Urban-rural disparities in wife-beating attitude among married women: a decomposition analysis from the 2017 Senegal Continuous Demographic and Health Survey
Bibliographic record
Abstract
BACKGROUND: Globally, intimate partner violence is one of the most common forms of gender-based violence, and wife beating is one component of intimate partner violence, with the problem being more severe among women living in rural settings. Little is known about the factors that explain the urban-rural disparity in the prevalence of wife beating attitude in Senegal. In this paper, we aimed to decompose the urban-rural disparities in factors associated with wife beating attitude among married women in Senegal. METHODS: Data were derived from the 2017 Senegal Continuous Demographic and Health Survey. We used the Blinder-Oaxaca decomposition method to decompose and explain the variation in the prevalence of disagreement to wife beating between urban and rural areas in Senegal. RESULTS: The results show that 48.9% of married women in Senegal disagreed with wife-beating. About 69% of urban women disagreed with wife beating, but only 36% of rural women disagreed with wife beating. About 68.7% of women in the sample reported that they disagreed to wife beating by their husbands for burning food and nearly 50% of women reported that they disagreed with wife beating when they refuse to have sex with their husbands. About 86% of the urban-rural disparities in disagreement with wife beating are explained in this study. Economic status (45.2%), subnational region (22.4%), women's educational status (13.3%), and husband's educational status (10.7%) accounted for 91.6% of the disparities. CONCLUSIONS: The study shows urban-rural disparities in the prevalence of wife-beating attitude (disagreement with wife beating) and this disfavored rural residents. We suggest the need for the government of Senegal to consider pro-rural equity strategies to narrow down the observed disparities. Moreover, socioeconomic empowerment and attitudinal changing interventions using existing socio-cultural institutions as platforms can be used to deliver such interventions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".