Preventing type 2 diabetes among Indigenous youth: A systematic review of community-based interventions
Bibliographic record
Abstract
A systematic review was conducted to assess evidence on effectiveness of community-based interventions promoting wellbeing and prevention of type 2 diabetes (T2D) among Indigenous youth. A convergent, segregated, mixed methods approach was used, with six databases and four grey literature sites searched from inception to May 2022. Articles selected for inclusion were about community-based interventions related to T2D prevention with Indigenous youth that evaluated effectiveness or youth experience published in English. Reference lists were also searched for relevant sources. Seven quantitative research articles met the inclusion and quality assessment criteria. No qualitative articles were identified. The results were synthesised through narrative analysis, while meta-analysis was not possible due to heterogenous study designs. Common foci across interventions included promoting physical wellness, improving physical activity and healthy eating patterns, enhancing knowledge, and psychosocial wellness. Interventions deemed effective addressed multiple areas, were school-based, and operated for at least a year. Findings support multi-strategy, community-based interventions implemented over longer periods of time. However, gaps in research and reporting included the extent to which interventions are culturally informed and based on community-driven priorities. Future research should include Indigenous, mixed and qualitative methods and Indigenous-driven measures of success to better understand effectiveness in alignment with Indigenous worldviews.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".