Global publications on Covid-19 and psychology: A scientometric assessment
Bibliographic record
Abstract
The article evaluates the global output on ‘COVID-19 and Psychology’ using bibliometric methods and indicators. The quantitative and qualitative analysis of all the publications in Scopus database was performed using ‘Covid- 19’ and its synonyms keywords in ‘Keyword’ and ‘Title’ tags. The results obtained were further restricted to the subject Psychology under the subject tag. A total of 8205 global publications were identified on the topic of ‘Covid- 19 and Psychology’ in Scopus database, that were cited 63361 times with an average of 7.72 citations per paper. About one-sixth (17.9%) of these publications received external funding support and registered 11.35 citations per paper. The maximum number of publications emerged from the USA, the U.K. and China (2640, 997 and 757 publications), and publications from Canada (16.68 and 2.16), Australia (15.25 and 1.98), U.K. (13.49 and 1.75) received the highest citation per paper and relative citation index. The organisations that produced the highest number of publications were Sapienza University of Rome, Italy (97 papers), University College London, U.K. (95 papers) and King's College London, U.K (91 papers). The organizations with highest citation impact per paper and relative citation index were: Peking University, China (46.46 and 6.02), University of Michigan, Ann Arbor, USA (41.74 and 5.41) and University of Queensland, Australia (39.48 and 5.11). The authors that produced the highest number of publications were G.J. Asmundson (26 papers), S. Grover (22 papers) and S. Taylor (22 papers). The authors who had the highest citation impact per paper and relative citation index were KM. Douglas (137.6 and 17.82), M.M. Paluszek (68.27 and 8.84) and S.K Kar (63.9 and 8.28). The journals that produced the highest number of publications were Frontiers in Psychology (1028 papers), Asian Journal of Psychiatry (324 papers) and the most impactful journals were Nature Human Behavior (52.18), Lancet Child & Adolescent Health (43.68) and Asian Journal of Psychiatry (19.78). The most studied subfields as reflected in keyword frequency were: Mental Health (1187), Anxiety (1176), Depression (881), Mental Disease (408), Distress Syndrome (217), etc. A significant amount ofliterature has emerged on psychological impact of COVID-19 since the beginning of the pandemic.
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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 | 0.062 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.187 | 0.237 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".