South Asian Youth Mental Health Organization
Bibliographic record
Abstract
Background: South Asians (SA) are the largest visible minority in Canada and experience higher rates of mental illness. SA youth, especially those under 24, face unique stressors including intergenerational conflict, migration stress, and academic pressures, which contribute to severe mental health symptoms. Furthermore, SA youth are less likely to seek mental health support due to stigma and a lack of mental health discussions within the community. Research in this area suggests that culturally informed community initiatives that promote health literacy and advocacy may be valuable in addressing this growing burden of disease. Objective: The South Asian Youth Mental Health (SAYMH) Ambassador Program was developed to improve mental health literacy and reduce stigma within the SA community. This program aims to empower SA youth, ages 16-25, to become mental health advocates and leaders in their communities. Methods: The SAYMH Ambassador Program includes an intensive four-day retreat where ambassadors learn about mental health literacy, cultural barriers to care, culturally informed treatments, and leadership skills. Ambassadors participate in a speaker series, group discussions, and an Addictions Medicine event focused on the SA population. Following the retreat, ambassadors form small teams and develop outreach projects addressing SA-specific mental health challenges. Projects this year include a podcast series discussing mental health topics for SA youth, social media content creation, and poetry and visual art in Urdu and Punjabi that address burnout in SA families. Regular check-ins by a leadership team member and a midpoint in-person meeting support the ambassadors and aid in the progression of their projects. Results: Feedback was collected after each retreat day, midpoint of the project, and will be gathered upon completion of the projects to assess mental health literacy and leadership growth. Major themes from this feedback will be analyzed to inform a final evaluation titled “Evaluation of the South Asian Youth Mental Health Training Program on the Development of Mental Health Ambassadors.” Conclusion: The SAYMH Ambassador Program provides a culturally relevant approach to addressing mental health challenges in the SA community. It empowers youth to reduce stigma and promote mental health literacy through unique and creative advocacy projects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.006 |
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".