Social Competence Versus Negative Symptoms as Predictors of Real World Social Functioning in Schizophrenia
Bibliographic record
Abstract
Social deficits are common in people with schizophrenia and the treatment of social skills deficits has been a long-time treatment strategy. However, negative (i.e., deficit) symptoms also appear to contribute to social dysfunction. In this study, we combined data from three separate studies of people with schizophrenia (total n=561) who were assessed with identical methods. We examined the prediction of real-world social functioning, rated by high contact clinicians, comparing the influence of global and specific ratings of negative symptoms and performance-based assessments of social skills on these social outcomes. Negative symptom severity accounted for 20% of the variance in social outcomes, with social competence adding an incremental 2%. This 2% variance contribution was the same when social competence was forced into a regression model prior to negative symptom severity. When we examined individual negative symptoms, prediction of social outcomes increased to 28%, with active and passive social avoidance entering the equation, and the influence of social competence was unchanged. Adding depression into the predictor model improved the prediction of social outcomes significantly, but minimally (4% variance). These data suggest that negative symptoms exert a substantial influence on social outcomes and that depression and social skills exert smaller, but independent influences. Treating negative symptoms appears to be a possible path for improving social outcomes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".