Oral health inequality in Canada, the United States and United Kingdom
Bibliographic record
Abstract
The objective of this study was to quantify the magnitude of absolute and relative oral health inequality in countries with similar socio-political environments, but differing oral health care systems such as Canada, the United States (US), and the United Kingdom (UK), in the first decade of the new millennium. Clinical oral health data were obtained from the Canadian Health Measures Survey 2007-2009, the National Health and Nutrition Examination Survey 2007-2008, and the Adult Dental Health Survey 2009, for Canada, the US and UK, respectively. The slope index of inequality (SII) and relative index of inequality (RII) were used to quantify absolute and relative inequality, respectively. There was significant oral health inequality in all three countries. Among dentate individuals, inequality in untreated decay was highest among Americans (SII:28.2; RII:4.7), followed by Canada (SII:21.0; RII:3.09) and lowest in the UK (SII:15.8; RII:1.75). Inequality for filled teeth was negligible in all three countries. For edentulism, inequality was highest in Canada (SII: 30.3; RII: 13.2), followed by the UK (SII: 10.2; RII: 11.5) and lowest in the US (SII: 10.3; and RII: 9.26). Lower oral health inequality in the UK speaks to the more equitable nature of its oral health care system, while a highly privatized dental care environment in Canada and the US may explain the higher inequality in these countries. However, despite an almost equal utilization of restorative dental care, there remained a higher concentration of unmet needs among the poor in all three countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".