First Nations Youths' Experiences with Wellness: A Four Directions Approach
Bibliographic record
Abstract
First Nations youth are a growing population at risk for multiple outcomes that affect their well-being. The effects of colonization and the residential school legacy continue to impact First Nations communities today, creating a cycle of intergenerational trauma to affect the next seven generations. As First Nations youth are at a social and economic disadvantage for maintaining balance in well-being, the purpose of this study was to identify through the Medicine Wheel teachings 1) what youth saw as contributors to well-being, 2) their vision for well-being, and 3) ways to achieve their vision. Using a qualitative approach, the results described the reality of wellness amongst First Nations youth in a holistic, cultural view. Face-to-face interviews were conducted with five First Nations youth in a rural First Nations community in Northern Ontario. Five themes emerged that were related to their experiences with wellness, including Balance Strategies and Challenges, Coping Strategies, Emotional Balance, Worldview, and Motivation, using a qualitative content analysis procedure. It was determined that the voices of First Nations youth are powerful, significant, and must be listened to. If an imbalance continues to affect the lives of First Nations youth, the imbalance will also be reflected in Canadian society. Further initiatives are needed to support and empower our First Nations youth on their journey to becoming tomorrow’s leaders.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".