Residential district multi-hazard risk is associated with childhood undernutrition: evidence from Bangladesh
Bibliographic record
Abstract
Child undernutrition and natural disasters are major public health concerns in Bangladesh, but research into their relationship is lacking. This study assessed the association between residential district multi-hazard-risk and undernutrition among children aged less than 5 years (under-5) in Bangladesh. Data for 22,055 under-5 children were extracted from the 2019 Multiple Indicator Cluster Survey of Bangladesh. Multi-hazard risk was categorized as low (score<10), moderate (score 10-20), and high (score>20) using a combined score of four major hazards: tornado, cyclone, earthquake, and flood. We found that children from high multi-hazard risk districts were 19% more likely to be stunted and 23% more likely to be underweight compared to low-risk districts. However, wasting was not associated with multi-hazard risk. Strategies such as agricultural adaptation and coping mechanisms, long-term post-disaster nutritional response, extended periods of relief supports, and enhanced quality maternal and child care services may help to reduce undernutrition burdens in Bangladesh.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".