Assessing Readiness and Sustainability for Integrated Care in Ontario, Canada with the Integrated Care Leadership Survey
Bibliographic record
Abstract
Introduction: Ontario, Canada, is shifting to a more integrated healthcare delivery system through the Ontario Health Team (OHT) initiative. The extent to which OHTs have the capabilities to engage in integrated care is unknown and important to designing implementation supports. This article describes the development and psychometric testing of the Ontario Integrated Care Leadership Survey (OICLS), in 30 OHTs. The OICLS was informed by the Context and Capabilities for Integrated Care framework (CCIC). Methods: The 42-item survey was distributed electronically to 765 eligible leaders across 30 OHTs; 480 (63%) responded representing approximately 600 organizations. Item analyses and scale psychometric analyses were undertaken to reduce the number of items in the CCIC survey tool while maintaining validity and reliability. Results: The OICLS survey is comprised of 10 domains covering 12 of 17 capabilities identified in the CCIC. In the total sample, Cronbach's alpha exceeded 0.7 for nine of the ten domains. Descriptive responses to each of the 39 OICLS closed-ended survey questions illustrate the areas of strength and weakness and where supports are warranted to advance the formation of integrated care delivery systems. Conclusion: The OICLS offers a brief and valid assessment of foundational aspects of multi-organizational integrated care initiatives.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".