Exploring Mechanisms that Facilitate the Development of Collaborative Governance Structures
Notice bibliographique
Résumé
Introduction: Globally, many health systems are moving toward integrated care. A Canadian example is the restructuring of care delivery in Ontario, to align with the Quadruple Aim. The vision for the Ontario Health Teams (OHTs), announced by the government in 2019, has prompted convergence across sectors with an initial focus on priority patient populations, and collaborative governance to achieve desired outcomes. The early implementation of OHTs was “low rules:” each OHT assembled its own leadership and governance infrastructure to meet local needs. This was (and remains) a significant task, requiring strategic thinking and inter-organizational collaboration. The ADVANCE program was created to support shared leadership, decision-making and accountability for leaders of OHT partner organizations. The purpose of this research was to analyze which partnerships within integrated health systems developed over time and to identify the interventions that support effective collaborative governance – specifically focused on leadership, decision making, and accountability. Methods: A qualitative study, framed by the model of Collective Impact, was undertaken to explore the mechanisms that supported the development of collaborative governance structures and processes within OHTs. We completed a document review to understand collaborative governance structures (e.g., collaborative decision-making frameworks, organizational charts, communication strategies etc.). We used these documents to create vignettes, which summarized information about each OHT. Concurrently, we conducted 15 interviews and two focus groups with members of OHTs’ senior leadership teams. We used a realist approach to frame data analysis, allowing us to summarize contextual factors and mechanisms that framed successful collaborative outcomes, as described by the study participants. Results: Participants were diverse in terms of their educational backgrounds, years of experience, position on the leadership council (e.g., CEO, Patient and Family Advisory Council member, community sector representative etc.) and their motivation for joining the leadership teams. There were a variety of contextual factors that were addressed by participants when describing their OHT’s journey toward collaborative governance; for example: the size of the OHT (e.g., the number and size of partners coming together), the level of partner engagement and the presence of historical relationships. Participants highlighted several mechanisms that facilitated collaborative governance including: (1) strong, effective intersectoral leadership (formal and informal), (2) the importance of backbone support, (3) development of trusting partnerships often based on past collaborations, (4) effective and widely distributed communication approaches, and (5) a continuing, articulated commitment to collaborative processes, guided by a clear, shared vision. These mechanisms facilitated outcomes that maximized performance on OHT-specific outcomes and supported synergy between partners. Conclusion and Next Steps: Study results provide insights into the contextual factors and mechanisms that contribute to successful collaborative outcomes. These insights may support others who are engaged in health care system transformation to consider varied cultural and operational approaches for building collaborative governance models.
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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 | 0,045 | 0,068 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,031 |
| Communication savante | 0,013 | 0,014 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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.
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 ».