Women’s autonomy in refusing risky sex in sub-Saharan Africa: Evidence from 30 countries
Bibliographic record
Abstract
Understanding the factors associated with women's autonomy to refuse risky sex is imperative to inform the development of policies and interventions to reduce the risk of unintended pregnancies, sexually transmitted infections, unsafe abortion, and maternal mortality. This study sought to examine the prevalence and factors associated with women's autonomy to refuse risky sex in sub-Saharan Africa (SSA). Data for the study were extracted from the most recent Demographic and Health Surveys (DHS) of thirty countries in SSA conducted from 2010 to 2020. We included a weighted sample of 260,025 women who were married or cohabiting in the final analysis. Percentages were used to present the results of the prevalence of women's ability to refuse risky sex. We used a multilevel logistic regression analysis to examine the factors associated with women's ability to refuse risky sex. Stata software version 16.0 was used for the analysis. We found that 61.69% (95% confidence interval [CI]: 56.22-67.15) of the women were autonomous to refuse sex if their partners have other women, and this was highest in Namibia (91.44% [95% CI: 90.77-92.18]) and lowest in Mali (22.25% [95% CI: 21.24-23.26]). The odds of autonomy in refusing risky sex was higher among women with higher education (adjusted odds ratio [aOR] = 1.88; 95% CI = 1.78-1.46) compared to those with no formal education. Employment status was also a significant predictor, with working women having higher odds of sex refusal compared to non-working women (aOR = 1.16; 95% CI = 1.13-1.18). Advocacy to improve women's autonomy to refuse risky sex must leverage the mass media as it emerged as a significant factor. Policies and intervention to enhance women's autonomy must also target high-risk sub-populations which constitutes adolescent girls, those with no formal education, and those without employment.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".