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Record W7018928642

Evaluating the Adequacy and Potential Use of Mental Health First Aid (MHFA) in Punjabi Communities

2021· dissertation· en· W7018928642 on OpenAlexaboutno aff

Bibliographic record

VenueTSpace (University of Toronto) · 2021
Typedissertation
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthCommissionPsychological interventionIntervention (counseling)First aidPublic healthHealth belief modelRace (biology)
DOInot available

Abstract

fetched live from OpenAlex

Mental health first aid (MHFA) is a psycho-educational intervention aimed at promoting mental health and training lay populations to intervene during mental health crises (Mental Health Commission of Canada, no date). Despite its increasing popularity, little research has been conducted into evaluating the adequacy of MHFA with ethno-racialized communities. Peel region, Ontario, is considered an ‘ethnic enclave’ for Canada’s South Asian migrant populations, with a distinctive presence of Punjabi communities (Qadeer, Agrawal and Lovell, 2010). This study critically considers the adequacy of MHFA and its application to Punjabi communities in Peel region. Through an approach based in Critical Race Theory (CRT), this study presents the obstacles and facilitators to MHFA, its perceived adequacy and use with an ultimate view to improving mental health outcomes for Punjabi communities. This study concludes that MHFA is generally perceived as adequate, its applicability is limited in the contexts of research participants.

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)0.011
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.003
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 head0.086
GPT teacher head0.398
Teacher spread0.312 · 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 designQualitative
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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