Nutrient intakes and top food categories contributing to intakes of energy and nutrients-of-concern consumed by Canadian adults that would require a ‘high-in’ front-of-pack symbol according to Canadian labelling regulations
Bibliographic record
Abstract
Canada recently mandated front-of-pack (FOP) labelling regulations, where foods meeting and/or exceeding recommended thresholds for nutrients-of-concern (i.e., saturated fat, sodium, and sugars) must display a 'high-in' FOP nutrition symbol. However, there is limited research on the amounts and sources of foods consumed by Canadians that would require a FOP symbol. The objective was to examine the intakes of nutrients-of-concern from foods that would display a FOP symbol and to identify the top food categories contributing to intakes for each nutrient-of-concern. Using the first day 24-hour dietary recall from the nationally representative 2015 Canadian Community Health Survey-Nutrition (CCHS), Canadian adults' intakes of nutrients-of-concern from foods that would display a FOP symbol was examined. Foods were assigned to 1 of 62 categories to identify the top food categories contributing to intakes of energy and nutrient-of-concern that would display a FOP symbol for each nutrient-of-concern. Canadian adults (n = 13,495) consumed approximately 24% of total calories from foods that would display a FOP symbol. Foods that would display a FOP symbol for exceeding thresholds for nutrients-of-concern accounted for 16% of saturated fat, 30% of sodium, 25% of total sugar, and 39% of free sugar intakes among Canadian adults. The top food category contributing intakes of each nutrient-of-concern that would display a FOP symbol were nutrient-specific: Processed meat and meat substitutes for saturated fat; Breads for sodium; and Fruit juices & drinks for total and free sugars. Our findings show that Canadian FOP labelling regulations have the potential to influence the intakes of nutrients-of-concern for Canadian adults. Using the findings as baseline data, future studies are warranted to evaluate the impact of FOP labelling regulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".