Promoting Therapists’ Use of Motor Learning Strategies within Virtual Reality-Based Stroke Rehabilitation
Bibliographic record
Abstract
PURPOSE: Therapists use motor learning strategies (MLSs) to structure practice conditions within stroke rehabilitation. Virtual reality (VR)-based rehabilitation is an MLS-oriented stroke intervention, yet little support exists to assist therapists in integrating MLSs with VR system use. METHOD: A pre-post design evaluated a knowledge translation (KT) intervention incorporating interactive e-learning and practice, in which 11 therapists learned how to integrate MLSs within VR-based therapy. Self-report and observer-rated outcome measures evaluated therapists' confidence, clinical reasoning and behaviour with respect to MLS use. A focus group captured therapists' perspectives on MLS use during VR-based therapy provision. RESULTS: The intervention improved self-reported confidence about MLS use as measured by confidence ratings (p <0.001). Chart-Stimulated Recall indicated a moderate level of competency in therapists' clinical reasoning about MLSs following the intervention, with no changes following additional opportunities to use VR (p = .944). On the Motor Learning Strategy Rating Instrument, no behaviour change with respect to MLS use was noted (p = 0.092). Therapists favoured the strategy of transferring skills from VR to real-life tasks over employing a more comprehensive MLS approach. CONCLUSION: The KT intervention improved therapists' confidence but did not have an effect on clinical reasoning or behaviour with regard to MLS use during VR-based therapy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".