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Record W4286253579 · doi:10.1027/2157-3891/a000051

Mental Health and Perceived Awareness of the South Asian Indian Community During the COVID-19 Pandemic

2022· article· en· W4286253579 on OpenAlexaff
Sagar S. Lad, Saajan Bhakta, Veena Hira

Bibliographic record

VenueInternational Perspectives in Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsCarleton University
Fundersnot available
KeywordsPandemicMental healthAnxietyCoronavirus disease 2019 (COVID-19)Psychological resilienceScope (computer science)Health carePsychologySouth asiaCommunity resiliencePolitical scienceMedicineDiseaseSociologyPsychiatrySocial psychologyInfectious disease (medical specialty)Resource (disambiguation)

Abstract

fetched live from OpenAlex

Abstract. The scope of the United Nations Development Programme (2019) emphasizes combating adaptive challenges, building resilience, and sustainability. This systematic review offers a range of perspectives through an in-depth overview of existing literature on the coronavirus disease 2019 (COVID-19) pandemic and the psychological impact it is having on the South Asian community in India. The selected studies focused on COVID-19 were mixed; however, the consensus among articles had themes relating to fear, stress, and anxiety. The results yielded 17 unique articles. Overall, the review supports the understanding of challenges related to mental healthcare and the attitudes and awareness of South Asians toward the COVID-19 pandemic. This review aims to assist healthcare providers to be better informed on the impact of COVID-19 on South Asians.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.481
Teacher spread0.368 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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