Prevalence of unplanned first pregnancy and socioeconomic factors in Benin: a cross-sectional and analytical study
Bibliographic record
Abstract
Background: Early or unplanned pregnancy is an obstacle to the well-being of adolescent girls, especially as early motherhood has health and social consequences on the young mother and the newborn. This also negatively impacts maternal and infant morbidity and mortality. The aim of this study is to determine the prevalence of unplanned first pregnancies and the explanatory factors among adolescent girls. Methods: Data were collected from a sample of 738 adolescent girls aged 15-19 years in the 12 departments of Benin. Analyses focused on teenagers who were pregnant for the first time or had at least one child at the time of the survey (337 individuals). Descriptive and bivariate analyses and logistic regression were used. Results: The prevalence of unplanned first pregnancies was 80.1% (n=270). The frequency of unplanned first pregnancies was higher among adolescents who had reached secondary school or higher (85.7%, p=0.027), those who had spent their socialisation period in big cities (88.5%, p=0.018) and those whose financial needs were not met (84.8%, p=0.014). The main explanatory factor identified was unmet financial needs. Indeed, adolescents with unmet financial needs were 1.7 times (p=0.041) more likely to have an unplanned first pregnancy than those whose needs were met. Conclusion: Multisectoral interventions are needed to reduce the prevalence of unplanned first pregnancies, which can compromise adolescent girls' academic and career plans. These interventions could focus on sexual and reproductive health education, girl empowerment, improvement of households' socioeconomic conditions, etc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".