Community Is Medicine: Understanding Indigenous Youth-Led Peer Support in Mental Health and Suicide Prevention
Bibliographic record
Abstract
Indigenous youth in Canada face complex mental health challenges, including disproportionately high rates of suicide. Peer support separates itself from typical mental health care provision by centering the value of lived experience to provide hope, challenge stigma, and build a sense of community and self-efficacy for young people. Through peer support, Indigenous youth can share their experiences, offer one another support, and honor their cultural identities. The purpose of this study is to understand what culturally grounded strategies Indigenous youth peer support models use to promote Indigenous youth resilience, mental wellness, and suicide prevention. This study adopts a partnership-oriented, community-based research approach that centers Indigenous youth leadership and knowledge, grounded in the concept of "wise practices." This study examines two Indigenous youth-led peer support programs: We Matter (operating nationally across Canada) and Yúusnewas (operating regionally in British Columbia, Canada) through interviews, program observation, and digital content analysis. Results indicate that We Matter and Yúusnewas both have four core culturally grounded program elements: cultural programming, intergenerational involvement, harm reduction education, and youth political advocacy and leadership development. Through centering Indigenous knowledges, these programs serve as an example of culturally relevant, sustainable, and effective Indigenous mental health promotion approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".