Low-Cost Virtual Reality to Support Imaginal Exposure Within PTSD Treatment: A Case Report Study Within a Community Mental Healthcare Setting
Bibliographic record
Abstract
Revisiting what happened during (or after) a traumatic event is an important part of the treatment process in trauma-focused cognitive therapy (TF-CT). However, clinicians may have difficulty helping patients to intentionally retrieve these memories in order to engage with their content. As such, clinical tools to support the access and delivery of imaginal exposure content within treatment may prove to be particularly useful for therapists. This case report introduces work undertaken with Mr. A, a 38-year-old male, who 2 years prior had experienced a city centre assault. Initial assessment revealed a PCL-5 score of 64 and he met DSM-5 criteria for posttraumatic stress disorder (PTSD). Mr. A received 10 sessions of TF-CT wherein the traditional imaginal exposure components were implemented via a newly developed virtual reality (VR) development workflow called “VR Photoscan.” After 10 sessions, results showed PCL-5 scores decreased from 64 to 19 and Mr. A no longer met DSM-5 PTSD criteria. VR Photoscan was used during 4/10 sessions and included (1) reliving, (2) memory updating, and (3) stimulus discrimination activities. Mr. A also reported VR Photoscan as helpful regarding preparation for site visits. In conclusion, VR Photoscan technology provided a more visceral exposure experience which supported Mr. A to revisit the trauma memory. He reported high levels of satisfaction with the quality of the virtual environment and no issues using the VR technology. Produced with lower costs and shorter development times than typical computer-generated environments, VR Photoscan may be more easily implemented within routine care, although further research is required.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".