Exploring the landscape of nutrition related marketing in Canada: is it guiding consumers to more healthful dietary patterns?
Bibliographic record
Abstract
A majority of Canadians report that they use food labels to obtain nutrition information and make food purchasing decisions. However, apart from the nutrition facts table, this information appears at the manufacturer's discretion. It is unclear whether the current landscape of Nutrition Related Marketing (NRM) on foods functions to promote healthy eating. Our objectives were to examine which foods are taking up NRM, what is being communicated to consumers, and what the implications of this practice are for population health. Front‐of‐package (FOP) NRM was recorded from all packaged foods (n=20520) in 3 large grocery stores in Toronto, representing the top three food retailers in Canada. Descriptive statistics were used to estimate the proportion of foods with NRM by food category and type of claim. 39% of all products had FOP NRM. It was especially prominent among beverages, cereals, snacks, and yogurt. Claims for the absence of undesirable nutrients (e.g. trans fat) were 1.5 times as common as claims for the presence of desirable nutrients (e.g. vitamin C). Claims for nutrients with a high prevalence of suboptimal intakes in the population (e.g. sodium, vitamin D, magnesium, and fibre) were found infrequently, even among foods able to bear these claims. FOP NRM is widespread in Canada, yet this practice provides limited nutritional guidance. This project is supported by CIHR.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".