Effect of a conditional cash transfer program on length-for-age and weight-for-age in Brazilian infants at 24 months using doubly-robust, targeted estimation
Bibliographic record
Abstract
OBJECTIVE: Conditional cash transfer programs are popular internationally and represent a large investment in child health. Evidence of their impact on child nutrition status remains weak and inconsistent, particularly for Bolsa Família, the Brazilian conditional cash transfer program and one of the world's largest. Our objective was to estimate the effect of the Brazilian conditional cash transfer program, Bolsa Família (BF), on child nutritional status as measured by length-for-age z-score (LAZ) and weight-for-age z-score (WAZ) at 24 months. METHODS: We analyzed the 1703 children eligible for BF from the 2004 Pelotas Birth Cohort. Children were divided into three exposure groups by total amount of money their household received from BF in 24 months: no BF, low BF (≤R$1000) and high BF (>R$1000). Using a doubly robust semiparametric estimation method we estimated the effect of receiving low and high levels of BF on LAZ and WAZ at 24 months. RESULTS: After adjustment for measured confounders, the expected difference in LAZ between children that received low or high levels of BF compared to no BF was -0.14 [95% confidence interval (CI): -0.27, -0.02] and -0.20 (95% CI: -0.33, -0.08) respectively. For WAZ the estimated differences were -0.04 (95% CI: -0.17, 0.08) for low levels versus no BF and -0.18 (95% CI: -0.30, -0.05) for high levels versus no BF. The expected difference in population LAZ had all eligible households received it and population LAZ under no BF was -0.15 (95% CI: -0.26, -0.04). Sensitivity analyses suggested only a strong confounder could explain away these results. CONCLUSIONS: Among participants of the 2004 Pelotas Birth Cohort, BF was associated with a reduction in LAZ and WAZ in 24 month old children.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".