The association between exposure to food marketing and dietary intake among youth in six countries
Bibliographic record
Abstract
Abstract Background While food marketing to youth is associated with harmful behavioural and dietary outcomes, few studies have assessed differences in this relationship between countries. This study examined the association between exposure to food marketing and dietary intakes among youth in six countries. Methods A cross-sectional analysis of International Food Policy Study 2023 Youth Survey data examined the relationship between self-reported exposure to marketing for less healthy (fast food, sugary drinks, sugary cereals, snacks, desserts/treats) and healthy (fruits, vegetables) food categories across various media/settings in the past 30 days and consumption of these foods yesterday among youth 10-17 years-old in Canada, Australia, Chile, Mexico, the United Kingdom and the United States (n=9057). Associations of food consumption with exposure to marketing of food categories and marketing techniques (e.g., characters, famous people) in food advertisements, and differences in associations between countries, were examined using binary and ordinal logistic regression. Results In all countries, youth reporting more frequent exposure to marketing of all less healthy food categories had higher odds of having consumed those foods yesterday (p < 0.05 for all), except snacks in Mexico. Compared with no exposure to marketing techniques, exposure to ≥ 1 marketing technique(s) in less healthy food advertisements was associated with higher odds of having consumed sugary drinks (AOR: 1.44; 95% CI: 1.21, 1.72), fast food (AOR: 1.69; 95% CI: 1.40, 2.03), sugary cereals (AOR: 1.26; 95% CI: 1.05, 1.51) and desserts/treats yesterday (AOR: 1.42; 95% CI: 1.18, 1.71) among youth in all countries. Consumption of snacks was associated with exposure to ≥ 1 marketing technique(s) in less healthy food advertisements in Australia (AOR: 1.61; 95% CI: 1.09, 2.34), Chile (AOR: 1.63; 95% CI: 1.12, 2.36) and Mexico (AOR: 2.13; 95% CI: 1.39, 3.26). Positive associations between frequency of exposure to marketing of fruits and vegetables and the number of times these foods were consumed yesterday were observed in all countries (p < 0.05), except vegetable consumption in the UK. Conclusions These results support the association between exposure to food marketing and consumption of marketed foods. Findings were similar between countries, reinforcing the need for global implementation of restrictions on food marketing to youth.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".