Patient-centered group psychotherapy for depression and negative emotions: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: Depressive disorders and negative emotions are a major global health challenge, affecting over 280 million people and worsened by the COVID-19 pandemic. Traditional treatments have limitations such as high relapse rates and accessibility issues. This study aimed to assess the efficacy of patient-centered group psychotherapy (PCGP) on depressive symptoms and functional outcomes, identify moderators, and provide recommendations. Methods: Following PRISMA guidelines, we searched PubMed, CNKI, and other databases through October 2024, including 7 randomized controlled trials (RCTs) and one Clinical study (total N = 1,989). Study quality was assessed using the Newcastle-Ottawa Scale. Random-effects meta-analyses via RevMan 5.4 calculated risk ratios (RRs) and standardized mean differences (SMDs), with heterogeneity evaluated via I² statistics. Results: Eligible participants comprised adults (≥18 years) with a principal diagnosis of major depressive disorder (DSM-5/ICD-10 criteria) or clinically significant negative emotional symptoms (e.g., PHQ-9≥15), excluding those with primary non-depressive psychiatric comorbidities. Studies involving mixed populations were included only if subgroup data for depressed participants were extractable. PCGP showed significant positive effects on overall effectiveness (RR = 1.10, 95% CI: 1.01-1.19, p = 0.03), symptom reduction (Positive and Negative Syndrome Scale (PANSS) scores, SMD = -1.96, 95% CI: -2.31 to -1.61, p < 0.001), and functional outcomes (Personal and Social Performance (PSP) scores, SMD = 1.96, 95% CI: 1.41-2.51, p < 0.001). It also improved negative mood (SMD = -4.28, 95% CI: -8.03 to -0.52, p = 0.03) but with high heterogeneity (I² = 99.0%). A positive trend was noted for medication adherence (RR = 1.11, 95% CI: 0.89-1.38, p = 0.35). Conclusion: PCGP is an effective first-line adjunct therapy for depression, particularly in resource-limited settings. It addresses both symptom reduction and functional recovery by combining personalized goal-setting with group dynamics.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".