Validating a Virtual Environment for Sexual Assault Victims
Bibliographic record
Abstract
Virtual reality has shown promising results in the treatment of posttraumatic stress disorder (PTSD) for some traumatic experiences, but sexual assault has been understudied. One important question to address is the relevance and safety of a virtual environment (VE) allowing patients to be progressively exposed to a sexual assault scenario. The aim of this study was to validate such a VE. Thirty women (victims and nonvictims of sexual assault) were randomly assigned in a counter-balanced order to 2 immersions in a virtual bar: a control scenario where the encounter with the aggressor does not lead to sexual assault and an experimental scenario where the participant is assaulted. Immersions were conducted in a fully immersive 6-wall system. Questionnaires were administered and psychophysiological measures were recorded. No adverse events were reported during or after the immersions. Repeated-measures analyses of covariance revealed a significant time effect and significantly more anxiety (Cohen's f = 0.41, large effect size) and negative affect (Cohen's f = 0.35, medium effect size) in the experimental scenario than in the control condition. Given the safety of the scenario and its potential to induce emotions, it can be further tested to document its usefulness with sexual assault victims who suffer from PTSD.
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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 | 0.004 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".