Healthier Together: co-creating healthy and well communities through intersectoral partnerships.
Notice bibliographique
Résumé
Background and rationale: Over 60% of health is shaped by the places where we spend our time, by our relationships, and the circumstances in which we live, work, learn, play, and age. Creating healthy environments with communities, workplaces, schools, and health care settings is one of the best ways to keep people healthy and well where we live, learn, work, play, and heal. For more than a decade, Alberta Health Services (AHS), in Alberta, Canada has implemented Healthier Together projects with partners across the province but has needed to focus on implementation and impact within single settings. To realize improvements in local health and wellbeing priorities, there is a need to integrate action across settings within communities through novel approaches that foster local decision making, collaboration and partnership. Who is it for? Healthier Together is a population health approach designed and adaptable to meet various communities' needs. From small rural communities to large urban centers the asset-based community development approach and population health principles are designed to impact the health and wellbeing of whole populations. Currently, we are engaging with 7 diverse communities across the province of Alberta with the plan to scale-up Healthier Together in 2024 and beyond. Who did you involve and engage with: At the Systems-level Healthier Together breaks down silos across the health system (public health, primary care, acute care, chronic disease prevention, data, and analytics, etc.) through a connected governance structure that has implementation, research, evaluation, analytics, communication, and engagement support at its core. At the local level, Healthier Together creates opportunities for intersectoral partners in health, education, employment, municipal government, social services, and citizens within diverse communities to work together across the pillars of integrated care. What did you do? Partners from health care, municipal governments, community organizations, schools, workplaces, and citizens come together to co-create a common vision, use asset-based approaches to understand the community context, create an evidence-informed action plan that responds to local priorities and acts on the determinants of health, collaboratively implement and evaluate, and sustain what works. Healthier Together Initiatives are locally led and supported by system-wide governance and leadership and processes that promote collaboration, partnership, and local effectiveness. Emerging local priorities include mental health, homelessness, and healthy youth and families. What results and impact did you have? Engagement within the health system and with partners on Healthier Together as a way of working has been received with great interest. There is a need to continually build relationships and trust to successfully work across systems at the community-level. Primary outcomes measures and stories of impact are centered on engagement, partnership and trust building in years 1-2 of Healthier Together. A common outcomes framework for measuring improved outcomes in supportive environments, protective and risk factors and health and wellbeing outcomes is under development and will be used to measure progress and results. Healthier Together has the potential to be a model way of working for health systems and communities and learnings will be applicable across jurisdictions.
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 | 0,018 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,020 | 0,016 |
| Communication savante | 0,014 | 0,010 |
| Science ouverte | 0,004 | 0,050 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,003 |
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 ».