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Record W3164810106 · doi:10.1080/09603123.2021.1932766

Residential district multi-hazard risk is associated with childhood undernutrition: evidence from Bangladesh

2021· article· en· W3164810106 on OpenAlexaff
Md. Belal Hossain, Jahidur Rahman Khan, Mahmood Parvez

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental healthMalnutritionUnderweightWastingHazardMedicineDisaster risk reductionRisk assessmentGeographyBody mass indexOverweightEnvironmental planning

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.089
GPT teacher head0.397
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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