Understanding the social determinants of health among Indigenous Canadians: priorities for health promotion policies and actions
Bibliographic record
Abstract
BACKGROUND: Indigenous Canadians have a life expectancy 12 years lower than the national average and experience higher rates of preventable chronic diseases compared with non-Indigenous Canadians. Transgenerational trauma from past assimilation policies have affected the health of Indigenous populations. OBJECTIVE: The purpose of this paper is to comprehensively examine the social determinants of health (SDH), in order to identify priorities for health promotion policies and actions. DESIGN: We undertook a series of systematic reviews focusing on four major SDH (i.e. income, education, employment, and housing) among Indigenous peoples in Alberta, following the protocol Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Equity. RESULTS: We found that the four SDH disproportionately affect the health of Indigenous peoples. Our systematic review highlighted 1) limited information regarding relationships and interactions among income, personal and social circumstances, and health outcomes; 2) limited knowledge of factors contributing to current housing status and its impacts on health outcomes; and 3) the limited number of studies involving the barriers to, and opportunities for, education. CONCLUSIONS: These findings may help to inform efforts to promote health equity and improve health outcomes of Indigenous Canadians. However, there is still a great need for in-depth subgroup studies to understand SDH (e.g. age, Indigenous ethnicity, dwelling area, etc.) and intersectoral collaborations (e.g. community and various government departments) to reduce health disparities faced by Indigenous Canadians.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".