The impact of exposure to sugary drink marketing on youth brand preference and recall: a cross-sectional and multi-country analysis
Bibliographic record
Abstract
BACKGROUND: Consumption of sugary drinks (SD) among children and adolescents is a prevalent public health issue both within Canada and worldwide. This problem is exacerbated by the powerful marketing of such beverages to youth, which is known to influence a wide range of dietary behaviours. METHODS: A cross-sectional, secondary analysis of the International Food Policy Survey Youth Wave 2019 was conducted to assess the relationship between self-reported exposure to SD marketing within the past 30 days or SD brand advertisements and brand preference and brand recall among youth aged 10-17 from Australia, Canada, Chile, Mexico, the United Kingdom, and the United States. Ordinal, multinomial, and binary logistic regression were used as appropriate to examine these associations. RESULTS: Youth brand preference and recall was positively associated with self-reported exposure to general and brand-specific SD marketing across all countries. No statistical interaction was observed between youth age and SD marketing overall or within countries. Soft drinks, sports drinks, and fruit juice brands were most commonly recalled by all youth. CONCLUSION: Similar results were observed among children and adolescents within all countries. Global marketing policies should consider older children and adolescents to adequately protect and support child health.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".