The effectiveness of self-regulation in limiting the advertising of unhealthy foods and beverages on children’s preferred websites in Canada
Bibliographic record
Abstract
OBJECTIVE: To assess the effectiveness of the self-regulatory Canadian Children's Food and Beverage Advertising Initiative (CAI) in limiting advertising of unhealthy foods and beverages on children's preferred websites in Canada.Design/Setting/SubjectsSyndicated Internet advertising exposure data were used to identify the ten most popular websites for children (aged 2-11 years) and determine the frequency of food/beverage banner and pop-up ads on these websites from June 2015 to May 2016. Nutrition information for advertised products was collected and their nutrient content per 100 g was calculated. Nutritional quality of all food/beverage ads was assessed using the Pan American Health Organization (PAHO) and UK Nutrient Profile Models (NPM). Nutritional quality of CAI and non-CAI company ads was compared using χ 2 analyses and independent t tests. RESULTS: About 54 million food/beverage ads were viewed on children's preferred websites from June 2015 to May 2016. Most (93·4 %) product ads were categorized as excessive in fat, Na or free sugars as per the PAHO NPM and 73·8 % were deemed less healthy according to the UK NPM. CAI-company ads were 2·2 times more likely (OR; 99 % CI) to be excessive in at least one nutrient (2·2; 2·1, 2·2, P<0·001) and 2·5 times more likely to be deemed less healthy (2·5; 2·5, 2·5, P<0·001) than non-CAI ads. On average, CAI-company product ads also contained (mean difference; 99 % CI) more energy (141; 141·1, 141·4 kcal, P<0·001, r=0·55), sugar (18·2; 18·2, 18·2 g, P<0·001, r=0·68) and Na (70·0; 69·7, 70·0 mg, P<0·001, r=0·23) per 100 g serving than non-CAI ads. CONCLUSIONS: The CAI is not limiting unhealthy food and beverage advertising on children's preferred websites in Canada. Mandatory regulations are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".