Influence of psycho-social factors on the emergence of depression and suicidal risk in patients with schizophrenia.
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the influence of certain psychosocial factors - insight, psycho-education, family and social support, loneliness and social isolation - on the appearance of depression and suicidal risk in schizophrenia. SUBJECTS AND METHODS: This was a cross-sectional study that comprised hospitalized patients with schizophrenia in the initial remission phase. The assessment of depression and suicidal risk was made by applying a semi-structured psychiatric interview that included scrutinized factors (insight, psycho-education, family and social support, loneliness and social isolation), Positive and Negative Syndrome Scale (PANSS), and Calgary Depression Scale for Schizophrenia (CDSS). On the basis of the assessment results, the sample was divided into two groups: Group of patients with depression and suicidal risk in schizophrenia (N = 53) and Control group (N = 159) of patients with schizophrenia without depression and suicidal risk. RESULTS: In the Group of patients with depression and suicidal risk, compared with the Control group, there was significantly higher frequency of insight in the mental status (χ² = 31.736, p < 0.001), number of patients without psycho-education (χ² = 10.039, p = 0.002), deficit of family support (χ² = 13.359, p = 0.001), deficit of social support (χ² = 6.103, p=0.047), loneliness (χ² = 6.239, p = 0.012), and social isolation (χ² = 47.218, p < 0.001). Using the model of multi-variant logistic regression, insight, deficit of psycho-education and social isolation (p < 0.05) were identified as predictors of depression and suicidal risk in schizophrenia. CONCLUSIONS: This study shows that considered psycho-social factors - insight in the mental status, lack of psycho-education, as well as social isolation - could be predictors for appearance of depression and suicidal risk in 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 | 0.000 | 0.001 |
| 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".