Virtual Reality at Workplace for Autistic Employees: Preliminary Results of Physiological-Based Well-Being Experience
Bibliographic record
Abstract
Emotional health problems in the workplace often hinder the integration and retention of autistic employees (AE), a challenge identified in many sectors. Recent literature highlights the consequences of these problems, such as burnout leading to reduced productivity and resignation. Previous research supports the effectiveness of virtual reality (VR) for training a variety of specific skills (e.g. riding a bus or plane travel), as well as more complex social skills, such as emotion recognition and functional communication. In addition, existing studies on using physiological self-monitoring in AE training offer a promising approach to promoting improved emotional health. The present paper reports on implementing a VR system that simulates workplace training and integration and enables real-time monitoring of three physiological signals, in five post-secondary autistic students. Using an Oculus Quest 2 and non-clinical grade sensors, the researchers delivered the VR intervention over three days to each participant. At the end of these interventions, the researchers measured the perceived satisfaction of these integrated systems, based on several technological criteria, on a 5-point scale. The integrated system received an overall rating of 4, suggesting its likelihood of acceptance and use. A preliminary analysis of a participant’s physiological responses to this VR intervention is also presented. This preliminary report suggests the efficacy of a VR workplace simulation and physiological self-monitoring in promoting emotional well-being and basic task training for post-secondary AE. The researchers’ observations and the proposal of a theoretical framework to enhance real-time emotional communication based on physiological markers for AE are also discussed.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".