Use of Nutrition Information and Understanding of “Percent Daily Value” on Nutrition Facts Tables: Evaluating the Impact of a National Public Education Campaign among Youth and Young Adults in Canada
Bibliographic record
Abstract
Purpose: In 2010, Health Canada implemented a national campaign to improve understanding of “percent daily value” (%DV) in Nutrition Facts Tables (NFTs). This study examined sources of nutrition information and knowledge of %DV information communicated in the campaign. Methods: Respondents aged 16–30 years completed the Canada Food Study in 2016 (n = 2665). Measures included sources of nutrition information, NFT use, and %DV knowledge based on the campaign message (“5% DV or less is a little; 15% DV or more is a lot”). A logistic regression examined correlates of providing “correct” responses to %DV questions related to the campaign messaging. Results: Overall, 7.2% (n = 191) respondents correctly indicated that 5% is “a little”, and 4.3% (n = 115) correctly indicated 15% DV was “a lot”. Only 4.0% (n = 107) correctly answered both. Correct recall of %DV amounts was not associated with number of information sources reported, but was greater among those who were female, were younger, and reported greater NFT understanding and serving size information use (P < 0.05 for all). Conclusions: Results show low awareness of messaging from the Nutrition Facts Education Campaign among young Canadians. Such a mass media campaign may be insufficient on its own to enhance population-level understanding of %DV.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".