Offline to online: a systematic mapping review of evidence to inform nutrition-related policies applicable to online food delivery platforms
Notice bibliographique
Résumé
BACKGROUND: Online food delivery (OFD) platforms offer easy access to an abundance of energy-dense and nutrient-poor takeaway foods and may exacerbate existing unhealthy food environments. Efforts to improve population diets include a range of policy recommendations focused on improving the healthiness of food environments; however, the way in which such policies may apply to OFD platforms is not clear. This paper aimed to synthesise the existing evidence to inform nutrition-related policies applicable to OFD platforms for population health and well-being. A secondary aim was to scan existing nutrition-related policies in Australia and internationally, which have the potential to be applicable to OFD platforms. METHODS: Seven electronic databases including Medline, Embase, CINAHL, Business Source Ultimate, Scopus, Web of Science, and Proquest were searched from January 2010 to October 2023. Evidence from studies was mapped to five existing policy domains outlined by the Healthy Food Environment Policy Index (Food-EPI) including (i) food labelling; (ii) food promotion; (iii) food composition and nutritional quality; (iv) food retail; and (v) food pricing. Relevant data sources were searched for currently implemented nutrition-related government policies that may have relevance to OFD platforms. RESULTS: A total of 2012 records were screened, and 43 studies were included. There were 70 relevant study outcomes across the included studies, which addressed one or more of the 5 domains. Of these, 21 were relevant to 'Food Promotion' (30%), 18 to 'Food Retail' (26%), 15 to 'Food Composition (21%), 11 to 'Food Prices' (16%), and six to 'Food Labelling' (9%). Three existing policies from international jurisdictions (England, Singapore, EU) included OFD platforms, of which one was a voluntary measure. Several existing policies under food labelling have the potential to be amended to include OFD platforms under regulatory definitions. CONCLUSION: OFD platforms have emerged as a disruptor to how people acquire their food and have yet to be widely included in existing nutrition-related policies. Advancing the evidence base to support the design of effective policy actions and mitigate the potential negative health impacts of OFD platforms will support efforts to improve population diets.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».