A knowledge translation intervention to enhance clinical application of a virtual reality system in stroke rehabilitation
Bibliographic record
Abstract
Despite increasing evidence for the effectiveness of virtual reality (VR)-based therapy in stroke rehabilitation, few knowledge translation (KT) resources exist to support clinical integration. KT interventions addressing known barriers and facilitators to VR use are required. When environmental barriers to VR integration are less amenable to change, KT interventions can target modifiable barriers related to therapist knowledge and skills. A multi-faceted KT intervention was designed and implemented to support physical and occupational therapists in two stroke rehabilitation units in acquiring proficiency with use of the Interactive Exercise Rehabilitation System (IREX; GestureTek). The KT intervention consisted of interactive e-learning modules, hands-on workshops and experiential practice. Evaluation included the Assessing Determinants of Prospective Take Up of Virtual Reality (ADOPT-VR) Instrument and self-report confidence ratings of knowledge and skills pre- and post-study. Usability of the IREX was measured with the System Usability Scale (SUS). A focus group gathered therapist experiences. Frequency of IREX use was recorded for 6 months post-study. Eleven therapists delivered a total of 107 sessions of VR-based therapy to 34 clients with stroke. On the ADOPT-VR, significant pre-post improvements in therapist perceived behavioral control (p = 0.003), self-efficacy (p = 0.005) and facilitating conditions (p =0.019) related to VR use were observed. Therapist intention to use VR did not change. Knowledge and skills improved significantly following e-learning completion (p = 0.001) and was sustained 6 months post-study. Below average perceived usability of the IREX (19th percentile) was reported. Lack of time was the most frequently reported barrier to VR use. A decrease in frequency of perceived barriers to VR use was not significant (p = 0.159). Two therapists used the IREX sparingly in the 6 months following the study. Therapists reported that client motivation to engage with VR facilitated IREX use in practice but that environmental and IREX-specific barriers limited use. Despite increased knowledge and skills in VR use, the KT intervention did not alter the number of perceived barriers to VR use, intention to use or actual use of VR. Poor perceived system usability had an impact on integration of this particular VR system into clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".