Perspectives on evidence based practice in ABI rehabilitation. “Relevant Research”: Who decides?
Bibliographic record
Abstract
Growing evidence suggests that acquired brain injury (ABI) rehabilitation and research should be guided by a philosophy that focuses on: restoration, compensation, function and participation in all aspects of daily life. Such a broad, more pluralistic approach influences ABI rehabilitation research at a number of levels, including both the generation of evidence, and in searching for, critiquing and applying the evidence to practice. The objective of evidence based medicine/practice (EBM/EBP) is to apply and integrate clinical expertise with evidence gained through systematic research and scientific inquiry to medical/clinical practice. While there is abundant literature debating the practical and sociological implications of EBP, there has been limited examination of EBP within the inherently complex nature of ABI rehabilitation and rehabilitation research. This paper provides a framework for clinical decision making regarding evidence based practice in the context of ABI rehab including: 1. A discussion of the purpose of evidence based practice, 2. Levels of evidence relevant to ABI rehabilitation research, and 3. A rationale for incorporating a broader, more pluralistic concept of evidence or "person-centred EBP". We conclude with a series of key questions for the evaluation and application of systematic reviews of the evidence in the context of ABI rehabilitation.
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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 | 0.301 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.013 | 0.141 |
| Scholarly communication | 0.046 | 0.050 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.060 | 0.056 |
| Insufficient payload (model declined to judge) | 0.004 | 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".