Long‐term trends in health status and determinants of health among the off‐reserve Indigenous population in Canada, 1991–2012
Bibliographic record
Abstract
The Indigenous population in Canada totals approximately 1.6 million individuals, representing about 5% of the total population. The off‐reserve Indigenous population represents the fastest growing segment of the Indigenous population, with over 50% living in urban settings. Despite the size of the off‐reserve population, research on the health of Indigenous peoples tends to remain focused on reserve‐based populations. The purpose of this paper is to contribute to a better understanding of health and social determinants of health among off‐reserve Indigenous peoples in Canada. Using data from the 1991 and 2012 Aboriginal Peoples Surveys this paper examines changes in health status and the social determinants of health over a 20‐year time span. Results show a decline in health care use and self‐reported health status in the period between 1991 and 2012. The results may be related to urbanization, aging, and increased prevalence of some chronic conditions. The findings may also be tied to barriers to achieving adequate off‐reserve health care—jurisdictional disputes, disjointed program coverage, systemic racism, and a lack of equity‐oriented health services. There remains a pressing need for Indigenous and non‐Indigenous governments, researchers, and policymakers to build new relationships that bridge these gaps in health and access to timely care.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".