Exploring Mechanisms that Facilitate the Development of Collaborative Governance Structures
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".