Early childhood development and its association with maternal parity
Bibliographic record
Abstract
Background: Maternal parity, which is usually measured as the number of children born to a mother, has a substantial impact on the social and environmental factors around children and their development. This paper estimates the Early Childhood Development Index (ECDI) of 3- and 4-year-old children in Bangladesh and examines the relationship between maternal parity and early childhood development. Methods: The study analysed nationally representative data from the Bangladesh Multiple Indicator Cluster Survey 2019. The dataset had 9453 children aged from 36 to 59 months. The ECDI was computed following the UNICEF's approach involving psychometric computation of four domains of development: physical, literacy-numeracy, learning and social–emotional. Since the dataset has a hierarchical structure, we used multilevel logistic regression. Results: A quarter (25%) of the children were not on track in their early childhood development. Seventy-one percent were not developmentally on track in the literacy-numeracy domain, 27% were not in the social–emotional and smaller percentages were not in learning (9%) and physical (1%) domains. There was a significant negative association between maternal parity and ECDI (adjusted odds ratio [AOR] 0.95; 95% CI: 0.91–0.99). Attendance at early childhood education programmes was significantly associated with early childhood development (AOR 1.73; 95% CI: 1.47–2.03). Also, female children, those who were not stunted, located in rural areas, received parental stimulation activities, lived in relatively wealthy households or had mothers who had received secondary or further education were more likely than others to be on track of early childhood development. Conclusions: Early childhood development is negatively correlated with maternal parity. The literacy-numeracy domain constitutes the major developmental delay. Programmes for parental awareness should be widely expanded.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".