Ready for Policy? Stakeholder Attitudes Toward Menu Labelling in Toronto, Canada
Bibliographic record
Abstract
OBJECTIVES: The purpose of this research was to assess key stakeholder attitudes regarding menu labelling in Toronto, the largest municipality in Canada. Menu labelling is a population health intervention where food-labelling principles are applied to the eating-out environment through disclosure of nutrient content of food items on restaurant menus at the point of sale. Menu-labelling legislation has been implemented in the United States, but has yet to be adopted in Canada. As provincial voluntary programs and federal analyses progress, municipal jurisdictions will need to assess the feasibility of moving forward with parallel interventions. METHODS: Data were collected and analyzed in late 2011 to early 2012, including: a consumer eating-out module incorporated into a public health surveillance telephone survey (n=1,699); an online survey of independent restaurant operators (n=256); in-depth key informant interviews with executives and decision makers at chain restaurants (n=9); and a policy consultation with local restaurant associations. RESULTS: Toronto residents, particularly men, younger adults, and those with higher income or education, frequently eat out. A majority indicated that nutrition information is important to them; 69% note that they currently use it and 78% reported they would use it if it were readily available. Resistance to menu-labelling requirements at the municipal level was articulated by franchise/chain restaurant executives and industry associations. Despite overall low interest among independent restaurant operators, 57% reported feeling some responsibility to provide nutrition information and 50% believed it could be good for business. CONCLUSIONS: This research supports earlier literature that indicates strong public support for menu labelling alongside perceived barriers among the restaurant and foodservices sector. Leverage points for effective operator engagement for menu-labelling adoption were identified, nonetheless, highlighting the need for public health support.
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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 | 0.001 | 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 | 0.000 |
| 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".