Report: The Canadian Best Practices Educational Toolkits: translating evidence-based stroke recommendations into practical implementation resources
Bibliographic record
Abstract
The Canadian Best Practice Recommendations for Stroke Care are intended to reduce variations in stroke care and facilitate closure of the gap between evidence and practice (Lindsay et al., 2010). The publication of best practice recommendations is only the beginning of this process. The guidelines themselves are not sufficient to change practice and increase consistency in care. Therefore, a key objective of the Canadian Stroke Network (CSN) Best Practices Working Group (BPWG) is to encourage and facilitate ongoing professional development and training for health care professionals providing stroke care. This is addressed through a multi-factorial approach to the creation and dissemination of inter-professional implementation tools and resources. The resources developed by CSN span pre-professional education, ongoing professional development, patient education and may be used to inform systems change. With a focus on knowledge translation, several inter-professional point-of-care tools have been developed by the CSN in collaboration with numerous professional organizations and expert volunteers. These resources are used to facilitate awareness, understanding and applications of evidence-based care across stroke care settings. Similar resources are also developed specifically for stroke patients, their families and informal caregivers, and the general public. With each update of the Canadian Best Practice Recommendations for Stroke Care, the BPWG and topic-specific writing groups propose priority areas for ongoing resource development. In 2010, two of these major educational initiatives were undertaken and recently completed—one to support continuing education for health care professionals regarding secondary stroke prevention and the other to educate families, informal caregivers and the public about pediatric stroke. This paper presents an overview of these two resources, and we encourage health care professionals to integrate these into their personal learning plans and tool kits for patients.
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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 | 0.055 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.042 |
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".