Overabundance of unhealthy food advertising targeted to children on Guatemalan television
Bibliographic record
Abstract
To assess, for the first time, the extent (by hour channel) and nature (e.g. persuasive marketing techniques (PMT) and health-related claims) of unhealthy food advertisements (ads) targeted at children (3-11 years) on the six most-watched television (TV) channels in Guatemala. We recorded 864 h of video on the six most popular channels featuring children's programmes. We classified food and beverage ads as permitted or non-permitted for marketing to children, according to the 2015 World Health Organisation (WHO) nutrient profile. Furthermore, we also analysed PMT (i.e. premium offers, promotional characters, brand benefit claims) and health-related claims. Most food ads (85%) were non-permitted to be marketed to children. Non-permitted food ads were six times more likely, either on weekdays or weekends, for all programme and channel categories compared with permitted food ads. There was no difference in the frequency of non-permitted food ads between peak and non-peak hours, weekend and weekdays or children and non-children programmes. PMT and health-related claims were present in all food ads (5.3 ± 1.9 techniques/claims per ad). There is a need to regulate food ads on TV channels featuring children's programmes in Guatemala as a result of a high frequency of non-permitted food ads and extensive use of PMT together with health-related claims.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".