The Role of Social Determinants on Substance Misuse and Cardiovascular Disease in Indigenous Communities: A Scoping Review
Bibliographic record
Abstract
Introduction: Cardiovascular disease (CVD) is the leading cause of death among Indigenous Peoples in Canada, who experience significantly higher rates of chronic illnesses compared to non-Indigenous populations. Despite this disparity, Indigenous communities remain underrepresented in CVD research, contributing to ongoing gaps in care and prevention. One major contributor is substance misuse (SM), which is both a consequence of intergenerational trauma and a key risk factor for CVD. This scoping review explores the social determinants that drive SM in Indigenous populations and contribute to their elevated CVD risk. Methods: This scoping review synthesized peer-reviewed studies from major academic databases using MeSH (medical subject headings) terms related to Indigenous health, SM, CVD, and social determinants of health. It identified and categorized key social determinants influencing health outcomes in Indigenous populations in Canada, guided by the WHO framework and assessed for relevance and feasibility based on available data. Results: Substance misuse among Indigenous communities is embedded in systemic, intergenerational, and structural inequities—many of which also elevate CVD risk. Three primary determinants emerged as most influential: (1) Historical trauma stemming from colonization, (2) barriers to healthcare quality, access and systemic discrimination, and (3) poverty and inadequate infrastructure. These interconnected factors contributed to different outcomes such as chronic stress, maladaptive coping strategies, and limited access to timely, quality care—all linked to poor cardiovascular health. Discussion: Our findings reveal a critical need for research and policy that center on Indigenous perspectives and address the structural roots of health disparities. Interventions must move beyond individual-level treatments and engage with the broader sociohistorical context of colonization, historical trauma, and systemic exclusion. Conclusion: Historical trauma, barriers to healthcare access and quality, built environment and poverty are central social determinants impacting SM and CVD in Canadian Indigenous populations. Long-term, ethical engagement with indigenous knowledge systems and community leadership is essential to achieving sustainable health equity.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".