<i>Discretionary Food Fortification:</i> Implications of Consumer Attitudes
Bibliographic record
Abstract
PURPOSE: The interest in, intent to, and impact of consuming foods fortified with vitamins and minerals, particularly foods of poor nutritional quality, were evaluated among Canadians. METHODS: A Canada-wide, online survey of 1200 adults and teens was used to assess the interest in, intent to, and impact of consuming or serving foods fortified under two fortification scenarios (10% and 20% of the Recommended Daily Value). Categories of foods tested were cereal bars, energy bars, flavoured bottled water, frozen desserts, fruit drinks, fruit juice, salty snacks, soda pop, sports drinks, sweet baked goods, and sweets. RESULTS: The majority of adults and teens were interested in consuming fortified foods and indicated that they would increase their current consumption of specific foods if they became fortified. These foods included soft drinks, salty snacks, fruit drinks, and fruit juice. A large proportion of adults also indicated that they would serve more of these fortified foods to their children. CONCLUSIONS: Our findings reveal that fortifying foods, particularly those of poor nutritional quality, could lead to increased consumption of these foods among children, teens, and adults. Potentially, this could have a negative impact on eating habits and, in turn, could exacerbate the current nutrition-related health issues that Canadians face.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".