Cognitive Remediation and Social Recovery in Early Psychosis (CReSt-R): protocol for a pilot randomised controlled study
Bibliographic record
Abstract
BACKGROUND: Psychosis, even in its early stages, is associated with significant disability, causing it to be ranked ahead of paraplegia and blindness in those aged 18-35 in terms of years lived with disability. Current pharmacological and psychological interventions intervention have focused primarily on the reduction of positive symptoms (hallucinations and delusions), with little benefit to domains of psychosis such as cognitive difficulties and social and occupational functioning. METHODS/DESIGN: The CReSt-R intervention trial is a single center, pilot randomised controlled study based at the National University of Ireland (NUI), Galway. The trial will recruit participants from four clinical sites with assessment and intervention completed by the primary NUI Galway team. The trial will explore the feasibility, acceptability, and effectiveness of a novel psychosocial intervention for early psychosis based on a combined cognitive remediation training and cognitive behavioural therapy approach focused on social recovery. Participants, aged 16-35 within the first 5 years of a diagnosed psychotic disorder, will be recruited from the Children and Adolescent Mental Health Service and the Adult Mental Health Services in the region. DISCUSSION: Cognitive remediation training (for improving cognition) and social recovery focused cognitive behavioural therapy, have both separately demonstrated effectiveness. This trial will evaluate the feasibility, acceptability, and explore the efficacy of a treatment approach that combines both approaches as part of an integrated, multicomponent intervention. TRIAL REGISTRATION: , 2021.
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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 | 0.053 | 0.036 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.093 | 0.017 |
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".