Cognitive Remediation for Adolescents With Mental Health Disorders: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: The effects of cognitive remediation therapy (CRT) in adults with mental health disorders have been widely documented, but its effects in adolescents with mental health disorders remain poorly understood. This review aims to (1) determine the effects of CRT on cognition, symptoms and functioning for adolescents with mental health disorders and (2) evaluate the methodological quality of studies on CRT. METHODS: A systematic review and meta-analysis were conducted of randomised controlled trials of CRT involving adolescents with mental health disorders. Searches were conducted in databases for studies pertaining to CRT effects on cognition, social functioning and clinical symptoms. Methodological quality was assessed using the Clinical Trials Assessment Measure. RESULTS: Fourteen studies (N = 14) were included, with 11 independent samples (k = 11, 592 participants). Participants had various mental health disorders. CRT showed a small significant effect on cognition (g = 0.14, p = 0.02), particularly on processing speed, working memory and episodic memory. No significant effects were found for clinical symptoms (g = 0.04, p = 0.58) and social functioning (g = 0.06, p = 0.39). Methodological quality of included studies was variable, ranging from poor to good quality. CONCLUSION: Included studies showed a small significant effect of CRT on cognition, and non-significant effects on clinical symptoms and social functioning in adolescents with mental health disorders. The lack of effects may be partly explained by limitations in the methodology of included studies. A critical analysis of current studies is presented and recommendations of core techniques to consider for future CRT studies are discussed.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".