Socioeconomic status, oral health and dental disease in Australia, Canada, New Zealand and the United States
Bibliographic record
Abstract
BACKGROUND: Socioeconomic inequalities are associated with oral health status, either subjectively (self-rated oral health) or objectively (clinically-diagnosed dental diseases). The aim of this study is to compare the magnitude of socioeconomic inequality in oral health and dental disease among adults in Australia, Canada, New Zealand and the United States (US). METHODS: Nationally-representative survey examination data were used to calculate adjusted absolute differences (AD) in prevalence of untreated decay and fair/poor self-rated oral health (SROH) in income and education. We pooled age- and gender-adjusted inequality estimates using random effects meta-analysis. RESULTS: New Zealand demonstrated the highest adjusted estimate for untreated decay; the US showed the highest adjusted prevalence of fair/poor SROH. The meta-analysis showed little heterogeneity across countries for the prevalence of decayed teeth; the pooled ADs were 19.7 (95% CI = 16.7-22.7) and 12.0 (95% CI = 8.4-15.7) between highest and lowest education and income groups, respectively. There was heterogeneity in the mean number of decayed teeth and in fair/poor SROH. New Zealand had the widest inequality in decay (education AD = 0.8; 95% CI = 0.4-1.2; income AD = 1.0; 95% CI = 0.5-1.5) and the US the widest inequality in fair/poor SROH (education AD = 40.4; 95% CI = 35.2-45.5; income AD = 20.5; 95% CI = 13.0-27.9). CONCLUSIONS: The differences in estimates, and variation in the magnitude of inequality, suggest the need for further examining socio-cultural and contextual determinants of oral health and dental disease in both the included and other countries.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".