Intellectual disability and COVID-19: A bibliometric review
Bibliographic record
Abstract
Background: During the COVID-19 pandemic, the vulnerabilities of certain groups of people have been highlighted, such as people with intellectual disability (ID). Although related research on ID has developed rapidly during the COVID-19 pandemic, the quantitative analysis of those research results has not been systematically performed through bibliometric analysis. Bibliometric analysis is a useful and rigorous method to explore large volumes of research data, and it allows researchers to extract quantitative information on distribution by author, time, country, and journal. Aim: The aim of the present study is to comprehensively analyze the current status and developing trends in publications on ID research related to and conducted during the COVID-19 pandemic. Methods: A bibliometric analysis was performed using the Web of Science database. Biblioshiny software was used to analyze and visualize the following information: main information of dataset, annual scientific production, journals which published the most relevant sources, most-cited authors, most-cited countries, most-cited global documents, word-cloud of keywords authors have used, and both the co-occurrence and co-citation networks. Results: A total of 450 publications were included. The average number of citations per document was 5.104. Among the top three journals, Journal of Applied Research in Intellectual Disabilities published 32 articles, Journal of Intellectual Disability Research published 29 articles, and British Journal of Learning Disabilities published 17 articles. The article with the title COVID-19 and People with Intellectual Disability: Impact of a Pandemic was the most cited with total 144 citations The United Kingdom had the most publications and had strong cooperative relationships with the United States, Canada, and Australia. The most popular keywords included mental health, autism, developmental disability, and lockdown. Thematic map analysis identified several possible clusters, including telemedicine, physical activities, and mental health. Conclusion: The present study provides a better understanding in this research field and may help clinicians, researchers and stakeholders to obtain more comprehensive view of ID and COVID-19. The insights gained from this analysis could inform future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.192 | 0.226 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".