Technology-enhanced psychological assessment and treatment of distressing auditory hallucinations: a systematic review
Bibliographic record
Abstract
Purpose Auditory hallucinations (“hearing voices”) are a relatively common experience, which is often highly distressing and debilitating. As mental health services are under increasing pressures, services have witnessed a transformative shift with the integration of technology into psychological care. This study aims to narratively synthesise evidence of technology-enhanced psychological assessment and treatment of distressing voices (PROSPERO 393831). Design/methodology/approach This review was carried out according to the preferred reporting items for systematic reviews and meta-analyses. Embase, MEDLINE, PsycINFO and Web of Science were searched until 30th May 2023. The Effective Public Health Practice Project (EPHPP) tool assessed methodological quality of studies. Findings Searching identified 9,254 titles. Fourteen studies (two assessment studies, twelve treatment studies, published 2010–2022, n = 1,578) were included in the review. Most studies were conducted in the UK, the USA or Canada. Technologies included avatar therapy, mobile apps, virtual reality, a computerised Web-based programme and a mobile-assisted treatment. Overall, technology-enhanced psychological assessments and treatments appear feasible, acceptable and effective, with avatar therapy the most used intervention. EPHPP ratings were “strong” (n = 8), “moderate” (n = 5) and “weak” (n = 1). Originality/value To the best of the authors’ knowledge, this is the first systematic review to investigate these technologies, specifically for distressing voices. Despite the relatively small number of studies, findings offer promising evidence for the clinical benefits of these technologies for enhancing mental health care for individuals with distressing voices. More high-quality research on a wider range of technologies is warranted.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".