Do health systems cover the mouth? Comparing dental care coverage for older adults in eight jurisdictions
Bibliographic record
Abstract
Oral health is an important component of general health, yet there is limited financial protection for the costs of oral health care in many countries. This study compares public dental care coverage in a selection of jurisdictions: Australia (New South Wales), Canada (Alberta), England, France, Germany, Italy, Sweden, and the United States. Drawing on the WHO Universal Coverage Cube, we compare breadth (who is covered), depth (share of total costs covered), and scope (services covered), with a focus on adults aged 65 and older. We worked with local experts to populate templates to provide detailed and comparable descriptions of dental care coverage in their jurisdictions. Overall most jurisdictions offer public dental coverage for basic services (exams, x-rays, simple fillings) within four general types of coverage models: 1) deep public coverage for a subset of the older adult population based on strict eligibility criteria: Canada (Alberta), Australia (New South Wales) and Italy; 2) universal but shallow coverage of the older adult population: England, France, Sweden; 3) universal, and predominantly deep coverage for older adults: Germany; and 4) shallow coverage available only to some subgroups of older adults in the United States. Due to the limited availability of comparable data within and across jurisdictions, further research would benefit from standardized data collection initiatives for oral health measures.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".