Every Team Needs a Coach: Insights from Ontario Health Teams
Bibliographic record
Abstract
Introduction: Globally, health systems are moving toward better integrated care organization and delivery. A current example within the Canadian context is the restructuring of care delivery in Ontario, in alignment with the Quadruple Aim. The vision for reorganizing care into Ontario Health Teams (OHTs) was announced by the government in early 2019 and this has prompted care providers across sectors to choose priority patient populations and engage in 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. In response, a team of faculty at the University of Toronto developed the ADVANCE program to guide shared leadership, decision-making and accountability for leaders of OHT partner organizations. The program, designed as a virtual constellation of educational supports included two main streams of activity: (1) a six-module series of interactive webinars to support leadership council members, and (2) adaptive support to local coaches embedded in the leadership teams. Methods: The Coaching Academy was intentionally designed with a responsive and adaptive curriculum, underpinned by the Collective Impact framework. OHT leadership teams nominated local coaches, based on specified coaching competencies and characteristics. We onboarded coaches through an intake interview which allowed us to better understand the collaborative culture/norms of their OHT, and their individual learning needs. These insights also informed the Coaching Academy curriculum which included virtual synchronous and asynchronous engagement methods. Monthly synchronous sessions included a blend of expert lectures and open-ended coach discussion. To encourage asynchronous engagement, we posted discussion questions and learning resources on an external collaborative platform. At regular intervals, we elicited feedback from coaches (via Google forms) and adapted our content and delivery format accordingly. Results: The coach participants were diverse in terms of their educational backgrounds, years of experience, position on the leadership council (e.g., CEO, Patient/Family Advisor, etc.) and willingness to participate (e.g., volunteered or ‘voluntold’). We learned that coaches functioned in a variety of contexts, dependent on the culture and the processes adopted by individual OHTs. We observed that coaches’ needs evolved throughout the program, likely due to the dynamic intersection of OHT implementation and the ongoing management of the COVID-19 pandemic. In early 2022, coach feedback indicated that less frequent touchpoints were needed, and coaches valued learning from other coaches and receiving asynchronous materials. We adapted the curriculum for the final cohort to include coach touchpoints every 2-3 months. Conclusion and Next Steps: When planning the Coaching Academy, our team anticipated the need to be flexible and adaptable in terms of the program format and content to meet the needs of a diverse participant group. These insights could be helpful for others who are engaged in health care system transformation and considering varying approaches for building a culture of collaborative governance.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".