Effect Conflict and Conflict-Related Sexual Violence on First-Order Marital and Premarital Adolescent Fertility in the Democratic Republic of the Congo
Bibliographic record
Abstract
The conflicts in the Democratic Republic of the Congo (DRC) have resulted in significant conflict-related sexual violence (CRSV) against girls under 18. This study examines the impact of conflict and CRSV on first-order marital and non-marital adolescent fertility in the DRC. We utilized data from the 2007 Demographic and Health Surveys, geo-referenced conflict data (UCDP-GED), and CRSV data (GEO-SVAC). Using the Mundlak approach, we first implemented a binary multilevel discrete-time event history model to assess how exposure to conflict and CRSV impacted the timing of first adolescent conception, both marital and premarital. Second, we treated first marital and premarital conceptions as two competing events. We employed a multinomial multilevel discrete-time event history model to examine the impact of exposure to conflict and CRSV on each type of event. Our findings reveal that (1) exposure to CRSV increased the risk of premarital adolescent conception; (2) conflict exposure reduced the likelihood of marital adolescent conception; (3) CRSV delayed adolescent conception within marriage. The first two findings align with existing literature, while the third offers new insights into how CRSV may delay the timing of first adolescent pregnancy, including through spousal rejection following extramarital sexual violence and resilience among survivors. These results underscore the need for (1) improved access to contraceptives for girls in conflict zones, (2) strengthened women’s empowerment programs, and (3) lasting peace in the DRC to enhance adolescent health outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".