Children’s measured exposure to food and beverage advertising on television in a regulated environment, May 2011–2019
Bibliographic record
Abstract
OBJECTIVE: To quantify food/beverage advertising on television in Montreal (Quebec), to estimate and characterise children's exposure and to examine trends over time. DESIGN: Television food advertising data were licensed for nineteen food categories and eighteen stations for May 2011, 2016 and 2019. The frequency of advertisements and the average number viewed per child aged 2-11 years overall, by food category and by station type (i.e. youth-appealing (n 3) and generalist (n 15) stations) were determined. The percent change in advertising frequency and exposure between May 2011 and 2019 was calculated. SETTING: Montreal, Quebec, Canada. PARTICIPANTS: This study used media data and did not directly involve human participants. RESULTS: The total number of television advertisements increased by 11 % between May 2011 (n 41 084) and May 2019 (n 45 406); however, exposure to food/beverage advertisements decreased by 53 %, going from 226 ads/child in May 2011 to 107 ads/child in May 2019. Overall, the most advertised food categories in both May 2011 and 2019 were fast food (29·8 % and 39·2 %, respectively) followed by chocolate (14·2 %) in 2011 and savory snacks (9·7 %) in 2019. In May 2019, children were predominantly exposed to unhealthy food categories such as fast food (41·3 % of exposure), savory snacks (7·5 %), chocolate (5·0 %) and regular soft drinks (4·5 %), and most (89·3 %) of their total exposure occurred on generalist television stations. CONCLUSION: Despite Quebec's restrictions on commercial advertising directed to children under 13 years, Quebecois children are still frequently exposed to unhealthy food advertising on television. Government should tighten restrictions to protect children from this exposure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".