Moving stroke rehabilitation research evidence into clinical practice: Consensus-based core recommendations from the Stroke Recovery and Rehabilitation Roundtable
Bibliographic record
Abstract
Moving research evidence to practice can take years, if not decades, which denies stroke patients and families from receiving the best care. We present the results of an international consensus process prioritizing what research evidence to implement into stroke rehabilitation practice to have maximal impact. An international 10-member Knowledge Translation Working Group collaborated over a six-month period via videoconferences and a two-day face-to-face meeting. The process was informed from surveys received from 112 consumers/family members and 502 health care providers in over 28 countries, as well as from an international advisory of 20 representatives from 13 countries. From this consensus process, five of the nine identified priorities relate to service delivery (interdisciplinary care, screening and assessment, clinical practice guidelines, intensity, family support) and are generally feasible to implement or improve upon today. Readily available website resources are identified to help health care providers harness the necessary means to implement existing knowledge and solutions to improve service delivery. The remaining four priorities relate to system issues (access to services, transitions in care) and resources (equipment/technology, staffing) and are acknowledged to be more difficult to implement. We recommend that health care providers, managers, and organizations determine whether the priorities we identified are gaps in their local practice, and if so, consider implementation solutions to address them to improve the quality of lives of people living with stroke.
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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 | 0.469 | 0.499 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.023 | 0.029 |
| Research integrity | 0.038 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 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".