Characterization of Violence Against Reproductive-age Women in Southwest Nigeria
Bibliographic record
Abstract
Background: Violence against women has been reported to be high across Nigeria. However, there are no specific data on this issue across individual states in southwestern Nigeria and their correlations. This study aimed to characterize the types, prevalence, and correlates of violence against reproductive-age women in the southwestern Nigeria states, thereby establishing an inter-state comparison that could stimulate a community-based intervention.Methods: This cross-sectional study was a secondary data analysis of the population-based 2018 Nigerian Demographic and Health Survey (NDHS) data. The NDHS collected data from 14th August to 29th December 2018 through a stratified three-stage cluster sample design using a sampling frame containing the list of enumeration areas prepared for 2006. Responses from 1516 women aged 15-49 were analyzed by descriptive and inferential statistics in SPSS version 25.Results: The overall percentage of intimate partner violence (IPV) was 22% (95% CI=19.9%-24.2%) and 17% (14.7%-18.5%) for non-IPV. Oyo State had the least percentage of IPV (11%) while Lagos State had the highest (25%). Women from Ogun State had a statistically significant risk of non-IPV, such that 47% of people with non-IPV came from the state (P=0.001). The odds of IPV among women with secondary education (OR=1.78, CI=1.25-2.55; P=0.002) was more than that of women with primary education (OR=1.68, CI=1.10-2.56; P 0.016). Alcohol consumption and husband’s controlling behavior were the most important predictors of IPV across the states (P<0.001). Only 3% of the respondents reported being sexually hurt by non-partners.Conclusion: Violence against reproductive-age women is very concerning. The current rate needs attention to reduce the ensuing risk of unintended pregnancies, suicides/self-harm, drug abuse, depression, and miscarriage. All of these will negatively impact the population’s health outcome. A community-based intervention using a socioecological model of behavioural changes is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".