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Enregistrement W4390945050 · doi:10.5334/ijic.icic23178

Designing with Older Adults: How University Health Network's NORC Innovation Centre creating an integrated health and social care community for seniors residing in naturally occurring retirement communities (NORC).

2023· article· en· W4390945050 sur OpenAlexaffabout
Joe Pedulla, Jen Recknagel, Melissa Chang, Howard Abrhams

Notice bibliographique

RevueInternational Journal of Integrated Care · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration, Aging, and Tourism Studies
Établissements canadiensUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésAging in placeFeelingGerontologyIntegrated careRetirement communitySociologyAgency (philosophy)Public relationsBlueprintPsychologyHealth careNursingMedicinePolitical scienceSocial psychologyEngineering

Résumé

récupéré en direct d'OpenAlex

It is well known that, with adequate supports, many seniors desire to age in place in their own homes. NIC’s data shows that in Toronto, 70,000 seniors are living in 495 NORCs with over 53% of these having 2 or more co-morbid conditions. Rising to this challenge, the NIC's vision is to implement a 21st-century model of integrated health and social care in NORC buildings by developing health, social, and digitally-enabled solutions that provide Canadians with new options for aging in place with dignity and choice. Phase one, created and refined the NORC Ambassadors program (norcambassadors.ca) – an aging-in-place model based on mutual support, community engagement, and seniors' leadership. It was founded on a multi-year exploration that incorporated senior input, ethnographic observation, documentary stories, literature reviews, journey mapping, and the co-creation of service blueprints. Seniors led the implementation of the Ambassadors program based on participatory decision-making, self-management, agency, and choice. From the 2021 final report, 100% of respondents indicated a desire to continue organizing aging-in-place activities with 78% feeling their awareness of aging-in-place issues improved. Interestingly, 75% reported challenges with improving overall building engagement. Based on learnings from phase 1 and guided by IFIC's 9 pillars of integrated care, phase 2 layered in a service design approach to creating an enhanced model of health and social care that increases access to place-based services and support for seniors living in Toronto’s high-rise communities. Phase 2 involves over 100 Senior Advisors, 37 Specialists, national partners, and a growing array of system partners. Central to phase 2 work is developing the NIC's Integrated Health and Social Care model. Inviting senior advisors to lead co-design activities ensured that their voice is front and centre in a system for seniors by seniors. Leveraging multi-sector involvement supported the development of one team to enable the provision of services most important to seniors that span the entire continuum of care and determinants of health. NIC's model focuses on two parts of a person's journey – ""I want to stay healthy"" where people can access an array of services that help them stay healthy, and connected, and address social isolation and loneliness. This approach added the introduction of a NORC Animator to the Ambassador model from phase 1. NORC Animators, are on site and function to create relationships with the residents, coordinate group health and social activities, and are a friendly resource for all resident needs. In addition, the NORC Animator can also watch for functional change and; where seniors state that ""I want to get healthier""; support the connection to one-on-one health and social services. This presentation will address the following questions: 1) How does the NIC model compare with other existing models in Ontario 2) What is required to effectively support seniors in participatory design? And how can you sustain involvement? 3) What is most important in designing what support is needed and how it is delivered? 4) What lessons learned have been identified from the early adopter site experiences?

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 candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,247
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,028
Tête enseignante GPT0,311
Écart entre enseignants0,283 · 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.

Devis d'étudeQualitatif
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

Citations1
Publié2023
Routes d'admission2
Résumé présentoui

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