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Enregistrement W4390942447 · doi:10.5334/ijic.icic23501

Neighbourhood Care Teams: Integrating Health Care and Social Services for Seniors in Toronto Community Housing

2023· article· en· W4390942447 sur OpenAlexaffabout
Jocelyn Charles, Einat Danieli, Kiara Fine, Jaipreet Kohli, Stacy Landau, Jagger Smith, Naomi Ziegler

Notice bibliographique

RevueInternational Journal of Integrated Care · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueAging, Elder Care, and Social Issues
Établissements canadiensPublic Health OntarioBaycrest HospitalCanada Mortgage and Housing CorporationSunnybrook Health Science Centre
Organismes subventionnairesnon disponible
Mots-clésIntegrated careNursingService providerHealth careBusinessNeighbourhood (mathematics)Service (business)MedicinePublic relationsMarketingPolitical science

Résumé

récupéré en direct d'OpenAlex

Toronto Seniors Housing Corporation (TSHC), owned by the City of Toronto, provides housing for 15,000 low-income seniors in 83 seniors-designated buildings across Toronto. The North Toronto Ontario Health Team (including primary care, hospital, community and home-care) partnered with one of the TSHC buildings in North Toronto to develop and implement a Neighbourhood Care Team (NCT) model to support TSHC’s Integrated Service Model to address tenants' health and social needs, co-designed with the tenants. The goal of the NCT is to provide an integrated model of care that is accountable to meeting the needs of people living within a specific neighbourhood so that people experience one system that provides simple access to service, and care that is coordinated with streamlined communication of health care providers. The NCT objectives include: Increasing primary care provider connections Increasing mental health & addictions care access and support options Increase Digital Health access and literacy to support primary care and specialist access, reduce social isolation and increase wellness Reduce avoidable ED and hospital use. The service design is guided by a co-design process with the tenants as follows: Door to door survey to engage tenants in identifying their barriers and the services and supports most meaningful to them Eliciting and voting on key education and support initiatives at an influenza vaccination clinic Communication back to tenants regarding the results of the survey and how the strategies/activities planned for the building have been prioritized based on their feedback. Multi-organization Education Fair focusing on the top issues addressed during the vaccination clinic survey which was well attended Regular educational sessions in response to tenant interest, combined with a self-screening component to help link the information to a concrete service/intervention to promote better health. Providing translation support to enable access and engagement by tenants from a variety of cultural backgrounds. Ongoing commitment to continue and co-design services and elicit tenants’ feedback. The team has worked to design structures to strengthen coordination and collaboration among the various delivery partners: Multi-organizational bi-weekly huddles to discuss residents identified with unmet needs (with consent or anonymized without consent) and identify options for improving their access to health care/social services and respond to their needs in a timely manner Designed pathways for ensuring attachment to primary care, access to primary care and specialist support, access to home care services and assistance with social determinants of health Established mechanisms to obtain informed consent and enable information sharing between delivery partners. Multi-modality tenant engagement to tailor services and supports to a TSHC building has led to increased involvement by tenants and a growing interest by tenants in strategies to improve their health and social inclusion. Cross-sector collaboration is an efficient and effective way to establish needs-based integration of health and social care services in this setting. Strong leadership as well as co-developed processes, frequent building meetings and cross-sector huddles were effective ways of sharing innovative ways of meeting needs with limited resources.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,502
Score d'incertitude au seuil0,990

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0050,001
Communication savante0,0020,001
Science ouverte0,0010,005
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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 Opus0,026
Tête enseignante GPT0,414
Écart entre enseignants0,388 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2023
Routes d'admission2
Résumé présentoui

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