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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".