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Enregistrement W3159979600 · doi:10.7488/era/882

Examination of the community food environment and the drivers affecting and impacting obesogenicity in a deprived urban neighbourhood in Scotland

2020· dissertation· en· W3159979600 sur OpenAlex

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Notice bibliographique

RevueERA · 2020
Typedissertation
Langueen
DomaineMedicine
ThématiqueObesity, Physical Activity, Diet
Établissements canadiensnon disponible
Organismes subventionnairesComisión Nacional de Investigación Científica y TecnológicaMedical Research CouncilUniversidad de Chile
Mots-clésNeighbourhood (mathematics)GeographyEnvironmental planningSocioeconomicsSociology

Résumé

récupéré en direct d'OpenAlex

Introduction: The condition of obesity has been classified as a pandemic , given that it is negatively impacted health in almost every country in the world (1). Scotland has one of the worst obesity records in the world and one of the highest rates of all OECD countries (2). Scottish men and women in the most deprived areas had higher rates of obesity in 2016 in comparison with less deprived areas (3,4). Its alarming increasing trend year on year and the magnitude of the level of obesity over the last 30 years, coupled with the causality network which appears to be rooted in health inequities has been made obesity a titan challenge of the 21st century (1,5,6). No country in the world has reversed the challenge of obesity. The community food environment has been identified as one of the environmental causes of obesity (7–9). The high presence and accessibility of less healthy food sources appears to determine an increased availability of high-energy dense food, and the lower presence and accessibility of healthier food outlets also decreases the availability and shopping possibilities for more nutritious products (7–9). Both scenarios encourage a more frequent consumption of obesogenic food, promoting a rapid and sustained weight increase in all age groups, but especially among adults and elders (6,7,10). This thesis is the first study in Scotland that has mapped a complete foodscape or food map in a deprived neighbourhood and identify some key contributors that promote obesity. Methodology and methods: the study was conducted in a Scottish urban neighbourhood, which is low-income with high levels of poverty and obesity and poor dietary patterns. Data collection made use of a combination of different databases and approaches, including ethnographic fieldwork and online validation. Predominance, proximity and density of all type food sources, and healthier and less healthy food sources were calculated, using the Quantum Geographic Information System (QGIS) software. Food sources were categorised using 15-point classification tool, proposed by Lake et al (11). Accessibility to these sources was assessed separately for general stores and healthier and less healthy categories. Results: Findings reported a wide range of outlet types and confirmed an obesogenic food environment in the neighbourhood. Food sources related to deprivation were also present, such as food banks, whereas others such as organic food outlets which are related to more affluent areas were absent. A set of ready-made food at a low price, easy to collect or delivery at home preparations was present in over 30% of the establishments and are described in the thesis. These preparations were highly popular among the residents, and almost all the menu options were served in extra-large portions. The food outlets’ showcases were often in a deteriorated state with a preponderance of special cheap offers. Most of the establishments had a small sit-in area, while promotion of food delivery and takeaway was high. A higher proportion of less healthy food sources (27.7%) than healthy food sources (5.5%) were present within the neighbourhood. Less healthy food sources, such as fast-food outlets, takeaways, and convenience stores, were highly accessible and offered a wide range of high-energy dense foods. This scenario is known as food swamp. On the opposite side, the few healthier food sources, such as supermarkets, and fruit and vegetable stores, were located further away from households than the less healthy food sources. This scenario is known as a food desert, and alongside a food swamp, they confirmed that the geographical area mapped, anonymised to Whitewood has a highly obesogenic food environment. This environment appeared to be encouraging unhealthy eating patterns among residents and people working and studying in the area. Conclusions: This complete food exposure mapping showed for the first time in an area of intense deprivation, the features of a low-income food environment. Regarding the obesogenic characteristics of the food environment, results resonated with previous investigations. The presence of a food swamp and a food desert and the high accessibility of less healthy food in comparison with healthier establishments, is a scenario described previously in literature in other countries, including the US and Canada (12,13). According to Glanz et al. and Story et al. there are common drivers related to deprivation that influence a less healthy food shopping behaviour among residents, contributing to the weight gain process (7,8,14). Although the obesity causality network is hugely complex and several determinants can potentially influence eating patterns, the community food environments quality and accessibility may be part of the factors that encourage inhabitants to eat less healthy food regularly. Obesity causes are potentiated by health inequities, and there is an urgent need to tackle the obesity problem from the roots, using a multilevel approach (5,6,15). Intervening within the food environments in deprived neighbourhoods is part of the Scottish government new food policy; however, more articulated initiatives are needed to fight against obesity, starting from tackling the roots of poverty (16,17).

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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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,035
Score d'incertitude au seuil0,510

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,014
Tête enseignante GPT0,235
Écart entre enseignants0,222 · 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