A bibliometric analysis of the knowledge related to mental health during and post COVID-19 pandemic
Bibliographic record
Abstract
Objective: COVID-19 led to a horrific global pandemic, with strict lockdowns and prolonged indoor stays increasing the risk of mental health problems, affecting people of different ages, genders, regions, and types of work to varying degrees. This study provides a bibliometric summary of the knowledge map related to mental health during and post COVID-19 pandemic. Methods: Publications related to mental health during and post COVID-19 pandemic were searched in the Web of Science Core Collection (WoSCC) database through March 19, 2024. After screening the search results, the literature included in the final was first quantitatively analyzed using GraphPad Prism software and then visualized using VOSviewer, CiteSpace, and R (the bibliometrix package). Results: The 7,047 publications from 110 countries were included, with the highest number of publications from China and the United States, and the number of publications related to mental health during and post the COVID-19 pandemic increased annually until 2023, after which it began to decline. The major institutions were University of Toronto, University of London, Harvard University, King's College London, University College London, University of California System, University of Melbourne, Institut National De La Sante Et De La Recherche Medicale (Inserm), Mcgill University, and University of Ottawa; Frontiers in Psychiatry had the highest number of publications, and the Journal of Affective Disorders had the highest number of co-citations; 36,486 authors included, with Xiang, Yu-Tao, Cheung, Teris, Chung, Seockhoon published the most papers, and World Health Organization, Kroenke K, and Wang CY were the most co-cited; epidemiologically relevant studies on mental health related to COVID-19, and the importance of mental health during normalized epidemic prevention and control are the main directions of this research area, especially focusing on children's mental health; "pandemic," "sars-cov-2," "epidemic," "depression," "coronavirus anxiety," "anxiety," "longitudinal," "child," "coronavirus anxiety," "longitudinal," "child," and "coronavirus" are the top keywords in recent years. Conclusion: This comprehensive bibliometric study summarizes research trends and advances in mental health during and after the COVID-19 Pandemic. It serves as a reference for mental health research scholars during and after the COVID-19 pandemic, clarifying recent research preoccupations and topical directions.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.181 | 0.236 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".