Bridging the Gap: Virtual Reality as a Tool for De-Escalation Training in Disability Support Settings
Bibliographic record
Abstract
Managing complex behaviors in disability support settings requires competent de-escalation skills. However, the current training methods often lack sufficient opportunities for realistic practice. This study details a three-stage development and evaluation of a virtual reality (VR) application for disability support staff to safely build de-escalation skills through simulated interactions. The first phase involved creating VR prototype scenarios depicting escalations with adolescent clients. Next, 12 disability support experts conducted content validation by refining the scenarios to confirm appropriateness and realism. Finally, a pilot study tested the tool’s usability and examined the initial construct validity in 20 participants. The prototype achieved high usability ratings (average 81.0 (SD 10.7) on the System Usability Scale). Additionally, a positive correlation between VR performance and empathy levels, as measured using the Toronto Empathy Questionnaire, was found (Pearson’s r = 0.487, p = 0.035). The promising results highlight the VR application’s potential as a transformative training tool. Future research should expand scenario diversity and compare VR with traditional methods to establish its efficacy in diverse settings and offer a path to enhance staff and student capabilities in challenging environments.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".