Child marriage and its impact on fertility and fertility-related outcomes in South Asian countries
Bibliographic record
Abstract
Although South Asia (SA) is reported as a home of child marriage, the association of child marriage with fertility and fertility-related outcomes in this region is poorly explored. The most recent data of the Demographic and Health Survey of six SA countries – Afghanistan, Bangladesh, India, Maldives, Nepal and Pakistan – have been used in this article. The unit of analysis is 584,213 currently married women aged 20–49. The outcomes of interest are fertility and fertility-outcomes. Quantitatively important and reliable estimates were obtained from the statistical analyses. The results are presented by odds ratios with 95% CIs. Findings reveal that, overall, 42.1% of the respondents were married-off before age 18. The prevalence of child marriage was lowest in the Maldives and highest in Bangladesh at 20.5% and 74.4% respectively. The likelihood of early childbirth and repeated childbirth were significantly ( p < 0.001) lower and that for high fertility, unintended pregnancy, lifetime pregnancy termination and use of a modern contraceptive method was significantly higher in the child married women compared to their adult married counterparts. Reforms should aim to have more girls remain in schooling for both personal and overall societal development and also to reduce adverse reproductive outcomes caused by child marriage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".