A systematic review of prevention strategies for type 2 diabetes in First Nations children and young people
Bibliographic record
Abstract
INTRODUCTION: There is a high prevalence of type 2 diabetes (T2D) in First Nations populations worldwide, increasingly at younger ages. This review aims to identify interventions for the prevention of T2D in First Nations children and young people aged 4-25 years. METHODS: A systematic search of both published and unpublished literature until March 2024 was performed using 15 electronic databases, including MEDLINE, CINAHL, EMBASE, Scopus, Cochrane Library, ATSIHealth, OpenGrey and specific First Nations databases. Eligible studies included First Nations participants aged 4-25 years without T2D, exploring interventions to prevent T2D. Outcomes included knowledge of diabetes, anthropometry and physiology, diet and nutrition, physical activity, glycemic indicators and psychosocial indicators. RESULTS: Fourteen pre-post exposure non-controlled studies were included, evaluating nine programs. Programs were culturally adapted and primarily school-based, focusing on individual-level behaviour modification in nutrition and physical activity. Most studies assessing knowledge outcomes reported improvement. There were inconsistent findings regarding impacts on dietary intake and glycemia. One home-based program achieved improvements across a range of outcomes, including body mass index, physical activity and psychosocial scores. CONCLUSION: Despite the increasing prevalence of T2D in First Nations children and young people, evidence of effective preventive strategies within these populations remains limited.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".