Associations between the overall nutritional quality of prepackaged food categories consumed at breakfast or as snacks and the presence of nutrition-related labelling messages: a cross-sectional analysis of products sold in the province of Québec (Canada)
Bibliographic record
Abstract
BACKGROUND: In Canada, there are no requirements regarding the healthfulness of foods carrying nutrition claims. Also, as of 2026, most prepackaged foods with a high content in saturated fat, sugars and/or sodium will be required to display Health Canada's front-of-pack symbol (HC-FOPS), regardless of their overall nutritional quality. This study aims to evaluate the associations between the overall nutritional quality of prepackaged foods and the presence of nutrition claims as well as of HC-FOPS on those foods. METHODS: The score from -15 (more nutritious) to + 40 (less nutritious) or the accompanying grade from A to E generated by the Nutri-Score was used to assess the overall nutritional quality of four food categories from the Food Quality Observatory: Breakfast cereals (n = 310), Sliced breads (n = 261), Granola bars (n = 234), and Yogurts and dairy desserts (n = 279). Data were sales-weighted to better represent what consumers are buying. RESULTS: In all categories, products with nutrition claims had a better overall nutritional quality (i.e. lower sales-weighted mean score) than products without claims (e.g. 8.00 ± 5.70 vs. 12.14 ± 4.41, respectively, for Breakfast cereals; -0.56 ± 3.01 vs. 1.31 ± 2.04 for Sliced breads; 10.16 ± 3.50 vs. 15.56 ± 5.00 for Granola bars; 0.43 ± 1.65 vs. 1.38 ± 2.70 for Yogurts and dairy desserts; all p ≤ 0.0004). Conversely, products which would be required to carry HC-FOPS generally had a lower overall nutritional quality than products which would not carry the symbol. However, additional analyses showed that some less nutritious foods (i.e. graded with letters D or E) carried nutrition claims or would not be required to display HC-FOPS, particularly in the categories of Granola bars (43.2% and 38.5%, respectively) and Breakfast cereals (23.8% and 11.8%). CONCLUSIONS: These findings show that while the presence of nutrition claims and the absence of HC-FOPS are generally indicative of a better overall nutritional quality, inconsistencies persist. This highlights the importance of strong educational campaigns to help consumers use nutrition-related labelling messages adequately. Results could also be used to support a more stringent regulatory framework for nutrition claims and HC-FOPS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".