Children’s exposure to unhealthy food advertising on Philippine television: content analysis of marketing strategies and temporal patterns
Bibliographic record
Abstract
BACKGROUND: This study conducted an exploratory content analysis of TV food advertisements on the top three most popular channels for Filipino children aged two to 17 during school and non-school days. METHODS: Data were collected by manually recording of aired advertisements from 16 non-school days (July to September 2020) and 16 school days (January to April 2021). Descriptive and inferential statistical analyses were used to assess children's rates of exposure to food advertisements (mean ± SD of advertisements aired per channel per hour), the healthiness of promoted foods (as permitted (healthier) or not permitted (unhealthy) according to nutrient profiling models from the World Health Organization), and persuasive techniques used in food advertisements, including promotional characters and premium offers. RESULTS: < 0.01). Both periods yield a similarly higher proportion of non-permitted food advertisements (e.g. 9.3 ± 9.7 ads/channel/hour for school days and 8.3 ± 8.5 ads/channel/hour for non-school days) than permitted ones. More non-permitted food advertisements during children's peak viewing times were observed than non-peak viewing times (e.g. 11.8 ± 10. vs. 8.3 ± 9.2 ads/channel/hour for school days). Non-permitted food advertisements employed persuasive techniques more frequently, accounting for 64-91% of all food ads during peak viewing times. CONCLUSION: Children are exposed to a large volume of television advertisements for foods that should not be permitted to be marketed to children based on authoritative nutrient criteria.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".