Sociodemographic factors associated with concurrent stunting and wasting among children experiencing extreme poverty in the Philippines: A cross-sectional study
Bibliographic record
Abstract
Background: The coexistence of stunting and wasting in a child increases the risk of mortality and requires more intensive treatment and care. However, there is limited research on the burden of concurrent stunting and wasting among children and the socioeconomic factors that are correlated with having both conditions. Aim: To understand the prevalence and sociodemographic correlates of stunting, wasting, and concurrent stunting and wasting among a sample of children ages 6–144 months experiencing poverty in the Philippines. Methods: Cross-sectional data were drawn from nutrition screening and sociodemographic surveys conducted by International Care Ministries in 2018-2019. Descriptive statistics were calculated to determine the prevalence of stunting, wasting, and concurrent stunting and wasting. Multilevel logistic regression modelling was conducted to understand the sociodemographic factors that were associated with stunting and wasting. Results: Among the 3005 children in this sample, the prevalence of stunting, wasting, and concurrent stunting and wasting was 49.9%, 9.3%, and 4.6%, respectively. Children experiencing concurrent stunting and wasting lived in households in lower wealth index quintiles, had a household head with fewer years of education, and were more likely to experience food insecurity compared to children who were not stunted or wasted. The education of the household head, the number of household members, and the wealth of the household were correlated with stunting across age groups, while food insecurity was correlated with wasting among younger children. Conclusion: The presence of concurrent stunting and wasting among children provides the impetus to integrate both conditions into nutrition monitoring, prevention, and treatment 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 | 0.001 | 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 | 0.001 | 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) | 0.001 | 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".