Adolescent marriage, maternity, and limited access to education in 106 countries: Bayesian analysis of prevalence, trend, and prediction
Bibliographic record
Abstract
Effectively addressing issues related to adolescent children being out of school and vulnerable to harmful practices is essential for advancing SDG 3-5. This study aimed to assess the prevalence, trends, and future projections of adolescent child vulnerability. We analyzed data from 386 datasets spanning 106 countries, encompassing 1,292,258 adolescent girls year 1990- 2023. Bayesian methods were employed to estimate prevalence, trends, and future projections. Sub-Saharan Africa and Latin America & the Caribbean showed a high prevalence of child marriage and early childbirth. Overall, these indicators decreased from 1990 to 2023, but recent years have seen increases in some countries. Projections for 2025 to 2030 suggest that changes in rates will occur in some countries, while most are expected to remain stable. The disparities are in underscore ongoing public health challenges. These issues risk derailing progress towards SDG targets of eliminating harmful practices and ensuring universal access to quality education. Addressing these challenges demands coordinated efforts from countries and the global community to implement effective interventions and strategies.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".