Exploration of the dialogue components in <scp>A</scp>vatar <scp>T</scp>herapy for schizophrenia patients with refractory auditory hallucinations: <scp>A</scp> content analysis
Bibliographic record
Abstract
Auditory verbal hallucinations are hallmark symptoms of schizophrenia and are amongst the most disturbing symptoms of the disorder. Although not entirely understood, the relationship between the voice hearer and their voices has been shown to be an important treatment target. Understanding voice hearers' standpoints through qualitative analysis is central to apprehend a deeper comprehension of their experience and further explore the relevance of interpersonal interventions. Compared with other dialogical intervention, virtual reality-assisted therapy (Avatar Therapy) enables patients to be in a tangible relation with a representation of their persecutory voice. This novel therapy has shown favourable results, though the therapeutic processes remain equivocal. We consequently sought to begin by characterizing the main themes emerging during the therapy by exploring the hearer's discussion with their avatar. The therapy sessions of 12 of our referrals were transcribed, and the patients' responses were analysed using content analysis methods. Five themes emerged from data saturation: emotional responses to the voices, beliefs about voices and schizophrenia, self-perceptions, coping mechanisms, and aspirations. All patients had at least one element within each of these themes. Our analyses also enabled us to identify changes that were either verbalized by the patients or noted by the raters throughout therapy sessions. These findings are relevant as they allowed to identify key themes that are hypothesized to be related to therapeutic targets in a novel relational therapy using virtual reality. Future studies to further explore the processes implicated within Avatar Therapy are necessary.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".