Barriers and enablers in designing regulations to restrict the exposure of children to unhealthy food and beverage marketing
Bibliographic record
Abstract
Background The insidious and pervasive nature of marketing of unhealthy food and beverages has been identified as one of several strategies the unhealthy food and beverage industry uses to exert their influence on population food choices and diet. Regulating the food and beverage industry's marketing practices is one mechanism to mitigate this commercial determinant of health. This paper seeks to understand the main barriers and enablers that governments face when attempting to design an appropriate regulatory system. Methods 14 semi-structured expert interviews were undertaken with participants across different jurisdictions (Ireland, United Kingdom, Chile, Canada, Norway, Portugal and Brazil) who were involved in introducing marketing restrictions; and a purposive documentary analysis was carried out. A thematic analysis of this data was conducted informed by the Health Policy Triangle. Results Multiple common technical and political issues were experienced by governments regarding the form and substance of the policy design regardless of the jurisdictional context. Such issues included: whether to introduce a mandatory approach; what age group to protect; what nutrient classification system to use; how to define “marketing to children”; and what mediums, settings and techniques to cover. The actors opposing regulation challenged the form and substance of each design element. However, having a strong political mandate to introduce regulation; multiple actors working together, including multiple government ministries, academics and civil society actors; and a strong evidence base supporting the policy design helped policymakers navigate the technical and political challenges faced when designing the regulatory approach. Conclusion Despite the different political contexts and actors involved in different jurisdictions internationally, there are many commonalities in the challenges and enabling factors faced by governments. Understanding the technical and political challenges experienced by governments and how these governments overcame those challenges is critical to improve capacity around designing more effective regulations to improve population's diets, and therefore NCDs.
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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 | 0.114 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".