MétaCan
Menu
Retour à la cohorte
Enregistrement W4413358459 · doi:10.5334/ijic.nacic24164

Weaving Networks: How University Health Network’s NIC is supporting aging adults to create helping communities

2025· article· en· W4413358459 sur OpenAlexaff
Joe Pedulla, Melissa Chang

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineEngineering
ThématiqueBiomedical and Engineering Education
Établissements canadiensUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésWeavingBusinessPublic relationsSociologyKnowledge managementGerontologyMedicineComputer scienceEngineeringPolitical science

Résumé

récupéré en direct d'OpenAlex

Over the past decade the team at UHN NORC Innovation Centre has had the privilege of learning from and working with aging adults living in naturally occurring retirement communities (NORCs). Together we have created a new integrated health and social care model that can be adapted to the needs of a diverse range of communities. The model currently supports up to ~4,000 aging adults including successfully addressing 95% of the needs identified through 0,000+ interactions. [PJ] Critical to the many successes has been the development and delivery of an Ambassador program as well as a steadfast commitment to take a participatory approach to build community and ensure aging adults have the agency to lead. The voices of aging adults guide implementation and improvement efforts as well as identify areas requiring innovation. Since many living in NORCs are relatively healthy and are living fairly independently there is a significant opportunity to preserve health system capacity by helping them stay healthy and, if needed, provide timely supports to get healthy. The community created through NORC programming as resulted in aging adults feeling they can take initiative and lead change, while their work in bringing community creates space to learn together, teach one another and normalize new ways to care for themselves and one another.These networks of mutual support resulted in the opportunity to address common health promotion and care challenges through care pathways that were initiated and co-developed by aging adults. In the first year of operation the team focused on three priorities- falls prevention, lung health and rapid access to appropriate care. We will share how care pathways have been developed and subsequently impacted care and health outcomes in participating sites. In one example, a resident who sustained a fall was cared for by a neighbour who kept a close eye for deterioration. Then, once a fractured hip was diagnosed, the person was supported by the community in the building both in the initial injury phase all through rehabilitation. Neighbors created a community of support for this person during rehab and worked closely with the NIC Team to ensure a smooth recovery.As a second example, aging adults indicated a desire to have a holistic end-to-end fall prevention, education and detection program. In this case on their won initiative, the aging adults in the building helped bring isolated individuals to the sessions, support the delivery of the intervention and assist with follow-up during the intervention.In a third example; a lung health initiative focusing on the early detection of COPD; neighbors were able to assist an individual in the building who presented as severely hypoxic. Working with the NIC Team, residents stepped up and assisted with the care delivery team to help get the person directly to the appropriate specialist while bypassing the emergency department.These and other similar instances provide several important learnings regarding people as partners in care: By providing agency to residents in buildings it possible to create a sense of confidence and courage for them to create supporting and engaged communities Responding to the needs identified in innovative ways can help create economies of scale by simultaneously reaching many individuals with one intervention. A community of aging adults can form and work with providers in delivering care to those who would normally be isolated and not have access to care. A group of aging adults can create a community of informal support for those who need extra help dealing with their conditions.

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)0,000
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: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,438
Score d'incertitude au seuil0,530

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 recherche0,0000,000
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 Opus0,006
Tête enseignante GPT0,238
Écart entre enseignants0,232 · 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'étudeSimulation ou modélisation
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
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueInternational Journal of Integrated CareMême sujetBiomedical and Engineering EducationTravaux en français237 207