Virtual Reality and Active Videogame-Based Practice, Learning Needs, and Preferences: A Cross-Canada Survey of Physical Therapists and Occupational Therapists
Bibliographic record
Abstract
OBJECTIVE: Describe the clinical use of virtual reality (VR)/active videogaming (AVG) by physical therapists (PTs) and occupational therapists (OTs) in Canada, identify usage barriers and facilitators, evaluate factors that predict intention to use VR/AVGs, and determine therapists' learning needs. DESIGN: Cross-sectional survey. MATERIALS AND METHODS: Online survey of therapists in Canada who were members of 1 of 26 professional PT or OT colleges or associations using the Assessing Determinants Of Prospective Take-up of Virtual Reality (ADOPT-VR2) Instrument. RESULTS: We received 1071 (506 PTs, 562 OTs, 3 dual-trained) responses. Forty-six percent had clinical VR/AVG experience; only 12% reported current use, with the Wii being the most clinically accessible (41%) system. Therapists used VR/AVGs primarily in rehabilitation (32%) and hospital (29%) settings, preferentially targeting balance (39.3%) and physical activity (19.8%) outcomes. Stroke (25.8%), brain injury (15.3%), musculoskeletal (14.9%), and cerebral palsy (10.5%) populations were most frequently treated. Therapists with VR/AVG experience rated all ADOPT-VR2 constructs more highly than did those without experience (P < 0.001). Factors predictive of intention to use VR included the technology's perceived usefulness and therapist self-efficacy in VR/AVG use (P < 0.001). Highest-rated barriers to VR/AVG use were lack of funds, space, time, support staff, and appropriate clients, whereas facilitators included client motivation, therapist knowledge, and management support. Most (76%) respondents were interested in learning more. CONCLUSION: Understanding use, predictors of use, and learning needs is essential for developing knowledge translation initiatives to support clinical integration of VR/AVGs. Results of this first national survey will inform the creation of resources to support therapists in this field.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".