Developing an Automated Virtual Reality Therapy for Improving Positive Self-Beliefs and Psychological Well-Being (Phoenix VR Self-Confidence Therapy): Tutorial
Bibliographic record
Abstract
Unlabelled: Virtual reality (VR) is an immersive technology in which delivery of psychological therapy techniques can be automated. Techniques can be implemented similarly to real-world delivery or in ways that are not possible in the real world to enhance efficacy. The potential is for greater access for patients to effective therapy. Despite an increase in the use of VR for mental health, there are few descriptions of how to build and design automated VR therapies. We describe the development of Phoenix VR Self-Confidence Therapy, designed to increase positive self-beliefs in young patients diagnosed with psychosis in order to improve psychological well-being. A double-diamond, user-centered design process conducted over the course of 18 months was used, involving stakeholders from multiple areas: individuals with lived experience of psychosis, clinical psychologists, treatment designers, and VR software developers. Thirteen meetings were held with young patients diagnosed with psychosis to increase the understanding and improve the assessment of positive self-beliefs, help design the scenarios for implementing therapeutic techniques, and conduct user testing. The resulting Phoenix therapy is a class I United Kingdom Conformity Assessed (UKCA)-certified medical device designed to be used on the standalone Meta Quest 2 (Meta Platforms) headset. Phoenix aims to build up 3 types of positive self-beliefs that are connected to psychological well-being. In a community farm area, tasks are designed to increase a sense of mastery and achievement ("I can make a difference"); in a TV studio, users complete an activity with graded levels of difficulty to promote success in the face of a challenge ("I can do this"); and in a forest by a lake, activities are designed to encourage feelings of pleasure and enjoyment ("I can enjoy things"). Phoenix is delivered over the course of approximately 6 weekly sessions supported by a mental health provider. Patients can take the headsets home to use in between sessions. Usability testing with individuals with lived experience of psychosis, as well as patients in the National Health Service (aged 16-26 years), demonstrated that Phoenix is engaging, easy to use, and has high levels of satisfaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".