An Answer to a Call for Dialogue on Advancing Rehabilitation Research
Bibliographic record
Abstract
We wholeheartedly agree with the ideas raised by Collins in his letter to the editor in the December 2005 issue,1 in which he speculates about why much of the research that is conducted in physical therapy is not relevant to clinicians. He declares that physical therapist practice is complex; a notion that, we believe, would be supported by most practicing clinicians. He contends, and we agree, that dynamic systems theories are useful in conceptualizing the multiple factors that contribute to the complexities of clinical situations. We also concur that reductionist approaches cannot provide evidence to support all aspects of physical therapist practice. Along with others,2,3 we advocate for an approach that makes use of, rather than eliminates, sources of variation to study phenomena holistically. Physical therapists need research evidence that reflects the “messiness” of their clinical realities. From our perspective, for research to become meaningful, it needs to go beyond consideration of complex systems to include both design and process. We believe that our approach: (1) acknowledges and evaluates complexity, (2) is consistent with client- and family-centered care, (3) brings clinicians and scientists together in collaboration, (4) bridges the gap between conducting research and incorporating it into practice, and (5) promotes embodiment of evidence-based practice. We acknowledge that this approach has challenges, ones that we believe can be overcome with enthusiastic collaboration.
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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 | 0.192 | 0.295 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.025 | 0.077 |
| Scholarly communication | 0.038 | 0.120 |
| Open science | 0.013 | 0.038 |
| Research integrity | 0.116 | 0.168 |
| Insufficient payload (model declined to judge) | 0.024 | 0.010 |
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".