Healthy Buddies™ Reduces Body Mass Index Z‐Score and Waist Circumference in Aboriginal Children Living in Remote Coastal Communities
Bibliographic record
Abstract
BACKGROUND: Aboriginal children are at increased risk for obesity and type 2 diabetes. Healthy Buddies™-First Nations (HB) is a curriculum-based, peer-led program promoting healthy eating, physical activity, and self-esteem. METHODS: Although originally designed as a pilot pre-/post-analysis of 3 remote Aboriginal schools that requested and received HB training, one school did not implement the program and was used as a control group. Outcomes included changes in body mass index z-score (zBMI), waist circumference (WC), blood pressure (BP), self-esteem, health behavior, and knowledge over 1 school year in kindergarten to grade 12 children. RESULTS: There was a significant decrease in zBMI (1.10 to 1.04, p = .028) and WC (77.1 to 75.0 cm, p < .0001) in the HB group (N = 118) compared with an increase in zBMI (1.14 to 1.23, p = .046) and a minimal WC change in the control group (N = 61). Prevalence of elevated BP did not change in the HB group, but increased from 16.7% to 31.7% in the control group (p = .026). General linear model analysis revealed a significant interaction between time, group, and zBMI (p = .001), weight status (p = .014), nutritious beverage knowledge (p = .018), and healthy living and self-esteem score (p = .005). CONCLUSIONS: The HB program is a promising school-based strategy for addressing obesity and self-esteem in Aboriginal children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".