Everything Is Related and It All Leads Up to My Mental Well-Being: A Qualitative Study of the Determinants of Mental Wellness Amongst Urban Indigenous Elders
Bibliographic record
Abstract
Indigenous peoples in Canada often experience a greater burden of poor health and wellness relative to non-Indigenous Canadians due to a legacy of colonisation and racism. However, Indigenous mental wellness outcomes vary by community, and it is essential to understand how a community has been impacted by the determinants to improve mental wellness outcomes. This article shares insight from a research partnership with the Ki-Low-Na Friendship Society, an urban Indigenous community service organisation. The study used a decolonising, qualitative methodology in which urban Indigenous Elders shared their knowledge of mental wellness and experiences of services and supports. Elders described mental wellness holistically, connected to their relationships, land, language and culture. They described several determinants of wellness including identity, poverty, transportation, abuse and trauma. Elders shared experiences of culturally unsafe care and identified colonisation as root causes of poor mental wellness. They shared how some determinants affect urban Indigenous communities uniquely. This included limited transportation to cultural activities outside urban centres, such as medicine picking, the importance of urban organisations (such as Aboriginal Friendship Centres) in developing social support networks, and the role of discrimination, racism and inequitable care as barriers to accessing services in urban centres.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| 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".