Nutritional quality of foods and non-alcoholic beverages advertised on Brazilian free-to-air television: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Evidence shows that foods marketed on television are often low-nutrient-dense foods associated with poor nutritional diet quality, obesity and non-communicable diseases. However, little research has been undertaken in Brazil around this issue. This study assessed the nutritional profile of foods and non-alcoholic beverages advertised on Brazilian television by applying the Pan American Health Organization (PAHO) and the World Health Organization (WHO/Europe) nutrient profiling models. METHODS: Cross-sectional study based on the International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support (INFORMAS) protocol. A total of 432 h on the three major Brazilian free-to-air TV channels was recorded from April 1st to 30th 2018. Recordings were done for eight non-consecutive and randomly selected days from 6 am to 12 am (midnight). All food-related ads were coded using a systematic approach and classified according to the PAHO and the WHO/Europe nutrient profile models as "eligible"/"not eligible" for marketing restrictions. Absolute and relative frequencies were used to describe absolute numbers and proportions of food and beverage advertisements. The nutritional profile of foods was compared by day, time of the day and types of TV program. For each advertisement, the parent company of promoted food products, supermarkets and restaurants was identified. RESULTS: A total of 1610 food and beverage ads were broadcast, representing 18.1% of the total ads shown on selected channels. Over 80.0% of all foods and beverages advertised on Brazilian TV channels did not meet the PAHO and the WHO/Europe nutritional quality standards and were considered eligible for marketing restrictions. The proportion of unhealthy food ads was significantly higher on weekends, in the afternoon, and during soap operas programming. Altogether, 10 transnational and local food and beverage companies, two large international fast food chains and two of Brazil's largest supermarket retailers accounted for almost 90% of all unhealthy food ads shown. CONCLUSIONS: The findings of the present study indicate a high exposure of the Brazilian population to unhealthy food marketing and an inefficient enforcement of existing regulations. Further research to monitor population exposure to unhealthy food marketing and understand the policy inertia that is preventing policy progress, is highly recommended.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".