Examining differences in exposure to digital marketing of unhealthy foods reported by Canadian children and adolescents in two policy environments
Bibliographic record
Abstract
BACKGROUND: There has been relatively little research on youth's exposure to food marketing on digital media, which is important as new digital platforms emerge and youth spend more time online. Evidence evaluating different policy approaches to restricting digital food marketing to children is also limited. This study examined differences in self-reported exposure to digital food marketing between children and adolescents in different policy environments: Ontario (where food marketing is self-regulated) and Quebec (where advertising is government regulated). METHODS: An observational cross-sectional online survey was conducted in April 2023 among children (aged 10-12 years) and adolescents (13-17 years) from Ontario and Quebec, recruited by Leger Marketing. Participants self-reported their frequency of exposure to food marketing via various digital platforms and marketing techniques. Logistic regression and proportional odds models examined differences in exposure by province and age group, adjusting for sociodemographic characteristics and digital device usage. RESULTS: The odds of reporting more frequent exposure to marketing of sugary drinks (OR: 0.48; 95% CI: 0.33, 0.69), sugary cereals (OR: 0.59; 95% CI: 0.41, 0.86), salty/savoury snacks (OR: 0.67; 95% CI: 0.47, 0.96), fast food (OR: 0.65; 95% CI: 0.45, 0.92), and desserts/sweet treats (OR: 0.54; 95% CI: 0.37, 0.78) were lower among Quebec children than Ontario children. Quebec children were less likely than Ontario children (OR: 0.56; 95% CI: 0.38, 0.84), but more likely than Quebec adolescents (OR: 1.58; 95% CI: 1.04, 2.42), to report exposure to unhealthy food marketing on one or more gaming/TV/music streaming platform/website(s). Compared with Ontario children, Quebec children were less likely to report exposure to marketing featuring characters or child/teenage actors (OR: 0.51; 95% CI: 0.34, 0.76), child-appealing subjects, themes and language (OR: 0.59; 95% CI: 0.40, 0.89), and visual design, audio and special effects (OR: 0.64; 95% CI: 0.41, 0.99), and to report exposure to a greater number of marketing techniques (OR: 0.60; 95% CI: 0.43, 0.84). CONCLUSIONS: Exposure to unhealthy food marketing on digital media is high for youth from Ontario and Quebec, particularly for Ontario children. These findings reinforce the need for federal regulations to protect Canadian youth from unhealthy food marketing on digital media.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".