Examining changes in income-related oral health inequality in Canada: a population-level perspective
Bibliographic record
Abstract
Introduction: Monitoring trends in oral health outcomes is key to identifying population needs and informing policy for improvements in oral health of Canadians. At present, effort to examine income-related inequalities in oral health and their changes over time has been minimal in Canada. Our objectives were to examine and compare income-related inequalities in decayed teeth in Canada between 1970 and 2000s. Methods: A secondary data analysis using the Nutrition Canada National Survey 1970-1972 and Canadian Health Measures Survey 2007-2009 was performed in order to examine individual- and population-level income-related inequalities in oral health. Income quintiles and concentration indices for the presence of one or more decayed teeth were derived using indirect standardization and multivariate logistic regression. Results: Results highlight that income-gradients in oral health have persisted over time, with lower reductions in decayed teeth in lower income quintiles than higher quintiles. Higher concentration indices were exhibited in more recent surveys suggesting an increase in income-related inequality in decayed teeth over time. Conclusion: Our findings provide a benchmark for measuring changes to income-related inequalities in oral health in the Canadian population and reveal that inequalities in untreated dental disease have persisted despite overall reductions in caries rates 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".