Effectiveness of Peer-Administered Interventions for Perinatal Depression or Anxiety
Bibliographic record
Abstract
This meta-analysis assessed the effectiveness of peer-administered interventions for treating perinatal depression or anxiety and whether variations in intervention characteristics impacted their effectiveness. Records were identified through MEDLINE, EMBASE, PsycINFO, CINAHL, and Web of Science until October 2024. We used terms related to the perinatal period, depression, anxiety, and peer support. We identified 5,700 articles, of which 19 were included and 18 were meta-analyzed. A total of 3,821 participants were included, with the majority from high-income countries. Studies involving a peer-administered intervention for perinatal depression or anxiety with a randomized controlled trial (RCT) design were eligible. Three intervention types were identified: peer-delivered psychotherapies, individual peer support, and peer discussion groups. Random-effects meta-analyses suggested that peer-administered interventions were more effective at improving depression symptoms than standard care (standardized mean difference [SMD]: -0.35; 95% CI, -0.54 to -0.17). Peer-delivered psychotherapy had the largest effect sizes (SMD: -0.51; 95% CI, -0.79 to -0.24), followed by individual support (SMD: -0.30; 95% CI, -0.63 to 0.04) and discussion groups (SMD: -0.09; 95% CI, -0.42 to 0.25). Subgroup analyses suggest that group interventions may lead to the greatest improvement. On the whole, peer-administered interventions were not effective for anxiety (SMD: -0.25; 95% CI, -0.57 to 0.08), but peer-delivered psychotherapies specifically improved anxiety symptoms (SMD: -0.63; 95% CI, -0.95 to -0.31). Peer-administered interventions are effective at improving perinatal depression, with peer-delivered psychotherapies being the most effective. Large-scale RCTs are needed to explore long-term effectiveness on perinatal depression and anxiety.
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 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.037 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".