Recreation Facility Food and Beverage Environments in Ontario, Canada: An Appeal for Policy
Bibliographic record
Abstract
Canadian, municipally funded recreation/sport facilities typically have unhealthy food environments. Ontario, unlike some provinces, lacks a voluntary recreation facility nutrition policy. This study assessed the healthfulness of food environments and vending sales in 16 Ontario recreation/sport facilities and, secondarily, compared data from facilities within municipalities that banned versus permitted plastic bottled-water sales (water-ban, n = 8; water, n = 8) to test the nutritional effects of environmental policy. Concession and vending packaged food/beverage offerings and vending sales were audited twice, eighteen months apart. The products were categorized using nutrition guidelines as Sell Most (SM), Sell Sometimes (SS), and Do Not Sell (DNS). Both water and water-ban facilities offered predominantly (>87%) DNS packaged food items. However, proportions of DNS and SM concession and vending beverages differed (p < 0.01). DNS beverages averaged 74% and 88% of vending offerings in water and water-ban facilities, respectively, while SM beverages averaged 14% and 1%, respectively. Mirroring offerings, DNS beverages averaged 79% and 90% of vending sales in water versus water-ban facilities. Ontario recreation/sport facilities provided unhealthy food environments; most food/beverage offerings were energy-dense and nutrient-poor. Water bans were associated with increased facility-based exposure to DNS beverage options. A nutrition policy is recommended to make recreation facility food/beverage environments healthier and to mitigate unintended negative consequences of bottled-water bans.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 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".