Shared environments: a multilevel analysis of community context and child nutritional status in Bangladesh
Bibliographic record
Abstract
OBJECTIVE: The goal of the present study was to examine the influence of community environment on the nutritional status (weight-for-age and height-for-age) of children (aged 0-59 months) in Bangladesh. In addition, we tested the association between specific characteristics of community environments and child nutritional status. DESIGN: Cross-sectional survey. SETTING: The data are from the nationally representative 2004 Bangladesh Demographic and Health Survey. SUBJECTS: Respondents were ever-married women (aged 15-49 years) and their children (n 5731), residing in 361 communities. Child nutritional outcomes are physical measurements of weight-for-age and height-for-age in sd units. We considered the following attributes of community environments potentially related to child nutrition: (i) community water and sanitation infrastructure; (ii) availability of community health and education services; (iii) community employment and social participation; and (iv) education level of the community. RESULTS: Multilevel regression analysis showed that the spatial distribution of maternal and child covariates did not entirely explain the between-community variation in child nutritional status. The education level of the community emerged as the strongest community-level predictor of child height-for-age (highest v. lowest tertile, β = 0.18 (SE 0.07)) and weight-for-age (highest v. lowest tertile, β = 0.21 (SE 0.06)). In the height-for-age model, community employment and social participation also emerged as being statistically significant (highest v. lowest tertile, β = 0.13 (SE = 0.06)). CONCLUSIONS: The community environment influences child nutrition in Bangladesh, and maternal- and child-level covariates may fail to capture the entire influence of communities. Interventions to reduce child undernutrition in developing countries should take into consideration the wider community context.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".