A Meta-Analysis of Group Cognitive Behavioral Therapy and Group Psychoeducation for Treating Symptoms and Preventing Relapse in People Living with Bipolar Disorder
Bibliographic record
Abstract
Objectives: This meta-analysis aims to evaluate the treatment outcomes of patients treated with Group cognitive behavioural therapy (GCBT) or group psychoeducation (GPE) as an adjunct to pharmacotherapy. Methods: Systematic search of PubMed, EMBASE, PsycINFO, and CENTRAL from inception till 1 March 2022 was conducted. Randomized-controlled-trials (RCTs) comparing GCBT/GPE with controls (treatment-as-usual/individualized therapy) in adults with bipolar disorder were eligible. The outcomes were relapse rates of any depressive or manic episodes and control of depressive and manic symptoms post-intervention. Overall odds-ratio was used to evaluate the relapse rates. Standard Mean Differences were pooled using a random-effects model for the control of depressive and manic symptoms. Results: 25 articles were assessed full-text independently by two members, and 11 studies were included in this meta-analysis. 601 and 590 participants were randomized into group-therapy (GCBT/GPE) and control, respectively. GPE significantly reduces relapse rates at post-intervention with Odds ratio of 0.43 (95% CI = 0.28-to-0.62, p < 0.0001) (I² = 41%) compared to control, however, no significant results were found for GPE on control of depressive or manic symptoms. No significant results were found for GCBT in all outcomes. Conclusion: This meta-analysis provides some evidence that GPE could be an efficacious treatment as an adjunct to treatment-as-usual in reducing the relapse rates of patients with bipolar disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.058 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".