Preliminary Study of the Thai Version of the Scale for the Assessment of Negative Symptoms (SANS-Thai)
Bibliographic record
Abstract
OBJECTIVES: The study aimed to evaluate content validity, convergent validity, internal consistency and test-retest reliability of the Thai version of the Scale for the Assessment of Negative Symptoms (SANS-Thai). METHODS: The content validity of the SANS-Thai was assessed using four experts. The average-content validity index and item level content validity index were analyzed. The SANS-Thai and the Thai versions of the Addenbrooke’s Cognitive Examination (ACE) were administered to 40 people with schizophrenia to examine convergent validity and internal consistency. Twenty participants took the second SANS-Thai assessment within four weeks to evaluate test-retest reliability. RESULTS: The results demonstrated that the SANS-Thai has excellent content validity with the average-content validity index of 0.94. The majority of the item level content validity index range from 0.75 to 1. The global and total SANS-Thai score moderately correlated with the ACE with the correlation coefficient of -0.48 (p = 0.002) and -0.49 (p=0.001), respectively. Internal consistency by the Cronbach alpha coefficient was 0.95. Test-retest reliabilities by intraclass correlation were 0.91 (p<0.001) for global SANS-Thai and 0.9 (p<0.001) for total SANS-Thai. The Bland-Altman plot demonstrated that only 5% of the participants fell outside the limits of agreement for both global SANS and total SANS scores. CONCLUSION: The SANS-Thai appears to be a valid and reliable measure of negative symptoms in schizophrenia and could be useful for patient care and research studies.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".