A Qualitative Analysis of Disclosure Patterns among Women with Sexual Violence-Related Pregnancies in Eastern Democratic Republic of Congo
Bibliographic record
Abstract
The Democratic Republic of the Congo (DRC) has experienced nearly two decades of civil conflict in the Eastern regions of North and South Kivu. This conflict has been notorious for the use of sexual violence as a weapon of war, leading in many cases to pregnancy after rape. The objectives of this analysis were: 1) to describe patterns of sexual violence-related pregnancy (SVRP) disclosure; 2) to consider why survivors chose to disclose to particular individuals; and 3) to examine the dialogue around SVRPs between women with SVRPs and their confidants. In South Kivu Province, Democratic Republic of Congo, two sub-groups of sexual violence survivors completed qualitative interviews, those currently raising a child from an SVRP (parenting group, N = 38) and those who had terminated an SVRP (termination group, N = 17). The findings show that a majority of SVRPs were conceived when participants were held in sexual captivity for prolonged periods of time. The SVRPs were disclosed to friends, family members, other sexual violence survivors, community members, spouses, health care providers, or perpetrators. The confidants were most often chosen because they were perceived by the participants as being discreet, trusted, and supportive. The confidants often provided advice about continuing or terminating the SVRP. Trust and discretion are the most important factors determining to whom women with SVRPs disclose their pregnancies. The vital role of confidants in giving support after disclosure cannot be overlooked. Providing opportunities for survivors to safely disclose their SVRPs, including to health care providers, is a necessary first step in allowing them to access safe and comprehensive post-assault care and services.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".