Data-Driven Discovery of Predictors of Virtual Reality Safety and Sense of Presence for Children With Autism Spectrum Disorder: A Pilot Study
Bibliographic record
Abstract
Virtual reality (VR) offers children with autism spectrum disorder (ASD) an inexpensive and motivating medium to learn and practice skills in a personalized, controlled, and safe setting; however, outcomes of VR interventions can vary widely. In particular, there is a need to understand the predictors of VR experience in children with ASD to inform the design of these interventions. To address this gap, a sample of children with ASD (n=35, mean age: 13.0 ± 2.6 years; 10 female) participated in a pilot study involving an immersive VR experience delivered through a head-mounted display. A data-driven approach was used to discover predictors of VR safety and sense of presence among a range of demographic and phenotypic user characteristics. Our results suggest that IQ may be a key predictor of VR sense of presence and that anxiety may modify the association between IQ and sense of presence. In particular, in low-anxiety participants, IQ was linearly related to experienced spatial presence and engagement, whereas, in high-anxiety participants, this association followed a quadratic form. The results of this pilot study, when replicated in larger samples, will inform the design of future studies on VR interventions for children with ASD.
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".