Frequency of food marketing in recreation and sport facilities differs by presence of food sponsorship agreements and food service contracts in Canada
Bibliographic record
Abstract
We aimed to evaluate whether food marketing frequency in recreation and sport facilities (RSFs) in Canada differed by the presence of food sponsorship policies, food sponsorship agreements, and food service contracts. We conducted a cross-sectional study of 85 RSFs using an observational audit using the Food and Beverage Marketing Assessment Tool for Settings (FoodMATS) and a facility survey. All instances of food marketing in RSF were recorded in the FoodMATS and the presence of food sponsorship policies, food sponsorship agreements, and food service contracts from the last fiscal year were reported in the survey by facility managers/directors. Mann–Whitney U tests evaluated differences in food marketing frequency by presence of policies (yes/no), agreements (1+/0), and contracts (1+/0). Food marketing frequency did not differ between RSF with and without a food sponsorship policy (14.5 vs. 18.0, p = 0.37). Food marketing frequency was significantly greater in RSF with food sponsorship agreements (26.5 vs. 12.5, p < 0.001) and food service contracts (60.0 vs. 21.0, p < 0.001), compared to RSF without. Only 22.4% and 16.8% of food marketing instances were linked to current food sponsorship agreements and food service contracts, respectively. Sponsorship agreements and contracts may contribute to food marketing in RSF, but they do not explain all marketing instances. Future research should seek to clarify the origin of food marketing exposures, and the opportunities to use policy documents (e.g., facility policies, sponsorship agreements, and food service contracts) to improve healthy food environments, including food marketing in RSF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".