Guilt and depression: Two different factors in individuals with negative symptoms of schizophrenia
Bibliographic record
Abstract
OBJECTIVE: Depression is common among schizophrenia patients and constitutes a major risk factor for suicide. Calgary Depression Scale (CDSS) is the most widely used instrument for measuring depression in schizophrenia. CDSS has never been examined in patients with predominant negative symptoms, thus possibly hindering both accurate assessment and understanding of the underlying mechanisms. The current study is the first to examine CDSS' structure in this population. METHODS: We conducted Principal Component Analysis (n=184) for the CDSS items. Thereafter, we correlated emerging factors with psychopathological, demographic and side effect variables. We assessed internal consistency and reliability of the emerging factors, as well as demographic correlations. RESULTS: The analysis yielded two factors: depression-hopelessness and guilt. Factors distinctly correlated with separate variables. Removal of item #7 (early waking) improved internal consistency. The depression-hopelessness factor had an inverse correlation with negative symptoms, and positive correlation with neuroleptic side effects. CONCLUSIONS: CDSS structure indicated of two separate factors, i.e., depression-hopelessness and guilt, suggesting separate underlying processes. The validity of the scale might benefit from a two-fold structure and the removal/replacement of item #7 (early waking). A noteworthy inverse correlation was found between the depression factor and negative symptoms, as well as a positive correlation between depression factor and neuroleptic side effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".