Estimated effects of the implementation of the Mexican Warning Labels regulation on the use of health and nutrition claims on packaged foods
Bibliographic record
Abstract
Abstract Background The use of health and nutrition claims on front-of-pack labels has a potential effect on consumers' food choices; therefore, many countries established regulations to avoid misperceptions. This study describes the use of nutrition and health claims on the front-of-pack of food products in retail stores in Mexico and analyze the potential effects of the new front-of-pack labelling regulation on the use of these claims. Methods This is a cross-sectional study in which nutrition and health claims, nutrition information panels, and the list of ingredients of all foods and beverages available in the main retail stores in Mexico City were collected. The products were grouped by level of processing according to the NOVA food system classification. Claims were classified into different types using the internationally harmonized INFORMAS taxonomy. The potential effect of the implementation of the warning label regulations on the use of nutrition and health claims was estimated by food group and by thresholds of energy and critical nutrients according to the new regulation. Results Of 17,264 products, 33.8% displayed nutrition claims and 3.4% health claims. In total 80.8% of all products on the Mexican market were classified as "less healthy"; 48.2% of products had excess calories, 44.6% had excess sodium, and 40.7% excess free sugars according to the new regulation. The new regulation would prevent 39.4% of products with claims from displaying health and nutrition claims (p<0.001); the largest reduction is observed for ultra-processed foods (51.1%, p<0.001). The regulation thresholds that contribute the most of the reduction in the use of claims were calories (OR 0.62, p<0.001) and non-sugar sweeteners (OR 0.54, p<0.001). Conclusions The new Mexican front-of-pack labelling regulation will prevent most of less healthy processed and ultra-processed foods from displaying HNC and will potentially increase the effectiveness of the warning labels for consumers.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".