Sexual violence as a predictor of unintended pregnancy among married young women: evidence from the 2016 Nepal demographic and health survey
Bibliographic record
Abstract
BACKGROUND: Sexual violence in marital relationship is higher among women married at young age. Although sexual violence has been found to increase risk for unintended pregnancy, there is a limited published data from Nepal linking sexual violence with unintended pregnancy. The current study aimed to investigate association of partner sexual violence with unintended pregnancy among young married women who experienced child birth in last 5 years. METHODS: Using data from Nepal Demographic and Health Survey, we studied the prevalence of sexual violence and unintended pregnancy, and their association among 560 married women (weighted sample) of 15-24 years who gave childbirth in last 5 years of the survey. We used multivariate logistic regression to analyse the association of sexual violence and other factors with unintended pregnancy. Analysis was conducted considering inverse probability weighting, clustering, and stratification to provide unbiased estimates of the population parameters. RESULTS: Nearly a quarter of women (22.7%) reported to have experienced unintended pregnancy in the last 5 years of the survey and almost one in 10 women (9%) reported to have ever experienced sexual violence from their husbands. Women who ever experienced sexual violence from their husbands were at 2.3 times higher odds to report an unintended pregnancy (aOR = 2.3; 95% CI = 1.1-4.8) compared to women who did not experience sexual violence from their husbands independent of important socio-demographic variables and ever use of contraception. CONCLUSION: The strong association of sexual violence within marital relationship with unintended pregnancy among young women in Nepal necessitates the provision of comprehensive sexual and reproductive health services. Women need routine assessment, and referral to appropriate services for sexual violence to reduce unintended pregnancy and its consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".