Best Practices for the Prevention and Management of Diabetes and Obesity-Related Chronic Disease among Indigenous Peoples in Canada: A Review
Bibliographic record
Abstract
OBJECTIVES: To carry out a systematic review of interventions that have aimed at improving screening, treatment, prevention and management of type 2 diabetes and obesity-related chronic disease in Indigenous communities in Canada from 2008 to 2014, with the aim of identifying current best practices. METHODS: A comprehensive literature review was carried out through an electronic database search using Medline, EMBASE, PubMED and Google scholar. RESULTS: We identified 17 publications, comprising 13 evaluated interventions. Of them, 7 were school-based programs focused on children, 5 focused on adults, and 1 included both adults and children. Most interventions aimed at encouraging behaviour change, especially dietary change, but did little to address the underlying context of systemic marginalization and colonialism experienced in many Indigenous communities. Interventions focused on improving fitness were more effective than those aimed at dietary change. Overall, we found a range of successes among these interventions. Those that met with limited success reported that complex social issues and poverty presented challenges to effective intervention work in these communities. Participatory action research methods and community ownership of the intervention were found to be essential for project success. CONCLUSIONS: Diabetes-focused intervention research in Indigenous communities appears to be a low priority for Canadian funders and policymakers. More intervention research is urgently needed in these communities. To be effective, this work must take an approach that is historically deep and sufficiently broad as to enable the ideologic, policy and institutional changes necessary in order to achieve true equity. This will involve addressing colonialism, racism and social exclusion as broader determinants of health.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".