Improving Canadian Indigenous Health: Diabetes & Cardiovascular Disease
Bibliographic record
Abstract
Background: This abstract explores improving health outcomes for Canada’s Indigenous peoples. The increased prevalence of cardiovascular diseases (CVD) and diabetes in this population makes this a public health problem because diabetes has long-term complications that affect the cardiovascular system and can result in disability and premature death. Methods: A literature review using specific search terms was performed to find 36 relevant articles. Search databases for the primary and secondary information were CINAHL and PubMED, respectively. Results: The results were classified into five groups: (1) Previous Genetic Protection; (2) Current Day Risk; (3) Diet; (4) Barriers in Developing and Maintaining Health; and (5) Strengths in Developing and Maintaining Health. Non-traditional foods have led to the increased likelihood of developing diabetes by 38%. Social determinants of health act as barriers in managing health. A dissonance between maintaining culture and adapting to modern society has led to acculturation stress, thus increasing the risk of CVD and diabetes in future generations. Discussion & Conclusion: While there is a huge knowledge gap, a vital strength displayed in the Indigenous population is a desire for culturally competent education, which can be addressed through health literacy. Support groups and spirituality can help build capacity in managing their health. By building upon these strengths, self-efficacy can be created within Indigenous communities leading to eventual turning-points that can transform barriers into strengths. Interdisciplinary Reflection: Concepts from the biological and social sciences are combined to show a better representation of the upstream issues that have resulted in an increased prevalence of diabetes and CVD in the Indigenous population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".