The relationship between youth’s exposure to unhealthy digital food marketing and their dietary intake in Canada
Bibliographic record
Abstract
There is limited evidence on how exposure to digital marketing of unhealthy foods affects youth’s dietary behaviours. This study therefore aimed to examine the association between youth’s self-reported digital food marketing exposure and dietary intakes, and explore predictors of frequent unhealthy food consumption. A survey was conducted among 1075 youth in Canada (aged 10–17 years) in April 2023. Proportional odds models examined associations between frequency of exposure to digital marketing of unhealthy foods and frequency of consumption of those foods, adjusted for sociodemographic characteristics and digital device usage. Compared with participants reporting no exposure to digital fast-food marketing in the past week, those exposed ≥4 times per week were more likely to consume fast food more frequently. Youth exposed to digital marketing of sugary drinks and salty/savoury snacks ≥1 time(s) in the previous week were more likely to consume these foods on a greater number of days, compared with those reporting no exposure to this marketing in the past week. Reporting exposure to digital marketing of desserts/sweet treats every day or more than once a day was associated with more frequent consumption of desserts/sweet treats. Province of residence (Ontario/Quebec) and total daily time spent online predicted more frequent consumption of fast food, sugary drinks, salty/savoury snacks, and desserts/sweet treats. Overall, more frequent self-reported exposure to digital marketing of unhealthy foods is associated with more frequent consumption of these foods by Canadian youth. Regulations are needed to help protect youth from digital food marketing, which may help reduce their unhealthy food consumption.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".