Comparing executive functioning and clinical and sociodemographic characteristics of people with schizophrenia who hold a driver’s license to those who do not
Bibliographic record
Abstract
BACKGROUND.: Community engagement requires driving. However, there is paucity of research focusing on the potential to drive for people with schizophrenia. PURPOSE.: This study aimed to characterize people with schizophrenia by comparing clinical signs, executive functions (EF), and sociodemographic aspects of those holding a driver's license to those without one. METHOD.: This cross-sectional study used convenience sampling to select 60 ambulatory individuals to participate: 31 with a driver's license and 29 without one. They completed the Wisconsin Card Sorting Test (WCST) for evaluation of EF and the Positive and Negative Syndrome Scale (PANSS) for symptoms severity evaluation. Data were analyzed using multivariate analyses of covariance and logistic regression models. FINDINGS.: Participants with a license had less severe negative symptoms and general psychopathology and better EF and sociodemographic aspects compared to those without a license. Logistic regression revealed significant odds ratios (OR) in general psychopathology (PANSS; OR = 0.963, p = .011) and in the WCST (OR = 0.504, p = .027). IMPLICATIONS.: This study offers occupational therapists a data-driven perspective on evaluating potential fitness to drive to enable participation in daily life and well-being of people with 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".