International school‐based interventions for preventing obesity in children
Bibliographic record
Abstract
The purpose of this article was to review international (excluding the United States) school-based interventions for preventing obesity in children published between 1999 and 2005. A total of 21 such interventions were found from Australia (1), Austria (1), Canada (1), Chile (1), France (1), Germany (3), Greece (1), New Zealand (1), Norway (1), Singapore (1) and the United Kingdom (9). The grade range of these interventions was from pre-school to high school with the majority (17) from elementary schools. Nine of these interventions targeted nutrition behaviours followed by seven aiming to modify both physical activity and nutrition behaviours. Only five interventions in international settings were based on any explicit behavioural theory which is different than the interventions developed in the United States. Majority of the interventions (9) were one academic year long. It can be speculated that if the interventions are behavioural theory-based, then the intervention length can be shortened. All interventions that documented parental involvement successfully influenced obesity indices. Most interventions (16) focused on individual-level behaviour change approaches. Most published interventions (16) used experimental designs with at least 1-year follow-up. Recommendations from international settings for enhancing the effectiveness of school-based childhood obesity interventions are presented.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".