Nutrient Profiling and Child-Targeted Supermarket Foods: Assessing a “Made in Canada” Policy Approach
Bibliographic record
Abstract
Marketing unhealthy food and beverages to children is a pervasive problem despite the negative impact it has on children's taste preferences, eating habits and health. In an effort to mitigate this influence on Canadian children, Health Canada has developed a nutrient profile model with two options for national implementation. This study examined the application of Health Canada's proposed model to 374 child-targeted supermarket products collected in Calgary, AB, Canada and compared this with two international nutrient profile models. Products were classified as permitted or not permitted for marketing to children using the Health Canada model (Option 1 and Option 2), the WHO Regional Office for Europe model, and the Pan-American Health Organization (PAHO) model. Results were summarized using descriptive statistics. Overall, Health Canada's Option 1 was the most stringent, permitting only 2.7% of products to be marketed to children, followed by PAHO (7.0%), WHO (11.8%), and Health Canada's Option 2 (28.6%). Across all models, six products (1.6%) were universally permitted, and nearly 60% of products were universally not permitted on the basis of nutritional quality. Such differences in classification have significant policy and health-related consequences, given that different foods will be framed as "acceptable" for marketing to children-and understood as more or less healthy-depending on the model used.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".