Intimate partner violence against ever-married women and its association with substance use in Ethiopia: A multilevel and spatial analysis from 2016 EDHS
Bibliographic record
Abstract
Abstract Background Intimate partner violence (IPV) is one of the most common forms of violence against women and includes physical, sexual, and emotional abuse. The most common IPV victims are women whose partners were financially insecure, uneducated, or substance users. Substance use has been related to an increase in the frequency and severity of IPV. Thus, we aimed to assess the prevalence of intimate partner violence, its spatial distribution, and its association with substance use among women who had ever-married in Ethiopia using the 2016 Ethiopian Demographic and Health Survey (EDHS) data. Methods Data from the 2016 EDHS was used and a total of 4962 ever-married women were involved in the analysis. The spatial autocorrelation statistic (Global Moran's I) was used to determine whether IPV and substance use were dispersed, clustered, or randomly distributed. The statistical software Sat Scan version 10.1 was used to identify the clusters with high IPV rates. A multi-level logistic regression model was used to examine the association of IPV with substance use, and statistical significance was declared at a p-value of less than 0.05 and 95% CI. Results Of all ever-married women, 33.2% (95% CI: 31.9, 34.6%) were currently experiencing at least one of the three types of IPV (physical, sexual, and emotional). The highest hotspot areas of IPV were observed in the Gambella and Oromia regions. The ever-married women whose husbands drink alcohol (AOR = 3.34; 95% CI: 2.70, 4.15), chew chat (AOR = 1.60; 95% CI: 1.22, 2.08), and smoke cigarettes (AOR = 1.95; 95% CI: 1.01, 3.79) were significantly associated with IPV. Conclusion One in every three ever-married women in this study experienced IPV. Following adjustment for potential confounders, at least one of the three substance uses (alcohol, chat and cigarette) was identified as a significant predictor of IPV. A concerted effort is required to reduce both substance abuse and IPV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".