Facilitating clinical decision-making about the use of virtual reality within paediatric motor rehabilitation: Application of a classification framework
Bibliographic record
Abstract
AIM: Multiple virtual reality (VR) systems are used to improve motor function in children and youth with neurological impairments. Galvin and Levac developed a classification framework to facilitate clinical decision-making about VR system use. This paper applies the classification framework to identify its strengths and limitations. METHOD: The classification framework is applied to three case studies where therapists may consider using VR with children involved in paediatric rehabilitation programmes. RESULTS: The classification framework identified VR systems that met each child's individual needs. The relevance of each category to clinical decision-making varied depending on each child's goals. Categories requiring further development and suggestions for additional categories are discussed. CONCLUSIONS: The classification framework facilitates child-centred decision-making about the use of VR as a therapeutic intervention. It has shown initial utility but requires further validation with clinicians working in a variety of clinical settings and with a range of client populations.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".