Abstract W P276: Developing Excellence in Stroke Care Through Knowledge Building and Interprofessional Collaborative Processes
Bibliographic record
Abstract
Background: As part of a system-wide collaborative change process, a comprehensive education needs assessment was conducted with 15 acute and rehabilitation stroke teams within the Toronto Stroke Networks (TSNs). Evidence-informed educational and knowledge translation (KT) approaches were planned to bridge identified gaps for successful implementation of stroke best practices and excellence in patient care. Purpose: The purpose of this collaborative KT process is to enable successful learning and sustained adoption of best practices for improved patient care. Methods: The Education and KT plan was launched in collaboration with local organization stroke champions to integrate feedback and stimulate engagement. Health care providers (HCPs) were oriented to the newly launched online platform, the TSNs Virtual Community of Practice (VCoP), for access to resources, ongoing knowledge exchange, and cross-system collaboration from both clinical and interprofessional perspectives. Co-creation of stroke core foundational posters facilitated team and individual competencies for select best practices and provided HCPs with an enhanced networking opportunity building virtual competence for online collaboration. Completed best practice posters and introduction of a small group learning guide and peer mentorship initiative will be launched to facilitate a team approach and enable practice change. A scheduled interprofessional event will reinforce online networking. Results: Formative evaluation of the online format has led to improved utility of the VCoP. Member checking with stroke champions has resulted in an adapted communication process with regular prompting to enable progress. Development of a strategic approach for increasing front line nurse participation was also highlighted. Further evaluation will be available at time of publishing. Conclusions: This innovative approach to enhance patient care has shown promise as an effective collaborative process. Iterative member checking with opportunity to adapt locally is key for successful system wide change.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".