Supporting Best Practice Stroke Care Through a Virtual Community of Practice : Challenges, Successes, and Sustainability
Bibliographic record
Abstract
BackgroundThe Toronto Stroke Networks (TSNs) Virtual Community of Practice (VCoP) provides a web-based knowledge translation (KT) strategy to support cross-continuum interprofessional collaborative learning and system-wide integration of stroke best practices. This work aims to:1) examine current VCoP use to support stroke best practices, and 2) share challenges and successes in implementing this KT strategy. MethodUsing a developmental evaluation approach, a review of VCoP use focused on three domains: 1) trend analysis in website analytics and user-generated content; 2) use of VCoP groups in supporting project-based work for regional stroke system planning; and 3) stakeholder feedback. ResultsVCoP membership has increased to 599 current users over 5 years, including a diverse clinical, community, and academic membership. To date, there are 125 resources and 549 user-generated comments related to stroke best practices on the VCoP. Of the 160 discussion groups, there are currently approximately 10 project-based discussions directly linked to regional stroke system planning. Strategies used to support engagement include: 1) facilitation of discussions by peer champions, 2) actively seeking regional expertise to answer practice-related questions, 3) weekly email digests of site activity, and 4) integrating stakeholder feedback for iterative improvement. ConclusionThe VCoP provides an interprofessional collaborative learning platform to support wide-spread KT for stroke best practices. Work is underway to examine optimizing system-wide adoption of the VCoP and impact on practice change. Knowledge created in this work supports the sustainability and spread of the VCoP to other regions and stroke systems.
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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 | 0.041 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".