Psychological and psychosocial interventions for negative symptoms in psychosis: Systematic review and meta-analysis
Bibliographic record
Abstract
Background Negative symptoms observed in patients with psychotic disorders undermine quality of life and functioning. Antipsychotic medications have a limited impact. Psychological and psychosocial interventions, with medication, are recommended. However, evidence for the effectiveness of specific non-biological interventions warrants detailed examination. Aims To conduct a meta-analytic and systematic review of the literature on the effectiveness of non-biological treatments for negative symptoms in psychotic disorders. Method We searched for randomised controlled studies of psychological and psychosocial interventions in psychotic disorders that reported outcome on negative symptoms. Standardised mean differences (SMDs) in values of negative symptoms at the end of treatment were calculated across study domains as the main outcome measure. Results A total of 95 studies met our criteria and 72 had complete quantitative data. Compared with treatment as usual cognitive–behavioural therapy (pooled SMD −0.34, 95% CI −0.55 to −0.12), skills-based training (pooled SMD −0.44, 95% CI −0.77 to −0.10), exercise (pooled SMD −0.36, 95% CI −0.71 to −0.01), and music treatments (pooled SMD −0.58, 95% CI −0.82 to −0.33) provide significant benefit. Integrated treatment models are effective for early psychosis (SMD −0.38, 95% CI −0.53 to −0.22) as long as the patients remain in treatment. Overall quality of evidence was moderate with a high level of heterogeneity. Conclusions Specific psychological and psychosocial interventions have utility in ameliorating negative symptoms in psychosis and should be included in the treatment of negative symptoms. However, more effective treatments for negative symptoms need to be developed.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".