Virtual Reality Could Help Assess Sexual Aversion Disorder
Bibliographic record
Abstract
Virtual reality (VR) may improve our understanding of sexual dysfunctions’ manifestations, although research in this area remains limited. This study assessed the potential use of a VR Behavior Avoidance Test (VR-BAT) as a tool for examining the clinical features of Sexual Aversion Disorder (SAD): the experience of fear, disgust, and avoidance when facing sexual cues/contexts. A sample of 55 adults (≥ 18y) with (n = 27) and without SAD (n = 28) completed a self-report measure of sexual avoidance. Their anxiety, disgust, electrodermal activity, heart rate, and visual and behavioral avoidance were then examined during two VR-BATs involving sexual or non-sexual stimuli. Mixed repeated measures ANOVAs, t-tests, and correlational analyses were performed. Results showed that individuals in the SAD group reported greater anxiety and disgust compared to their non-SAD counterparts during the sexual stimuli condition. Sexual avoidance scores were largely positively related to anxiety and disgust during the VR sexual condition, and moderately negatively related to the time spent touching the virtual character’s genitals. This study is important given the prevalence of sexual difficulties, such as SAD, and the new research avenues offered by emerging technologies, like VR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".