Social functioning in early psychosis: are all the domains predicted by the same variables?
Bibliographic record
Abstract
AIM: The study aims to determine the predictive value of negative symptoms, depression, short-term verbal learning and gender on three areas of social functioning--social life, vocational functioning and independent living skills--in a sample of 88 individuals with early psychosis. METHODS: Participants were recruited from early psychosis intervention programmes and community mental health clinics in British Columbia, Canada, and completed the following measures: client's assessment of strengths, interests, and goals, brief psychiatric rating scale, Beck depression inventory and California verbal learning task. RESULTS: Multiple linear regressions revealed that: more negative symptoms and higher depression predicted a less active social life; more negative symptoms and poorer short-term verbal learning ability predicted lower vocational functioning; and more negative symptoms and male gender predicted lower independent living skills. CONCLUSION: Results suggest that negative symptoms are predictive of all three areas of functioning but that specific variables add significant unique variance to individual areas of social functioning. Although a global social functioning score can be considered useful, greater precision can be gained by the use of domain-specific measures.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".