Age at First Birth in Uttar Pradesh: How Much Has It Actually Changed? Over Inter-NFHS Period?
Bibliographic record
Abstract
Culturally, there is always pressure among newly-wed to conceive early and have births in India. Previous studies have documented relationship between age at first birth & fertility, besides the socio-demographic factors that influence age at first birth. The current study aims answering directions and quantum of such relationships using frailty models. The successive rounds of NFHS data (1, 2, 3 & 4) from Uttar Pradesh is used in the study. Fertility in India is characterized as too-early-too-fast. By age-30 majority women would have completed the childbearing. However, the data from NFHS-4 shows some striking changes in the initiation of child bearing in Uttar Pradesh breaking away from the stereotypes of too early too fast characterization. While 44.67 percent of the women aged 30-34 had experienced first birth by age 18 in the year 1992-93 (NFHS-1), the percentages declined during 2015-16 (NFHS-4) to 28.25%. However, by ages 26 majority of women (>95%) aged 30-34 have had experienced first birth. Births at younger age are also a reflection on enforcement of child-marriage restraint act & adherence to legal minimum age at marriage which is 18 for girls & 21 for boys. The data from NFHS-4 have some quality issues. Women aged as low as 5 have shown to have experienced first birth by that age. This may not be possible. The Kaplan Meier survival Graph provided the survival probabilities with respect of each predictor sub groups. The log rank test was used to test the equality of survivor function for each sub group of the predictor variable. The survivor function was significantly different among sub groups of the predictor variables except for the categories of ever use of contraception at NFHS1 and categories of religion across rounds of NFHS data. The Cox Proportional Hazards model was used to study the risk of first birth by socio demographic characteristics. The Frailty model capturing the unobserved heterogeneity in the event time was preferred over standard survival model. For the current study, gamma frailty with Weibull-hazard is used as it fits the data well. Age at marriage and women’s literacy significantly determines the Age at First Birth. The inverse relationship with regard to ever use of contraception needs further analysis. The model also predicts significant frailty with variance parameter (theta) greater than one across the NFHS datasets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".