How soon do single mothers have another child? A competing risk analysis of second premarital childbearing in sub-Saharan African countries
Bibliographic record
Abstract
BACKGROUND: A considerable number of previous studies have examined the trends, correlates, and consequences of premarital childbearing among adolescents and young women in Africa. However, very little is known about whether and how soon single mothers have another premarital birth in sub-Saharan African countries. This study examines the timing of a second premarital birth among single mothers and assesses how it may differ across key socio-demographic variables. METHODS: We pooled recent Demographic and Health Surveys from 25 sub-Saharan African countries to create a database of 57, 219 single mothers aged 15-49 years. Cumulative incidence graphs and Fine and Gray's competing risk models were used to delineate the timing of a second premarital birth and its socio-demographic correlates. RESULTS: More than one-third of single mothers in 16 countries have had a second premarital birth in their reproductive life. We also observed that more than 15% of the single mothers in Angola, Benin, the Republic of Chad, Liberia, Namibia, Nigeria, Sierra Leone, and Uganda, have had another premarital birth three years after the first. The incidence of a second premarital birth was significantly lower among women with secondary or higher education, compared to women with less than secondary education (p < 0.05) in most countries. Residence in an urban area compared to rural, was also significantly associated with a low incidence of second premarital birth in 10 countries (p < 0.05). CONCLUSIONS: Findings indicate a rapid progression to having a second premarital birth in some sub-Sahara African countries, particularly among socio-economically disadvantaged women. The findings suggest the need for tailored interventions for improving the quality of life of single mothers, to reduce the associated burden and consequences of having a premarital birth.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".