Association between sexual violence and multiple high-risk fertility behaviours among women of reproductive age in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence has adverse outcomes on the sexual and reproductive health of women. In this study, we examined the association between sexual violence and multiple high-risk fertility behaviours (MHRFB) among women in sub-Saharan Africa (SSA). METHODS: We conducted a cross-sectional analysis of data pooled from the most recent Demographic and Health Surveys of 20 countries in SSA. We included countries with most recent datasets conducted from 2015 to 2021 and had data on all variables included in the study. A weighted sample of 88,011 was included in the study. We used a multilevel binary logistic regression to examine the association between sexual violence and MHRFB, controlling for other covariates. The regression results were presented using adjusted odds ratio (aOR) with 95% confidence interval (CI). Statistical significance was set at p < 0.05. RESULTS: The overall prevalence of MHRFB was 22.53% (95% CI: 22.26-22.81), which ranged from 9.94% in South Africa to 30.38% in Chad. For sexual violence, the pooled prevalence was 7.02% (95% CI: 6.86-7.19). Burundi (20.58%) and the Gambia (2.88%) reported the highest and lowest proportions, respectively. Women who experienced sexual violence were more likely to engage in MHRFB compared to those who did not experience sexual violence [aOR = 1.11, 95% CI: 1.02, 1.21]. CONCLUSION: There is a positive association between sexual violence and the risk of MHRFB. Our findings underscore a need for sub-Saharan African countries to strengthen their efforts to reduce the occurrence of sexual violence in intimate partner relationships. To augment efforts and accelerate social change, sub-Saharan African countries can introduce pro-poor policies and interventions to improve the wealth status of women. Also, empowering women through the encouragement of attaining higher education would be a useful step in lowering the risk of MHRFB in SSA.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".