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Enregistrement W7072089456

Urban design and health

2019· article· en· W7072089456 sur OpenAlexaboutno aff

Notice bibliographique

RevueVirtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2019
Typearticle
Langueen
DomaineMedicine
ThématiquePregnancy and preeclampsia studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPopulationPublic healthGovernment (linguistics)Work (physics)Context (archaeology)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Recent trends such as globalisation and urbanisation, combined with an ageing population and population growth, result in new challenges for public health. To tackle these emerging public health issues, novel approaches are required. The paradigm shift in public health supports this needed change. Public health is moving from a medical model, focused on the individual, to a social model, where public health is the result of various socio-economic, cultural and environmental factors. As stated in the Ottawa Charter1 and the "Health in All Policies" strategies2 of the World Health Organization (WHO), environment and living spaces are considered as global, social and political entities that determine the health status of populations.
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\nThis e-collection examines the relationship between built environment and health by presenting evidence from the papers that are recently published in the European Journal of Public Health. This evidence can support decision-makers in innovative policies, strategies and tangible actions in order to face contemporary public health challenges.
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\nSocial inequalities and social cohesion. In recent years, urban regeneration has widened its approach not only to give cities a new and more competitive look but also to boost cultural, economic and societal aspects. Those operations might result in gentrification processes which are demonstrated to have negative impacts on the population with a lower socioeconomic status (SES)3. Negative impacts include social relationship and daily routine disruption, psychosocial stress, health accessibility, stigmatization and discrimination resulting in anxiety and depression. Other studies highlight how adults who live in lower SES areas are more prone to develop psychological distress (Erdem et al., 2015). Instead, an inclusive approach to urban regeneration can improve the living conditions of the inhabitants with new services, resources, safety and social relationships (Mehdipanah et al., 2018).
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\nTo maximise health gains for the whole population, draft urban policies should be assessed. Pennington and colleagues (2017) present tools to measure the impact of urban policies on the health of residents in urban areas and potential variations within the urban population.
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\nPhysical activity and green areas. According to the WHO, over 3.2 million deaths are caused by insufficient physical activity. Physical inactivity is a risk factor for several non-communicable diseases, and as Dallat et al. (2014) underline, an increase of 10% of physical activity could lead to reduced cases and deaths from ischaemic heart disease, type 2 diabetes, stroke, colon and breast cancer. Urban areas with public space, walking circuits, and pedestrian paths can contribute to improved well-being, especially in elderly people (Bailly et al., 2018). A study on children (from 3 to 5 years old) investigating the relationship between obesity risk and the presence of green space, demonstrates that the quality and quantity of green areas has an impact on public health (Schalkwijk et al., 2018).
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\nNevertheless, the availability of green areas is not always correlated with high levels of physical activity. The perceived quality of green spaces is detrimental, as a study in 13 cities in the United Kingdom shows (Ali et al., 2017). Similarly, Pope et al. (2018) showed that the risk of psychological distress in people with access to lower quality green areas is up to 54% higher than those close to high quality spaces.
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\nAssessment of urban areas and neighbourhoods. The quality of urban areas and neighbourhoods is very important for the public's health. A collection of 13 tools to measure the perceived quality of urban area residents are presented by Hofland and colleagues (2018). These tools survey residents about amenities, landscape, public space, sidewalks, and more. Consulting residents about the quality of their living areas can give insight in important living quality aspects that cannot be retrieved from registries. Innovative methodologies (e.g. mobile applications) could provide tools to receive feedback from residents about the quality of living areas and this information could support policy and decision makers in strategic choices. Furthermore, the importance of using locally based aggregate measures in urban health policy making, is highlighted by Gemmell et al., 2017.
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\nA comparison of the health of people living in urban areas versus those not living in urban areas shows interesting differences between and within Eastern and Western European countries (Koster et al., 2017). In general, people living in Western European cities have a better health status than those living in Eastern European cities. While the urban population in Western European countries are less healthy than the country's average and people living in Eastern European cities are more healthy than the general population of that country.
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\nAs the papers in this e-collection demonstrate, there is an urgent need for joint actions in order to involve communities and policy makers as main stakeholders of the urban planning process. Starting from the concepts of evidence-based medicine and evidence-based design, future studies should develop a multidisciplinary approach for evidence-based urban health planning.

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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,345
Score d'incertitude au seuil0,524

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,050
Tête enseignante GPT0,272
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

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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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