Structural determinants explain caries differences among preschool children in Chile’s Metropolitan Region
Bibliographic record
Abstract
OBJECTIVE: To estimate the association between Social Determinants of Health (structural and intermediate) and caries indicators in Chile's Metropolitan Region preschool children. METHODS: A multilevel cross-sectional study of Social Determinants of Health (SDH) and caries in children aged 1 to 6 years in Chile's Metropolitan Region was conducted in 2014-2015, with three levels: district, school and child. Caries were assessed by the dmft-index and the prevalence of untreated caries. The structural determinants analyzed were Community Human Development Index (CHDI), urban/rural location, school type, caregiver's education and family income. Poisson multilevel regression models were fit. RESULTS: The sample size was 2,275 children from 40 schools in 13 districts. While the highest CHDI district had an untreated caries prevalence of 17.1% (12.3-22.7%), in the most disadvantaged district it was 53.9% (95% CI 46.0-61.6%). As family income increased, the probability of untreated caries prevalence decreased (PR = 0.9 95% CI 0.8-1.0). Rural districts had an average dmft-index of 7.3 (95% CI 7.2-7.4), while in urban districts, it was 4.4 (95% CI 4.3-4.5). Higher probabilities of untreated caries prevalence (PR = 3.0 95% CI 2.3-3.9) were observed in rural children. Greater probabilities of untreated caries prevalence (PR = 1.3 95% CI 1.1-1.6) and prevalence of caries experience (PR = 1.3 95% CI 1.1-1.5) were observed in children whose caregivers had a secondary educational level. CONCLUSIONS: A strong association was observed between the social determinants of health, specifically the structural ones, and the caries indicators studied in children of the Metropolitan Region of Chile. There were notable differences in caries between districts according to social advantage. Rurality and caregiver's education were the most consistent predictors.
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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 | 0.000 | 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".