Youth exposure to unhealthy digital food marketing in relation to race/ethnicity and income adequacy in Canada
Bibliographic record
Abstract
Abstract Background Youth of racial/ethnic minority groups and lower-income households are disproportionately exposed to unhealthy food marketing on television; however, there is limited evidence concerning digital marketing. This study examined differences in Canadian youth’s exposure to digital food marketing by race/ethnicity and household income adequacy. Methods Frequency of food marketing exposure via digital platforms and digital food marketing techniques were self-reported by 996 youth in Canada aged 10–17 years. Proportional odds and logistic regression models explored differences between racial/ethnic (White vs. racial/ethnic minority) and income adequacy groups (low vs. medium vs. high), adjusted for sociodemographic and digital device usage variables. Results White participants had lower odds of more frequent exposure to digital marketing of sugary drinks (OR: 0.70; 95% CI: 0.52–0.94), sugary cereals (OR: 0.56; 95% CI: 0.42–0.76), fruits/vegetables (OR: 0.63; 95% CI: 0.45–0.87), salty/savoury snacks (OR: 0.63; 95% CI: 0.47–0.85), fast food (OR: 0.74; 95% CI: 0.55–0.99), and desserts/sweets (OR: 0.68; 95% CI: 0.50–0.91) than racial/ethnic minority youth. Compared to youth from low income adequacy households, those with medium income adequacy were less likely to report more frequent exposure to marketing of sugary drinks (OR: 0.67; 95 CI: 0.51–0.89), fast food (OR: 0.66; 95% CI: 0.50–0.87), and desserts/sweets (OR: 0.65; 95% CI: 0.49–0.87). White youth were less likely than racial/ethnic minority youth to report exposure to unhealthy food marketing on ≥ 1 social media platform(s) (OR: 0.45; 95% CI: 0.30–0.68) and gaming/TV/music streaming platform/website(s) (OR: 0.71; 95% CI: 0.51–0.99); no differences were observed between income groups. White youth were less likely than racial/ethnic minority youth to report exposure to marketing featuring incentives/premiums (OR: 0.72; 95% CI: 0.52–0.99) and cross-promotions (OR: 0.71; 95% CI: 0.51–0.99). Participants of higher (OR: 0.68; 95% CI: 0.49–0.95) and medium (OR: 0.69; 95% CI: 0.50–0.93) income adequacy were less likely to report exposure to marketing featuring celebrities than those with low income adequacy. Conclusions Youth of racial/ethnic minorities report more frequent exposure to digital food marketing, especially for unhealthy foods, than White youth in Canada. Differences were also observed between income groups. Comprehensive marketing regulations are needed to limit all youths’ exposure to unhealthy digital food marketing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.084 | 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".