Factors Associated With Dental Care Utilization in Early Childhood
Bibliographic record
Abstract
OBJECTIVES: To identify sociodemographic, dietary, and biological factors associated with families who do not receive dental care in early childhood and to identify risk factors associated with having cavities among children who receive early dental care. METHODS: A cross-sectional study of healthy Canadian children seen for primary health care between September 2011 and January 2013 was conducted through the TARGet Kids! practice-based research network in Toronto, Canada. Adjusted logistic regression was used to determine factors associated with children who were not seen by a dentist in early childhood and to determine risk factors associated with having dental cavities among children who received early dental care. RESULTS: Of the 2505 children included in the analysis, <1% were seen by a dentist by 1 year of age. Older children were less likely to have never been to the dentist (odds ratio [OR], 0.88; 95% confidence interval [CI], 0.87-0.90). Low family income (OR, 2.73; 95% CI, 1.47-5.06), prolonged bottle use (OR, 1.43; 95% CI, 1.03-2.00), and higher intakes of sweetened drinks (OR, 1.20; 95% CI, 1.01-1.42) were associated with increased risk for never having been to the dentist. Among those who had been to the dentist, older children (OR, 1.04; 95% CI, 1.03-1.05), children of low income families (OR, 1.90; 95% CI, 1.17-3.10), and those of East Asian maternal ethnicity (OR, 1.91; 95% CI, 1.10-3.29) were more likely to have dental cavities. CONCLUSIONS: Among healthy urban children seen by a primary care provider, those most susceptible to cavities were least likely to receive early dental care. These findings support the need for publicly funded universal early preventive dental care and underscore the importance for primary care physicians to promote dental care in early childhood.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".