USING A KNOWLEDGE-TO-ACTION CYCLE TO HELP IMPROVE ADHERENCE TO CANADIAN STROKE BEST PRACTICE RECOMMENDATIONS FOR REHABILITATION AND INCREASE REHABILITATION INTENSITY
Bibliographic record
Abstract
BackgroundElisabeth Bruyu00e8re (EB) Hospital and the Champlain Regional Stroke Network established a partnership to help improve adherence to Canadian Stroke Best Practice Recommendations (CSBPR) for rehabilitation and increase rehabilitation intensity (RI) at EB. The Knowledge-to-Action (KTA) Cycle1 was utilized to guide this process.MethodstCSBPRs and Quality Based Procedures informed the knowledge creation funnel for this partnership. Know-do gaps (KDG) were determined by surveying clinicians and management in order to identify barriers/facilitators to knowledge use. Systemic, process, educational, and human resource issues were identified and adapted to the local context in order to select, tailor, and implement interventions, including education and partnership days. Some facets of the Partnership are ongoing including: monitoring, evaluating, and sustaining knowledge use. ResultsMany leading practices were already in place on EBu2019s stroke rehab unit. KTA approach generated ideas to improve adherence to CSBPRs, increase efficiency and RI. Forty-two opportunities for improvement were identified and grouped into seven categories. Some changes have been implemented or are in process, including: improving rounds and communication, refresher education on RI, streamlining admission assessments and developing new therapy schedule.Conclusions/ImplicationsKTA approach enabled a systematic method to identifying facilitators/barriers to implementation of CSBPRs and achieving RI. KTA facilitated the Partnership to achieve its objectives step-by-step in a non-punitive manner and promoted active engagement at all organizational levels, from clinicians to directors. 1.tStraus S, Tetroe J, Graham ID, editors. Knowledge translation in health care: moving from evidence to practice. John Wiley & Sons; 2013 May 31.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.030 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.021 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".