Psychometric properties of the Icelandic version of the Calgary depression scale for schizophrenia
Bibliographic record
Abstract
This paper presents the psychometric properties of the Icelandic version of the Calgary depression scale for schizophrenia (CDSS). The aim of the study was to evaluate the reliability and validity of the Icelandic translation and to suggest appropriate cut-off score for the CDSS. The CDSS is a 9-item depression rating scale that was especially developed to assess depression in schizophrenia. The rationale behind its development is that depression is often seen in patients with schizophrenia and is thought to be a different construct than the negative symptoms related to schizophrenia. Between 25% and 50% of patients with acute symptoms of schizophrenia are estimated to suffer from some depressive symptoms. The participants in this study were 35, 27 men and 8 women. The mean age was 24.36. After signing a form for informed consent, PANSS, M.I.N.I., CDSS and DASS, were administered. The data for each participant was collected within a period of one week. The psychometric properties of the Icelandic version of the CDSS are not as good as the original and in fact not good enough to recommend its use. The internal consistency and the convergent validity are not satisfactory. The discriminant validity of the Icelandic version of the CDSS was good. The CDSS does have an excellent predictive ability and discriminates well between subjects with a diagnosis of depression from those who are not depressed. The signal detection analysis found an optimal cut-off score of 6 which is similar to the original scale. \nKey words \n Calgary depression scale for schizophrenia, depression, negative symptoms
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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 | 0.010 |
| 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) | 0.002 | 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".