Scrutinizing the impact of two self-regulation policies on unhealthy food marketing in children’s popular television in Malaysia: a multiple-year repeated evaluation using a harmonized protocol
Bibliographic record
Abstract
BACKGROUND: Regulating unhealthy food marketing is critical as it is a recognized driver of childhood obesity. Two voluntary self-regulatory policies governing food advertising in the media were introduced in Malaysia in 2008 and 2013. OBJECTIVES: To assess food advertising on Malaysian children's popular television channels across a decade using the standardized INFORMAS protocol. METHODS: The main dataset was collected cross-sectionally from 2020 to 2022 evaluating three television channels. Additionally, a retrospective comparison between the 2022 and 2012 datasets was limited to two channels commonly available for both years. Advertised foods were classified as permitted (healthy) or not-permitted (unhealthy) using a nutrient profile model of the World Health Organization. We compared advertising rates and use of persuasive marketing techniques during children's peak viewing time (PVT) versus non-PVT. RESULTS: > 0.05). In 2022, fast foods emerged as the most frequently advertised unhealthy food (1.33 ± 2.23 ads/h/channel), a six-fold increase compared to 2012 (0.21 ± 0.47 ads/h/channel). CONCLUSIONS: Unhealthy food advertising dominates Malaysian children's popular television channels, especially during PVT despite the presence of voluntary self-regulatory policies. These findings underscore the need for government-led mandatory regulations to control unhealthy food marketing targeting children.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".