The Growing Epidemic of Diabetes Among the Indigenous Population of Canada: A Systematic Review
Bibliographic record
Abstract
Diabetes is one of the most well-known and well-researched non-communicable diseases known to humankind. The goal of this article is to show that the prevalence of diabetes is constantly increasing among indigenous people, a major population subgroup in Canada. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used to conduct this systematic review, and the databases used were PubMed and Google Scholar. Studies that were published in the last 15 years (2007-2022) were selected for this review, and after applying the inclusion and exclusion criteria, screening, and removing duplicates, 10 articles were selected for the final review - three qualitative studies, three observational studies, and four studies without a specified methodology. We used the JBI (Joanna Briggs Institute) checklist, NOS (Newcastle-Ottawa Scale) checklist, and SANRA (Scale for the Assessment of Narrative Review) checklist for quality assessment. We found that all the articles showed that the prevalence of diabetes is increasing in all the Aboriginal communities despite all the interventional programs already in place. Rigorous health plans, health education, and wellness clinics for primary prevention can all be effective in reducing the potential risks of diabetes. More studies exploring the prevalence, effects, and outcomes of diabetes in the indigenous population of Canada are needed to effectively understand the disease and its complications in this group.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".