Towards an understanding of the structural determinants of oral health inequalities: A comparative analysis between Canada and the United States
Bibliographic record
Abstract
OBJECTIVE: To compare the magnitude of, and contributors to, income-related inequalities in oral health outcomes within and between Canada and the United States over time. METHODS: The concentration index was used to estimate income-related inequalities in three oral health outcomes from the Nutrition Canada National Survey 1970-1972, Canadian Health Measures Survey 2007-2009, Health and Nutrition Examination Survey I 1971-1974, and National Health and Nutrition Examination Survey 2007-2008. Concentration indices were decomposed to determine the contribution of demographic and socioeconomic factors to oral health inequalities. RESULTS: Our estimates show that over time in both countries, inequalities in decayed teeth and edentulism were concentrated among the poor and inequalities in filled teeth were concentrated among the rich. Over time, inequalities in decayed teeth increased and decreased for measures of filled teeth and edentulism in both countries. Inequalities were higher in the United States compared to Canada for filled and decayed teeth outcomes. Socioeconomic characteristics (education, income) contributed greater to inequalities than demographic characteristics (age, sex). As well, income contributed more to inequalities in recent surveys in both Canada and the United States. CONCLUSIONS: Inequalities in oral health have persisted over the past 35 years in Canada and the United States, and are associated with age, sex, education, and income and have varied over time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| 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) | 0.002 | 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".