Sexual violence and self-reported sexually transmitted infections among women in sub-Saharan Africa
Bibliographic record
Abstract
Sexual violence has proven to be associated with sexually transmitted infections (STIs) in sub-Saharan Africa (SSA). We examined the association between sexual violence and self-reported STIs (SR-STIs) among women in sexual unions in 15 sub-Saharan African countries. This was a cross-sectional study involving the analysis of data from the Demographic and Health Surveys (DHS) from 15 countries in SSA. A total sample of 65,392 women in sexual unions were included in the final analysis. A multilevel binary logistic regression analysis was carried out and the results were presented using adjusted odds ratios (aOR) at 95% Confidence Interval (CI). Women who experienced sexual violence in the last 12 months were more likely to self-report STIs compared to those who did not experience sexual violence [aOR = 1.76, 95% CI = 1.59-1.94]. Compared to women in Angola, those who were in Mali, Nigeria, Sierra Leone, Uganda, and Liberia were more likely to self-report STIs while those in Burundi, Cameroon, Chad, Ethiopia, Malawi, Rwanda, South Africa, Zambia, and Zimbabwe were less likely to self-report STIs. The study has revealed variations in the country level regarding the prevalence of sexual violence and SR-STI in the last 12 months among women in sexual unions in the selected countries. This study has demostrated that sexual violence in the last 12 months is associated with SR-STIs among women in sexual unions. Moreover, factors that predict SR-STIs were observed in this study. Policymakers and agencies that matter could consider the factors identified in this study when designing policies or strengthening existing ones to tackle STIs among women in SSA. To accelerate the progress towards the achievement of Sustainable Development Goal 3, its imperative efforts and interventions must be intensified in SSA to reduce sexual violence which will go a long way to reduce SR-STIs among women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".