Health profile differences between recipients and non-recipients of the Brazilian Income Transfer Program in a low-income population
Bibliographic record
Abstract
We investigated the relationship between living in a household that receives the Brazilian Income Transfer Program (Bolsa Família, in Portuguese - BF), a Brazilian conditional cash transfer program, and aspects of health and whether these relationships are heterogeneous across the 27 Brazilian states. According to data from the 2013 Brazilian National Health Survey, 18% of households participated in BF. Among households with household per capita income below BRL 500, many aspects of health differed between people living in BF and non-BF houses. For example, BF households were less likely to have medical coverage but more likely to have visited the doctor in the last 12 months as well as being more likely to smoke and less likely to do exercise. They ate nearly one less serving of fruits and vegetables a week but were less likely to substitute junk food for a meal. They reported worse self-rated health but did not differ importantly on reporting illnesses. Moderate amounts of heterogeneity in the difference in health characteristics were found for some variables. For instance, medical coverage had an I2 value of 40.7% and the difference in coverage between BF and non-BF households ranged from -0.09 to -0.03. Some illnesses differed qualitatively across states such as high cholesterol, asthma and arthritis. This paper is the first to outline the health profile of people living in households receiving payments from a cash transfer program. It is also the first to find geographic heterogeneity in the relationship between a cash transfer program and health variables. These results suggest the possibility that the effect of cash transfer programs may differ based on the population on which it is implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".