Compensatory Interventions for Cognitive Impairments in Psychosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Cognitive compensatory interventions aim to alleviate psychosocial disability by targeting functioning directly using aids and strategies, thereby minimizing the impact of cognitive impairment. The aim was to conduct a systematic review and meta-analysis of cognitive compensatory interventions for psychosis by examining the effects on functioning and symptoms, and exploring whether intervention factors, study design, and age influenced effect sizes. METHODS: Electronic databases (Ovid Medline, PsychINFO) were searched up to October 2018. Records obtained through electronic and manual searches were screened independently by two reviewers according to selection criteria. Data were extracted to calculate estimated effects (Hedge's g) of treatment on functioning and symptoms at post-intervention and follow-up. Study quality was assessed using Cochrane Collaboration's risk of bias tool. RESULTS: Twenty-six studies, from 25 independent randomized controlled trials (RCTs) were included in the meta-analysis (1654 participants, mean age = 38.9 years, 64% male). Meta-analysis revealed a medium effect of compensatory interventions on functioning compared to control conditions (Hedge's g = 0.46, 95% CI = 0.33, 0.60, P < .001), with evidence of relative durability at follow-up (Hedge's g = 0.36, 95% CI = 0.19, 0.54, P < .001). Analysis also revealed small significant effects of cognitive compensatory treatment on negative, positive, and general psychiatric symptoms, but not depressive symptoms. Estimated effects did not significantly vary according to treatment factors (ie, compensatory approach, dosage), delivery method (ie, individual/group), age, or risk of bias. Longer treatment length was associated with larger effect sizes for functioning outcomes. No evidence of publication bias was identified. CONCLUSION: Cognitive compensatory interventions are associated with robust, durable improvements in functioning in people with psychotic illnesses.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".