Evaluation of a voluntary nutritional information program versus calorie labelling on menus in Canadian restaurants: a quasi-experimental study design
Bibliographic record
Abstract
BACKGROUND: A significant proportion of the Canadian diet comes from foods purchased in restaurant settings. In an effort to promote healthy eating, the province of British Columbia (BC) implemented the Informed Dining Program (IDP), a voluntary, industry supported information program in 2012, while the province of Ontario implemented mandatory calorie labelling on menus in 2017. The study examined differences in awareness and the self-reported influence of nutrition information on food choices in restaurants with voluntary nutrition information, calorie labelling on menus, and no nutrition information program. METHODS: Exit surveys were conducted outside of nine chain restaurants in Toronto, Ontario and Vancouver, British Columbia (Canada) in 2012, 2015, and 2017 with varying nutrition information programs implemented. Logistic regression analyses compared self-reported noticing and influence of nutrition information in restaurants with: 1) the IDP which provided nutrition information upon request, 2) calorie labelling on menus, and 3) control restaurants with no specific nutrition information program in place, adjusted for year, city and socio-demographic characteristics. Awareness and knowledge of the IDP were also examined. RESULTS: There were no significant differences in noticing and self-reported influence of nutrition information on food choices between restaurants with the IDP and restaurants with no program. Participants were more likely to notice nutrition information in restaurants when calorie information was provided on menus (57%) compared to in restaurants with the IDP (22%, AOR = 6.20, 95%CI 3.51-10.94, p < 0.001) or restaurants with no nutrition information program (20%, AOR = 7.44, 95%CI 4.21-13.13, p < 0.001). Participants in restaurants with menu labelling were also more likely to report that nutrition information influenced their food purchase (38%) compared to restaurants with the IDP (12%, AOR = 4.43, 95%CI 2.36-8.30, p < 0.001) and restaurants with no nutrition information program (12%, AOR = 5.29, 95%CI 2.81-9.95, p < 0.001). Fewer than 1 in 5 participants who visited an IDP restaurant had heard of the IDP across all data collection years in both cities. CONCLUSIONS: There was no evidence that voluntary programs which provide nutrition information upon request were effective. Providing calorie information on menus increased the likelihood that consumers noticed and that their food choices were influenced by nutrition information in restaurant settings.
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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 | 0.000 |
| 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".