Development and Validation of Virtual Reality Scenarios to Improve Disability Awareness among Museum Employees
Bibliographic record
Abstract
To improve inclusion of persons with disabilities (PWD), it is important to create suitable physical and social environments. This can be done by improving awareness about disability, specifically for employees working in the service and cultural sectors. Virtual reality (VR) simulation can be advantageous by providing an engaging experience highlighting physical accessibility issues, as well as social interactions with virtual avatars. This study’s objective was to validate the content of two disability awareness VR scenarios in museum employees and individuals with disabilities in terms of perceived usefulness. Five PWD and seven museum employees experienced two VR scenarios illustrating a museum visit for a person with low vision or using a wheelchair. The scenarios consisted of different scenes such as finding an accessible entrance and interacting with virtual employees. Participants were interviewed about their experience, with questions related to the realism of the scenarios and their perceived usefulness. Four main themes were identified specifically: emotions, experience, usefulness, and realism. Our scenarios were seen as useful in describing social and physical barriers experienced by PWD. VR can be a valid tool to promote disability awareness among employees in a sociocultural setting, representing a step towards the inclusion of PWD.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".