Use of the School Setting During the Summer Holidays: Mixed‐Methods Evaluation of Food and Fun Clubs in Wales
Bibliographic record
Abstract
OBJECTIVE: School summer holiday clubs in deprived areas of Wales were evaluated to examine opportunities for healthy eating and physical activity and explore delivery processes. METHODS: Ten Food and Fun clubs participated in 2016. Quantitative data (child and parent surveys; N = 196, N = 84) assessed the opportunity to provide children with breakfast and lunch. A sub-sample of children wore an accelerometer (N = 41) to evaluate the opportunity for achieving 1-hour of moderate to vigorous activity (MVPA) at club. Features of successful club delivery were identified through; focus groups (child and parent; N = 74, N = 69) and interviews (staff/volunteer; N = 32). RESULTS: Opportunities for healthy eating were delivered with high fidelity: 86% of children reported breakfast consumption and 75% eating a healthy lunch. On club days, children reported consuming fewer sugary snacks (66%), fewer sugary drinks (81%), and more fruits and vegetables (67%). About 71% of children achieved the recommended MVPA at club, with children engaging in more MVPA (+17 minutes/day, p < .01) on average compared to non-club days. Successful delivery processes were: use of school facilities and staff; flexible partnership-working; and whole family involvement. CONCLUSIONS: Schools appear to provide a suitable setting for the delivery of healthy eating and physical activity opportunities during school summer holidays.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".