Who perpetrates violence against children? A systematic analysis of age-specific and sex-specific data
Bibliographic record
Abstract
OBJECTIVE: The epidemiology of violence against children is likely to differ substantially by sex and age of the victim and the perpetrator. Thus far, investment in effective prevention strategies has been hindered by lack of clarity in the burden of childhood violence across these dimensions. We produced the first age-specific and sex-specific prevalence estimates by perpetrator type for physical, sexual and emotional violence against children globally. DESIGN: We used random effects meta-regression to estimate prevalence. Estimates were adjusted for relevant quality covariates, variation in definitions of violence and weighted by region-specific, age-specific and sex-specific population data to ensure estimates reflect country population structures. DATA SOURCES: Secondary data from 600 population or school-based representative datasets and 43 publications obtained via systematic literature review, representing 13 830 estimates from 171 countries. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Estimates for recent violence against children aged 0-19 were included. RESULTS: The most common perpetrators of physical and emotional violence for both boys and girls across a range of ages are household members, with prevalence often surpassing 50%, followed by student peers. Children reported experiencing more emotional than physical violence from both household members and students. The most common perpetrators of sexual violence against girls aged 15-19 years are intimate partners; however, few data on other perpetrators of sexual violence against children are systematically collected internationally. Few age-specific and sex-specific data are available on violence perpetration by schoolteachers; however, existing data indicate high prevalence of physical violence from teachers towards students. Data from other authority figures, strangers, siblings and other adults are limited, as are data on neglect of children. CONCLUSIONS: Without further investment in data generation on violence exposure from multiple perpetrators for boys and girls of all ages, progress towards Sustainable Development Goals 4, 5 and 16 may be slow. Despite data gaps, evidence shows violence from household members, peers in school and for girls, from intimate partners, should be prioritised for prevention. TRIAL REGISTRATION NUMBER: PROSPERO 2015: CRD42015024315.
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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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".