Examining the Prevalence, Nutritional Quality and Marketing of Foods with Voluntary Nutrient Additions in the Canadian Food Supply
Bibliographic record
Abstract
Foods with voluntary nutritional additions are a fast-growing sector of the global food industry. In Canada, while the addition of nutrients to foods has been regulated through fortification regulations, parallel policies which aim to encourage product innovation have also allowed for the voluntary addition of nutrients and other novel ingredients to 'supplemented' and 'functional' foods. Concerns have been raised that the consumption of these products may have negative repercussions on population health, such as high nutrient intakes inappropriate for certain population subgroups (e.g., children) and the shifting of dietary patterns to include more unhealthy foods. The aim of this study was to evaluate the prevalence, nutritional quality, and marketing characteristics of foods with added nutrients in the Canadian market. We found many nutritionally-enhanced foods contained high levels of nutrients beyond recommended intakes, despite these nutrients having no evidence of inadequacy in the Canadian population. Additionally, a large proportion of foods with added nutrients had poor nutrient profiles (were deemed 'less healthy' than their non-enhanced counterparts) and carried heavy marketing on their labels, regardless of their nutritional quality. Taken together these findings raise concerns about foods with voluntary nutrient additions and suggest the need to further investigate consumer attitudes and decision-making towards these foods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".