Designing legislative responses to restrict children’s exposure to unhealthy food and non-alcoholic beverage marketing: a case study analysis of Chile, Canada and the United Kingdom
Notice bibliographique
Résumé
INTRODUCTION: Introducing legislation that restricts companies from exposing children to marketing of unhealthy food and beverage products is both politically and technically difficult. To advance the literature on the technical design of food marketing legislation, and to support governments around the world with legislative development, we aimed to describe the legislative approach from three governments. METHODS: A multiple case study methodology was adopted to describe how three governments approached designing comprehensive food marketing legislation (Chile, Canada and the United Kingdom). A conceptual framework outlining best practice design principles guided our methodological approach to examine how each country designed the technical aspects of their regulatory response, including the regulatory form adopted, the substantive content of the laws, and the implementation and governance mechanisms used. Data from documentary evidence and 15 semi-structured key informant interviews were collected and synthesised using a directed content analysis. RESULTS: All three countries varied in their legislative design and were therefore considered of variable strength regarding the legislative elements used to protect children from unhealthy food marketing. When compared against the conceptual framework, some elements of best practice design were present, particularly relating to the governance of legislative design and implementation, but the scope of each law (or proposed laws) had limitations. These included: the exclusion of brand marketing; not protecting children up to age 18; focusing solely on child-directed marketing instead of all marketing that children are likely to be exposed to; and not allocating sufficient resources to effectively monitor and enforce the laws. The United Kingdom's approach to legislation is the most comprehensive and more likely to meet its regulatory objectives. CONCLUSIONS: Our synthesis and analysis of the technical elements of food marketing laws can support governments around the world as they develop their own food marketing restrictions. An analysis of the three approaches illustrates an evolution in the design of food marketing laws over time, as well as the design strengths offered by a legislative approach. Opportunities remain for strengthening legislative responses to protect children from unhealthy food marketing practices.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».