The food and beverage marketing monitoring framework for Canada: Development, implementation, and gaps
Bibliographic record
Abstract
As many countries are considering the restriction of marketing to children for unhealthy foods and beverages to improve population health, systematic monitoring of this marketing to inform and evaluate policies is critical. The objective of this research was to develop and describe the Food and Beverage Marketing Monitoring Framework for Canada, a framework commissioned by Health Canada to help guide their monitoring efforts. Following a literature review and expert consultation, the questions to be answered by the framework, the frequency and scope of monitoring activities, the short-/long- term outcome indicators and the methodologies to be employed were determined. The resulting Framework aims to assess the frequency and power of food marketing in various media and settings and monitor children and adolescents’ exposure to food marketing, food company practices, and changes in children’s attitudes, behaviours, and health. It proposes that monitoring occur annually in six regions across Canada. Considering probable budget constraints and research capacity, television, digital media, schools, convenience stores, packaging and children’s sport/event sponsorship were identified as priority media/settings. Short- and long-term outcomes include: food marketing (e.g., advertising rate, marketing technique use), company-level (e.g., ad expenditures, product reformulation) and behavioral/health indicators (e.g., children’s marketing awareness and recall, food requests and consumption). While significant efforts have been made in monitoring food marketing in Canada via the implementation of the Framework into the Health Canada M2K Monitoring Strategy, gaps remain (e.g., within diverse sociodemographic groups). The Framework can be leveraged to inform policy in Canada and the development process and content of the Framework could be adapted and implemented for global use.
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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 | 0.082 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".