[Predictors of social functioning in schizophrenia].
Bibliographic record
Abstract
OBJECTIVE: Impaired social functioning is a characteristic feature of schizophrenia. The aim of this study was to determine the factors that predict social functioning in schizophrenia. METHOD: The study included 100 patients that met DSM-IV diagnostic criteria for schizophrenia. All the patients were evaluated using the Scale for the Assessment of Negative Symptoms, Scale for the Assessment of Positive Symptoms, Calgary Depression Scale for Schizophrenia, Facial Emotion Identification Test, Facial Emotion Discrimination Test, Digit Span Test, Rey's Auditory Verbal Learning Test, Trail Making Test, and Social Functioning Scale. Marital status, living arrangements, and working status of the patients were also recorded. Pearson's correlation analysis and multiple stepwise regression analysis were used to determine the relationships between the research variables and social functioning. Categorical variables were compared with the independent samples t test. RESULTS: Negative symptoms, level of education, and facial emotion identification were significant predictors of social functioning total score. Negative symptoms were the most important predictor of almost all domains of social functioning. Age and duration of illness were not associated with social functioning. There were no gender differences in terms of social functioning. Married patients had higher social functioning scores. Employed patients had lower negative symptom scores and higher social functioning scores. CONCLUSION: Negative symptoms were the most important predictors of social functioning in our study.
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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 | 0.001 | 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) | 0.005 | 0.001 |
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".