Virtual Reality and Sex Therapy: Future Directions for Clinical Research
Bibliographic record
Abstract
Rapidly growing new technologies are revolutionizing the field of mental health, in terms of both understanding and treating mental disorders. Among these, virtual reality (VR) is a powerful tool providing clients with new learning experiences benefiting their psychological well-being. This article offers an overview of the current literature on VR in psychotherapy, highlighting its relevance to sexual dysfunction (SD) treatment.A literature review of PubMed and Google Scholar databases was used to provide a description of the theoretical frameworks and clinical indications associated with VR use in psychotherapy and SD treatment. The effectiveness of VR exposure-based therapy has been empirically validated for several mental disorders, notably anxiety disorders. The emerging combined use of VR and mindfulness tends to focus on chronic pain treatment. Experimental research examining the use of immersive technologies in the treatment of SDs is lacking.Given the shortcomings of conventional SD treatments, exploring and developing specialized VR interventions may prove beneficial. VR offers promising avenues in sex therapy, particularly for the treatment of genital pain disorders or SDs in which anxiety plays a significant etiological role.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".