Reclaiming overall well-being: an analysis of individual- and community-level characteristics contributing to well-being in Yukon First Nations
Bibliographic record
Abstract
This collaborative study implemented a new conceptual framework for health research relevant to Yukon First Nations people and actively involved Yukon First Nations as partners into all steps of the research. Selected characteristics from the Yukon Adult RHS data-set (individual-level characteristics) and the ecological variable survey (community-level characteristics) underwent a sequence of bivariate and multivariate comparisons to explore associations with three outcome measures for overall well-being: no depression, no suicidal thoughts and no suicide attempts. Six individual-level characteristics were identified that had a significant association with the outcome measures: traditional foods modern and traditional health care emotional supports and loving relationships spirituality physical well-being and socio-economic characteristics. The following community-level characteristics emerged as being significantly associated with the outcome measures: geographic characteristics community control community engagement and cultural continuity. Limitations of the study, implications for practice and policy and recommendations for future research and summary comments are identified and discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".