Children’s measured exposure to food and beverage advertising on television in Toronto (Canada), May 2011–May 2019
Bibliographic record
Abstract
OBJECTIVE: Exposure to unhealthy food advertising is a known determinant of children's poor dietary behaviours. The purpose of this study was to quantify and characterize Canadian children's exposure to food advertising on broadcast television and examine trends over time. METHODS: Objectively measured advertising exposure data for 19 food categories airing on 30 stations broadcast in Toronto were licenced for May 2011 and May 2019. Using ad ratings data, the average number of food advertisements viewed by children aged 2-11 years, overall, by food category and by type of television station (child-appealing, adolescent-appealing and generalist stations), was estimated per time period. RESULTS: In May 2019, children viewed an average of 136 food advertisements on television, 20% fewer than in May 2011. More than half of advertisements viewed in May 2019 promoted unhealthy food categories such as fast food (43% of exposure), candy (6%), chocolate (6%) and regular soft drinks (5%) and only 17% of their total exposure occurred on child-appealing stations. Between May 2011 and May 2019, children's exposure increased the most, in absolute terms, for savory snack foods (+7.2 ad exposures/child), fast food (+5.4) and regular soft drinks (+5.3) with most of these increases occurring on generalist stations. CONCLUSION: Canadian children are still exposed to advertisements promoting unhealthy food categories on television despite voluntary restrictions adopted by some food companies. Statutory restrictions should be adopted and designed such that children are effectively protected from unhealthy food advertising on both stations intended for general audiences and those appealing to younger audiences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".