The Impact of Covid-19 on Mental Health: A Global Analysis of Publications
Bibliographic record
Abstract
Covid-19 infection, which emerged in late 2019, spread across the world rapidly and was declared as a pandemic on 24th March 2020 by the World Health Organization. Besides other implications, Covid-19 pandemic led to significant mental health issues in the general public, those infected with the virus and the health care workers. Over the period of 15-16 months, a significant amount of literature has emerged on the mental health issues in the context of Covid-19 pandemic. This paper aims to evaluate the research trends in mental health related to Covid-19 infection by using the bibliometric analysis. Using the Scopus database, as on 21st of March 2021, 15,223 records focusing on “Covid-19 and Mental Health” were identified. The research on this theme averaged 8.90 citations per paper with 13.77% publications supported by funding agencies from global research agencies/firms were published. Researchers from 158 countries participated in mental health research on Covid-19, with top 12 countries accounting for 95.91% share of the global output and a major share of global citations in the subject. Although researchers from USA, U.K. and China led the global publication share (ranging from 10.40% to 26.56%), but researchers from China, France and Australia registered higher relative citation index (ranging from 1.19 to 2.31). Researchers from Harvard Medical School, USA, University of Toronto, Canada, and King’s College, London, U.K. were the most productive (with 299, 270 and 222 papers). Researchers from the National University of Singapore (51.84 and 5.83), King’s College, London, U.K. (27.23 and 3.06), Huazhong University of Science and Technology, China (23.65 and 2.66) were most impactful in terms of citation per paper and relative citation index. To conclude, this bibliometric analysis provides an overview of the extent of research activities in Covid-19 and mental health. Key words: Covid-19, Mental Health, Global Publications, Scientometrics, Bibliometrics.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.109 | 0.190 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".