Clinical and School-based Intervention strategies for Youth Obesity Prevention: A systematic Review
Bibliographic record
Abstract
In the last decades, numerous interventions strategies (IS) have been set up in school/community or clinical sectors using physical activity (PA) in order to prevent youth obesity. Those two sectors have shown interesting elements in terms of efficient results and best practices mechanisms but they have been rarely compared to learn one from the other. The aims of the systematic review was to analyze and synthesize PA IS from school/community or clinical domains, for the period 2013-2017, in French or English, targeting youth 5-19 years old through primary, secondary and tertiary prevention. In total, 68 full articles were kept for data extraction and synthesis and 617 were excluded because didn’t meet eligibility criteria. Results identified a number of differences between the studies of the various IS sectors and identified a third type of IS: mixed sectors. They should be privileged because it can add at a time school/community-based and clinical-based strength. Mixed IS showed the most promising results. This review also showed differences between sectors and their IS on intervention team, prevention objective, duration, material and efficiency. Future studies should focus on establishing a prevention program in a given geographical area involving all stakeholders with their respective skills/knowledge, in decision making and in the development of the IS, that it be the most efficient and best adapted to its environment.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".