Food and beverage advertising expenditures in Canada in 2016 and 2019 across media
Notice bibliographique
Résumé
Abstract Background Food and beverage advertising has been identified as a powerful determinant of dietary intake and weight. Available evidence suggests that the preponderance of food and beverage advertising expenditures are devoted to the promotion of unhealthy products. The purpose of this study is to estimate food advertising expenditures in Canada in 2019 overall, by media and by food category, determine how much was spent on promoting healthier versus less healthy products and assess whether changes in these expenditures occurred between 2016 and 2019. Methods Estimates of net advertising expenditures for 57 selected food categories promoted on television, radio, out-of-home media, print media and popular websites, were licensed from Numerator. The nutrient content of promoted products or brands were collected, and related expenditures were then categorized as “healthy” or “unhealthy” according to a Nutrient Profile Model (NPM) proposed by Health Canada. Expenditures were described using frequencies and relative frequencies and percent changes in expenditures between 2016 and 2019 were computed. Results An estimated $628.6 million was spent on examined food and beverage advertising in Canada in 2019, with television accounting for 67.7%, followed by digital media (11.8%). In 2019, most spending (55.7%) was devoted to restaurants, followed by dairy and alternatives (11%), and $492.9 million (87.2% of classified spending) was spent advertising products and brands classified as “unhealthy”. Fruit and vegetables and water accounted for only 2.1 and 0.8% of expenditures, respectively, in 2019. In 2019 compared to 2016, advertising expenditures decreased by 14.1% across all media (excluding digital media), with the largest decreases noted for print media (− 63.0%) and television (− 14.6%). Overall, expenditures increased the most in relative terms for fruit and vegetables (+ 19.5%) and miscellaneous products (+ 5%), while decreasing the most for water (− 55.6%) and beverages (− 47.5%). Conclusions Despite a slight drop in national food and beverage advertising spending between 2016 and 2019, examined expenditures remain high, and most products or brands being advertised are unhealthy. Expenditures across all media should continue to be monitored to assess Canada’s nutrition environment and track changes in food advertising over time.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».