Supporting Indigenous youth to live with continued resilience, meaning and hope
Bibliographic record
Abstract
Indigenous Life Promotion and strengths-based approaches to mental wellness and suicide prevention are important strategies that promote sovereignty, holistic wellness, and healing at the individual, family, and community levels. As part of the Patient and Community Engagement Research (PaCER) program, our team facilitated three focus groups with Indigenous young people in Alberta to better understand how they wish to be supported to live life with continued resilience, meaning and hope. Our team conducted a thematic analysis on the focus group transcripts and derived five key themes from their contents: 1. Accessible, meaningful, and ongoing supports; 2. Indigenous-centered, culturally meaningful and safe supports; 3. Fostering meaningful connections and relationships; 4. Surviving/ ‘Existing’; and 5. Thriving; living with purpose and meaning beyond surviving. Based on these themes, six recommendations for better supporting the wellbeing of Indigenous youth were developed: 1. Indigenous-centred resources; 2. Accountability; 3. Person-centred support; 4. Enhancing empowerment in children & youth; 5. Holistic health liaison/navigators; 6. Increased funding. Attending to the voices of Indigenous youth in planning and enhancing supports will continue to bolster their inherent resilience and contribute to the process of reconciliation in Alberta and Canada.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".