Exploring the extent of digital food and beverage related content associated with a family-friendly event: a case study
Notice bibliographique
Résumé
Background: Exposure to unhealthy food and beverage content is a contributing factor to the obesity epidemic. Youth are susceptible to unhealthy digital food marketing including content shared by their peers, which can be as influential as commercial marketing. Current Canadian regulations do not consider the threat digital food marketing poses to health. No research to date has examined the prevalence of food related posts on social media surrounding family-friendly events. The aim of this study was to explore the frequency of food related content (including food marketing) and the marketing techniques employed in social media posts related to a family-friendly event in Canada. Methods: In this case study, a content analysis of social media posts related to a family-friendly event on Facebook, Twitter, and Instagram was conducted between January to February 2019. Each post containing food related content was identified and categorized by source and food category using a coding manual. Marketing techniques found in each food related post were also assessed. Results: A total of 732 food and beverage related posts were assessed. These posts were most commonly promoted through Instagram (n = 561, 76.6%) with significantly more individual users (61.5%; p < 0.05) generating food and beverage related content (n = 198, 27%) than other post sources. The top most featured food category was fast food (n = 328, 44.8%) followed by dine-in restaurants (n = 126, 17.2%). The most frequently observed marketing techniques included predominantly featuring a child in the post (n = 124, 16.9%; p < 0.0001), followed by products intended for children (n = 118, 16.1%; p < 0.05), and the presence of family (n = 57, 7.8%; p < 0.0001). Conclusions: The present study highlights the proliferation of unhealthy food and beverage content by individuals at a family-friendly event as well as the presence of food marketing. Due to the unfettered advertising found in digital spaces, and that they are largely unregulated, it is important for future policies looking to combat childhood obesity to consider incorporating social media into their regulations to safeguard family-friendly events. General awareness on the implications of peer to peer sharing of unhealthy food and beverage posts should also be considered.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».