Household Income, Food Insecurity, and Nutrition in Bangladeshi Youth
Bibliographic record
Abstract
The role of nutrition in health inequities, particularly among children, is poorly understood. The goal of this study was to look at the impact of income, as well as the combined effects of low income and food insecurity, on a variety of dietary parameters in a sample of Bangladeshi youth. The diets of 8,938 youth aged 9–18 years were studied using a nationally representative population-based sample. Dietary data were collected using a single 24-hour recall. Anthropometric measures were available for 71% of the population, and interviews were conducted in person. The variance estimates were calculated. The connections between anthropometric measurements, food and nutrient intakes, and low-income and low-income food insecurity were investigated using generalized linear models. The height percentiles of children from low-income families were lower than those of children from higher-income families. Low-income girls were more likely than their higher-income peers to have a BMI in the 85th percentile. Boys in low-income food-insecure homes had a greater prevalence of BMI 85th percentile than low-income boys in food-secure households. Boys and girls from low-income families had lower calcium and vitamin D intakes. Milk intake was also lower among low-income boys. Low-income, food-insecure girls consumed less milk and consumed more sweetened beverages. We discovered some evidence of nutritional deprivation among Canadian children from low-income families. These findings were supported by longer-term markers of nutritional health, such as decreased height and weight in impoverished households. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".