A systematic review of the effectiveness of school‐based obesity prevention programmes for <scp>F</scp>irst <scp>N</scp>ations, <scp>I</scp>nuit and <scp>M</scp>étis youth in <scp>C</scp>anada
Bibliographic record
Abstract
First Nations, Inuit and Métis (FNIM) youth are disproportionately affected by obesity and represent known a high-risk group in Canada. School-based prevention programmes may have the potential to effectively influence obesity-related health behaviours (i.e. healthy eating and physical activity) among this population. We conducted a systematic review of nine electronic databases (2003-2014) to identify studies that describe school-based programmes that have been developed to improve obesity-related health behaviours and outcomes among FNIM youth in Canada. The objectives of this review were to identify and evaluate the effectiveness of these programmes and assess the strength of the methodologies used to evaluate them. Fifteen studies, representing seven distinct interventions, met our inclusion criteria. The majority of these programmes did not result in significant improvements in outcomes related to obesity, healthy eating, or physical activity among FNIM youth. The studies varied in design rigour and use of evaluation activities. The lack of literature on effective school-based programmes for FNIM youth in Canada that target obesity-related outcomes highlights a priority area for future intervention development, evaluation and dissemination within the peer-reviewed literature. Further research is needed on interventions involving Métis and Inuit youth, secondary school-aged FNIM youth and FNIM youth living in urban settings.
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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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".