The association of the Bolsa Familia Program with children’s oral health in Brazil
Bibliographic record
Abstract
BACKGROUND: Several studies have demonstrated that Conditional Cash Transfer (CCT) programs reduce poverty/inequity and childhood mortality. However, none of these studies investigated the link between CCT programs and children's oral health. This study examines the association between receiving the Brazilian conditional cash transfer, Bolsa Familia Program (BFP), and the oral health of five-year-old children in the Northeast of Brazil. METHODS: We conducted a cross-sectional study with 230 caregivers/children randomly selected in primary health care clinics in the city of Fortaleza in 2016. Interviews and oral health examinations were performed. Descriptive statistics and multiple logistic regression analyses were conducted to identify factors associated with dental caries among five-year-old children enrolled in the BFP. RESULTS: Around 40% of children enrolled in the BFP had dental caries. However, those who received Bolsa Familia (BF) for a period up to two years (OR = 0.13, 95% CI 0.05-0.35) had substantially lower adjusted odds of having dental caries than those who had never received BF. In addition, the association of BF and dental caries was more prominent among extremely poor families (OR = 0.05, 95% CI 0.01-0.28). CONCLUSIONS: Although initial enrolment in the BFP predicted low dental caries among five-year-old children, the prevalence of dental caries in this population is still high, thus, public health programs should target BF children's oral health. An ongoing effort should be made to reduce oral health inequalities among children in Brazil.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".