Beverage industry TV advertising shifts after a stepwise mandatory food marketing restriction: achievements and challenges with regulating the food marketing environment
Bibliographic record
Abstract
OBJECTIVE: Sugar-sweetened beverages (SSB) are heavily advertised globally, and SSB consumption is linked to increased health risk. To reduce unhealthy food marketing, Chile implemented a regulation for products classified as high in energies, sugar, saturated fat or sodium, starting with a 2016 ban on child-targeted advertising of these products and adding a 06.00-22.00 daytime advertising ban in 2019. This study assesses changes in television advertising prevalence of ready-to-drink beverages, including and beyond SSB, to analyse how the beverage industry shifted its marketing strategies across Chile's implementation phases. DESIGN: Beverage advertisements were recorded during two randomly constructed weeks in April-May of 2016 (pre-implementation) through 2019 (daytime ban). Ad products were classified as 'high-in' or 'non-high-in' according to regulation nutrient thresholds. Ads were analysed for their programme placement and marketing content. SETTING: Chile. RESULTS: < 0·001). Additionally, total ready-to-drink beverage ads increased by 5·4 p.p. and brand-only ads (no product shown) by 7 p.p. CONCLUSIONS: After the regulation implementation, 'high-in' ads fell significantly, but 'non-high-in' ads rose and continued using strategies targeting children and being aired during daytime. Given research showing that advertising one product can increase preferences for a different product from that same brand and product categories, broader food marketing regulation approaches may be needed to protect children from the harmful effects of food marketing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".