Effect of Poverty Reduction Program on Nutritional Status of the Extreme Poor in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Poverty alleviation programs for the extreme poor improve participants' economic status and may impact other important outcomes that are seldom evaluated. A program targeted to the extreme poor by BRAG, a development organization in Bangladesh, has been successful in significantly alleviating extreme poverty. OBJECTIVE: We hypothesized that the program also improved the nutritional status of women and preschool children. METHODS: A nonequivalent control, pre- and posttest quasi-experimental design that was longitudinal at the village level was used to test the hypotheses. Data were collected from a random sample of 4,131 children and 3,551 women from 3,409 households in 159 villages of 3 northern districts of Bangladesh in 2002 and 2006. Linear mixed random-intercept models accounted for clustering effects and potential confounders. RESULTS: The weight-for-height of children between 24 and 35 months of age from program households was significantly higher (p < .05) than that of children from control households. We found no significant differences between control and program households in three other growth and body-composition indicators in three other age categories of preschool children or in women. CONCLUSIONS: These results are important, as this is a large-scale program that has already been extended to more than half the country. The findings will contribute to judging the cost-benefit and cost-effectiveness of the program and in garnering support for the expansion of such programs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".