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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.077 |
| Open science | 0.015 | 0.033 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".