The co-design and co-creation of an integrated geriatric care system: A case study from Ontario, Canada, in partnership with older adult patients and providers
Notice bibliographique
Résumé
Background: Integrated care has been heavily researched, but additional efforts are required to move knowledge of integrated care into action, ideally at a larger scale and in a more sustainable fashion. We need a stronger grasp of how integrated care models are implemented, and how they might be tailored to unique contexts. Within the context of complex health care interventions, it is relatively rare that researchers engage in evaluations of the process (Lewin et al., 2009); the focus tends to be the outcomes not the process (Moore et al., 2015). To support the implementation of sustained and tailored models of integrated care, we must endeavor to better capture the process in which change is enacted. This is particularly prudent in countries with aging societies, as health care integration is especially important for older adults, whose care needs cut across a range of systems, providers, and settings. Purpose: This work seeks to understand the knowledge to action (KTA) process in which knowledge is co-created within a co-design setting, and then implemented within a health region to improve integrated care for older adults. Here, we focus on the processes and results of the co-design approach with care providers and older adults. Older adult patient partners have been integrated in each step of this process. Methods: In this study, we have partnered with health care providers and older adult patients and caregivers in Southwestern Ontario, Canada, to document and evaluate efforts to co-design and implement an integrated model of care for frail older adults in two regions. We have worked with each region to understand and document their co-design process from late 2020 through to the present. Employing a qualitative multiple case study design, we have observed and documented virtual co-design sessions which occurred monthly, in two regions, and conducted individual interviews regarding the co-design process with older adult (n=4) and health care provider (n=9) working group members. Within these co-design sessions, we also facilitated the development and identification of goals for each region’s approach to integrating geriatric care. Qualitative data were analyzed using appropriate coding techniques. Results: Through observations and interviews with older adult and health care provider co-design working group members, a number of themes emerged. First, working group (WG) members identified that having an external facilitator leading the co-design sessions was a benefit. Co-design sessions needed to be flexible, informative, and productive to keep members engaged in the co-design work. WG members also identified a number of goals that needed to be achieved to indicate that co-designed improvements were made to the geriatric health system. Goals included, decrease referral duplication, improve patient and caregiver experience, improve provider collaboration, and decrease service wait time. Conclusion: This work has demonstrated practices and techniques for both creating and then tailoring an integrated care model for unique regions, including a novel application of the GAS guide as a tool to support co-design. These learnings may facilitate the future participation of older adults and their caregivers in improving health care, in Canada and elsewhere.
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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 | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,039 | 0,009 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».