Stroke rehabilitation adaptive approaches: A theory-focused scoping review
Bibliographic record
Abstract
BACKGROUND: Stroke rehabilitation consists of restorative and adaptive approaches. Multiple adaptive approaches exist. AIMS/OBJECTIVES: The objective of this study was to develop a framework for categorising adaptive stroke rehabilitation interventions, based on underlying theory. MATERIAL AND METHODS: We searched multiple databases to April 2020 to identify studies of interventions designed to improve participation in valued activities. We extracted the name of the intervention, underlying explicit or implicit theory, intervention elements, and anticipated outcomes. Using this information, we proposed distinct groups of interventions based on theoretical drivers. RESULTS: Twenty-nine adaptive interventions were examined in at least one of 77 studies. Underlying theories included Cognitive Learning Theory, Self-determination Theory, Social Cognitive Theory, adult learning theories, and Psychological Stress and Coping Theory. Three overarching theoretical drivers were identified: learning, motivation, and coping. CONCLUSIONS: At least 29 adaptive approaches exist, but each appear to be based on one of three underlying theoretical drivers. Consideration of effectiveness of these approaches by theoretical driver could help indicate underlying mechanisms and essential elements of effective adaptive approaches. SIGNIFICANCE: Our framework is an important advance in understanding and evaluating adaptive approaches to stroke 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 | 0.028 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.037 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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; 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".