Food advertising in cooking shows on Brazilian free-to-air tv channels: the predominance of ultra-processed foods
Bibliographic record
Abstract
Introduction: Cooking at home is linked to better diet quality. Cooking shows are a popular source for learning about home cooking. These programs usually feature food advertising. Objective: This cross-sectional study analyzed food advertising during cooking shows on Brazilian free-to-air TV channels. Methods: All the cooking shows on Brazil's four most popular free-to-air TV channels were recorded for two days in 2019. Advertisements identified during commercial breaks and within cooking shows were categorized as food-related or non-food-related ads. Food ads were classified and analyzed according to the NOVA classification. Descriptive analyses were used to identify the frequency of food-related ads, distribution throughout food groups, and advertising/sponsoring companies. A Chi-square test was used to compare UPF and non-UPF ads identified during commercial breaks and cooking shows. Results: Among all identified ads (n 828), 32% were food-related. Food ads were the fourth most prevalent in commercial breaks (11.1%) and the second during cooking shows (18.7%). UPF was the most advertised food (57.8%), especially soft drinks, mayonnaise, and other sauces. The difference between the frequency of UPF and non-UPF advertisements was statistically significant. Approximately a quarter of all food ads were from seven companies, five were sponsors of cooking shows, and six were promoting UPF. Conclusion: Foods advertised on cooking shows on Brazilian free-to-air TV channels were mainly UPF. Actions to promote home cooking should consider UPF ads' influence on home environments and culinary practices.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".