Awareness, use and understanding of nutrition labels among children and youth from six countries: findings from the 2019 – 2020 International Food Policy Study
Bibliographic record
Abstract
BACKGROUND: Nutrition facts tables (NFTs) on pre-packaged foods are widely used but poorly understood by consumers. Several countries have implemented front-of-package labels (FOPLs) that provide simpler, easier to use nutrition information. In October 2020, Mexico revised its FOPL regulations to replace industry-based Guideline Daily Amount (GDA) FOPLs with 'Warning' FOPLs, which display stop signs on foods high in nutrients of concern, such as sugar and sodium. This study examined self-reported awareness, use, and understanding of NFTs and FOPLs among young people in six countries with different FOPLs, with an additional focus on changes before and after implementation of Mexico's FOPL warning policy. METHODS: A 'natural experiment' was conducted using 'pre-post' national surveys in Mexico and five separate comparison countries: countries with no FOPL policy (Canada and the US), countries with voluntary FOPL policies (Traffic Lights in the UK and Health Star Ratings in Australia), and one country (Chile) with mandatory FOPL 'warnings' (like Mexico). Population-based surveys were conducted with 10 to 17-year-olds in 2019 (n = 10,823) and in 2020 (n = 11,713). Logistic regressions examined within- and between-countries changes in self-reported awareness, use, and understanding of NFTs and FOPLs. RESULTS: Across countries, half to three quarters of respondents reported seeing NFTs 'often' or 'all the time', approximately one quarter reported using NFTs when deciding what to eat or buy, and one third reported NFTs were 'easy to understand', with few changes between 2019 and 2020. In 2020, awareness, use and self-reported understanding of the Warning FOPLs in Mexico were higher than for NFTs in all countries, and compared with GDA FOPLs in Mexico (p < .001). Mandated Warning FOPLs in Mexico and Chile had substantially higher levels of awareness, use, and understanding than the voluntary Traffic Lights in the UK and Health Star Ratings in Australia (p < .001 for all). CONCLUSIONS: Mandated easy-to-understand FOPLs are associated with substantially greater levels of self-reported awareness, use and understanding at the population-level compared to NFT and GDA-based labeling systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".