Linking Heart Health and Mental Wellbeing: Centering Indigenous Perspectives from across Canada
Bibliographic record
Abstract
Indigenous peoples have thrived since time immemorial across North America; however, over the past three to four generations there has been a marked increase in health disparities amongst Indigenous peoples versus the general population. Heart disease and mental health issues have been well documented and appear to be interrelated within Indigenous peoples across Canada. However, Western medicine has yet to clearly identify the reasons for the increased prevalence of heart disease and mental health issues and their relationship. In this narrative review, we discuss how Indigenous perspectives of health and wholistic wellness may provide greater insight into the connection between heart disease and mental wellbeing within Indigenous peoples and communities across Canada. We argue that colonization (and its institutions, such as the Indian Residential School system) and a failure to include or acknowledge traditional Indigenous health and wellness practices and beliefs within Western medicine have accelerated these health disparities within Indigenous peoples. We summarize some of the many Indigenous cultural perspectives and wholistic approaches to heart health and mental wellbeing. Lastly, we provide recommendations that support and wholistic perspective and Indigenous peoples on their journey of heart health and mental wellbeing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".