Examining differences in children and adolescents' exposure to food and beverage marketing in Canada by sociodemographic characteristics: Findings from the International Food Policy Study Youth Survey, 2020
Bibliographic record
Abstract
BACKGROUND: Many countries, including Canada, are considering regulations to restrict food and beverage marketing to children. However, little evidence is available outside of the US on how marketing exposure differs across sociodemographic subgroups. OBJECTIVE: To investigate potential associations between child and adolescent sociodemographic characteristics and exposure to food and beverage marketing in Canada. METHODS: Participants (n = 3780) aged 10-17 self-reported exposure to food and beverage marketing across food categories, locations and marketing techniques. Logistic regression models tested relationships between sociodemographics (age, sex, ethnicity and income adequacy) and marketing exposure. RESULTS: Among other differences identified, 13-17 years old were more likely than 10-12 years old to report seeing unhealthy food marketing online. Girls were more likely than boys to see such marketing online and in retail settings, while boys were more likely to see it in video games. Minority ethnicities (including Indigenous youth) and respondents with lower income adequacy generally reported more exposure than White and higher income respondents, respectively. CONCLUSIONS: This study highlights important differences in marketing exposure among youth of different sociodemographic groups in Canada, including greater exposure to marketing among those most disadvantaged and emphasizes the essential need to consider food marketing across equity groups when developing restrictions on marketing to kids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".