Child-appealing Marketing on Food and Beverage Product Packaging: Evidence to Inform Regulatory Development, Implementation and Monitoring in Canada
Bibliographic record
Abstract
Child-appealing marketing for unhealthy foods and beverages is prolific globally and is contributing to the development of poor dietary habits among children. Canadian attempts to establish federally-mandated marketing restrictions have been unsuccessful to date. Therefore, comprehensive evidence to monitor the marketing landscape and support policy development was needed during the period of this thesis research. In Studies 1 and 2, the effectiveness of Canadian Nutrient Profiling Models (NPMs) developed for purposes related to marketing (i.e., the Canadian Advertising Initiative’s NPM and Health Canada’s proposed NPM) were evaluated. Results demonstrated their stringency in identifying foods and beverages of poor nutritional quality, however, their effectiveness was limited by the breadth of scope of marketing instances/food products they were applied to (i.e., child-appealing marketing/products). Study 3 found significant heterogeneity in how researchers have defined and evaluated “child-appeal” in the literature, which was synthesized into a thematically-categorized inventory of marketing techniques to inform the development of future research methodologies. The Child-Appealing Packaging (CAP) tool was developed for this purpose (Study 4) and was found to be valid to assess the presence, type, and power of child-appealing marketing using core and broad marketing techniques that children (5-12 years) reported to find pertinent. Study 5 evaluated packaged foods using the CAP tool, and found that powerfully marketed, unhealthy, child-appealing products were prevalent in the Canadian food supply, and that these products were often of poorer nutritional composition and more likely to exceed Health Canada’s NPM thresholds than non-child-appealing products. Taken together, the five studies presented in this thesis highlight the necessity of continued broad monitoring of the food marketing landscape in Canada and the need for comprehensive marketing restrictions. Collectively, this research identifies key aspects of the regulatory approach, namely the criteria for 1) identifying child-appealing marketing and 2) the types of foods that are “unhealthy”, both of which will be critical to ensuring policy effectiveness and best protecting children from harmful marketing practices.
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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 | 0.026 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".