Comparing the magnitude of oral health inequality over time in Canada and the United States
Bibliographic record
Abstract
OBJECTIVES: To assess the magnitude of, and changes in, absolute and relative oral health inequality in Canada and the United States, from the 1970s till the first decade of the new millennium. METHODS: Data were obtained from four national surveys; two Canadian (NCNS 1970-1972 and CHMS 2007-2009) and two American (HANES 1971-1974 and NHANES 2007-2008). The slope and relative index of inequality were used to measure absolute and relative inequality, respectively. Percentage change in inequality was also calculated. RESULTS: Relative inequality for untreated decay increased by 91% in Canada and 189% in the United States, while for filled teeth it declined by 63% in Canada and 16% in the United States. Relative inequality in edentulism rose by 200% and 78% in Canada and United States, respectively. Absolute inequality declined in both countries. CONCLUSIONS: There was persistent absolute and relative inequality in Canada and the United States. An increase in relative inequality for adverse outcomes suggests that improvements in oral health were occurring primarily among the rich, while reductions in relative inequality for filled teeth indicate higher utilization of restorative services among the poor. These results point to the necessity of tackling the sociopolitical determinants of health to mitigate oral health inequality in Canada and the United States.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".