Speech and Language Therapists’ Perspectives of Virtual Reality as a Clinical Tool for Autism: Cross-Sectional Survey
Bibliographic record
Abstract
Background: Persistent difficulties with social skills form part of the diagnostic criteria for autism and in the past have required speech and language therapy (SLT) management. However, many speech and language therapists are moving toward neuro-affirmative practices, meaning that social skills approaches are now becoming redundant. Research demonstrates that virtual reality (VR) interventions have shown promise in overcoming challenges and promoting skill generalization for autistic children; however, the majority of these focus on social skills interventions. While VR is emerging as an SLT intervention, its application for autism remains unexamined in clinical practice. Objective: This research aimed to examine speech and language therapists' knowledge and attitudes toward immersive VR as a clinical tool for autistic children and explore the reasons for its limited integration into clinical practice. Methods: A web-based cross-sectional survey was available from April 3, 2023 to June 30, 2023. The survey, consisting of 23 questions, focused on VR knowledge, attitudes, and the support required by speech and language therapists to incorporate VR into clinical practice. Dissemination occurred through the Royal College of Speech and Language Therapists Clinical Excellence Networks to recruit speech therapists specializing in autism. Results: Analysis included a total of 53 responses from the cross-sectional survey. Approximately 92% (n=49) of speech and language therapists were aware of VR but had not used it, and 1.82% (n=1) had used VR with autistic children. Three key themes that emerged were (1) mixed general knowledge of VR, which was poor in relation to applications for autism; (2) positive and negative attitudes toward VR, with uncertainty about autism specific considerations for VR; and (3) barriers to adoption were noted and speech and language therapists required an improved neuro-affirming evidence base, guidelines, and training to adopt VR into clinical practice. Conclusions: While some speech and language therapists perceive VR as a promising intervention tool for autistic children, various barriers must be addressed before its full integration into the clinical toolkit. This study establishes a foundation for future co-design, development, and implementation of VR applications as clinical tools for autistic children., This study is the first to explore clinical implementation factors for the use of VR in SLT field, specifically with autistic children. Poor autism-specific VR knowledge, and mixed attitudes toward VR, highlight that specific barriers must be addressed before the technology can successfully integrate into the SLT clinical toolkit., Speech and language therapists require support from employers, funding, a robust neuro-affirming evidence base, and education and training to adopt VR into practice. Recommendations for a SLT VR education and training program for use with autistic children, are provided.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".