Unmet need for contraception among women in Benin: a cross-sectional analysis of the Demographic and Health Survey
Bibliographic record
Abstract
BACKGROUND: The aim of the current study was to examine the prevalence and predictors of unmet need for contraception among women in sexual unions in Benin. METHODS: Data for the study was extracted from the recent 2017-2018 Benin Demographic and Health Survey. A weighted sample of 9513 women of reproductive age was included in the study. We used multivariable multilevel binary logistic regression analysis to examine the factors associated with unmet need for contraception. RESULTS: The prevalence of unmet need for contraception was 38.0% (36.7, 39.2). The odds of unmet need for contraception was higher among women with ≥4 births compared with those with no births, and among those who reported that someone else or others usually made decisions regarding their healthcare compared with those who make their own healthcare decisions. Wealth index was associated with a higher likelihood of unmet need for contraception. Also, the region of residence was associated with unmet need for contraception, with the highest odds being among women from the Mono region (adjusted odds ratio [aOR]=2.18, 95% CI 1.33 to 3.58). CONCLUSIONS: Our study shows that the unmet need for contraception among women in Benin is relatively high. Our findings call on relevant stakeholders, including government and non-governmental organisations, to enhance women's empowerment as part of interventions that seek to prioritise contraceptive services for women.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".