Community-level environmental characteristics predictive of childhood stunting in Bangladesh - a study based on the repeated cross-sectional surveys
Bibliographic record
Abstract
Coastal morphology makes Bangladesh vulnerable to environmental hazards and climate change. Therefore, environmental characteristics may shape population health, including child health. The prevalence of stunting among under-five aged (U5) children is high in Bangladesh. However, there is a lack of research on environmental predictors of stunting. This study aimed to assess the association between community-level environmental characteristics and stunting using pooled data from the three latest Bangladesh demographic and health surveys (BDHS). According to the multilevel model, rainfall, distance to protected areas, and vegetation index showed a nonlinear association with stunting. The temperature was inversely, and distance to water bodies was positively related to stunting. Overall, results evidence the environmental characteristics are predictive of stunting, and these characteristics should be taken into account during intervention design to minimise the negative effects of environmental change on child health. Further research is also necessary to comprehend the causal pathways between environmental characteristics and stunting 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 | 0.007 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".