Virtual Reality Human–Human Interface to Deliver Psychotherapy to People Experiencing Auditory Verbal Hallucinations: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Digital technologies have expanded the options for delivering psychotherapy, permitting for example, the treatment of schizophrenia using Avatar Therapy. Despite its considerable potential, this treatment method has not been widely disseminated. As a result, its operability and functionality remain largely unknown. OBJECTIVE: We aimed to study the usability of a therapeutic virtual reality human-human interface, created in a game engine. METHODS: Participants were psychiatric hospital staff who were introduced to the therapeutic platform in a hands-on session. The System Usability Scale (SUS) was employed for evaluation purposes. Statistical evaluation was conducted using descriptive statistics, the chi-square test, analysis of variance, and multilevel factor analysis. RESULTS: =6.136; P=.003) than nursing staff (mean 79.01, SD 13.30) and administrative personnel (mean 77.98, SD 10.72). A multilevel factor analysis demonstrates a different factor structure for each profession. CONCLUSIONS: In all professional groups in this study, the usability of a digital psychotherapeutic tool developed using a game engine achieved the benchmark for an excellent system, scoring highest among the professional target group (psychotherapists). The usability of the system seems, to some extent, to be dependent on the professional background of the user. It is possible to create and customize novel psychotherapeutic approaches with gaming technologies and platforms. TRIAL REGISTRATION: Clinicaltrials.gov NCT04099940; http://clinicaltrials.gov/ct2/show/NCT04099940.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".