Meta-Analysis on Interventions for Children Exposed to Intimate Partner Violence
Bibliographic record
Abstract
Several reviews have been conducted on children’s outcomes following exposure to intimate partner violence (IPV), but there remain inconsistent findings. We conducted a meta-analysis on child emotional and behavioral outcomes of IPV exposure interventions, based on published reviews that included a child component. We also explored relative effect sizes by examining moderators of the effect sizes across studies. This meta-analysis included 21 evaluation studies across 12 published reviews, which were located using a multiple database systematic search of English publications between 2000 and 2019. Studies were required to evaluate IPV interventions that included children, to gather quantitative pre- and post-intervention data on child outcomes, to use standardized instruments, and to present data in a format that could be used in a meta-analysis. Results indicated an overall pre- to post-intervention medium effect size ( d = 0.49), with effect sizes ranging from small to large depending on the specific outcome. Improvements at follow-up were maintained for internalizing behaviors but decreased for trauma-related symptoms and social, externalizing, and total behaviors. However, externalizing and total behavior outcomes still had significant effect sizes in the small-to-medium range ( d = 0.36 and 0.44). There were greater intervention effects when treatment was not exclusively trauma-specific. It appears that IPV exposure interventions are generally effective for improving children’s emotional and behavioral well-being, although interventions would benefit from greater tailoring to children’s specific needs. Interventions may also benefit from incorporating various content areas (both trauma-specific and non-trauma-specific) and from greater focus on ensuring the maintenance of treatment gains.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.058 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".