Television food and beverage marketing to children in Costa Rica: current state and policy implications
Bibliographic record
Abstract
OBJECTIVE: To examine the frequency of television (TV) food and beverage advertisements (F&B ads) to which children (4-11 years) are likely exposed and the nutrient profile of products advertised. DESIGN: TV broadcasting between September and November 2016 was recorded (288 h of children's programming; 288 h of family programming) resulting in 8980 advertisements, of which 1862 were F&B ads. Of those, 1473 could be classified into one of the seventeen food groups, and into permitted/non-permitted according to the WHO-EU nutrient profile model. Persuasive marketing techniques used were also identified. SETTING: TV programming was recorded for four weekdays and four weekend days, between 06.00 and 00.00 hours (576 total hours), for four channels (two national and two cable), in Costa Rica. RESULTS: Mean (sd) number of F&B ads/h was greater in cable than national channels (3·7 (0·4) v. 2·8 (0·4), P < 0·05) and during children's peak viewing hours (4·4 (0·4) v. 2·9 (0·3)). Of F&B ads classified with WHO-EU nutrient profile model (n 1473, 71·1 %), 91·1 % were non-permitted to be marketed to children. Categories most frequently advertised were ready-made/convenience foods (16 %), chocolates/confectionery/desserts (15 %), breakfast cereals (14 %), beverages (15 %), edible ices (9 %) and salty snacks (8 %). Non-permitted F&B ads were more likely to use promotional characters, brand benefit claims, and nutrition and health claims than permitted F&B ads. CONCLUSIONS: Children watching popular TV channels in Costa Rica are exposed to a high number of unhealthy F&B ads daily. Our findings help justify the need for regulatory actions by national authorities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".