Social Determinants of Health Inequities in Indigenous Canadians Through a Life Course Approach to Colonialism and the Residential School System
Bibliographic record
Abstract
Indigenous populations in Canada have experienced social, economic, and political disadvantages through colonialism. The policies implemented to assimilate Aboriginal peoples have dissolved cultural continuity and unfavorably shaped their health outcomes. As a result, indigenous Canadians face health inequities such as chronic illness, food insecurity, and mental health crises. In 2015, the Canadian government affirmed their responsibility for indigenous inequalities following a historic report by the Truth and Reconciliation Commission of Canada. It has outlined intergenerational traumata imposed upon Aboriginals through decades of systemic discrimination in the form of the Residential School System and the Indian Act. As these policies have crossed multiple lifespans and generations, societal conceptualization of indigenous health inequities must include social determinants of health (SDOH) intersecting with the life course approach to health development to fully capture the causes of intergenerational maintenance of poor health outcomes. To provide culturally sensitive care for those who have experienced intergenerational trauma, health care providers should be aware of and understand two key SDOH inequity influencing the indigenous life course, including the residential school system and loss of socioeconomic status, over time due to colonialism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".