Understanding Social Determinants of First Nations Health Using a Four-Domain Model of Health and Wellness Based on the Medicine Wheel: Findings from a Community Survey in One First Nation
Bibliographic record
Abstract
We examined the explanatory roles of social determinants of health (SDOH) for First Nations people using a four-domain model of health and wellness based on the Medicine Wheel (i.e., physical, mental, emotional, and spiritual health), including colonial-linked stressors (i.e., historical trauma, childhood adversities, racial discrimination) and cultural resilience factors (i.e., cultural strengths, traditional healing practices, social support). Data were collected in partnership with a First Nation in Ontario, Canada in 2013 through a community survey (n = 194). For each outcome (physical, mental, emotional, and spiritual health), a modified Poisson regression model estimated prevalence ratios for the SDOH, adjusting for age, sex, education, and marital status. Negative associations were found for historical trauma with physical, mental, emotional, and spiritual health; for childhood adversities with mental health; and for racial discrimination with physical, mental, and emotional health. Positive associations were found for cultural strengths with physical, mental, and emotional health and for social support with physical, mental, emotional, and spiritual health. We observed negative associations between use of traditional healing practices and mental and emotional health. Our findings suggest that these SDOH may play important roles in relation to wellness through associations with the domains of health modelled by the Medicine Wheel.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".