A feasibility study of a new computerised cognitive remediation for young adults with schizophrenia
Bibliographic record
Abstract
Cognitive remediation therapy is effective for improving cognition, symptoms and social functioning in individuals with schizophrenia; however, the impact on visual episodic memory remains unclear. The objectives of this feasibility study were: (1) to explore whether or not CIRCuiTS--a new computerised cognitive remediation therapy programme developed in England--improves visual episodic memory and other cognitive domains in young adults with early course schizophrenia; and (2) to evaluate acceptability of the CIRCuiTS programme in French-Canadians. Three participants with visual episodic memory impairments at baseline were recruited from clinical settings in Canada, and consented to participate. Neuropsychological, clinical and social functioning was evaluated at baseline and post-treatment. Intervention involved 40 sessions of cognitive remediation. First, the reliable change index (RCI) revealed that each participant demonstrated significant post-therapy change in episodic memory and in other cognitive domains. The response profile was characterised by the use of organisational strategies. Second, the treatment was considered acceptable to participants in terms of session frequency (number of sessions per week), intensity (hours per week; total hours), and number of missed sessions and total completed sessions. This preliminary study yielded encouraging data demonstrating the feasibility of the CIRCuiTS programme in French-Canadian young adults 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".