Validation of the Lithuanian version of the brief Negative Symptoms Scale
Bibliographic record
Abstract
Purpose of the article To validate the Lithuanian version of the Brief Negative Symptoms Scale (Lith-BNSS) in a Lithuanian speaking sample.Materials and methods We performed a double translation of BNSS from English into Lithuanian. Four clinicians conducted psychometric validation. We checked the internal consistency of the 13 items and six subscales of BNSS. Convergent and discriminant validity were calculated by applying BNSS in clinical practice with other psychometric tools for negative, positive, and depressive symptoms and cognitive deficit assessment. The psychometric tools used were BNNS, Self-assessment of Negative Symptoms Scale (SNS), Positive and Negative Symptoms Scale (PANSS), Montgomery Asberg Depression Rating Scale (MADRS), and Calgary Depression Scale for Schizophrenia (CDSS). We calculated the convergent and discriminant validities using Pearson and Spearman correlations.Results We have included 130 patients. Excellent internal consistency was observed for the 13 items (alpha = 0.944) and the six subscales (alpha = 0.874) of BNSS. Good convergent validity is illustrated by strong Pearsons’s correlations with the PANSS negative subscale (r = 0.77, p < 0.001) and the PANSS Marder negative factor (r = 0.77, p < 0.001). Adequate discriminant validity is shown by a non-significant correlation with PANSS positive subscore (r = 0.13, p = 0.15), PANSS Marder positive factor (r = 0.14, p = 0.13), CDSS total score (r = 0.02, p = 0.83) and MADRS total score (r = 0.12, p = 0.2).Conclusions Lith-BNSS has good psychometric properties and can be used as a valuable addition to the available Lithuanian evaluation tools for negative symptoms of 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 | 0.006 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".