“Hitting” Voices of Schizophrenia Patients May Lastingly Reduce Persistent Auditory Hallucinations and Their Burden: 18-Month Outcome of a Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the outcome of an 18-month randomized controlled trial (RCT) on subjective burden and psychopathology of patients suffering from schizophrenia. METHOD: An RCT was used to compare hallucination-focused integrative treatment (HIT) and routine treatment (RT) in schizophrenia patients who persistently hear voices. We performed an intent-to-treat analysis on each of the 63 patients who were assessed at baseline, 9, and 18 months. On each of the 3 occasions, the differential effects of the treatment conditions were tested repeatedly. Sex, age, education, and illness (hallucination) duration were used as covariates. RESULTS: Patients in the experimental group retained improvements over time. Improvements in hallucinations, distress, and negative content of voices remained significant at the 5% level. CONCLUSION: HIT seems to be an effective treatment strategy with long-lasting effects for treatment-refractory voice-hearing patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".