Extent and Nature of Television Food and Nonalcoholic Beverage Marketing in 9 Asian Countries: Cross-Sectional Study Using a Harmonized Approach
Bibliographic record
Abstract
BACKGROUND: Rising childhood obesity rates in Asia are adding risk for the future adult burden of obesity and noncommunicable diseases. Weak policies across most Asian countries enable unrestricted marketing of obesogenic foods and beverages to children. Television is the common medium for food marketing to reach this audience. OBJECTIVE: This study aimed to assess the extent and nature of television food and nonalcoholic beverage marketing in 9 Asian countries (Bangladesh, China, India, Malaysia, Mongolia, Nepal, the Philippines, Sri Lanka, and Vietnam) with capacity building support from the International Network for Food and Obesity/Non-Communicable Disease Research, Monitoring and Action Support, who enabled harmonization of data collection method and content analyses. METHODS: Advertised foods were categorized as permitted or not permitted based on the nutrient profile models established by the World Health Organization regional offices for South-East Asia (SEARO) and the World Health Organization regional offices for Western Pacific (WPRO). Overall rates of food advertisements (advertisements per hour per channel) and persuasive strategy use were analyzed along with comparisons between children's peak viewing time (PVT) and non-PVT. RESULTS: Cross-country comparisons, irrespective of country income level, indicated that not permitted food advertising dominated children's popular television channels, especially during PVT with rates as per WPRO or SEARO criteria ranging from 2.40/2.29 (Malaysia) to 9.70/9.41 advertisements per hour per channel (the Philippines). Persuasive strategy rates were also comparatively higher during PVT. Sugar-sweetened beverages, sugar-containing solid foods, and high salt- and fat-containing snacks and fast foods were frequently advertised. Evaluation of the application of WPRO and SEARO nutrient profile models identified inconsistencies due to regional taste and cuisine variations across Asia. CONCLUSIONS: This study clearly showed that unhealthy food marketing through popular children's television channels is widely occurring in Asia and is a clear breach of child rights. Evidence outcomes will benefit advocacy toward stronger policy regulations to control unhealthy food marketing and strengthen strategies to promote a healthier food environment for Asia's children.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".