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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".