Abstract T P345: Knowledge Translation in Stroke Care in Toronto: Impact of member informed improvements to a Virtual Community of Practice
Bibliographic record
Abstract
Background: The Toronto Stroke Networks Virtual Community of Practice (VCoP) is a secure social media platform, part of an evidence-informed knowledge translation (KT) approach to bridge identified gaps in knowledge for successful implementation of stroke best practices. In 2014, a formative developmental evaluation examined members’ perspectives about the value of the VCoP. Key themes emerged: networking, response time, validation of practice, and prioritization of best practices. Themes informed subsequent improvements: enhanced search functions, ability to share documents within a discussion forum, and formal facilitation. Objective: To examine the impact of VCoP enhancements on KT using value cycles 1-5 of the Wenger (2011) model: 1.immediate value: activity / interactions 2.potential value: knowledge capital 3.applied value: changes in practice 4.realized value: performance improvement 5.reframing value: redefining success Methods: Each theme was explored to identify potential improvement opportunities. Discussion with developers informed feasibility. Three priorities were identified: enhanced search functions, ability to share documents within discussion forums, and formal facilitation to prompt discussion. VCoP members were informed of improvements. Qualitative (members’ narratives) and quantitative indicators (e.g. response times, number of discussion threads) were examined. Results: Preliminary results indicate that formal facilitation supports increased value in activity and interaction (Cycle 1) on the VCoP. Within the first month of formal facilitation, posts in discussion forums increased from 3.75/month (FY13/14) to 11; average response times decreased from 18 to 8 days. Early examination of narratives indicates themes aligning with Cycle 2: members express value in VCoP information. Further evaluation, in progress, examines the impact of improved search functions and document upload capabilities including analysis of themes aligning with the Wenger Cycles, number of uploaded documents, and search effectiveness. Conclusions: Member informed enhancements to the VCoP contribute to KT through enhanced activity, member interactions, and knowledge capital.
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".