Trends in sexual violence patterns and case management: a sex disaggregated analysis in Goma, Democratic Republic of Congo
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».