Validation of the Turkish version of the self-evaluation of negative symptoms scale (SNS)
Bibliographic record
Abstract
OBJECTIVES: The Self-Evaluation of Negative Symptoms Scale (SNS) is a self-report scale that evaluates a patient's subjective experience on all five domains of the negative symptoms. This study aimed to present the adaptation and validation study of the Turkish version of SNS(SNS-TR). METHODS: Seventy-five patients and 50 controls were recruited for this study. After the approval of the translation, participants were asked to fill out SNS-TR by themselves. They were interviewed with the Brief Negative Symptoms Scale (BNSS), Positive and Negative Syndrome Scale (PANSS), and Calgary Depression Scale for Schizophrenia (CDSS). RESULTS: < 0.01). In the validity analyses, the total and subscale scores of SNS-TR showed positive correlations with the total and subscales of BNSS, with only one exception of BNSS lack of distress subscales. The total score of SNS-TR demonstrated a significant correlation with PANSS-total, PANSS-negative subscale, PANSS-general subscale, and CDSS scores. Confirmatory factor analysis showed acceptable values for the five-factor structure, similar to the original version. CONCLUSION: To conclude, our study indicates that SNS-TR is an easily applicable self-evaluation tool with good psychometric properties for assessing negative symptoms. KEY POINTSSNS is a novel and easily applicable self-report scale for examining negative symptoms in schizophrenia patients, allowing them to evaluate their subjective experience on all five domains of the negative symptoms.It shows good internal consistency (α= 0.873) which is similar to the original version (α = 0.867).Confirmatory factor analysis scores were found in acceptable ranges and SNS-TR confirm the five-factor structure.Using this scale in clinical practice would empower both the physician's examinations and patient participation through treatment and follow-up course.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".