Evaluating the strengths and challenges of PAX dream makers approach to mental health promotion: perspectives of youth and community members in indigenous communities in Manitoba, Canada
Bibliographic record
Abstract
PAX Good Behaviour Game (PAX-GBG) is an evidence-based approach to co-create a nurturing environment where all children can thrive. This school-based approach was identified as a promising intervention for suicide prevention by First Nations communities in Manitoba, Canada. To enhance this mental health promotion approach, PAX Dream Makers was developed. It is a youth-led addition to PAX-GBG for middle and high school students. This study's aim was to examine, from the communities' perspectives, the influence of PAX Dream Makers on youth as well as its strengths, challenges and suggestions for future improvements. A case study method was conducted using interviews and focus groups with 30 youth and 17 adult mentors and elders. Participants reported that PAX Dream Makers provided support and encouragement to the youth, increased their resilience and provided an opportunity to be positive role models. It strengthened PAX-GBG implementation in schools. Challenges included: adult mentors availability, frequent teacher turn-over and community mental distress. Suggestions expressed were: being mindful of cultural and community contexts, increasing community leadership's understanding of PAX-GBG and better recruitment of mentors and youth. PAX Dream Makers approach was well-received by communities and holds great promise for promoting the well-being of First Nations youth.
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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 | 0.004 | 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 | 0.021 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".