Development and implementation of the Ontario Stroke System: the use of evidence
Bibliographic record
Abstract
INTRODUCTION: The Ontario Stroke System was developed to enhance the quality and continuity of stroke care provided across the care continuum. RESEARCH OBJECTIVE: To identify the role evidence played in the development and implementation of the Ontario Stroke System. METHODS: This study employed a qualitative case study design. In-depth interviews were conducted with six members of the Ontario Stroke System provincial steering committee. Nine focus groups were conducted with: Regional Program Managers, Regional Education Coordinators, and seven acute care teams. To supplement these findings interviews were conducted with eight individuals knowledgeable about national and international models of integrated service delivery. RESULTS: Our analyses identified six themes. The first four themes highlight the use of evidence to support the process of system development and implementation including: 1) informing system development; 2) mobilizing governmental support; 3) getting the system up and running; and 4) integrating services across the continuum of care. The final two themes describe the foundation required to support this process: 1) human capacity and 2) mechanisms to share evidence. CONCLUSION: This study provides guidance to support the development and implementation of evidence-based models of integrated service delivery.
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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 | 0.085 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 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".