Contribution of socio-economic and demographic factors to the trend of adequate dietary diversity intake among children (6–23 months): evidence from a cross-sectional survey in India
Bibliographic record
Abstract
BACKGROUND: The present study aims to estimate the factors contributing to the change adequate diversified dietary intake (ADDI) from 2005-06 to 2015-16 among children aged 6-23 months in India. METHODS: A cross-sectional study was conducted using a large representative survey data. Data from the National Family Health Survey 2005-06 and 2015-16 was used. The effective sample size for the present study was 14,422 and 74,132 children aged 6-23 months in 2005-06 and 2015-16, respectively. The outcome variable was minimum adequate dietary diversity intake. Binary logistic regression was used to evaluate the factors associated with ADDI. Additionally, the Fairlie method of decomposition was used, which allows quantifying the total contribution of factors explaining the decadal change in the probability of ADDI among children aged 6-23 months in India. RESULTS: There was a significant increase in ADDI from 2005-06 to 2015-16 (6.2%; p < 0.001). Additionally, compared to the 2005-06 years, children were more likely to have ADDI [AOR; 1.29, CI: 1.22-1.35] in 2015-16. Mother's education explained nearly one-fourth of the ADDI change among children. Further, the regional level contribution of 62.3% showed that the gap was widening across regions between the year 2005-06 and 2015-16 in ADDI among children. The child's age explained 5.2% with a positive sign that means it widened the gaps. Whereas the household wealth quintile negatively contributed and explained by -5.2%, that means between the years the gaps has reduced in ADDI among children aged 6-23 months. CONCLUSION: Our findings indicate that increasing awareness of the use of mass media and improving the education levels of mothers would be beneficial for adequate dietary diversity intake among children aged 6-23 months. Investments should support interventions to improve overall infant and young children feeding practices in India.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".