Immersive Virtual Reality–Assisted Therapy for Distressing Voices in Psychosis: Qualitative Study of Participants’ and Therapists’ Experiences in the Challenge Trial
Notice bibliographique
Résumé
Background: Immersive virtual reality-assisted therapy (VRT) is a relational therapy for distressing voices in psychosis. Like AVATAR therapy (AT), VRT centers on therapist-facilitated dialogues with a digital avatar representing a voice. Unlike AT, VRT uses immersive virtual reality (VR). While participant experiences of AT have been explored, therapist perspectives remain unexamined, and for VRT, neither participant nor therapist experiences have been studied. Understanding these perspectives is essential to inform optimization of therapy, future research, and implementation. Objective: The objective of this qualitative study was to explore both trial participants' and therapists' experience of VRT in the Challenge trial. Methods: Semistructured interviews were conducted with 10 trial participants and 8 therapists across the 3 Challenge trial sites. Trial participants were purposively sampled to ensure site representation and variation in voice-hearing duration. Individual interviews were conducted with trial participants, while therapists participated in site-based groups with 2-3 in each. Interviews were audio-recorded, transcribed, and subjected to reflexive thematic analysis from a critical realist position. Coding and theme development were inductive. People with lived experience were invited to an initial focus group for topic guide development and a later theme validation workshop. Reporting followed the Standards of Reporting Qualitative Research (SRQR). Results: A total of five overarching themes were generated: (1) using technology to meet the voice, (2) a different approach to voice-hearing and treatment, (3) on a tight schedule, (4) a toolbox for transformation, and (5) a price to pay. Trial participants and therapists generally found VRT acceptable, appropriate, and feasible. Highlights included the acknowledging approach to the voice(s), facilitation of engagement with the voice(s), and opportunity to share the otherwise private experience of voice-hearing. Externalizing and embodying the voice(s) in VR-supported avatar role-plays was seen as a key affordance. Positive outcomes included increased trial participant empowerment and self-worth, enabling or improving voice dialogue, new understanding of voice intentions, and changes in voice frequency or content. Challenges included instances of participant anxiety, exhaustion, or suboptimal sense of (voice) presence; adverse voice reactions; technological malfunctions and limitations to avatar design; measurement insensitivity; tensions between assertiveness and compassion; difficulties with reproducing negative voice content; and the demanding nature of the therapy and the nontraditional skills required of therapists. Conclusions: The study provided comprehensive insights into trial participants' and therapists' experiences of VRT in the Challenge trial. Findings share several similarities with qualitative research on other relational therapies for distressing voices and highlight VRT's potential for positive change. Key considerations for future research and implementation include monitoring anxiety and voice reactions, ensuring operational reliability of hardware and software, and addressing the additional effort required by therapists, which may be unsustainable in routine practice. As a demanding intervention, the successful implementation of VRT will require adequate training, supervision, and structural support.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,011 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».