Psychometric evaluation of the Persian version of the Beliefs About Voices Questionnaire: revised in patients with schizophrenia spectrum disorders
Bibliographic record
Abstract
BACKGROUND: The Beliefs About Voices Questionnaire-Revised (BAVQ-R) is a widely used measure in research and clinical settings designed to assess beliefs about auditory hallucinations and the corresponding response styles. Despite its common usage, the psychometric properties of the BAVQ-R are understudied. This study aimed to contribute to this gap in the literature by examining the psychometric properties of the Persian version of the BAVQ-R in Iran. METHOD: A total of 298 patients diagnosed with Schizophrenia Spectrum Disorder (aged 19–50 years; M = 37.32, SD = 1.14; 85.23% male) were recruited from Razi Hospital in Tehran. Participants completed the BAVQ-R alongside self-report measures (e.g., the Beck Depression Inventory-II [BDI-II] and the Schizophrenia Quality of Life Scale [SQLS]) and underwent interview-based assessments (e.g., the Psychotic Symptom Rating Scales [PSYRATS] and the Calgary Depression Scale for Schizophrenia [CDSS]). RESULTS: A confirmatory factor analysis supported a 31-item four-factor model comprising two belief subscales (Malevolence/Omnipotence and Benevolence) and two response subscales (Resistance and Engagement), with excellent model fit indices (CFI = 0.99, TLI = 0.99, RMSEA = 0.042). The subscales demonstrated high internal consistency (Cronbach’s α and McDonald’s ω = 0.91–0.95) and correlated in theoretically consistent ways with external constructs (e.g., measures of hallucinations, delusions, depression, and quality of life). CONCLUSION: Findings suggest that the Persian BAVQ-R is a reliable and valid instrument for assessing beliefs about voices and response styles in Iranian patients with Schizophrenia Spectrum Disorder. These findings support its use in both research and clinical settings and suggest avenues for further research to explore its applicability across different populations and settings. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".