TV advertising and dietary intake in adolescents: a pre- and post- study of Chile’s Food Marketing Policy
Bibliographic record
Abstract
BACKGROUND: The first phase of a comprehensive marketing policy was implemented in Chile in 2016. The policy restricted child-directed marketing of foods and beverages considered high in energy, total sugars, sodium or saturated fat ("high-in"). The objective of this study was to examine the role of high-in TV food advertising as a mediator in the association between policy implementation and consumption of high-in foods and beverages between 2016 and 2017. METHODS: Dietary data were from 24-hour diet recalls collected in 2016 and 2017 in a cohort of 12-14 y children (n = 721). Television use was assessed concurrently and linked to analyses of food advertisements on broadcast and paid television to derive individual-level estimates of exposure to high-in food advertising. A multilevel mediation analysis examined direct and indirect effects of the policy through advertising exposure. RESULTS: Following the policy implementation, high-in advertising exposure was significantly reduced (p < 0.01). High-in food intake decreased in adolescents with lower levels, but not higher levels, of high-in advertising at baseline. We did not find evidence of mediation by changes in high-in ad exposure. CONCLUSIONS: Adolescents' exposure to high-in TV advertising decreased after the 2016 implementation of the Chilean Food Labeling and Marketing Law. However, evidence that changes in advertisement mediated dietary changes was not found. Further research is needed to understand how marketing changes will relate to dietary changes after full implementation of the law and in the long term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".