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Enregistrement W4390957083 · doi:10.5334/ijic.icic23539

Healthy@Home, An Integrated Health and Social Care Program for a Marginalized Population Group in Toronto, Canada

2023· article· en· W4390957083 sur OpenAlexaffabout
Siu Mee Cheng

Notice bibliographique

RevueInternational Journal of Integrated Care · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Disease Management Strategies
Établissements canadiensToronto Metropolitan University
Organismes subventionnairesnon disponible
Mots-clésFocus groupHealth careIntegrated carePopulationGeneral partnershipPublic relationsAccountabilityContext (archaeology)NursingMedicineSociologyPolitical scienceEnvironmental healthGeography

Résumé

récupéré en direct d'OpenAlex

Introduction: Integrated health and social care (IHSC) can support improved health and social care outcomes for highly vulnerable and marginalized population groups. Toronto has one of the most diverse populations in the world. Immigrants account for 46% of the city's population. Russian-speaking Jewish older adults are a population group who are at significant risk of isolation and poor access to health and social services. In response an adult day program, Healthy@Home (H@H) was established through multi-organizational partnership among community-based health and social care outcomes led by social services agencies has spread to 20 sites across Canada. Methodology: a qualitative study of H@H was undertaken to explore the factors that support successful integrated services delivery for this high-risk population group. Key informant interviews and focus groups were completed involving eight health and social care agencies to explore the factors that support successful IHSC. Results: The qualitative study identified eleven critical factors that support IHSC among many health and social care agencies. They include strong communications, shared vision and goals, team-based care, dedicated resources, inter-organizational culture, and pre-existing relationships were overwhelmingly regarded as being key to the success of H@H partnerships. Leadership and accountability agreements were also regarded as being important factors. The H@H case also identified pre-existing relationships, role clarity and champions that contributed towards its integration success. The H@H case offers up some unique insights into how context can have an important role in influencing IHSC and that may be unique to services provision in Toronto. Ten contextual factors were identified as having exerted an influence on services partnership. They include aging population, immigration population, transportation, differences in the healthcare and social care sectors, accountability agreements with governing authorities and funders, public funding, governance authorities, sense of community among the providers, public policies and being part of the not-for-profit sector. Lessons Learned: The H@H study has shown that IHSC can occur voluntarily and at the grass-roots level. It also provides insights into how IHSC can be initiated and driven by social services agencies who are united by a shared vision and goal, and a strong sense of community. Despite limited funding and external resources, these organizational partners have been able to engage in IHSC for more than a decade in order to respond to a significant unmet need for a population group that was being ignored and marginalized due to their socio-economic status, their immigration status, and language barriers by existing health and welfare systems. Their isolation from mainstream society put them at significant risk of falling through the Canadian welfare system cracks. These community-based agencies, with close ties to their neighbourhoods were able to engage, respond, coordinate, and integrate flexibly. Contextual factors including a supportive policy environment that was aimed at supporting ageing in place, seniors care, poverty reduction and immigrants and a vibrant community of culturally competent providers has made H@H a success.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,046
Score d'incertitude au seuil0,337

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0130,002
Communication savante0,0020,000
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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 Opus0,025
Tête enseignante GPT0,377
Écart entre enseignants0,352 · 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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