Validation of Turkish version of brief negative symptom scale
Bibliographic record
Abstract
OBJECTIVE: Negative symptoms in schizophrenia have been assessed by many instruments. However, a current consensus on these symptoms has been built and new tools, such as the Brief Negative Symptom Scale (BNSS), are generated. This study aimed to evaluate reliability and validity of the Turkish version of BNSS. METHODS: The scale was translated to Turkish and backtranslated to English. After the approval of the translation, 75 schizophrenia patients were interviewed with BNSS, Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS) and Extrapyramidal Symptom Rating Scale (ESRS). Reliability and validity analyses were then calculated. RESULTS: In the reliability analysis, the Cronbach's alpha coefficient was 0.96 and item-total score correlation coefficients were between 0.655-0.884. The intraclass correlation coefficient was 0.665. The inter-rater reliability was 0.982 (p < 0.0001). In the validity analysis, the total score of BNSS-TR was correlated with PANSS Total Score, Positive Symptoms Subscale, Negative Symptoms Subscale, and General Psychopathology Subscale. CDSS and ESRS were not correlated with BNSS-TR. The factor structure of the scale was consisting the same items as in the original version. CONCLUSIONS: Our study confirms that the Turkish version of BNSS is an applicable tool for the evaluation of negative symptoms in 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".