Association between knowledge and use of contraceptive among women of reproductive age in sub‐Saharan Africa
Bibliographic record
Abstract
Background and Aims: The use of contraceptives has been considered relevant in reducing unintended pregnancies in sub-Saharan Africa (SSA). However, despite evidence of knowledge of contraceptives, their use remains low in SSA. This study examined the association between knowledge of contraceptive methods and the use of contraceptives in SSA. Methods: Data for the study were extracted from the Demographic and Health Surveys of 21 countries in SSA spanning from 2015 to 2021. A weighted sample of 200,498 sexually active women of reproductive age were included in the final analysis. We presented the results on the utilization of contraceptives using percentages with their respective 95% confidence intervals (CI). We examined the association between knowledge of contraceptive methods and the use of contraceptives using multilevel binary logistic regression analysis. Results: Overall, 24.32% (95% CI: 24.15-24.50) of women in SSA used contraceptives. Chad had the lowest prevalence of contraceptive use (5.07%) while Zimbabwe had the highest prevalence (66.81%). The odds of using any method of contraception were significantly higher for women with medium [Adjusted odds ratio (AOR) = 1.89; 95% CI = 1.80-1.98] and high [AOR = 2.22; 95% CI = 2.10-2.33] knowledge of contraceptive methods compared to those with low knowledge, after adjusting for all covariates. Conclusion: Our study has shown that the use of contraceptives among women in SSA is low. Women's knowledge of any contraception method increases their likelihood of using contraceptives in SSA. To improve contraceptive use in SSA, targeted interventions and programmes should increase awareness creation and sensitization, which can improve women's knowledge on methods of contraception. Also, programmes implemented to address the low uptake of contraceptives should consider the factors identified in this study. In addition, specific subregional strategies could be implemented to narrow the subregional disparities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".