Contraceptive use pattern based on the number and composition of children among married women in sub-Saharan Africa: a multilevel analysis
Bibliographic record
Abstract
BACKGROUND: The relationship between composition of children and contraception use has received limited scholarly attention in sub-Saharan Africa. In this study, we examined the relationship between contraceptive methods, the number and composition of children in SSA. METHODS: Data on 21 countries in sub-Saharan Africa (SSA) countries that had a Demographic and Health Survey on or before 2015 were analysed. We applied a multilevel multinomial logistic regression model to assess the influence of family composition on contraceptive use. Adjusted relative risk ratio (aRRR) and 95% CI were estimated. The significant level was set at p < 0.05. All the analyses were conducted using weighted data. RESULTS: Women who had one son and two daughters (aRRR = 0.85, CI = 0.75, 0.95), two sons and one daughter (aRRR = 0.81 CI = 0.72, 0.92), one son and three daughters (aRRR = 0.66, CI = 0.54, 0.80), two sons and two daughters (aRRR = 0.59, CI = 0.50, 0.69), and three or more sons (aRRR = 0.75, CI = 0.63, 0.91) were less likely to use temporary modern contraceptive methods. Those with two sons and two daughters were less likely to use traditional methods (aRRR = 0.52, CI = 0.35, 0.78). Women in the older age group (35-49 years) were less likely to use temporary modern methods (aRRR = 0.60; 95%CI; 0.57, 0.63). However, this group of women were more likely to use permanent (sterilization) (aRRR = 1.71; 95%CI; 1.50, 1.91) and traditional methods (aRRR = 1.28; 95%CI; 1.14, 1.43). CONCLUSION: These findings suggest that contraception needs of women vary based on the composition of their children, hence a common approach or intervention will not fit. As a result, contraception interventions ought to be streamlined to meet the needs of different categories of women. The findings can inform policymakers and public health professionals in developing effective strategies to improve contraceptive use in SSA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".