Sociodemographic differences in nutrition labels effect on Chilean and Mexican youth
Bibliographic record
Abstract
Objective: To examine sociodemographic differences in the awareness, understanding, use and effect of nutrition labels among Mexican and Chilean youth. Methods: Online surveys among youth (10-17 years) were obtained in 2019 (n=2631). Participants reported their awareness, understanding, and use of their country-specific nutrition facts tables (NFT) and front-of-pack labels (FOPL) (Chile: warning labels [WLs]; Mexico: guideline daily amounts [GDA]). Additionally, participants reported their perceived healthfulness of a sweetened fruit drink after viewing one of six versions of it with different FOPL (no-label control, Health Star Rating, WLs, GDAs, Traffic Light, or Nutri-Score) during an experimental task. Results: Higher self-reported nutrition knowledge was associated with higher NFT and FOPL awareness, understanding, and use, except for WL use. WLs were the most effective FOPL in decreasing the perceived healthfulness of the sweetened fruit drink compared to a no-label condition and other FOP labels. In Chile, the effect of GDA differed by income adequacy, while in Mexico Nutri-Score differed by age. Conclusions: Results suggest that nutrition label awareness, use, understanding, and impact differ across demographics, favoring higher income and nutrition knowledge. Despite this, WLs are likely to have a positive impact on nutrition-related knowledge and behaviors among Mexican and Chilean youth, independently of their socio-demographic groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".