The Generation of Integration: The Early Experience of Implementing Bundled Care in Ontario, Canada
Notice bibliographique
Résumé
Policy Points Policymakers interested in advancing integrated models of care may benefit from understanding how integration itself is generated. Integration is analyzed as the generation of connectivity and consensus-the coming together of people, practices, and things. Integration was mediated by chosen program structures and generated by establishing partnerships, building trust, developing thoughtful models, engaging clinicians in strategies, and sharing data across systems. This study provides examples of on-the-ground integration strategies in 6 programs, suggests contexts that better lend themselves to integration initiatives, and demonstrates how programs may be examined for the very thing they seek to implement-integration itself. CONTEXT: By bundling services and encouraging interprofessional and interorganizational collaboration, integrated health care models counter fragmented health care delivery and rising system costs. Building on a policy impetus toward integration, the Ministry of Health and Long-Term Care in the Canadian province of Ontario chose 6 programs, each comprising multiple hospital and community partners, to implement bundled care, also referred to as integrated-funding models. While research has been conducted on the facilitators and challenges of integration, there is less known about how integration is generated. This article explores the generation of integration through the dynamic interplay of contexts and mechanisms and of structures and subjects. METHODS: For this qualitative study, we conducted 48 interviews with program stakeholders, from organization leaders and managers to physicians and integrated care coordinators, across the hospital-community spectrum. We then used content analysis to explore the extent to which themes were shared across programs and to identify idiosyncrasies, followed by a realist evaluation approach to understand how integration was produced in structural and everyday ways in local program contexts. FINDINGS: Integration was generated through the successful production of connectivity and consensus-the coming together of people, practice, and things, as perceived and experienced by stakeholders. When able, the programs harnessed existing cultures of clinician engagement, and leveraged established partnerships. However, integration could be achieved even without these histories, by building trust, developing thoughtful models, using clinicians' existing engagement strategies, and implementing shared systems and technologies. The programs' structures (from their scale to their chosen patient population) also contextualized and mediated integration. CONCLUSIONS: This article has both practical and theoretical implications. It provides transferable insights into the strategies by which integration is generated. It also contributes conceptually to realist approaches to evaluation by advancing an understanding of mechanisms as contextually and temporally contingent, with the capacity to produce new contexts, which in turn generate new sets of mechanisms.
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 | 0,010 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,038 | 0,011 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».