South Asian youth mental health in Peel Region, Canada: Service provider perspectives
Bibliographic record
Abstract
The Peel Region of Toronto, Canada is home to over a third of the province's South Asian population. Youth are at a vulnerable time period in terms of their mental health. South Asian youth populations may face additional challenges to their mental health such as acculturative stress, intergenerational conflict, and racism and discrimination. This qualitative study set out to understand the mental health concerns and service access barriers experienced by South Asian youth populations in the Peel Region of Toronto, Canada from the perspective of mental health service providers. In-depth semi-structured interviews were carried out with mental health service providers (n = 22) who work with South Asian youth living in Peel Region. Thematic analysis was used to elucidate themes related to mental health stressors and service access barriers experienced by youth. According to mental health service providers, South Asian youth navigate a number of unique stressors related to the domains of culture, religion, and family dynamics, experiences of discrimination, the impact of migration, beliefs around mental illness and help-seeking, help-seeking trajectories and therapy recommendations, and lastly, sex differences. Mental health service providers outlined steps needed to effectively address the unique mental health challenges, best practice guidelines, and recommendations for working with South Asian youth, families, and communities to provide a practical and nuanced overview on how a multi-level strategy for mental health care can effectively meet the needs of South Asian youth populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".