Association between sexual violence and unintended pregnancy among adolescent girls and young women in South Africa
Bibliographic record
Abstract
BACKGROUND: Unintended pregnancy has dire consequences on the health and socioeconomic wellbeing of adolescent girls and young women (AGYW) (aged 15-24 years). While most studies tend to focus on lack of access to contraceptive information and services, and poverty as the main contributing factor to early-unintended pregnancies, the influence of sexual violence has received limited attention. Understanding the link between sexual violence and unintended pregnancy is critical towards developing a multifaceted intervention to reduce unintended pregnancies among AGYW in South Africa, a country with high teenage pregnancy rate. Thus, we estimated the magnitude of unintended pregnancy among AGYW and also examined the effect of sexual violence on unintended pregnancy. METHODS: Our study adopted a cross-sectional design, and data were obtained from AGYW in a South African university between June and November 2018. A final sample of 451 girls aged 17-24 years, selected using stratified sampling, were included in the analysis. We used adjusted and unadjusted logistic regression analysis to examine the effect of sexual violence on unintended pregnancy. RESULTS: The analysis shows that 41.9% of all respondents had experienced an unintended pregnancy, and 26.3% of those unintended pregnancies ended in abortions. Unintended pregnancy was higher among survivors of sexual violence (54.4%) compared to those who never experienced sexual abuse (34.3%). In the multivariable analysis, sexual violence was consistently and robustly associated with increased odds of having an unintended pregnancy (AOR:1.70; 95% CI: 1.08-2.68). CONCLUSION: Our study found a huge magnitude of unintended pregnancy among AGYW. Sexual violence is an important predictor of unintended pregnancy in this age cohort. Thus, addressing unintended pregnancies among AGYW in South Africa requires interventions that not only increase access to contraceptive information and services but also reduce sexual violence and cater for survivors.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".