The Relationship Between Depressive Symptoms and Psychological Variables in Patients With Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Depressive symptoms are common in schizophrenia and can be seen at any stage of the disease. Although various models have been proposed to explain the development of depression in schizophrenia, studies investigating related psychological factors are scarce and the studies that have been done usually focus on only a small number of possible factors. OBJECTIVE: The goal of this study was to investigate the predictability of some psychological factors on depression in patients with schizophrenia. For this purpose, patients with high and low depression scores were compared. METHODS: Two groups of individuals with schizophrenia, with (n=29) and without (n=31) depression, as determined by scores on the Calgary Depression Scale in Schizophrenia, were compared using a sociodemographic data form, the Positive and Negative Syndrome Scale (PANSS), the Beck Anxiety Inventory, the Rotter Internal-External Locus 2024 of Control Scale, the Multidimensional Scale of Perceived Social Support, and the Stress Coping Styles Scale. RESULTS: No differences were found between the 2 groups in terms of sociodemographic and clinical characteristics, social support scores, and coping styles. Statistically significant differences were found between the groups on the PANSS positive, negative, and general psychopathology subscales, in PANSS total scores, in anxiety scores, and in locus of control scores. CONCLUSIONS: This study showed that high levels of negative, positive, and general psychopathological symptoms, external locus of control, and high anxiety scores may be predictive of depression in individuals with schizophrenia. Studies that examine psychological factors in larger patient groups may provide the opportunity to detect and target these factors earlier in the course of schizophrenia, thereby reducing morbidity and mortality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".