Tailored Immersion: Implementing Personalized Components Into Virtual Reality for Veterans With Post-Traumatic Stress Disorder
Bibliographic record
Abstract
With the application of virtual reality (VR), tailored interventions can be created that mirror the traumatic experiences of veterans with post-traumatic stress disorder (PTSD). Visual elements can be mimicked, and auditory and other senses stimulated. In doing so, the degree of immersion can be adjusted to optimize the therapeutic process. Objectively measuring the sensory immersion is key to keep subjects within their personal window of tolerance. Based on this information the therapist can decide manipulate the sensory stimulation embedded in the treatment. The objectives of this article are to explore the different immersive design aspects of VRET that can be modified to influence the experienced presence in veterans with PTSD, and to discuss possible methods of measuring the emotional response facilitated by immersive design aspects and experienced presence. Four design aspects are discussed: system, sensory cues, narrative and challenge. We also report on a user experiment in three veterans that informed on quality and depth of immersion. Believability of the neutral virtual environment was important for maintaining the veterans’ presence within the VR experience. The immersive design aspects that were personalized and supportive in the narrative of the veteran such as music and self-selected images appeared to have a strong influence on recall and reliving of the traumatic events. Finally, in order to increase the therapeutic effect in veterans with PTSD, the highlighted design aspects should be recognized and tailored to maximize immersion in virtual reality exposure therapy.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".