Intention to use contraceptives among married and cohabiting women in sub-Saharan Africa: a multilevel analysis of cross-sectional data
Bibliographic record
Abstract
OBJECTIVE: To examine the factors associated with intention to use contraceptives among married and cohabiting women in sub-Saharan Africa (SSA). DESIGN: Data for the study were extracted from the most recent Demographic and Health Surveys of 29 countries in SSA conducted from 2010 to 2020. We included a total of 180 682 women who were married or cohabiting. Multilevel regression analysis was carried out and the results were presented as adjusted odds ratio (AOR), with 95% confidence interval (CI). SETTING: 29 countries in SSA. PARTICIPANTS: Women aged 15-49 years in sexual unions. OUTCOME MEASURE: Intention to use contraceptives. RESULTS: The pooled prevalence of intention to use contraceptives among married and cohabiting women in the 29 countries was 41.46%. The prevalence ranged from 18.28% in Comoros to 71.39% in Rwanda. Intention to use contraceptives was lower among women aged 45-49 (AOR=0.06, 95% CI= 0.05 to 0.07), those with no education (AOR=0.60, 95% CI= 0.58 to 0.61), and primary education (AOR=0.90, 95% CI 0.88 to 0.93), married women (AOR=0.81, 95% CI= 0.79 to 0.84), those of the poorest wealth quintile (AOR=0.78, 95% CI= 0.75 to 0.82), and women who were not exposed to mass media (AOR=0.87, 95% CI= 0.86 to 0.90). Women with four or more births (AOR=2.09, 95% CI= 1.99 to 2.19) had greater likelihood of contraceptive use intention compared to those with no birth. Women in rural settings were found to have greater likelihood of intention to use contraceptives compared to those in urban settings (AOR=1.10, 95% CI= 1.07 to 1.14). CONCLUSION: There is a low prevalence of contraceptive use intention among married and cohabiting women in SSA with differences between countries. It is imperative for policymakers to consider these factors when developing and executing contraceptive programmes or policies to enhance contraceptive intents and use among married and cohabiting women. To resolve discrepancies and increase contraceptive intention among women, policymakers and other key stakeholders should expand public health education programmes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".