L’amélioration de la qualité de vie chez les patients atteints d’une schizophrénie réfractaire ayant suivi la Thérapie assistée par la Réalité Virtuelle : une analyse de contenu
Bibliographic record
Abstract
Objectives Schizophrenia, particularly treatment-resistant schizophrenia (TRS), is one of the most disabling psychiatric disorders in terms of adverse effects on the quality of life (QOL) of patients. Subjective QOL has become a particularly crucial target that should be improved with treatment, since improved QOL may lead to recovery in patients with schizophrenia. However, there is little evidence on the efficacy of recommended psychosocial interventions on non-symptomatic measures such as QOL. In this regard, the treatment of schizophrenia can be enhanced if, in addition to the treatment of symptoms, therapeutic emphasis is placed on other areas of importance to patients. With advancements in technology, Virtual Reality assisted Therapy (VRT) allows voice hearers to enter in a direct dialogue with an avatar, fully animated by the therapist, who represents their most persecuting voice. This is in the aim to allow them to gain better control over their voices and to work on their self-esteem. Beyond symptomatology, the results of the pilot projects on this innovative therapy have shown significant results on QOL. Method To refine the observed quantitative results, this article will explore emerging themes from a content analysis arising from the spontaneous discourse of 10 patients who responded well to TRV. Results Four general themes emerged: (i) impact of therapy on voices, (ii) interpersonal relationships, (iii) psychological well-being, and (iv) lifestyle. This content analysis has identified several spheres of life that are further improved in patients with TRS using TRV. Conclusion TRV highlights the future of patient-oriented approaches that integrate several relevant processes to potentially improve QOL. TRV can have potentially immense implications for the health and quality of life of patients. This study was a first step towards exploring the subjective effects of TRV on the lives of patients beyond the symptoms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".