Proof of Concept of an Eclectic, Integrative Therapeutic Approach to Mental Health and Well-Being Through Virtual Reality Technology
Bibliographic record
Abstract
Across three studies we provide a proof of concept evaluation of an integrative psychotherapeutic application of virtual reality (VR) technology. Study 1 (n = 36) evaluated an unguided “safe-place” imagery task, where participants were instructed “to create a safe space… [such as] a scene, item, design, or any visual representation that makes you feel safe” using either the Google Tilt Brush application (VR condition), the standard Microsoft Paint application (2-D condition), or via eyes-closed mental imagery alone (IMG condition). Study 2 (n = 48) evaluated a narrative episodic recall task, where participants viewed their childhood and adult homes and places of schooling either using either the Google Earth VR application (VR condition), the standard Google Earth application (2-D condition), or recalled these places with their eyes closed via mental imagery alone (IMG condition). Finally, study 3 (n = 48) evaluated a guided wilderness imagery task, during which different scripts were narrated, specifically, a trail walk in autumn, a spring meadow, and a hillside walk in snowy winter, while either these same scenes were visually presented using the Nature Treks VR application (VR condition), the scenes were presented using the same software but shown on standard computer monitor (2-D condition), or participants’ eyes were closed (IMG condition). Order of intervention format was randomized across participants. Across all three studies, quantitative survey ratings showed that the VR format of intervention delivery produced greater positive affect and satisfaction and perceived credibility ratings as an intervention for trauma- and stressor-related disorders and psychological wellbeing as rated by university students who varied in traumatic and stressful life event history and symptoms of posttraumatic stress disorder (PTSD), while qualitative findings revealed additional themes of experiential response including increased experience of presence and vividness in the VR condition. Future research directions and clinical applications are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".