MétaCan
Menu
Retour à la cohorte
Enregistrement W4410566279 · doi:10.1017/s0029665125000606

Outcomes and impacts of community food hubs: a rapid review

2025· review· en· W4410566279 sur OpenAlexaboutno aff
Kate Wingrove, Penelope Love, Kristy A. Bolton, Patrícia Batista Melo, Erica Reeve, Colin Bell, Gavin L. Sacks, Steven Allender, Vivien Yii, A. Parsot, Dheepa Jeyapalan, Rebecca Lindberg

Notice bibliographique

RevueProceedings of The Nutrition Society · 2025
Typereview
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueUrban Agriculture and Sustainability
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEnvironmental planningBusinessEnvironmental science

Résumé

récupéré en direct d'OpenAlex

In Australia and other high-income countries, communities are experiencing diet-related diseases due to social inequities and food systems that promote the production and consumption of unhealthy foods (1) . Community food hubs have the potential to strengthen local food systems and improve access to healthy, affordable, culturally appropriate food by selling local food to local people (2) . The primary aim of this rapid review was to identify short- and medium-term outcomes and long-term impacts associated with community food hubs. In January 2024, four databases and the grey literature were searched for relevant studies and reports published in English between 2013 and 2023. Empirical evaluations of food hubs in high-income countries that included a physical market selling healthy local food were eligible for inclusion. A narrative synthesis was conducted, and descriptive statistics were used to summarise outcomes and impacts under five categories: economic development and viability; ecological sustainability; access to and demand for healthy local food; personal and community wellbeing; and agency and re-localisation of power (3,4) . A total of 16 studies/reports were included, reporting on 24 community food hubs (USA n = 16; Australia n = 7; Canada n = 1). Food hubs were often described as farmers’ markets (n = 9, 37% of food hubs), some of which offered financial incentives/subsidies to people living on low incomes. Some food hubs also sold food wholesale and/or provided nutrition education and community gardens. Across the 24 food hubs, a total of 83 short- and medium-term outcomes were assessed. No long-term impacts were evaluated. Outcomes were considered ‘positive’ if evaluation results reflected desirable changes. Overall, 86% of outcomes were positive (n = 71). Within the personal and community wellbeing category, 42 outcomes were assessed, and 83% (n = 35) were positive (e.g., increased fruit and vegetable consumption, increased community connection). Within the access to and demand for healthy local food category, 25 outcomes were assessed, and 96% (n = 24) were positive (e.g., increased access to and/or demand for affordable local produce). Outcomes under the remaining three categories were assessed less frequently. Within the economic development and viability category, 6 outcomes were assessed, and 50% (n = 3) were positive (e.g., access to new markets for food hub suppliers). Within the ecological sustainability category, 6 outcomes were assessed, and 100% (n = 6) were positive (e.g., reduction in food packaging and food waste). Within the agency and re-localisation of power category, 4 outcomes were assessed, and 75% (n = 3) were positive (e.g., integration of community members from low income and cultural minority groups into local food systems). Community food hubs can promote personal and community wellbeing, access to and demand for healthy local food, economic development and viability, ecological sustainability, and agency and re-localisation of power. Future research should focus on methods for evaluating long-term impacts under all five categories.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,829
Score d'incertitude au seuil0,295

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,043
Tête enseignante GPT0,283
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueProceedings of The Nutrition SocietyMême sujetUrban Agriculture and SustainabilityTravaux en français237 207