<p>Validation of the Chinese Version of the 16-Item Negative Symptom Assessment</p>
Bibliographic record
Abstract
PURPOSE: The Negative Symptom Assessment-16 (NSA-16) is an instrument with significant validity and utility for assessing negative symptoms associated with schizophrenia. This study aimed to validate the Chinese version of the NSA-16. PATIENTS AND METHODS: A total of 172 participants with schizophrenia were assessed with the NSA-16, Scale for Assessment of Negative Symptoms (SANS), Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS) and Rating Scale for Extrapyramidal Side Effects (RSESE). The factor structure of the NSA-16 was evaluated via exploratory and confirmatory factor analysis. Cronbach's α and intraclass correlation coefficients were computed. Correlations were evaluated via Spearman correlation coefficient. RESULTS: The original five-factor model of the NSA-16 did not fit our sample. Exploratory factor analysis followed by confirmatory factor analysis suggested a three-factor structure, consisting of communication, emotion and motivation, with 15 items. The NSA with 15 items was termed as the NSA-15. The NSA-15 showed excellent convergent validity by high correlations with the SANS and PANSS total and negative factor scores and good divergent validity by independence from the PANSS positive factor, CDSS and RSESE. The NSA-15 showed good internal consistency, interrater reliability and test-retest reliability. CONCLUSION: The NSA-15 is best characterized by a three-factor structure and is valid for assessing negative symptoms of schizophrenia in Chinese individuals.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".