An Intervention To Enhance the Food Environment in Public Recreation and Sport Settings: A Natural Experiment in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Publicly funded recreation and sports facilities provide children with access to affordable physical activities, although they often have unhealthy food environments that may increase child obesity risk. This study evaluated the impact of a capacity-building intervention (Healthy Food and Beverage Sales; HFBS) on organizational capacity for providing healthy food environments, health of vending machine products, and food policy development in recreation and sport facilities in British Columbia, Canada. METHODS: Twenty-one HFBS communities received training, resources, and technical support to improve their food environment over 8 months in 2009-2010, whereas 23 comparison communities did not. Communities self-reported organizational capacity, food policies, and audited vending machine products at baseline and follow-up. Repeated-measures analysis of variance evaluated intervention impact. RESULTS: Intervention and comparison communities reported higher organizational capacity at follow-up; however, improvements were greater in HFBS communities (p<0.001). Healthy vending products increased from 11% to 15% (p<0.05), whereas unhealthy products declined from 56% to 46% (p<0.05) in HFBS communities, with no changes in comparison communities. At baseline 10% of HFBS communities reported having a healthy food policy, whereas 48% reported one at follow-up. No comparison communities had food policies. CONCLUSIONS: This is the first large, controlled study to examine the impact of an intervention to improve recreation and sport facility food environments. HFBS communities increased their self-rated capacity to provide healthy foods, healthy vending product offerings, and food policies to a greater extent than comparison communities. Recreation and sport settings are a priority setting for supporting healthy dietary behaviors among children.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".