Food and Beverage Advertising to Children and Adolescents on Television: A Baseline Study
Bibliographic record
Abstract
The progressive rise in Canadian child obesity has paralleled trends in unhealthy food consumption. Industry has contributed to these trends through aggressive food and beverage marketing in various media and child settings. This study aimed to assess the extent of food and beverage advertising on television in Canada and compare the frequency of food advertising broadcasted during programs targeted to preschoolers, children, adolescents and adults. Annual advertising from 2018 was drawn from publicly available television program logs. Food and beverage advertisement rates and frequencies were compared by, target age group, television station, month and food category, using linear regression modelling and chi-square tests, in SAS version 9.4. Rates of food and beverage advertising differed significantly between the four target age groups, and varied significantly by television station and time of the year, in 2018. The proportion of advertisements for food and beverage products was significantly greater during preschooler-, child-, and adult-programming [5432 (54%), 142,451 (74%) and 2,886,628 (48%), respectively; p < 0.0001] compared to adolescent-programming [27,268 (42%)]. The proportion of advertisements promoting fast food was significantly greater among adolescent-programming [33,475 (51%), p < 0.0001] compared to other age groups. Legislation restricting food and beverage advertising is needed in Canada as current self-regulatory practices are failing to protect young people from unhealthy food advertising and its potential negative health effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".