Child and adolescent exposure to unhealthy food marketing across digital platforms in Canada
Bibliographic record
Abstract
Abstract Background Children and adolescents are exposed to a high volume of unhealthy food marketing across digital media. No previous Canadian data has estimated child exposure to food marketing across digital media platforms. This study aimed to compare the frequency, healthfulness and power of food marketing viewed by children and adolescents across all digital platforms in Canada. Methods For this cross-sectional study, a quota sample of 100 youth aged 6–17 years old (50 children, 50 adolescents distributed equally by sex) were recruited online and in-person in Canada in 2022. Each participant completed the WHO screen capture protocol where they were recorded using their smartphone or tablet for 30-min in an online Zoom session. Research assistants identified all instances of food marketing in the captured video footage. A content analysis of each marketing instance was then completed to examine the use of marketing techniques. Nutritional data were collected on each product viewed and healthfulness was determined using Health Canada’s 2018 Nutrient Profile Model. Estimated daily and yearly exposure to food marketing was calculated using self-reported device usage data. Results 51% of youth were exposed to food marketing. On average, we estimated that children are exposed to 1.96 marketing instances/child/30-min (4067 marketing instances/child/year) and adolescents are exposed to 2.56 marketing instances/adolescent/30-min (8301 marketing instances/adolescent/year). Both children and adolescents were most exposed on social media platforms (83%), followed by mobile games (13%). Both age groups were most exposed to fast food (22% of marketing instances) compared to other food categories. Nearly 90% of all marketing instances were considered less healthy according to Health Canada’s proposed 2018 Nutrient Profile Model, and youth-appealing marketing techniques such as graphic effects and music were used frequently. Conclusions Using the WHO screen capture protocol, we were able to determine that child and adolescent exposure to the marketing of unhealthy foods across digital media platforms is likely high. Government regulation to protect these vulnerable populations from the negative effects of this marketing is warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".