Childhood Sexual Violence in Indonesia: A Systematic Review
Bibliographic record
Abstract
There has been relatively little research into the prevalence of childhood sexual violence (CSV) as well as the risk and protective factors for CSV in low- and middle-income countries including Indonesia. Systematic searches conducted in English and Bahasa Indonesia in this review identified 594 records published between 2006 and 2016 in peer-reviewed journals and other literature including 299 Indonesian records. Fifteen studies, including nine prevalence studies, met the quality appraisal criteria developed for this review. The review found that CSV research is scarce: Only one study included nationally representative prevalence estimates. Varying definitions for CSV, survey methods, and sample characteristics limited the generalizability of the data. The available evidence points to significant risk of sexual violence affecting both girls and boys across many geographical and institutional settings. Married adolescent girls are vulnerable to sexual violence by partners in their homes. Children in schools are vulnerable to CSV by peers and adults. Victims seldom disclose incidents and rarely seek support. In addition, early childhood experiences of trauma were strongly associated with later perpetration of sexual violence and revictimization. Limited information is available about protective factors. This review synthesizes evidence about what is currently known about CSV in Indonesia and identifies the strengths and weaknesses of the existing research. A more robust evidence base regarding CSV is required to better inform policy and justify investment into prevention programs.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".