Sexual Violence as a Predictor of Unwanted Pregnancy: Evidence from the 2013 Nigeria Demographic and Health Survey
Bibliographic record
Abstract
Gender-based domestic violence (GBDV) continues to pose a serious threat to woman folk and the society at large. All efforts to reduce the menace have not yielded an impressive result and thus, the prevalence rate is still unacceptably high. Employing analytic nationally representative weighted sample size, 15,941women aged 15-49 years who were currently pregnant or ever had at least one pregnancy experience were interviewed for domestic violence through quantitative instrument (questionnaire). The data were analysed with a chi-squared test and binary logistic regression using STATA 13. Overall, one quarter (24.7%) of the total respondents who ever experienced domestic violence from their spouses or intimate sexual partners reported having experienced unwanted/unintended pregnancy. It was evident in the study that GBDV is significantly related to unwanted pregnancy even after controlling for all other tested independent variables like age, educational attainment, wealth index, religion, place of residence and other fertility related variables such as number of children ever born, contraceptive use and pregnancy termination experience. Spousal abuse in any form is a crucial predictor of unwanted pregnancy in Nigeria. Therefore, addressing gender-based domestic violence is critical to reducing the menace of unwanted pregnancy and taming unnecessary population growth in Nigeria.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".