methodology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What OpenQuestion holds
Registered trials
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.