A comparison of self-reported exposure to fast food and sugary drinks marketing among parents of children across five countries
Bibliographic record
Abstract
Exposure to unhealthy food and beverage marketing is an important environmental determinant of dietary intake. The current study examined self-reported exposure to marketing of unhealthy foods and beverages across various media channels and settings among parents of children younger than 18 years in five high and upper-middle income countries. Data from 4827 parents living with their children were analyzed from the International Food Policy Study (2017), a web-based survey of adults aged 18-64 years from Canada, the United States (US), the United Kingdom (UK), Australia, and Mexico. Respondents reported their exposure to marketing of fast food and of sugary drinks across media channels/settings overall and how often they see fast food and sugary drink marketing while viewing media with their children. Regression models examined differences across countries and correlates of marketing exposure. Parents in Mexico and the US reported greater exposure to marketing for fast food and sugary drinks compared to parents in Australia, Canada, and the UK. Patterns of exposure among parents were generally consistent across countries, with TV, digital media, and radio being the most commonly reported media channels for both fast food and sugary drinks. Exposure to marketing of fast food and sugary drinks was associated with a variety of sociodemographic factors, most strongly with ethnicity and education, and sociodemographic trends differed somewhat between countries. The findings demonstrate differences in self-reported parental exposure to marketing of fast food and sugary drinks between countries, and may help to evaluate the impact of marketing restrictions implemented over time.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".