Domestic violence: a hidden barrier to contraceptive use among women in Nigeria
Bibliographic record
Abstract
BACKGROUND: The nonuse of family planning methods remains a major public health concern in the low-and-middle-income countries, especially due to its impact on unwanted pregnancy, high rate of abortion, and transmission of sexually transmitted diseases. Various demographic and socioeconomic factors have been reported to be associated with the nonuse of family planning methods. In the present study, we aimed at assessing the influence of domestic violence (DV) on contraceptive use among ever married women in Nigeria. METHODS: Data on 22,275 women aged between 15 and 49 years were collected from the most recent Nigeria Demographic and Health Survey conducted in 2013. The outcome variable was contraceptive utilization status, and the main exposure variable was DV, which was assessed by the self-reported experience of physical and psychological abuse. Complex survey method was employed to account for the multistage design of the survey. Data analyses were performed by using bivariate and multivariable techniques. RESULTS: The mean age of the participants was 31.33±8.26. More than four fifths (84%) of the participants reported that they were not using any contraceptive methods at all. Lifetime prevalence of psychological and physical abuse was, respectively, 19.0% (95% CI =18.0-20.1) and 14.1% (95% CI =13.3-14.9). Women who reported physical abuse were 28% (adjusted odds ratio [AOR] =1.275; 95% CI =1.030-1.578), and those reported both physical and psychological abuse had 52% (AOR =1.520; 95% CI =1.132-2.042) higher odds of not using any contraception. CONCLUSION: The rate of contraception nonuse was considerably high and was found to be significantly associated with DV. Thus, the high prevalence of DV may compromise the effectiveness of the family planning programs in the long run. Evidence-based intervention strategies should be developed to protect the health and reproductive rights of the vulnerable women and to reduce DV by giving the issue a wider recognition in public policy making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.000 |
| 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 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".