Determinants of children’s development (cognitive, language and motor) and growth (length for age Z-score) at 6-16 months of age in urban Bangladesh
Bibliographic record
Abstract
Abstract Background Children’s growth and development are multifactorial. Little is known about the maternal and children’s factors associated with children’s development and growth in urban areas of low- and middle-income countries. The objective of this study was to determine the important factors associated with children’s development (cognitive, language and motor) and growth (length for age Z score) at age 6–16 months Methods This cross-sectional study was conducted in an urban area of Bangladesh. Bayley III and the WHO methods were used to assess children’s development and growth respectively. Multivariable linear regression model and dominance analysis were used to determine the important factors associated with development and growth. Results Out of the total 599 mother-child dyads, 303 (50.58%) were girls. The common determinants of children’s development were Length-for-age-Z-score (LAZ) [cognitive: B = 1.21 (95%CI = 0.31, 2.11), P = 0.008, Language: 1.67 (0.79, 2.55) P < 0.001; motor: 2.15 (1.01, 3.29) P < 0.001] and home environment [cognitive: 0.58 (0.27, 0.89) P < 0.001, Language: 0.59 (0.27, 0.92), P < 0.001, motor: 0.44 (0.09, 0.79) P = 0.013]. Girls had higher cognitive and language development and LAZ scores than the boys. LAZ score was predicted by their age and gender and the children of families with middle wealth quintile were taller than the children of lower wealth quintile. Conclusion Children’s nutritional status and home environment are important factors for children’s development in low resource urban settings of Bangladesh. Poverty is an important predictor of children’s growth.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".