Drivers of Stunting Among 0-23 Months Old Filipino Children Included in the 2003 and 2011 National Nutrition Survey
Bibliographic record
Abstract
This study aims to evaluate household and individual level drivers of stunting among Filipino children aged 0-23 months in the 2003 National Nutrition Survey (NNS) and identified which factors pushed these same children to or out of stunting in middle childhood (8-9 years old) in 2011 Updating National Nutrition Survey (UNNS). All children aged 0-23 months in 2003 NNS were tracked if they're still in 2011 UNNS by matching identifiers: region, province, municipality/city, name, and birthdate. There are 290 children included in both surveys. Children were categorized as: stunted in 2003 but not in 2011 (catch-up); stunted in 2003 & 2011 (persistently stunted); stunted in 2011 but not in 2003 (stunted later). The prevalence of stunting increased from 17.2% in 2003 to 35.2% in 2011. About 22.1% became stunted later; persistently stunted (13.1%); catch-up (4.1%). The individual-level factors that contributed towards persistent stunting are older age onset of stunting, underweight, and a <2 years birth interval; while the household level factors are those with ≥ 5 dependents, and a higher number of under-fives in the family. Households usage of water-sealed toilets and availability of electricity decrease the odds of persistent stunting and stunting later. No significant factors were found on what moves a child out of stunting. Living in shanties (Huts) pushed a normal child to be stunted in 2011. This study reflects the strong influence of both individual and household factors on stunting. These results could be useful in crafting area and problem-specific interventions.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".