Best practise use in stroke rehabilitation: From trials and tribulations to solutions!
Bibliographic record
Abstract
PURPOSE: This article explores the use of best practises among stroke rehabilitation professionals, salient barriers that influence their knowledge uptake/application and effective knowledge translation (KT) strategies that meet the needs of this clinician group. METHOD: Relevant literature on evidence-based practise in stroke rehabilitation and the use of KT strategies among rehabilitation professionals is summarised and discussed. RESULTS: Although adherence to rehabilitation guidelines translates into improved patient outcomes, best practises are not routinely applied by clinicians when treating individuals with a stroke. Lack of protected work time to search and appraise the research literature is by far the largest organisational barrier to knowledge uptake/application. Personal barriers, such as the lack of confidence and skills to interpret, synthesise and apply research findings, also limit clinicians' uptake of best practises. Studies involving rehabilitation professionals found that active KT strategies were more effective than passive strategies to produce change in their evidence-based knowledge and practise behaviours. As such, interactive e-learning resources are likely to be a relevant KT solution to meet rehabilitation professionals' specific learning needs, guide their clinical decision-making and ultimately increase their best practise behaviours. CONCLUSION: We have the knowledge of best practises in stroke rehabilitation, a means to disseminate that knowledge internationally through interactive e-learning resources, and information about effective KT interventions. With these opportunities in place, rehabilitation professionals can expand their capacity by adopting stroke best practises and producing better outcomes 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 | 0.239 | 0.538 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 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; 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".