Association between the survey-based women’s empowerment index (SWPER) and intimate partner violence in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is high among women of reproductive age in sub-Saharan Africa (SSA). However, empowering women enables them to confront and mitigate IPV. In this study, we examined the association between the survey-based women's empowerment index (SWPER) and IPV in SSA. METHODS: We used data from the Demographic and Health Surveys of 19 countries conducted from 2015 to 2021. Our study was restricted to a weighted sample of 82,203 women of reproductive age who were married or cohabiting. We used spatial maps to show the proportions of women who experienced past-year IPV. A five-modelled multilevel binary logistic regression analysis was adopted to examine the association between SWPER and IPV. The results were presented using the adjusted odds ratio (AOR) with their respective 95% confidence interval (CI). Statistical significance was set at p < 0.05. RESULTS: With physical and emotional violence, the country with the highest prevalence was Sierra Leone, with a prevalence of 39.00% and 38.97% respectively. Rwanda (10.34%), Zambia (11.09%), Malawi (15.00%), Uganda (16.88%), and Burundi (20.32%) were the hotspot countries for sexual violence. Angola (34.54%), Uganda (41.55%), Liberia (47.94%), and Sierra Leone (59.98%) were the hotspot countries for IPV. A high SWPER score in attitudes to violence significantly decreased the odds of IPV [AOR = 0.70; 95% CI = 0.66, 0.75]. Also, women with medium score in decision-making were less likely to experience IPV compared to those with lower scores [AOR = 0.89; 95% CI = 0.83, 0.95]. However, higher odds of experiencing IPV was found among women with medium score in autonomy compared to those with low scores [AOR = 1.07; 95% CI = 1.01, 1.14]. CONCLUSIONS: Our study has shown that the three dimensions of SWPER significantly predict IPV among women. Consequently, it is crucial that sub-Saharan African countries implement various initiatives, such as IPV advocacy programs and economic livelihood empowerment initiatives. These initiatives should not only aim to improve women's attitudes to domestic violence but also to enhance their social independence, autonomy, and decision-making capacity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".