Effectiveness of In Virtuo Exposure and Response Prevention Treatment Using Cognitive–Behavioral Therapy for Obsessive–Compulsive Disorder: A Study Based on a Single-Case Study Protocol
Bibliographic record
Abstract
Obsessive-compulsive disorder (OCD) is characterized by the presence of distressing, recurrent and intrusive thoughts, impulses, or doubts as well as behavioral or mental rituals. OCD has various subtypes, including the fear of contamination in which individuals fear bacteria, germs, disease, or bodily secretions, and engage in clinically significant cleaning and avoidance rituals. Cognitive-behavioral therapy (CBT) is an effective treatment for OCD and involves, among other therapeutic strategies, exposing patients to feared stimuli while preventing them to engage in compulsive behaviors. In recent years, virtual reality (VR) has shown the potential of in virtuo exposure with people suffering from anxiety disorders and OCD. The objective of this pilot study is to examine the effectiveness of a CBT program where exposure in conducted in virtuo. Three adults suffering from OCD with a dominant subtype of contamination were enrolled in a single-case design with multiple baselines across participants. The presence and intensity of obsessions and compulsions were assessed daily during baselines of 3-, 4-, or 5-week, and a 12-session treatment. Follow-up information was gathered after 4 and 8 months. Treatment outcome is assessed with visual inspection of the graphs and ARMA time-series analyses. Clinical information, self-reports, and details of the treatment are provided for each patient. Statistical analyses for the time-series data revealed a statistically significant improvement in all three participants, but global improvement is considered positive for only two. This study innovates in proving preliminary support for the usefulness of VR in the CBT of OCD with contamination features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".