Virtual Reality Cognitive Therapy in Inpatient Psychiatric Wards: Protocol for a Qualitative Investigation of Staff and Patient Views Across Multiple National Health Service Sites
Bibliographic record
Abstract
BACKGROUND: Patients in psychiatric wards typically have very limited access to individual psychological therapy. Inpatients often have significant time available, and an important transition back to everyday life to prepare for-but historically, there have been few trained therapists available on wards for the delivery of evidence-based therapy. Automated virtual reality (VR) therapy may be one route to increase the provision of powerful psychological treatments in psychiatric hospitals. The gameChange automated VR cognitive therapy is targeted at helping patients overcome anxious avoidance and re-engage in everyday situations (such as walking down the street, taking a bus, or going to a shop). This treatment target may fit well for many patients preparing for discharge. However, little is known about how VR therapy may be viewed in this setting. OBJECTIVE: The objectives of the study are to explore psychiatric hospital staff and patients' initial expectations of VR therapy, to gather patient and staff views of an automated VR cognitive therapy (gameChange) after briefly experiencing it, and to identify potential differences across National Health Service (NHS) mental health trusts for implementation. Guided by an implementation framework, the knowledge gained from this study will be used to assess the feasibility of VR treatment adoption into psychiatric hospitals. METHODS: Focus groups will be conducted with NHS staff and patients in acute psychiatric wards at 5 NHS mental health trusts across England. Staff and patients will be interviewed in separate groups. Individual interviews will also be conducted when preferred by a participant. Within each of the 5 trusts, 1 to 2 wards will be visited. A total of 8-15 staff and patients per ward will be recruited, with a minimum total of 50 staff and patients recruited across all sites. Focus group questions have been derived from the nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability (NASSS) framework. Focus groups will discuss expectations of VR therapy before participants are given the opportunity to briefly try the gameChange VR therapy. Questions will then focus on opinions about the therapy and investigate feasibility of adoption, with particular consideration given to site specific issues. A thematic analysis will be conducted. RESULTS: As of May 15, 2020, 1 patient focus group has been conducted. CONCLUSIONS: The study will provide unique insight from patients and staff into the potential for implementing automated VR therapy in psychiatric wards. Perspectives will be captured both on the use of immersive technology hardware and therapy-specific issues in such settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/20300.
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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 | 0.053 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".