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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".