Incorporating the Concept of Relevance in Clinical Rehabilitation Research and Its Reviews May Improve Uptake by Stakeholders
Bibliographic record
Abstract
ABSTRACT: The "relevance" of research to stakeholders is an important factor in influencing the uptake of new knowledge into practice; however, this concept is neither well defined nor routinely incorporated in clinical rehabilitation research. Developing a uniform definition, measurement standards, stakeholder engagement strategies, and guiding frameworks that bolster relevance may help incorporate the concept as a key element in research planning and design. This article presents a conceptual argument for why relevance matters, proposes a working definition, and suggests strategies for operationalizing the construct in the context of clinical rehabilitation research. We place special emphasis on the importance of promoting relevance to patients, caregivers, and clinicians and provide preliminary frameworks and innovative study designs that can assist clinical rehabilitation researchers in doing so. We argue that researchers who include a direct statement regarding why and to whom a study is relevant and who incorporate considerations of relevance throughout all phases of study design produce more useful research for patients, caregivers, and clinicians, increasing its chance of uptake into practice. Consistent consideration of relevance, particularly to nonacademic audiences, during the conceptualization, study design, presentation, and dissemination of clinical rehabilitation research may promote the uptake of findings by patients, caregivers, and providers.
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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 | 0.800 | 0.893 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.025 | 0.036 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".