Digital food and beverage marketing appealing to children and adolescents: An emerging challenge in Mexico
Bibliographic record
Abstract
BACKGROUND: Digital food marketing is increasing and has an impact on children's behaviour. Limited research has been performed in Latin America. OBJECTIVES: To determine the extent and nature of Mexican children's and adolescents' exposure to digital food and beverage marketing during recreational internet use. METHODS: A crowdsourcing strategy was used to recruit 347 participants during the COVID-19 lockdown. Participants completed a survey and recorded 45 minutes of their device's screen time using screen-capture software. Food marketing was identified and nutrition information for each marketed product was collected. Healthfulness of products was determined using the Pan-American Health Organization and the Mexican Nutrient Profile Model (NPM). A content analysis was undertaken to assess marketing techniques. RESULTS: Overall, 69.5% of children and adolescents were exposed to digital food marketing. Most frequently marketed foods were ready-made foods. Children and adolescents would typically see a median of 2.7 food marketing exposures per hour, 8 daily exposures during a weekday and 6.7 during a weekend day. We estimated 47.3 food marketing exposures per week (2461 per year). The most used marketing technique was brand characters. Marketing was appealing to children and adolescents yet most of the products were not permitted for marketing to children according to the NPMs (>90%). CONCLUSIONS: Mexican children and adolescents were exposed to unhealthy digital food marketing. The Government should enforce evidence-based mandatory regulations on digital media.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".