Integrating health and social care in the community to support a new model of Long-term Life Care at home.
Notice bibliographique
Résumé
Background: Long-term care (LTC) reform was an international priority well before the COVID-19 pandemic. While strategies to promote de-institutionalization, rehabilitation, caregiver support and enhanced home and community care have varied by country in terms of implementation and success, the pandemic universally reinforced existing system-specific barriers and weaknesses. In Canada, heightened access issues and silo-ed delivery of community-based medical, functional and social care and support services contributed to increased caregiver burnout and growing residential care waitlists. Aims: This study aimed to develop an alternative model to residential LTC that would enable older adults to live, age and receive care at home long-term. The specific objectives were to: 1) describe variation in medical, functional and psychosocial ‘life care’ needs of community-dwelling older adults; 2) develop a model of needs-based care with packages to support variation in needs; and 3) to assess preliminary feasibility of the model using the Ontario, Canada (population 15 million) health care market. Approach: An exploratory, sequential, mixed methods design was applied (5). Phase 1 involved historical analysis of 2017-18 Ontario interRAI home care assessments (n=283,601) and 2018-19 Ontario service utilization data (n=115,000) to develop unique patient vignettes. Phase 2 was a 6-week modified eDelphi process with interdisciplinary home care clinicians (n=42) to develop care packages for the model, including types and dose of care and services. Six focus groups (n=67) were then conducted with older adults, caregivers and health and social care providers across Ontario to validate and refine the model. Phase 3 explored feasibility of the emerging model through comparison of the home care patient vignettes with the needs of the residential LTC population using 2017-18 Ontario interRAI data (n=115,000). Preliminary costing of the model was based on existing system per diems and direct care costs in comparable transitional care models. Results: A model of ‘Long-term Life Care’ at home (LTLifeC model) includes care packages to meet the dominant life care needs of 6 unique patient groups representing known predictors of LTC home admission: social frailty, caregiver distress, chronic disease, cognition/ behaviours, medical complexity, and geriatric syndromes. Overlap in care needs of home care and LTC populations confirms potential to shift care to the community; yet current home care clients receive six times less daily care hours on average, compared to residential LTC standards. New LTLifeC packages of home-based interdisciplinary care ranged from 3.1-8.9 hours daily, including comprehensive assessment, integrated care planning, direct care provision and community referral(s). Initial cost comparisons suggest plausible short and long-term system benefits of model adoption. Learnings: Evidence-informed decision making for sustainable home and community care as part of an integrated system of LTC requires attention to both routinely collected health information, or ‘big data’, and expertise by lived-experience. Adoption of the LTLifeC model will require decision-making to prioritize societal values for living and aging at home and in community. Next Steps: An evaluation framework will be developed to guide pilot implementation and testing of the LTLifeC model through the lens of the quadruple aim.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».