From What We Know to What We Do: Translating Stroke Rehabilitation Research into Practice
Bibliographic record
Abstract
Despite the recent advances in stroke rehabilitation research, the translation of research evidence into practice remains a challenge. The purpose of this article is to communicate practical experience and describe research methodologies used to promote change and implementation of stroke rehabilitation research in three international settings. In England, the development of an evidence-based consensus document, combined with qualitative and quantitative methods, was used to promote practice change in community-based stroke services. The Canadian research program involved synthesis of evidence, creation of user friendly information, and development of multimodal knowledge transfer strategies to promote change at an individual clinician level. Australian researchers followed a multistep process, involving audit and feedback, identification of barriers, and tailored education to improve implementation of one clinical guideline recommendation. Reducing the evidence-practice gap requires the development of active management strategies. This article highlights the importance of close collaboration between stakeholders - both in terms of the transfer of evidence into clinical practice and for optimizing future Phase IV implementation research endeavours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".