Socio-economic Inequalities and Oral Health in Canada and the United States
Bibliographic record
Abstract
This paper describes and compares the magnitude of socio-economic inequalities in oral health among adults in Canada and the US over the past 35 years. We analyzed data from nationally representative examination surveys in Canada and the US: Nutrition Canada National Survey (1970-1972, N = 11,546), Canadian Health Measures Survey (2007-2009, N = 3,508), The First National Health and Nutrition Examination Survey (1971-1974, N = 13,131), and National Health and Nutrition Examination Survey (2007-2008, N = 5,707). Oral health outcomes examined were prevalence of edentulism, proportion of individuals having at least 1 untreated decayed tooth, and proportion of individuals having at least 1 filled tooth. Sociodemographic indicators included in our analysis were place of birth, education, and income. Data were age-adjusted, and survey weights were used to account for the complex survey design in making population inferences. Our findings demonstrate that oral health outcomes have improved for adults in both countries. In the 1970s, Canada had a higher prevalence of edentulism and dental decay and lower prevalence of filled teeth. This was also combined with a more pronounced social inequality gradient among place of birth, education, and income groups. Over time, both countries demonstrated a decline in absolute socio-economic inequalities in oral health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".