"Pause-2-Play": a pilot schoolbased obesity prevention program
Bibliographic record
Abstract
OBJECTIVES: "Pause-2-Play" is an obesity prevention program targeting screen-related sedentary behaviours and increasing physical activity among elementary school students. The program consisted of a Behavioural Modification Curriculum and a Health Promoting Afterschool Program. This pilot study reports program feasibility, practicability, and impact. METHODS: the 12-week pilot program was implemented with 32 grade five and six students. Program feasibility and practicability were assessed using a qualitative approach. Intervention effects were assessed by comparing pre-post changes in BMI, body composition, fitness scores, screen time, and cognitive variables related to screening viewing behaviours. RESULTS: "Pause-2-Play" was perceived as a useful, fun program with numerous benefits including: children trying new snacks, feeling fitter and better about one's own body shape, and becoming more aware of a healthy lifestyle. The intervention resulted in a statistically significant reduction in percent body fat and an increase in fat-free mass index in overweight children; a decrease in waist circumference and an increase in fat-free mass index were observed in normal weight children. The intervention also statistically improved fitness scores in both normal weight and overweight children. CONCLUSIONS: "Pause-2-Play" was feasible, practical, and favourably changed body composition and fitness level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".