Trends in sexual violence patterns and case management: a sex disaggregated analysis in Goma, Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: Both conflict and non-conflict sexual violence have been well described in the Democratic Republic of Congo (DRC). However, there is little empiric data comparing sexual violence patterns for males and females in the DRC, and little is known about how post-sexual assault care experiences may differ between the two sexes. METHODS: This was a retrospective, registry-based study at HEAL Africa Hospital. Researchers extracted and analyzed available data for all patients seeking post-sexual assault care between July 2013 and December 2017. Comparative analysis was conducted using SAS to document patterns of sexual violence among male and female survivors and to describe the clinical management of males and females seeking post-assault care. RESULTS: Between July 2013 and December 2017, the hospital provided post-sexual assault care to 1766 patients (1623 female and 93 male). Female survivors were more likely to be minors under the age 18 (p < 0.0001) with a mean age 16.5 years versus 22.3 years for males. For both sexes, approximately half of all perpetrators were civilians who were known to the survivor (friends, family members, colleagues or neighbors). After sexual assault, males (79.6%) were more likely than females (55.7%) to present to the hospital within 72 h (p-value < 0.0001). Among female patients, 12% had a positive pregnancy test at the time of presentation and another 43% received emergency contraception. Male survivors were more likely to test positive for HIV (p-value = 0.0032) and to receive HIV post-exposure prophylaxis as well as prophylactic antibiotics (p-value < 0.0001). CONCLUSIONS: In this single-centre registry, non-conflict-related sexual violence affected both women and girls as well as men and boys in North Kivu with civilian-perpetrated assaults being most common, and girls under the age of 18 being disproportionately affected. Overall, delays to seeking post-assault care appear to have decreased over time, although females presented later than males. These differences, as well as sex discrepancies in receiving HIV prophylaxis and prophylactic antibiotics, are not well understood. Additional research is needed to understand these phenomena such that equitable and optimal care can be ensured for both female and male sexual violence survivors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".