Virtual Reality and Active Video Game Integration within an Intensive Bimanual Therapy Program for Children with Hemiplegia
Bibliographic record
Abstract
AIMS: To describe the nature of custom and non-custom virtual reality and active video game (VR/AVG) implementation within a Hand-Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE) intervention program for children with hemiplegia. METHODS: Six children aged 8-11 years participated in a 10-day HABIT-ILE intervention (65 h; 6.5 planned VR/AVG hours). VR/AVG implementation details were recorded daily and summarized with descriptive statistics; active motor engagement was quantified as minutes of active game participation. Post-intervention interviews with interventionists were analyzed with qualitative content analysis. RESULTS: On average, participants received 79% of the planned VR/AVG dosage (314/400 planned minutes, range 214-400 min), of which the per-session active motor engagement average was 68% (27 min, SD 12 min). Participation involved equivalent amounts of custom (49%) and non-custom (51%) VR/AVG system use. Material and verbal adaptations facilitated alignment with HABIT-ILE principles. Interventionists identified type of task (gross versus fine motor), children's perceived motivation, and VR/AVG attributes as factors influencing active motor engagement and alignment with HABIT-ILE principles. CONCLUSIONS: Describing individual and technological challenges of VR/AVG integration within HABIT-ILE can advance knowledge about VR/AVG use in intensive interventions and identify directions for subsequent research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".