Exploring the emerging concept of precision rehabilitation: a qualitative study
Bibliographic record
Abstract
Abstract Purpose This descriptive qualitative study explored stakeholders’ perspectives on precision rehabilitation concepts, barriers, facilitators, and future directions as part of a convergent mixed methods scoping review. Materials and methods Sixteen clinicians, administrators, and researchers from three North American tertiary care rehabilitation centers were recruited using convenience and snowball sampling to participate in individual semi-structured interviews. Conventional qualitative content analysis followed a deductive thematic approach based on predetermined categories. Results Analyses revealed three main themes: 1) Although precision rehabilitation shares foundational concepts with precision medicine, there are certain elements, such as personalization, that are uniquely expressed; 2) Rehabilitation-specific facilitators to precision approaches include the use of unobtrusive technology to collect large amounts of data in real-world contexts, while barriers include rehabilitation’s typically small, heterogeneous sample sizes; and 3) The future of precision rehabilitation will require collaborative data-sharing to focus on determining care trajectories that enhance functional outcomes. Conclusion Findings provide the first qualitative synthesis of stakeholder perspectives to complement quantitative evidence and inform the emerging field of precision 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 | 0.059 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".