Disrupting structural racism: A qualitative study on how to address structural racism and its impacts on the health of First Nations Peoples in Canada
Bibliographic record
Abstract
Structural racism is a critical determinant of Indigenous peoples' health and wellbeing in Canada.The effects of structural racism are evident in the persistent health inequities faced by First Nations peoplesincluding disproportionately higher rates of chronic disease, infant mortality and lower life expectancycompared with other Canadians.Mounting reports and cases of anti-Indigenous racism in the healthcare system have called for more attention to address structural racism.However, there is limited research to date that has explored ways to address structural racism against First Nations peoples.This qualitative study explored how to address structural racism and its impact on the health of First Nations peoples in Canada.Semi-structured interviews with 25 participants were conducted with experts in health, justice/ legal, child welfare, education, politics and racism scholarship, and First Nations living with a chronic condition(s).Collected data were analysed using thematic analysis.Five themes emerged on how to address structural racism against First Nations: accountability and consequences; Indigenous authority and representation; anti-racism praxis; education and training; and legislative and policy reform.The findings suggest that addressing an entire ecosystem of structural racism requires a whole-of-society approach that includes strategies targeting individual, policy and structural levels.Ultimately, efforts to address structural
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.019 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".