Self-reported reactions to the front-of-package warning labelling in Mexico among parents of school-aged children
Bibliographic record
Abstract
ABSTRACT Background To improve the food environment and guide consumers to select healthier foods, the implementation of a front of package warning labelling (FOPWL) started in Mexico in October 2020. We aimed to identify the self-reported support, understanding, use and perceived impact of the FOPWL 1-5 months after its implementation among parents of school-aged children across socioeconomic categories and nutrition knowledge and attitudes. Methods EPHA-niñ@s is a national web-based cohort of Mexican children 5-10 y and one of their parents aiming to monitor their food and food policy perception and opinions and children’s dietary intake. Recruitment was conducted primarily through paid advertisements on social media. Data was collected online with a self-administered questionnaire answered by the parent and an interviewer-administered questionnaire answered by the child during a video call. This analysis was conducted with data from the parent’s questionnaire from the first wave of data collection (November 2020-March 2021) which included 2,071 participants from all over the country. We evaluated differences by socioeconomic status (SES), education and nutrition knowledge and consciousness, while adjusting by other sociodemographic characteristics using multinomial logistic regression. Results The sample was predominantly from middle and high socioeconomic status (SES). Most parents (85%) agree/strongly agree with the FOPWL (support), 86% correctly identified that a product with one warning is healthier than a product with three (understanding), 65% compared the number of warnings sometimes to very often (use), and 63% reported buying less and 25% stopped buying products with warnings for their children (perceived impact). The perceived impact was higher when products were for their children than for themselves. Perceived impact also differed by food group, being higher for sodas, juices, and cereal bars and lower for chips and chocolate powder. Responses were more favorable for five-six questions (out of seven) among those with higher nutrition knowledge, and higher nutrition consciousness, and for three questions among those with higher education level. Conclusion Within six months of implementation, the immediate self-reported responses related to support, understanding, use, and perceived impact to the Mexican FOPWL were favorable. Further studies in other populations including low SES participants and impact evaluations, are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".