Food Allergy Labeling and Disclosure Practices on Restaurants’ Online Menus in Toronto, Canada
Bibliographic record
Abstract
Restaurants have a responsibility to mitigate food-allergic reactions by nonverbally disclosing allergens on their menus and websites. In Canada, there are no laws requiring allergen labeling on non-pre-packaged food, leaving it up to restaurant managers to decide how to accommodate allergic customers. A cross-sectional study was conducted to assess allergen disclosure and labeling on online menus in Toronto, Canada. A random sample of 1,000 nonchain restaurants was sourced from DineSafe, Toronto's food inspection system. The online menus of each restaurant were accessed and assessed using a checklist in 2023-2024 to determine the presence of allergen menus, statements, or symbols. Mixed-effect logistic regression models were developed to assess the relationship between restaurant characteristics (cuisine type, Google review rating, cost indicator, and number of locations) and two outcomes: (1) presence of at least one allergen symbol on the menu, and (2) presence of an allergen statement on the menu. Only 16% (n = 159) of restaurants included allergen statements, and only 10% (n = 100) used allergen symbols. Regression models predicted that vegan and vegetarian restaurants were the most likely cuisine type to have at least one allergen symbol on their menu (19%, 95% CI: 13-24%), and Southeast Asian restaurants were the most likely to have an allergen statement (28%, 95% CI: 20-36%). Additionally, higher restaurant costs and multiple locations were linked to more allergen disclosures. This study highlights the need for improved allergen labeling in nonchain restaurants. Policies requiring allergen disclosures can improve menu transparency and encourage proactive customer-waiter interactions, preventing allergic reactions in restaurants.
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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 | 0.001 |
| 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 | 0.001 |
| 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".