Influence of type of violence on women’s help-seeking behaviour: Evidence from 10 countries in sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is a major public health concern that mostly impacts women's health and social well-being. This study explored how the various types of IPV (physical, sexual, and emotional) including women's experience of childhood violence influence their help-seeking behavior in sub-Saharan Africa (SSA). METHODS: We analyzed data from the most recent Demographic and Health Surveys (DHS), carried out between 2018 and 2021. The outcome variable was help-seeking behavior. Descriptive and inferential analyses were carried out. The descriptive analysis looked at the bivariate analysis between the country and outcome variables. Using a binary logistic regression model, a multivariate analysis was utilized to determine the association between the outcome variable and the explanatory variables. Binary logistic regression modelling was used based on the dichotomous nature of the outcome variable. The results were sample-weighted to account for any under- or over-sampling in the sample. RESULTS: The proportion of women who sought help for intimate partner violence was 36.1 percent. This ranged from 19.2 percent in Mali to 49.6 percent in Rwanda. Women who experienced violence in childhood (OR = 0.75, CI = 0.69, 0.82) have a lower likelihood of seeking help compared to those who did not experience violence in their childhood. Women who had experienced emotional violence (OR = 1.94, CI = 1.80, 2.08), and physical violence (OR = 1.37, CI = 1.26, 1.48) have a higher likelihood of seeking help compared to those who have not. Women with secondary educational levels (aOR = 1.13, CI = = 1.02, 1.24) have a higher likelihood of seeking help compared to those with no education. Cohabiting women have a higher likelihood (aOR = 1.22, CI = 1.10, 1.35) of seeking help compared to married women. CONCLUSION: The study highlights the importance of early identification of IPV and fit-for-purpose interventions to demystify IPV normalization to enhance women's willingness to seek help. The study's findings suggest that education is crucial for increasing women's awareness of the legalities surrounding IPV and available structures and institutions for seeking help.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".