Unhealthy food advertising on Costa Rican and Guatemalan television: a comparative study
Bibliographic record
Abstract
This study aimed to exhaustively explore the characteristics of food advertising on TV in Guatemala and Costa Rica. The International Network for Food and Obesity Non-Communicable Diseases (NCDs) Research, Monitoring and Action Support (INFORMAS) methodology was applied. In 2016, we recorded 1440 h of video among 10 TV channels. We used the Pan American Health Organization (PAHO) Nutrient Profile (NP) Model to identify 'critical nutrients', whose excessive consumption is associated with NCDs. We created a nutritional quality score (0 if the product did not exceed any critical nutrient, 1 if the product exceeded one and 2 if it exceeded ≥2). We classified food ads as permitted (score = 0) and not-permitted (score 1 or 2) for marketing. Persuasive marketing techniques were classified as promotional characters (e.g. Batman), premium offers (e.g. toys), brand benefit claims (e.g. tasty) and health-related claims (e.g. nutritious). In Guatemala, foods that exceeded one critical nutrient were more likely to use persuasive marketing techniques, and in Costa Rica were those with an excess of ≥2 critical nutrients, compared with foods without any excess in critical nutrients [Guatemala: promotional characters (odds ratio, OR = 16.6, 95% confidence interval, CI: 5.8, 47.3), premium offers (OR = 3.4, 95% CI: 1.4, 8.2) and health-related claims (OR = 3.5, 95% CI: 2.2, 5.7); Costa Rica: health-related claims (OR = 4.2, 95% CI: 2.0, 8.5)]. In conclusion, Guatemalan and Costa Rican children are exposed to an overabundance of not-permitted food ads on TV. This justifies implementing national policies to reduce exposure to not-permitted food for marketing, including on TV and other 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".