Health effects of ‘Juntos’, a conditional cash transfer programme in Peru
Bibliographic record
Abstract
In some countries, conditional cash transfer (CCT) programmes show an impact on maternal and child health. Juntos, the CCT programme in Peru, has been evaluated several times operationally, but seldom for maternal and child health outcomes. The objective of this study is to evaluate the impact of Juntos on children under 6 years, pregnant women and mothers of children under 17 years. Outcomes evaluated included (1) anaemia in women and children; (2) acute malnutrition in children; (3) post-partum complications in mothers; and (4) underweight and overweight in mothers. We identified Juntos eligible respondents from the Demographic and Health Surveys of Peru for years 2007 to 2013. Propensity score matching was used to identify comparable treatment and control groups, including eligible respondents enrolled in Juntos vs. those not enrolled in Juntos (individual-level analysis), as well as eligible respondents living in Juntos districts vs. those not residing in Juntos districts (district-level analysis). We then used generalized linear models to estimate prevalence ratios. Individual level analysis showed that Juntos reduced underweight in women (PR:0.39, 95%CI:0.18 - 0.85) and anaemia in children (PR:0.93, 95%CI:0.86 - 1.00). In the district level analysis, the programme was associated with a reduction of overweight in women (PR:0.94, 95%CI:0.90 - 0.98) and acute malnutrition in children (PR:0.49, 95%CI:0.32 - 0.73), but an increase in the prevalence of anaemia in children (PR:1.09, 95%CI:1.01 - 1.17). We found that Juntos had an effect on maternal and child health indicators, but further studies are required to overcome some limitations encountered here.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".