Socio-economic determinants of household food security and women's dietary diversity in rural Bangladesh: a cross-sectional study
Bibliographic record
Abstract
There has been limited decline in undernutrition rates in South Asia compared with the rest of Asia and one reason for this may be low levels of household food security. However, the evidence base on the determinants of household food security is limited. To develop policies intended to improve household food security, improved knowledge of the determinants of household food security is required.Household data were collected in 2011 from a randomly selected sample of 2,809 women of reproductive age. The sample was drawn from nine unions in three districts of rural Bangladesh. Multinomial logistic regression was conducted to measure the relationship between selected determinants of household food security and months of adequate household food provisioning, and a linear regression to measure the association between the same determinants and women's dietary diversity score.The analyses found that land ownership, adjusted relative risk ratio (RRR) 0.28 (CI 0.18, 0.42); relative wealth (middle tertile 0.49 (0.29, 0.84) and top tertile 0.18 (0.10, 0.33)); women's literacy 0.64 (0.46, 0.90); access to media 0.49 (0.33, 0.72); and women's freedom to access the market 0.56 (0.36, 0.85) all significantly reduced the risk of food insecurity. Larger households increased the risk of food insecurity, adjusted RRR 1.46 (CI 1.02, 2.09). Households with vegetable gardens 0.20 (0.11, 0.31), rich households 0.46 (0.24, 0.68) and literate women 0.37 (0.20, 0.54) were significantly more likely to have better dietary diversity scores.Household food insecurity remains a key public health problem in Bangladesh, with households suffering food shortages for an average of one quarter of the year. Simple survey and analytical methods are able to identify numerous interlinked factors associated with household food security, but wealth and literacy were the only two determinants associated with both improved food security and dietary diversity. We cannot conclude whether improvements in all determinants are necessarily needed to improve household food security, but new and existing policies that relate to these determinants should be designed and monitored with the knowledge that they could substantially influence the food security and nutritional status of the population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".