Inequalities in Indigenous Oral Health: Findings from Australia, New Zealand, and Canada
Bibliographic record
Abstract
The objective was to compare absolute differences in the prevalence of Indigenous-related inequalities in dental disease experience and self-rated oral health in Australia, Canada, and New Zealand. Data were sourced from national oral health surveys in Australia (2004 to 2006), Canada (2007 to 2009), and New Zealand (2009). Participants were aged ≥18 y. The authors measured age- and sex-adjusted inequalities by estimating absolute prevalence differences and their corresponding 95% confidence intervals (95% CIs). Clinical measures included the prevalence of untreated decayed teeth, missing teeth, and filled teeth; self-reported measures included the prevalence of “fair” or “poor” self-rated oral health. The overall pattern of Indigenous disadvantage was similar across all countries. The summary estimates for the adjusted prevalence differences were as follows: 16.5 (95% CI: 11.1 to 21.9) for decayed teeth (all countries combined), 18.2 (95% CI: 12.5 to 24.0) for missing teeth, 0.8 (95% CI: –1.9 to 3.5) for filled teeth, and 17.5 (95% CI: 11.3 to 23.6) for fair/poor self-rated oral health. The I 2 estimates were small for each outcome: 0.0% for decayed, missing, and filled teeth and 11.6% for fair/poor self-rated oral health. Irrespective of country, when compared with their non-Indigenous counterparts, Indigenous persons had more untreated dental caries and missing teeth, fewer teeth that had been restored (with the exception of Canada), and a higher proportion reporting fair/poor self-rated oral health. There were no discernible differences among the 3 countries.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".