Using Integrative Thinking Techniques to Design Integrated care Programming in Ontario, Canada
Bibliographic record
Abstract
Integrated Care connects care providers across sectors and often from different agencies or organizations to deliver more effective, efficient and client centered care. Creating new or redesigned programs to achieve these goals typically requires new care models delivered by a range of providers who have varying experiences and mental models about what is required for effective care. Effective care designs must also consider factors such as variations in populations, available services, and resources. Thus, creating care models for integrated care can be challenging, creating conflicts that can undermine efforts to arrive at effective delivery models. Ontario Health Teams (OHTs), a new model for integrating care, launched in staged cohorts across the province in 2019, are developing new population health-focused care delivery models for specified target populations, and, eventually, for the 15 million people of Ontario, Canada. OHT leaders and staff come from a variety of agencies with varying care models, and, in some cases, limited prior collaborations with a mandate to integrate service delivery. Leaders in each OHT can design services to fit local needs, and their performance will be assessed on specified metrics. To assist in the design of new care models that meet patient needs and support providers from multiple and different agencies, a team from the ADVANCE program at the Dalla Lana School of Public Health and the Rotman School of Management at the University of Toronto have used Integrative Thinking approaches to help leaders to select and combine elements of opposing care designs to create more effective strategies for integrated care. Integrative Thinking is an innovative method developed by Roger Martin and Jennifer Riel that helps decision-makers to reframe decisions by shifting efforts away from seeking compromises among competing approaches, and, instead helps decision-makers to identify the core elements of each potential model and then reassemble these components into a new, even better design. Integrative Thinking thus helps decision makers combine seemingly competitive solutions into a stronger option through a carefully structured, facilitated process. This presentation will describe the methods used for Integrative Thinking within the context of integrated care, and home care programming specifically, to better serve local populations in different care 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".