BlueHealth: a study programme protocol for mapping and quantifying the potential benefits to public health and well-being from Europe's blue spaces
Notice bibliographique
Résumé
INTRODUCTION: Proximity and access to water have long been central to human culture and accordingly deliver countless societal benefits. Over 200 million people live on Europe's coastline, and aquatic environments are the top recreational destination in the region. In terms of public health, interactions with 'blue space' (eg, coasts, rivers, lakes) are often considered solely in terms of risk (eg, drowning, microbial pollution). Exposure to blue space can, however, promote health and well-being and prevent disease, although underlying mechanisms are poorly understood. AIMS AND METHODS: The BlueHealth project aims to understand the relationships between exposure to blue space and health and well-being, to map and quantify the public health impacts of changes to both natural blue spaces and associated urban infrastructure in Europe, and to provide evidence-based information to policymakers on how to maximise health benefits associated with interventions in and around aquatic environments. To achieve these aims, an evidence base will be created through systematic reviews, analyses of secondary data sets and analyses of new data collected through a bespoke international survey and a wide range of community-level interventions. We will also explore how to deliver the benefits associated with blue spaces to those without direct access through the use of virtual reality. Scenarios will be developed that allow the evaluation of health impacts in plausible future societal contexts and changing environments. BlueHealth will develop key inputs into policymaking and land/water-use planning towards more salutogenic and sustainable uses of blue space, particularly in urban areas. ETHICS AND DISSEMINATION: Throughout the BlueHealth project, ethics review and approval are obtained for all relevant aspects of the study by the local ethics committees prior to any work being initiated and an ethics expert has been appointed to the project advisory board. So far, ethical approval has been obtained for the BlueHealth International Survey and for community-level interventions taking place in Spain, Italy and the UK. Engagement of stakeholders, including the public, involves citizens in many aspects of the project. Results of all individual studies within the BlueHealth project will be published with open access. After full anonymisation and application of any measures necessary to prevent disclosure, data generated in the project will be deposited into open data repositories of the partner institutions, in line with a formal data management plan. Other knowledge and tools developed in the project will be made available via the project website (www.bluehealth2020.eu). Project results will ultimately provide key inputs to planning and policy relating to blue space, further stimulating the integration of environmental and health considerations into decision-making, such that blue infrastructure is developed across Europe with both public health and the environment in mind.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,088 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,005 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,117 | 0,036 |
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 source (Gemma direct ou Codex distillé), 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 ».