Nutrition‐related food marketing: a case study of whole grain promotion on breads
Bibliographic record
Abstract
With the release of recommendations for increased whole grain consumption, the term ‘whole grain’ is now increasingly appearing on packaged food products in Canada. Manufacturers’ use of this term is voluntary and unregulated, raising questions about the nature of nutrition guidance being provided. To examine the relationship between front‐of‐package (FOP) references and whole grain content, nutritional content and price, we collected nutrition facts table information, ingredient list and price for all breads sold in 3 major supermarket chains in Toronto (n=1,002). Bread was selected for study because it is a staple food, widely consumed, and 55% of products have FOP nutrition‐related marketing. 21% of breads bore a reference to whole grain; this included 51% with whole grain ingredients in the first position meeting USDA or Whole Grains Council criteria, but we could not quantify whole grain content. The presence of a whole grain reference was associated with significantly higher fibre, lower sodium, and higher fat content. Mean price did not differ by presence of a whole grain reference, but few breads with whole grain references fell within the lower tertile of price. Our results suggest that manufacturers’ applications of this term are providing valuable nutrition guidance, but mandatory, standardized information on whole grain content would enable consumers to make more informed purchases. Funded 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".