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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".