Current prevalence and socio-demographic factors associated with unmet need for contraception among women in union in sub-Saharan Africa.
Bibliographic record
Abstract
This study analyzes the prevalence and sociodemographic factors associated with unmet need for contraception among women in union in sub-Saharan Africa. Using secondary data from recent Demographic and Health Surveys, statistical tests revealed an overall prevalence of 20.7% of unmet need for contraception (13.9% for birth spacing and 6.8% for birth control). Furthermore, multinomial logistic regression shows that women aged 25-34 and 35-49 years have a reduced risk of unmet need for spacing but an increased risk for control. Women who have reached their desired fertility have a reduced need for spacing [RRR=0.87; CI=0.80-0.95, p<0.01], while those with unwanted children have an increased need for control [RRR=3.80; CI=0.80-0.95, p<0.01]. CI=3.50-4.12, p<0.001]. Similarly, women living with a partner have a higher risk of unmet need for spacing [RRR=1.07; CI=1.01-1.13, p<0.05], but those with at least primary education have a low risk for limitation. Therefore, the study recommends that family planning (FP) program managers and Non-Governmental Organizations redouble their efforts to improve access to FP services, promote women's education. It is also essential to increase awareness among small families and men aspiring to high fertility, men about the benefits of FP for health and well-being.
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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 | 0.003 |
| 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) | 0.001 | 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".