Connecting with clinicians: Opportunities to strengthen rehabilitation research
Bibliographic record
Abstract
PURPOSE: This article examines the distinctive opportunities and challenges involved in connecting with clinicians to strengthen rehabilitation research. METHOD: The relevant literature on various factors that link researchers and clinicians is summarized and discussed. RESULTS: Links between researchers and clinicians are demonstrated by evidence-based practice, common conceptual background and the development of research capacity. Sustainable partnerships can evolve throughout the research process by using various enduring strategies such as experts' committee as well as novel approaches like communities of practice. CONCLUSION: This paper reflects the conviction that reducing the gap between research and clinical practice will be facilitated by implementing partnerships originating from both researchers and clinicians.
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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 | 0.325 | 0.452 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.033 | 0.057 |
| Open science | 0.007 | 0.053 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".