The impact of fast food marketing on brand preferences and fast food intake of youth aged 10-17 across six countries
Bibliographic record
Abstract
Abstract Background Consumption of fast food, which is associated with poor diet, weight gain and the development of noncommunicable diseases, is high amongst youth. Fast food marketing, a modifiable determinant of excess weight and obesity, affects youth’s food-related behaviours. This study aimed to examine the relationship between exposure to fast food marketing and the fast food brand preferences and intake amongst youth aged 10-17 across six countries. Methods Data from 9,695 youth respondents living in Australia, Canada, Chile, Mexico, the United Kingdom (UK) and the United States (US) were analyzed from the 2019 International Food Policy Youth Study. Survey measures assessed exposure to fast food marketing and brand-specific marketing, and preference for these brands and fast food intake. Regression models adjusted for age, sex, income adequacy and ethnicity were used to examine the associations. Results Exposure to fast food marketing was positively associated with brand preferences and intake consistently across most countries. Overall, preference for McDonald’s (OR:1.97; CI:1.52, 2.56), KFC (OR:1.61; CI:1.24, 2.09) and Subway (OR:1.73; CI:1.34, 2.24) were highest when exposed to general fast food marketing ≥2x/week compared to never. Preference for McDonald’s (OR:2.32; CI:1.92, 2.79), KFC (OR:2.28; CI:1.95, 2.68) and Subway (OR:2.75; CI:2.32, 3.27) were also higher when exposed to marketing for each brand compared to not. Fast food intake was highest in Chile (IRR:1.90; CI:1.45, 2.48), the UK (IRR:1.40; CI:1.20, 1.63), Canada (IRR:1.32; CI:1.19, 1.48), Mexico (IRR:1.26; CI:1.05, 1.53) and the US (IRR:1.21; CI:1.05, 1.41) when exposed to general fast food marketing ≥2x/week compared to never and was higher across most countries when exposed to brand-specific marketing compared to not. Respondents classified as ethnic minorities were more likely to report consuming fast food than ethnic majorities, and females were less likely to report consuming fast food than males. Conclusions Exposure to fast food marketing is consistently and positively associated with brand preferences and fast food intake in all six countries. Our results highlight the need for strict government regulation to reduce exposure of unhealthy food marketing to youth in all six countries.
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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) | 0.002 | 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".