Bibliographic record
Abstract
BACKGROUND: A consistent amount of empirical research suggests that depression, besides interfering with quality of life and social functioning, may influence other symptom dimensions in schizophrenia, thus constituting an important domain for treatment strategies, outcome, and prognosis. AIM: This study investigated the factorial structure of the Calgary depression scale for schizophrenia (CDSS) in a sample of schizophrenic patients and explored the relationships between such factors, major symptom dimensions and subjective experiences. METHODS: One hundred and sixty-one subjects were examined to assess the severity of schizophrenic symptoms (scored according to the five-dimensional model of Toomey et al. [28]), the distress due to the subjective experience of negative symptoms, and the degree of subjectively-felt cognitive-affective vulnerability (i.e. basic symptoms). RESULTS: Principal component analysis revealed CDSS to include three main factors, namely: "depression-hopelessness" (factor I), "guilty idea of reference-pathological guilt" (factor II) and "early wakening" (factor III). Whereas the last factor did not correlate with any of the other psychopathological domains, the first two factors revealed multiple correlations with both diagnostic symptoms and subjective experiences. CONCLUSIONS: The results confirm the threefold factorial structure of the CDSS previously reported by the authors of the scale and could shed further light on the psychopathological nature of the components of depression in schizophrenia. The specific correlation patterns with diagnostic and subjective psychopatholgy substantiate the clinical distinction between a general depression factor ("depression-hopelessness") and a cognitive-guilt factor ("guilty idea of reference-pathological guilt").
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".