The influence of demographic factors on functional capacity and everyday functional outcomes in schizophrenia
Bibliographic record
Abstract
Patients with schizophrenia have impaired everyday living and social outcomes. Performance-based measures, including neuropsychological (NP) performance and functional capacity (FC) measures have demonstrated usefulness in predicting these outcomes. We examined the correlation of demographic factors (race, age, and education) and FC measures, and the relative ability of NP performance, FC, and demographic factors to predict real-world outcomes in social, vocational, and residential domains in 194 outpatients with schizophrenia. Age, education, sex, and racial status were significantly, but modestly, associated with performance-based measures of everyday functioning, while, in addition, age and education had a similar modest relationship with social competence. Age, but none of the other demographic variables, contributed to the prediction of all three domains of everyday functioning. Functional capacity variables predicted everyday outcomes even when demographic variables were entered into a predictive equation first. These data suggest a similar and modest but detectable effect of demographic factors on performance-based measures of functional capacity as seen with NP performance in schizophrenia populations. Older age contributed to poorer everyday functioning even after consideration of functional capacity, which seems similar to findings in healthy populations without clinically notable cognitive decline.
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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 | 0.005 |
| 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) | 0.001 | 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".