Mapping the Scholarship on Mental Health during COVID-19 Pandemic: A Scientometric View
Bibliographic record
Abstract
Mental health has been a major concern worldwide even before the emergence of novel coronavirus. The evolving nature of the virus and the fatality rate has increased the psychological distress among people across all age groups. This study is an attempt to explore the research productivity on mental health research during COVID-19 pandemic to combat the disease by means of enhancing awareness and preventive measures as some countries are going through the second wave of the viral attack. The research contribution on mental health during the ongoing COVID-19 pandemic appears to be slow in pace not going with the need of the time. During the study period 1st January 2020 to 5th November 2020, only 1690 scholarly documents were published with average number of articles per author less than one. United States emerged as the most prolific country in the research on ‘Mental Health’ followed by China and U.K. Most of the scholarly output were predominantly in English language and most of the universities were in the forefront in conducting research on mental health. Many researchers got funding encouragement from multiple agencies for their research on mental health stimulating collaborative research trend with Canadian Institutes of Health Research being the top funder. Most of the research publications got concentrated in only few top ranked journals in the field of mental health. These findings reinforce the need to increase the research on mental health.
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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 | 0.015 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.117 | 0.212 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".