Historical and Continued Colonial Impacts on Heart Health of Indigenous Peoples in Canada: What’s Reconciliation Got to Do With It?
Bibliographic record
Abstract
Colonization and enforced genocidal strategies have collectively fractured and changed Indigenous people by attempting to erase and dismiss their voices and knowledge. Nearly a decade ago, we were reminded by Dr Ku Young of the cardiovascular health disparities, in evidence among Indigenous people in Canada. compared with White people. He went on to say that beyond a biomedical understanding of this health status is the ongoing impact of long-standing marginalization and oppression faced by Indigenous people. Limited attention has been afforded to advance our understanding of these colonial impacts on Indigenous people and their heart health. This article contributes to our collective understanding of Indigenous people and their cardiac health by covering the following topics: layers of foundational truths of relevance to healthcare contexts and Indigenous people; a critical reflection of Western (biomedical) perspectives concerning cardiac health among Indigenous people; and materials from 2 studies, funded by the Canadian Institutes of Health Research, in which Indigenous voices and experiences were privileged concerning the heart and caring for the heart. In the final section, 3 topics are offered as starting points for self-reflection and acts of reconciliation within healthcare practice, decision-making, and research: reflections on self and one's worldview; anti-racist healthcare practice; and 2-eyed seeing approaches to work within healthcare contexts. A common thread is the imperative for "un-silencing" Indigenous people's voices, experiences, and knowledge, which is a requirement if addressing the identified cardiovascular health disparities is truly a health priority.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.030 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.008 |
| 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".