A Population Health and Lifestyle Survey of a Coastal City in England (Health Counts 2024): Protocol for a Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Health and lifestyle population surveys are important in public health to identify trends, provide data to monitor the effectiveness and reach of public health initiatives and policies, and help allocate health resources more equitably. Surveys are a methodologically robust way of examining the inequalities in health outcomes or access to resources across a number of sociodemographic groups in a defined geographic area. This particular public health survey will provide information that cannot be obtained from other sources. OBJECTIVE: The aim of the study is to generate comprehensive public health-relevant data from an adult population in a defined geographic coastal area of South East England. METHODS: A cross-sectional, noninterventional (observational) health and lifestyle population survey was developed using a mobile-first design approach to recruitment, with the content drawing on a previous iteration of the survey in 2012. Previous Health Counts surveys in this region have been conducted approximately every 10 years since 1992 to provide data about trends over time. Extensive rounds of consultation and testing took place between October 2023 and February 2024. The final survey comprised 102 questions structured around 13 contemporary public health-related issues in the United Kingdom. Survey distribution was carried out in 2 rounds of SMS text messaging through all general practices in Brighton and Hove involving all adults registered with a general practice and having a mobile phone who had not opted out of communications. Advertising across a range of public-facing initiatives was also used, as well as targeted outreach activities for potentially marginalized groups, for example, in public libraries and community groups. Enrollment took place between March 18, 2024, and April 28, 2024. RESULTS: A total of 26,014 eligible people responded. Data analysis has started, and results will be reported later in 2025. CONCLUSIONS: Understanding trends in population inequalities over time as well as gaining insights into new areas for the very first time, Health Counts 2024 data can inform decision-making on strategies to improve health and reduce inequalities by local authorities in not only England and the National Health Service but also potentially across other European cities through the effective dissemination and sharing of promising practices as an integral part of evidence-based public health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64001.
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 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 | 0,115 | 0,013 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».