Making it stick: policy levers for people centred integrated care across systems, sectors and networks
Bibliographic record
Abstract
Advancing integrated care has both top-down and bottom-up approaches. Policy levers including payment, regulations and standards are essential to support sustainable integrated care under either approach. In this dynamic, interactive workshop, speakers from Health Standards Organization and IFIC Canada will bring practical realities and lessons learned from extensive experience in co-creating and supporting transformative change across systems, sectors, and networks. From the co-design of a National Standard for Integrated People Centered Health Systems in Canada, to building capacity for implementation along different types of integrated care journeys, participants will gain valuable insights to support making change that lasts in their own local context. After a "fire side chat" type of exchange with our two speakers and expert facilitator, participants will be actively involved in a creative, productive and enriching interactive exercise. Using mock scenarios provided by the workshop team, participants will experiment with using various policy levers to co-create pathways toward integrated care across systems, sectors and networks. They will then self-assess their progress along an integrated care journey using evidence-based standards, tools and resources.Groups will then share their insights and and action ideas for applying lessons learned from the workshop in their own jurisdiction. The workshop will close with final reflections from facilitator and speakers. Faculty and Specific Format and Workshop Outline: Facilitator: Jodeme Goldhar, Co-Founder of North American Center for Integrated Care, IFIC Canada , Vice Chair IFIC Board, Adjunct lecturer and co-instructor at University of Toronto Institute for Health Policy, Managment and Evaluation( HPME) Speakers: 1) Leslee Thompson, CEO Health Standards Organization and Accreditation Canada, and Executive in Residence, U of T Rotman School of Management. 2) Walter Wodchis, Professor Institute of Policy, Management and Evaluation, Research Chair and Senior Scientist. Format: Opening Remarks & Context Setting: Facilitator (5 Min) Facilitator leads "" Fireside Chat"" with 2 speakers (25 min) 3, Q & A ( 10 min) with participants Workshop Exercise In small groups at round table - with scenarios for each table, using tools and resources provided by workshop team to support exercise as described( 30 min) Feedback from Tables: (10 min) - insights & ideas for how to apply lessons learned in their own jurisdictions Final reflections - facilitator and speaker summary (10 min)
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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 | 0.111 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.041 | 0.045 |
| Open science | 0.008 | 0.033 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".