Developmental health in the context of an early childhood program in Brazil: the “Primeira Infância Melhor” experience
Bibliographic record
Abstract
Design and evaluation of early child development (ECD) programs are poorly documented in low- or middle-income countries. The study aimed to identify family and child characteristics associated with developmental health outcomes among children aged from 4 to 6 years who participated in the "Primeira Infância Melhor" - PIM (Better Early Childhood), a home visiting program in Rio Grande do Sul State, Brazil. We also evaluated the impact of PIM on developmental vulnerability at school entry using a comparison group. Multistage sampling was first used to select cities, then families, in different regions of the state, resulting in a sample of eight cities and 571 children (364 PIM; 207 comparison). We used a sociodemographic questionnaire, completed by parents, and the Early Development Instrument (EDI), completed by teachers. Among PIM children, lower family income, time of exit from the program, city, and younger age were associated with higher risk of developmental vulnerability and/or with lower mean scores in EDI domains. Multivariate analysis controlling for covariates found no differences between the study groups in EDI outcomes even though the gaps in equity of the outcomes were smaller in the PIM group. These results are discussed in the context of challenges faced by home visiting programs in addressing complex social conditions of high-risk families and difficulties in finding an adequate comparison group in communities where an ECD program is universally accessible. We also note the importance of setting structured and longitudinal monitoring systems together with the implementation of ECD policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".