Drivers of desire for more children among childbearing women in sub-Saharan Africa: implications for fertility control
Bibliographic record
Abstract
BACKGROUND: Despite the extensive research on fertility desires among women the world over, there is a relative dearth of literature on the desire for more children in sub-Saharan Africa (SSA). This study, therefore, examined the desire for more children and its predictors among childbearing women in SSA. METHODS: We pooled data from 32 sub-Saharan African countries' Demographic and Health Surveys. A total of 232,784 married and cohabiting women with birth history, who had complete information on desire for more children made up the sample for the study. The outcome variable for the study was desire for more children. Multilevel logistic regression analysis was conducted. Results were presented using adjusted odds ratios (aOR), with their corresponding 95% confidence intervals (CI). RESULTS: The overall prevalence of the desire for more children was 64.95%, ranging from 34.9% in South Africa to 89.43% in Niger. Results of the individual level predictors showed that women aged 45-49 [AOR = 0.04, CI = 0.03-0.05], those with higher education [AOR = 0.80, CI = 0.74-0.87], those whose partners had higher education [AOR = 0.88; CI = 0.83-0.94], women with four or more births [AOR = 0.10, CI = 0.09-0.11], those who were using contraceptives [AOR = 0.68, CI = 0.66-0.70] and those who had four or more living children [AOR = 0.09 CI = 0.07-0.12] were less likely to desire for more children. On the other hand, the odds of desire for more children was high among women who considered six or more children as the ideal number of children [AOR = 16.74, CI = 16.06-17.45] and women who did not take decisions alone [AOR = 1.58, CI = 1.51-1.65]. With the contextual factors, the odds of desire for more children was high among women who lived in rural areas compared to urban areas [AOR = 1.07, CI = 1.04-1.13]. CONCLUSIONS: This study found relatively high prevalence of women desiring more children. The factors associated with desire for more children are age, educational level, partners' education, parity, current contraceptive use, ideal number of children, decision-making capacity, number of living children and place of residence. Specific public health interventions on fertility control and those aiming to design and/or strengthen existing fertility programs in SSA ought to critically consider these factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".