Intimate Partner Violence and the Sexual and Reproductive Health Outcomes of Women in Ghana
Bibliographic record
Abstract
Few studies have examined the relationships between intimate partner violence (IPV) and the sexual and reproductive health outcomes of women in sub-Saharan countries in general and Ghana specifically. This study began to fill the gap by investigating whether individual- and community-level IPV influenced unwanted pregnancy and pregnancy loss among women in Ghana. Nationally representative cross-sectional data were collected from 2,289 ever-married women, and multilevel modeling was used to estimate individual- and community-level effects. At the individual level, IPV was significantly associated with unwanted pregnancy and pregnancy loss. Women with experience of both physical and sexual violence were more likely to have reported an unwanted pregnancy and a pregnancy loss. However, only those experiencing sexual violence reported unwanted pregnancies. Similarly, community-level IPV was associated with sexual health outcomes. Respondents in communities with higher levels of sexual violence were significantly more likely to have had unwanted pregnancies. The findings corroborate calls for policy makers to consider IPV a reproductive health issue. They also emphasize the need to move beyond individual-level interventions to consider structural and community contexts when addressing the sexual and reproductive health outcomes of women in Ghana.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".