Integrating virtual reality video games into practice: Clinicians' experiences
Bibliographic record
Abstract
The Nintendo Wii is a popular virtual reality (VR) video gaming system in rehabilitation practice and research. As evidence emerges related to its effectiveness as a physical therapy training method, clinicians require information about the pragmatics of its use in practice. The purpose of this descriptive qualitative study is to explore observations and insights from a sample of physical therapists (PTs) working with children with acquired brain injury regarding practical implications of using the Wii as a physical therapy intervention. Six PTs employed at a children's rehabilitation center participated in semi-structured interviews, which were transcribed and analyzed using content analysis. Two themes summarize the practical implications of Wii use: 1) technology meets clinical practice; and 2) onus is on the therapist. Therapists described both beneficial and challenging implications arising from the intersection of technology and practice, and reported the personal commitment required to orient oneself to the gaming system and capably implement this intervention. Findings include issues that may be relevant to professional development in a broader rehabilitation context, including suggestions for the content of educational initiatives and the need for institutional support from managers in the form of physical resources for VR implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".