Supplementary Material for: PSYCHOMETRIC PROPERTIES AND FACTOR STRUCTURE OF THE ARABIC TRANSLATION OF THE BRIEF NEGATIVE SYMPTOM SCALE
Bibliographic record
Abstract
Objectives: The current study embarked on an Arabic translation of the BNSS and an examination of its psychometric properties in a Tunisian sample of inpatients and outpatients with schizophrenia. Method: Care recipients with schizophrenia (N=178) completed administrations of the A-BNSS, the Scale for the Assessment of Negative Symptoms (SANS), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), and the St. Hans Rating Scale (SHRS). Results: The A-BNSS produced strong evidence for the reliability of the scale with Cronbach’s alpha and interrater ICC estimates for the full measure and its subscales falling in the good to excellent range. The A-BNSS showed excellent convergent validity with large correlations of its full scale and subscale scores with the SANS and PANSS negative symptom scores. The A-BNSS showed minimal correlations with PANSS positive and emotional distress scores, CDSS depression, and SHRS extrapyramidal symptoms, suggesting strong discriminant validity. CFA favored a five-factor model consistent with the NIMH consensus domains. Conclusion: The study supports the robust psychometric properties of the Arabic translation of the BNSS rendering it promising for the assessment of negative symptoms in Arabic speaking individuals with schizophrenia. Along with pre-existing translations, this extension of the language repertoire of the BNSS would support cross-cultural deconstruction of the phenomenology of negative symptoms and outcome evaluation in global clinical trials. The A-BNSS is a promising tool for the assessment of negative symptoms in Arabic-speaking individuals with schizophrenia.
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.747 | 0.216 |
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".