Interventions to Reduce Child Maltreatment: A Systematic Review with a Narrative Synthesis
Bibliographic record
Abstract
Abstract Child maltreatment has been a prominent topic on the political agenda for the past decade. However, while there are several types of interventions that can potentially benefit the prevention of child maltreatment, uncertainties remain regarding the transferability of these interventions to different contexts and their overall impact. Consequently, we conducted a systematic review of intervention studies aimed at preventing child maltreatment. We searched for studies published between 2016 and 2021, using predefined keywords from various bibliographical databases including PsycINFO, SocINDEX, Social Care Online, Web of Science, and ASSIA. The initial literature search yielded 3221 studies based on titles and abstracts, after removing duplicates. Out of these, 251 studies were screened based on full texts, resulting in the selection of 56 studies that met our inclusion criteria and were retained for extraction and analysis. The screening and data extraction processes were conducted by at least two independent reviewers. Given the heterogeneity of the included studies, we performed a narrative synthesis and categorized the 56 studies based on intervention type, control condition, outcomes, effects and quality. The results indicated that most of the studies employed individual randomization, with the control group most often receiving treatment as usual. Home visiting programs and educational interventions emerged as the most prevalent types of interventions. The review also demonstrated that a significant number of the included studies reported positive effects on one or more outcomes, such as indicators of maltreatment, suboptimal parenting practices, and problematic child behaviors. While nearly one-third of the studies did not report an effect size, those that did reported varying types of effect sizes. Additionally, only a few studies met the assessed quality criteria.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".