Food marketing and gender among children and adolescents: a scoping review
Notice bibliographique
Résumé
BACKGROUND: Pervasive marketing of unhealthy foods is a contributing factor to the growth of the global epidemic of childhood and adolescent overweight and obesity. Sex and gender differences come into play in the design of and responses to these marketing strategies, contributing to the perpetuation of stereotyped behavior and generating disparities in food choices and health. The purpose of this paper is to review the current literature regarding gender differences in food marketing design and perception among children and adolescents to facilitate evidence-based policy dialogues to address gender-based health disparities in NCD prevention. METHODS: Scoping review of articles published in scientific journals in English and Spanish, from 2003 to 2018, that addressed the influence of food marketing among children and adolescents including a gender perspective. The methodological quality of each article was assessed following criteria specific to each study design. RESULTS: From a total of 37 articles (39 studies) included in the review, 17 were experimental and 22 had descriptive, cross-sectional designs. Twenty-one studies were found to have low methodological quality, while 10 and 8 were of medium and high quality, respectively. A total of 23 studies among children and adolescents found gender-based differences. Differences were found in the following dimensions: food marketing on intake; responses to specific marketing; perceptions and attitudes towards food marketing and marketing regulation initiatives; exposure to food advertising and gendered marketing content. The evidence was not conclusive in any of the dimensions. CONCLUSIONS: The evidence suggests that food marketing has a similar impact on the consumption of unhealthy foods on boys and girls, but boys were found to be exposed to food advertising more intensively and their preferences to be more affected by this exposure, coinciding with a male-dominant advertising content. Limitations of these studies include taking gender as an unproblematic construct equivalent to biological sex and the lack of studies focused on developing countries. As gender is a cross-sectional dimension that interacts with other factors driving health disparities, an integrated gender perspective is needed to develop effective, evidence-based policies to control food marketing and tackle the childhood overweight and obesity pandemic.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».