Sex differences in children’s exposure to food and beverage advertisements on broadcast television in four cities in Canada
Bibliographic record
Abstract
INTRODUCTION: Sex differences exist in children's obesity rates, dietary patterns and television viewing. Television continues to be a source of unhealthy food advertising exposure to children in Canada. Our objective was to examine sex differences in food advertising exposure in children aged 2 to 17 years across four Canadian English language markets. METHODS: We licensed 24-hour television advertising data from the company Numerator for January through December 2019, in four cities (Vancouver, Calgary, Montréal and Toronto) across Canada. Child food advertising exposure overall, by food category, television station, Health Canada's proposed nutrient profiling model, and marketing techniques were examined on the 10 most popular television stations among children and compared by sex. Advertising exposure was estimated using gross rating points, and sex differences were described using relative and absolute differences. RESULTS: Both male and female children were exposed to an elevated level of unhealthy food advertising and a plethora of marketing techniques across all four cities. Differences between sexes were evident between and within cities. Compared to females, males in Vancouver and Montréal viewed respectively 24.7% and 24.0% more unhealthy food ads/person/year and were exposed to 90.2 and 133.4 more calls to action, 93.3 and 97.8 more health appeals, and 88.4 and 81.0 more products that appeal to children. CONCLUSION: Television is a significant source of children's exposure to food advertising, with clear sex differences. Policy makers need to consider sex when developing food advertising restrictions and monitoring efforts.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".