Association of Sexual Violence and Human Rights Violations With Physical and Mental Health in Territories of the Eastern Democratic Republic of the Congo
Bibliographic record
Abstract
CONTEXT: Studies from the Eastern Region of the Democratic Republic of the Congo (DRC) have provided anecdotal reports of sexual violence. This study offers a population-based assessment of the prevalence of sexual violence and human rights abuses in specific territories within Eastern DRC. OBJECTIVE: To assess the prevalence of and correlations with sexual violence and human rights violations on residents of specific territories of Eastern DRC including information on basic needs, health care access, and physical and mental health. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional, population-based, cluster survey of 998 adults aged 18 years or older using structured interviews and questionnaires, conducted over a 4-week period in March 2010. MAIN OUTCOME MEASURES: Sexual violence prevalence and characteristics, symptoms of major depressive disorder (MDD) and posttraumatic stress disorder (PTSD), human rights abuses, and physical and mental health needs among Congolese adults in specific territories of Eastern DRC. RESULTS: Of the 1005 households surveyed 998 households participated, yielding a response rate of 98.9%. Rates of reported sexual violence were 39.7% (95% confidence interval [CI], 32.2%-47.2%; n = 224/586) among women and 23.6% (95% CI, 17.3%-29.9%; n = 107/399) among men. Women reported to have perpetrated conflict-related sexual violence in 41.1% (95% CI, 25.6%-56.6%; n = 54/148) of female cases and 10.0% (95% CI, 1.5%-18.4%; n = 8/66) of male cases. Sixty-seven percent (95% CI, 59.0%-74.5%; n = 615/998) of households reported incidents of conflict-related human rights abuses. Forty-one percent (95% CI, 35.3%-45.8%; n = 374/991) of the represented adult population met symptom criteria for MDD and 50.1% (95% CI, 43.8%-56.3%; n = 470/989) for PTSD. CONCLUSION: Self-reported sexual violence and other human rights violations were prevalent in specific territories of Eastern DRC and were associated with physical and mental health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".