Reliability and validity of the Thai version of the Calgary Depression Scale for Schizophrenia
Bibliographic record
Abstract
AIM: The purpose of this study was to assess the reliability and validity of the Thai version of the Calgary Depression Scale for Schizophrenia (CDSS) for the evaluation of depression in patients with schizophrenia. METHODS: Sixty patients with schizophrenia according to Diagnostic and Statistical Manual of Mental Disorders: Fourth Edition; Text Revision (DSM-IV-TR) criteria were recruited to the study The Thai version of the CDSS, the Montgomery-Åsberg Depression Rating Scale (MADRS), the Hamilton Depression Rating Scale, 17-item version (HDRS-17), and the Positive and Negative Syndrome Scale (PANSS) were administered. A major depressive episode diagnosed by a psychiatrist according to the DSM-IV-TR was used as a gold standard. RESULTS: The internal consistency of the Thai version of the CDSS was very good (Cronbach's alpha = 0.869). The inter-rater reliability was found to be in substantial agreement with the intra-class correlation coefficient of 0.979. The test-retest reliability over a period of 3 days was high, with an intra-class correlation coefficient of 0.861. The Thai version of the CDSS showed significant correlations with the MADRS (r = 0.887), the HDRS-17 (r = 0.865), and the depression item of the Positive and Negative Syndrome Scale (PANSS-G6) (r = 0.833). The areas under the receiver operating characteristic curve of the CDSS, MADRS, HDRS-17, and PANSS-G6 against the DSM-IV-TR criteria for major depressive episode were 0.993, 0.954, 0.966, and 0.933, respectively. The optimal cut-off score to discriminate between depressed and non-depressed patients was 6/7, with a sensitivity of 92.31% and specificity of 97.87%. CONCLUSION: The Thai version of the CDSS is a reliable and valid measure for the evaluation of depression in Thai patients with 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".