Collaborative Governance for Integrated Care: Insights from a Policy Stakeholder Dialogue
Bibliographic record
Abstract
INTRODUCTION: Integrated care is a goal of many health care systems. However, operationalizing and implementing integrated care remains challenging especially in continuously evolving policy environments. We report on a policy symposium held in 2017 focused on operationalizing a particular integrated care policy in the context of policy evolution in Ontario, Canada. METHODOLOGY: Forty-five participants attended the symposium including government employees, health care leaders, researchers, clinicians, and patient representatives. The symposium included presentations from representatives of each group and breakout sessions. Two trained observers recorded observational field notes. RESULTS: We report four recommendations and fourteen sub-recommendations which arose regarding the implementation of the policy. We highlight four important tensions which characterize challenges regarding its implementation, and discuss the recommendations in the context of Collaborative Governance. DISCUSSION: We outline how the recommendations could be strengthened by collaborative governance and identify where this framework could support governance and leadership challenges associated with implementing integrated care. We describe the unique challenges posed by working towards these goals in an evolving policy environment. CONCLUSION: We draw on collaborative governance to generate insights for leaders implementing integrated care and conclude by addressing the importance of maintaining collaborative governance initiatives under circumstances of unstable policy environments.
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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 | 0.092 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.046 | 0.063 |
| Scholarly communication | 0.032 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".