Leadership Strategies for Developing Integrated Care: Learning from Ontario Health Teams
Bibliographic record
Abstract
"What are key challenges and enablers when it comes to leading integrated care? How does one develop new forms of governance, manage tensions between local and system priorities, or lead through uncertainty? Ontario Health Teams (OHTs) have been engaging with such questions for years. Join us for a workshop where we will explore these issues through conversation with leaders from multiple OHTs The workshop begins with a brief overview followed by a panel discussion with OHT leads. After that, participants will have a chance to engage in small group discussions where they can add their insights and experiences. Topics include strategies, challenges, and lessons learned, with a focus on improving leadership and governance to enable collective impact. The workshop is aimed at leaders, staff and patients/caregivers who are planning or working on integrated care initiatives and who would like to learn more about the current Ontario efforts and to share their own experiences in an interactive format.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".