Two Level Logistic Regression Model of Factors Influencing in Early Childbearing and its Consequences on Nutritional Status of Bangladeshi Mothers: Nationally Representative Data
Bibliographic record
Abstract
Background: Early marriage and early pregnancy is a social as well as a medical problem in developing countries, which may have an impact on the health and nutritional status of teenage mothers. Therefore, the objective of this study was to determine the influencing factors of early childbearing (ECB) and its consequences on the nutritional status of Bangladeshi mothers. Methods: Data was extracted from Bangladesh Demographic and Health Survey (BDHS-2014). Women who delivered their first baby before the age of 20 years are considered ECB mothers. Nutritional status was measured by body mass index (BMI). Chi-square test and both univariable and multivariable logistic regressions, and z-proportional test were used in this study. Results: The prevalence of ECB among currently non-pregnant mothers in Bangladesh was 83%. The logistic regression model provided the following six risk factors of ECB: (i) living location (division) (p<0.01), (ii) respondents’ education (p<0.05), (iii) husbands’ education (p<0.05), (iv) household wealth quintiles (p<0.01), (v) respondents’ age at first marriage (p<0.05), and (vi) number of family members (p<0.05). Still, 17.6% of mothers were undernourished in Bangladesh; among them, 18.5% and 13.4% were ECB and non- ECB mothers respectively. ECB mothers had a greater risk to be undernourished than non-ECB mothers [COR=1.26, 95% CI: 1.11-1.43; p<0.01]. Conclusions: In this study, some modifiable factors were found as predictors of ECB in Bangladesh. ECB mothers were more prone to become under-nourished. These findings can be considered to reduce the number of ECB mothers in Bangladesh consequently improve their nutritional status.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".