Intimate partner violence as a predictor of marital disruption in sub-Saharan Africa: A multilevel analysis of demographic and health surveys
Bibliographic record
Abstract
INTRODUCTION: Intimate partner violence has gained momentum as health, social, and human right issue across the globe. Women within sub-Saharan Africa often do not report any case of violence due to the acceptance of violence which is rooted in their socio-cultural beliefs and practices. With a high prevalence of marital disruption in sub-Saharan Africa, it is important that we understand the role intimate partner violence plays in this phenomenon. Hence, this present study assessed the association between intimate partner violence and marital disruption among women in sub-Saharan Africa. METHODS: This study involved a cross-sectional analysis of data from the Demographic and Health Survey of 25 countries in sub-Saharan Africa. Multilevel binary logistic regression analysis was carried out and the results were presented as adjusted odds ratios (aOR) at 95% Confidence Interval (CI). RESULTS: The prevalence of physical, emotional, and sexual violence in the 25 countries considered in this study were 29.3%, 28%, and 11.5%, respectively. The highest prevalence of physical violence was in Sierra Leone (50.0%) and the lowest prevalence was in Comoros (5.7%). For emotional violence, the highest prevalence was in Sierra Leone (45.9%) and the lowest prevalence was in Comoros (7.9%). The highest prevalence of sexual violence was in Burundi (25.5%) and the lowest prevalence was in Comoros (1.8%). The average prevalence of marital disruption was 7.7%. This ranged from 1.3% in Burkina Faso to 20.2% in Mozambique. We found that women who had ever experienced physical violence were more likely to experience marital disruptions compared to those who had never experienced physical violence [aOR = 1.42, 95% CI = 1.35-1.50]. Women who had ever experienced sexual violence were more likely to experience marital disruption compared to those who had never experienced sexual violence [aOR = 1.29, 95% CI = 1.21-1.37]. Finally, women who had ever experienced emotional violence were more likely to experience marital disruption compared to those who had never experienced emotional violence [aOR = 1.86, 95% CI = 1.76-1.96]. CONCLUSION: Findings from this study call for proven effective intimate partner violence reduction interventions such as strengthening laws against intimate partner violence in sub-Saharan Africa. Again, marital counseling and health education interventions should be implemented to address the role of intimate partner violence on the wellbeing of women and the stability of couples in sub-Saharan Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".