The impact of evidence-based interventions on depression in survivors of intimate partner violence: A Systematic review and meta-analysis
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) is a pervasive global issue that substantially increases the risk of depression, post-traumatic stress disorder (PTSD), and substance use disorders among survivors. Despite the known mental health consequences, there is limited synthesis of evidence regarding the effectiveness of therapeutic interventions targeting depression in this population. Objectives: To evaluate the effectiveness of pharmacological and psychosocial interventions in reducing depressive symptoms among survivors of IPV through a systematic review and meta-analysis. Methods: A comprehensive literature search was conducted in PubMed, PsycINFO, Scopus, Web of Science, and CINAHL for studies published between 2008 and 2025. Eligible studies included randomized controlled trials and quasi-experimental designs evaluating depression outcomes in IPV survivors aged ≥16 years. Data extraction and quality assessment were performed independently by two reviewers using standardized tools (RoB 2, ROBINS-I, and Newcastle-Ottawa Scale). Meta-analyses were conducted using random-effects models, with subgroup and meta-regression analyses to explore heterogeneity. Results: Eight studies met inclusion criteria, comprising six psychosocial and two combined psychosocial-pharmacological interventions. The pooled mean difference (MD) was -1.22 (95% CI: -1.27 to -1.18; p < 0.001), indicating a significant reduction in depressive symptoms. Digital interventions yielded a MD of -1.29 (95% CI: -1.87 to -0.70), while participatory models showed a MD of -0.79 (95% CI: -1.42 to -0.16). High heterogeneity was observed (I² = 99.58%). Metaregression revealed digital interventions were significantly associated with reduced depressive symptoms (coefficient = -1.41, p = 0.014). Conclusions: Evidence-based interventions, particularly digital and participatory models, can effectively reduce depression among IPV survivors. However, standalone interventions often yield limited outcomes. Integration with pharmacological treatments and long-term trauma-informed care is critical to enhance mental health recovery. Future research should focus on tailoring interventions to diverse populations and ensuring methodological rigor in evaluating long-term efficacy.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.042 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".