Culturally Adapted Cognitive Behaviour Therapy (CaCBT) to Improve Community Mental Health Services for Canadians of South Asian Origin: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: South Asian (SA) Canadians are disproportionately affected by higher rates of mood and anxiety disorders. SA Canadians with depression report significant barriers to accessing mental health care and the highest proportion of unmet mental health needs. The Mental Health Commission of Canada (MHCC) advocates for culturally and linguistically relevant services for SA Canadians. Culturally adapted cognitive behavior therapy (CaCBT) has shown to be more effective than standard cognitive behavior therapy (CBT). Adapting CBT for the growing SA population in Canada will ensure equitable access to effective, culturally-appropriate mental health interventions. METHOD: The study used a qualitative design to elicit stakeholder consultation via in-depth interviews. This study is reported using the criteria included in Consolidated Criteria for Reporting Qualitative Studies (COREQ). The analysis follows an ethnographic approach and was informed by the principles of emergent design. RESULTS: Five themes were identified from the analysis, (i) Awareness and preparation: factors that impact the individual's understanding of therapy and mental illness. (ii) Access and provision: SA Canadians' perception of barriers, facilitators, and access to treatment. (iii) Assessment and engagement: experiences of receiving helpful treatment. (iv) Adjustments to therapy: modifications and suggestions to standard CBT. (v) Ideology and ambiguity: racism, immigration, discrimination, and other socio-political factors. CONCLUSIONS: Mainstream mental health services need to be culturally appropriate to better serve SA Canadians experiencing depression and anxiety. Services must understand the family dynamics, cultural values and socio-political factors that impact SA Canadians to reduce attrition rates in therapy.
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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".