The Association Between Income Inequality and Oral Health in Canada
Bibliographic record
Abstract
Societies exhibiting higher levels of economic inequality experience poorer health outcomes, and the proposed pathways used to explain these patterns are also relevant to oral health. This study therefore examines the relationship between the level of income inequality and the oral health and dental care services utilization of residents from eleven Canadian metropolitan areas. We calculated Pearson correlation coefficients (r) between each metropolitan area's Gini coefficient (used as a proxy for income inequality, calculated from 2006 Canadian census data) and each area's experience of dental pain, self-reported oral health, and use of dental care services (provided by data from the 2003 Canadian Community Health Survey). Greater levels of income inequality in the selected metropolitan areas were related to an increased likelihood of residents self-reporting their oral health as poor/fair and reporting a prolonged absence from visiting a dentist. There was, however, no relationship between the level of income inequality and the likelihood of respondents reporting a recent toothache, tooth sensitivity, or jaw pain. Policies designed to improve the oral health of the population, and Canadians' access to dental care generally, may therefore work best when supported by policies that promote greater economic equality within Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".