Cognitive adaptation training for forensic psychiatry inpatients with schizophrenia spectrum diagnoses
Bibliographic record
Abstract
Cognitive deficits affect 70–75% of individuals with schizophrenia and significantly impact functioning. Cognitive Adaptation Training (CAT) is an evidence-based compensatory intervention that improves functioning through personalized environmental supports. Research has explored adaptations to CAT for specific contexts and sub-populations. The present study explored the feasibility and preliminary outcome data for CAT adapted for inpatient forensic psychiatry settings (finCAT). This study employed a single group mixed-method design collecting data at baseline, post-intervention, and 2-months follow-up. Forensic psychiatry inpatients with schizophrenia spectrum diagnoses (N = 18) participated. Outcomes included room organization, self-care, goal attainment, and qualitative interviews with patients (n = 4) and staff (n = 4), as well as secondary measures of unit climate and clinician attitudes. Data analyses with repeated-measures ANOVA revealed a significant effect of time on blind-rated room organization, with significant improvements at post-intervention sustained during follow-up. There were no significant changes to self-care ratings or secondary measures. Qualitative themes identified included (1) improvement in patients’ self-care and organization; (2) clinicians’ increased awareness of the relationship between cognitive deficits and functional outcomes; (3) improvements beyond self-care and room organization; and (4) increased opportunities for interprofessional collaboration. These results support the feasibility of adapting CAT for inpatient forensic psychiatry settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".