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Record W4200488553 · doi:10.5530/jyp.2021.13s.72

The Impact of Covid-19 on Mental Health: A Global Analysis of Publications

2021· article· en· W4200488553 on OpenAlexaboutno aff
Sandeep Grover, Gupta BM Gupta, Ghouse Modin Nabeesab Mamdapur, Swapnajeet Sahoo, Aseem Mehra

Bibliographic record

VenueJournal of Young Pharmacists · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPandemicContext (archaeology)ScopusChinaPublic healthCoronavirus disease 2019 (COVID-19)Science Citation IndexBibliometricsGlobal healthMedicineCitationHealth carePolitical scienceLibrary scienceMEDLINEGeographyPsychiatryNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.1090.190
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.128
GPT teacher head0.549
Teacher spread0.421 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of Young PharmacistsSame topicCOVID-19 and Mental HealthFrench-language works237,207