The Good Food Junction: a Community-Based Food Store Intervention to Address Nutritional Health Inequities
Notice bibliographique
Résumé
BACKGROUND: This is a 2-year study to assess the early impacts of a new grocery store intervention in a former food desert. OBJECTIVE: The purpose of the study is to understand the early health effects of the introduction of a large-scale food and nutrition-focused community-based population health intervention, the Good Food Junction (GFJ) Cooperative Store, in a geographically bounded group of socially disadvantaged neighborhoods (the "core neighborhoods") in a midsized Canadian city. The GFJ grocery store was tasked with improving the access of residents to healthy, affordable food. The 5 research questions are: (1) What is the awareness and perception of the GFJ store among residents of the core neighborhoods? (2) Are there differences in awareness and perception among those who do and do not shop at the GFJ? (3) Will healthy food purchasing at the GFJ by residents of the core neighborhoods change over time, and what purchases are these individuals making at this store? (4) What early impact(s) will the GFJ have on key health-related outcomes (such as household food security status, vegetable and fruit intake, key aspects of self-reported mental health, self-reported health)? and (5) Are the effects of the intervention seen for specific vulnerable population groups, such as Aboriginal people, seniors (65 years old or older) and new immigrants (settled in Saskatoon for less than 5 years)? METHODS: The research project examined initial impacts of the GFJ on the health of the residents in surrounding neighborhoods through a door-to-door cross-sectional survey of food access and household demographics; an examination of GFJ sales data by location of shoppers' residences; and a 1-year, 3-time-point longitudinal study of self-reported health of GFJ shoppers. RESULTS: Analyses are on-going, but preliminary results show that shoppers are using the store for its intended purpose, which is to improve access to healthy food in a former food desert. CONCLUSIONS: To our knowledge this is the first large-scale study of a full-service grocery store intervention in a former food desert in Canada that has used multiple data sources, as well as longitudinal analyses, to examine its effects. Its findings will contribute significantly to the knowledge base on food environment interventions.
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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 | 0,010 | 0,001 |
| 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 | 0,011 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».