UPPER EXTREMITY MANAGEMENT POST-STROKE u2013 MOVING EVIDENCE-BASED PRACTICE INTO ACTION
Bibliographic record
Abstract
UPPER EXTREMITY MANAGEMENT POST-STROKE u2013 MOVING EVIDENCE-BASED PRACTICE INTO ACTIONMariu00e8ve Legrand, BSc. (OT); Melissa Austin, MSc. (OT)The Canadian Stroke Best Practice Recommendations (CSBPR) provide guidelines for stroke prevention and management, while promoting optimal recovery for stroke survivors. While this information is readily available, the real-life challenge is the dissemination and implementation of these guidelines. Frontline clinicians rarely have time to review such recommendations when they have full and busy caseloads. Efficient Knowledge Translation (KT) is therefore vital to reduce practice variations while striving to provide the best care.In July 2016, a working group at North Vancouver's Lions Gate Hospital (LGH) developed templates based on 5 upper extremity management techniques.The templates were intended to provide frontline therapists with practical information, including evidence, use, contraindications, equipment, protocols, progression and outcome measures. Although these templates were immediately distributed, it remained unclear whether their availability had had any impact on practice.A KT project was therefore implemented to promote the use of these templates over a specific 6 month period, under guidance and supervision of resource therapists, practice leads and the stroke clinician at LGH. Occupational therapists and physiotherapists working with stroke patients were educated on the templates and overall best-practice guidelines, and asked to record their intervention strategies with stroke patients who had upper extremity impairment.The goal of this KT project was to raise therapist awareness regarding the CSBPR, by facilitating implementation and use of specific treatment techniques for upper extremity management. It was found to be an efficient way to promote learning and increase therapist confidence in integrating best practice guidelines into a clinical setting.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".