Sexual Violence toward Children and Youth in War-Torn Eastern Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: The epidemic of gender-based violence in the Democratic Republic of the Congo (DRC) has garnered popular media attention, but is incompletely described in the medical literature to date. In particular, the relative importance of militarized compared to civilian rape and the impact on vulnerable populations merits further study. We describe a retrospective case series of sexual abuse among children and youth in eastern DRC. METHODS: Medical records of patients treated for sexual assault at HEAL Africa Hospital, Goma, DRC between 2006 and 2008 were reviewed. Information extracted from the chart record was summarized using descriptive statistics, with comparative statistics to examine differences between pediatric (≤ 18 yrs) and adult patients. FINDINGS: 440 pediatric and 54 adult sexual abuse cases were identified. Children and youth were more often assaulted by someone known to the family (74% vs 30%, OR 6.7 [95%CI 3.6-12], p<0.001), and less frequently by military personnel (13% vs 48%, OR 0.14 [95%CI 0.075-0.26], p<0.001). Delayed presentation for medical care (>72 hours after the assault) was more common in pediatric patients (53% vs 33%, OR 2.2 [95%CI 1.2-4.0], p = 0.007). Physical signs of sexual abuse, including lesions of the posterior fourchette, hymeneal tears, and anal lesions, were more commonly observed in children and youth (84% vs 69%, OR 2.3 [95%CI 1.3-4.4], p = 0.006). Nine (2.9%) pediatrics patients were HIV-positive at presentation, compared to 5.3% of adults (p = 0.34). INTERPRETATION: World media attention has focused on violent rape as a weapon of war in the DRC. Our data highlight some neglected but important and distinct aspects of the ongoing epidemic of sexual violence: sexual abuse of children and youth.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".