Towards an understanding of the structural determinants of oral health inequalities: A comparative analysis between Canada and the United States
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it