Evaluating the application of front-of-package labelling regulations to menu labelling in the Canadian restaurant sector using menu food label information and price (Menu-FLIP) 2020 data
Bibliographic record
Abstract
OBJECTIVE: To evaluate the application of front-of-package (FOP) labelling regulations to menu labelling in the Canadian restaurant sector by assessing the proportion of menu items that would be required to display the 'high-in' FOP symbol if the policy were extended to the restaurant sector. DESIGN: Nutrition information of 18 760 menu items was collected from 141 chain restaurants in Canada. Menu items were evaluated using the mandatory FOP labelling regulations promulgated in Canada Gazette II by Health Canada in July of 2022. SETTING: Chain restaurants with ≥20 establishments in Canada. PARTICIPANTS: Canadian chain restaurant menu items including beverages, desserts, entrées, sides and starters. RESULTS: Overall, 77 % of menu items in the Canadian restaurant sector would display a 'high-in' FOP symbol. Among these menu items, 43 % would display 'high-in' one nutrient, 54 % would display 'high-in' two and 3 % would display 'high-in' all three nutrients-of-concern. By nutrient, 52 % were 'high-in' sodium, and 24 and 47 % were 'high-in' total sugars and saturated fat, respectively. CONCLUSIONS: Given the poor nutritional quality of restaurant foods, the current regulations, if applied to restaurant foods, would result in most menu items displaying a FOP symbol. Therefore, expanding the Canadian FOP labelling regulations to the restaurant sector can be key to ensuring a healthy food environment for Canadians. Furthermore, menu labelling along with other multi-faceted approaches such as reformulation targets are necessary to improve the dietary intake of Canadians from restaurant foods.
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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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".