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Record W7065261031

Development and Evaluation of Culturally Adapted CBT to Improve Community Mental Health Services for Canadians of South Asian Origin - Final Report 2023

2023· other· en· W7065261031 on OpenAlexaboutno aff

Bibliographic record

VenueKent Academic Repository (University of Kent) · 2023
Typeother
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMulticulturalismAffect (linguistics)Adaptation (eye)PerceptionSouth asiaEthnic group
DOInot available

Abstract

fetched live from OpenAlex

Phase 1: Five themes were identified from the analysis: Awareness and preparation: matters that impact the individual’s cognizance of therapy and mental illness Access and delivery of care: SA Canadians’ perception of barriers, facilitators and access to treatment Assessment and engagement: experiences of receiving helpful treatment Adjustments to therapy: modifications and suggestions to standard CBT Ideology and ambiguity: racism, immigration, discrimination and other socio-political factors that affect mental health and access to care. Phase 2: The CaCBT group scored lower than standard CBT on all symptom measures. The CaCBT group exhibited significantly greater levels of engagement and satisfaction than the standard CBT group, as evidenced by VSSS and WAI results. South Asians born in Canada showed greater reduction in depressive symptoms (approaching statistical significance) than those born outside of Canada, indicating that CaCBT may be more widely accepted among those born in Canada. The study had a high recruitment response and retention rate, demonstrating the feasibility of CaCBT. Phase 3: There was significant increase in knowledge of both multicultural counselling skills and cultural adaptation after training. There was a significant increase in Southampton Adaptation Framework knowledge after training, with a 37% average normalized gain in knowledge Average satisfaction post-training was 91.66%.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.277
Teacher spread0.253 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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