Knowledge Translation in Rehabilitation: A Shared Vision
Bibliographic record
Abstract
PURPOSE: Advances in rehabilitation provide the infrastructure for research and clinical data to improve care and patient outcomes. However, gaps between research and practice are prevalent. Knowledge translation (KT) aims to decrease the gap between research and its clinical use. This special communication summarizes KT-related proceedings from the 2016 IV STEP conference, describes current KT in rehabilitation science, and provides suggestions for its application in clinical care. SUMMARY OF KEY POINTS: We propose a vision for rehabilitation clinical practice and research that includes the development, adaptation, and implementation of evidence-based practice recommendations, which will contribute to a learning health care system. A clinical research culture that supports this vision and methods to engage key stakeholders to innovate rehabilitation science and practice are described. CONCLUSIONS: Through implementation of this vision, we can lead an evolution in rehabilitation practice to ultimately prevent disabilities, predict better outcomes, exploit plasticity, and promote participation.
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 | 0.382 | 0.172 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.018 | 0.107 |
| Scholarly communication | 0.064 | 0.068 |
| Open science | 0.012 | 0.071 |
| Research integrity | 0.044 | 0.050 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".