Using a Virtual Community of Practice to Support Stroke Best Practice Implementation: Mixed Methods Evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND Successful best practice implementation is influenced by access to peer support and knowledge exchange. The Toronto Stroke Networks Virtual Community of Practice, a secure social media platform, is a knowledge translation tool supporting dissemination and adoption of stroke best practices for interprofessional stroke stakeholders. OBJECTIVE The aim of this study is to evaluate the use of a virtual community of practice (VCoP) in supporting regional stroke care best practice implementation in an urban context. METHODS A mixed methods approach was used. Qualitative data were collected through focus groups and interviews with stroke care provider members of the VCoP working in acute and rehabilitation settings. Thematic analysis was completed, and the Wenger Value Creation Model and developmental evaluation were used to reflect practice change. Quantitative data were collected and analyzed using website analytics on VCoP use. RESULTS A year after implementation, the VCoP had 379 members. Analysis of web analytics data and transcripts from focus groups and interviews conducted with 26 VCoP members indicated that the VCoP provided immediate value in supporting user networking, community activities, and interactions. Skill acquisition and changes in perspective acquired through discussion and project work on the VCoP were valued by members, with potential value for supporting practice change. Learning about new stroke best practices through the VCoP was a starting point for individuals and teams to contemplate change. CONCLUSIONS These findings suggest that the VCoP supports the early stages of practice change and stroke best practice implementation. Future research should examine how VCoPs can support higher levels of value creation for implementing stroke best practices.
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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 | 0.131 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".