Promoting Sustained Real-Life Benefits of Virtual Reality–Based Interventions in People With Mental Health and Substance Use Disorders: Qualitative Study
Bibliographic record
Abstract
Background: Concurrent mental health and substance use disorders (MHD/SUD) are one of the most prominent public health problems as of today, and the worldwide prevalence of MHD/SUD is currently increasing. Modern virtual reality technology may provide easy, unlimited, and safe access to social experiences and interactions that hold the potential to promote individuals' new learning for the benefit of their social participation and recovery. However, the clinical adoption of virtual reality-based interventions (VRIs) is still in its infancy. Human limitations in skills transfer from virtual to actual reality are a major challenge in designing efficient VRIs. Key working mechanisms of the interactive, digital social environments in virtual realities have yet to be identified. There is a lack of knowledge on how immersive learning experiences may be designed and structured to promote sustained real-life benefits for people with mental health and substance use disorders. Objective: The main aims of this paper were to explain the factors affecting the outcomes of learning in multisensory virtual reality environments and to examine how they affect our particular target group. The overall purpose of this study was to understand how learning experiences in VRIs may be designed and orchestrated to promote sustained real-life benefits of VRIs in people with MHD/SUD. Methods: Eight individual in-depth interviews with adults in recovery from mental health and substance use disorders were conducted in a medium-sized municipality in eastern Norway in fall 2022. The interviews were analyzed using template analysis, a form of codebook thematic analysis, in a process involving peer researcher collaboration. Results: This study suggests that the human capacity to achieve sustained learning outcomes from multisensory immersive learning experiences was limited in general. This study also indicates that people with mental health and substance use disorders struggle with attention deficit, concentration, and memory to an extent that it affects their daily functioning. Conclusions: Altogether, the theoretical framework and empirical findings provide added information on how we may develop learning experience designs in VRIs that accommodate human perceptual processes. VRI scenarios that may be repeated and structured according to individual learning prerequisites may enable the restructuring of maladaptive social schema. This may possibly promote the storage of new, repaired schemas in the user's long-term memory. It is therefore suggested that short, focused VRI scenarios, orchestrated in a sequenced and deliberately structured learning workflow, may promote sustained real-life benefits from VRIs in people with mental health and substance use disorders.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".