Effects of front-of-package caffeine and sweetener disclaimers in Mexico: cross-sectional results from the 2020 International Food Policy Study
Bibliographic record
Abstract
Abstract Objective: Front-of-package warning labels introduced in Mexico in 2020 included disclaimers that caution against allowing children to consume products with non-sugary sweeteners and caffeine. We examined the awareness and use of the disclaimers among Mexican adults and youth 1 month after the regulation was implemented. We also investigated their impact on the perceived healthfulness of industrialised beverages designed for children. Design: Data on the awareness and use of the disclaimers were analysed. Two between-subjects experiments examined the effect of a sweetener disclaimer (Experiment 1, youth and adults) or a caffeine disclaimer (Experiment 2, only adults) on the perceived healthfulness of industrialised beverages. Interactions between experimental conditions and demographic characteristics were tested. Setting: Online survey in 2020. Participants: Mexican adults (≥18 years, n 2108) and youth (10–17 years, n 1790). Results: Most participants (>80 %) had seen the disclaimers at least rarely, and over 60 % used them sometimes or frequently. The sweetener disclaimer led to a lower perceived healthfulness of a fruit drink (adults: 2·74 ± 1·44; youth: 2·04 ± 0·96) compared with the no-disclaimer condition (adults: 3·17 ± 1·54; youth: 2·32 ± 0·96) (t’s: >4·0, P values: <0·001). This effect was larger among older adults and male youth. The caffeine disclaimer did not affect adult’s perceived healthfulness of a caffeinated drink (t = 0·861, P value = 0·3894). Conclusions: There were high awareness and use of the sweeteners and caffeine disclaimers shortly after the warning labels were implemented. The sweetener disclaimer appears to be helping consumers modify their perceptions regarding industrialised beverages for children. Findings may help decision-makers improve the regulation and better target communication strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".