Filling the Cavities: Improving the Efficiency and Equity of Canada’s Dental Care System
Bibliographic record
Abstract
Ensuring that all members of the community, including the poor, have access to urgently needed healthcare is a central objective of Canadian social policy. Yet, in the current system, there are many population groups in which individuals have difficulty accessing even urgently needed dental care. Moreover, the number of Canadians unable to access dental care is likely to grow rapidly in the next decade as the babyboom generation retires and loses insurance coverage, and the number of Canadians working in the gig economy, where benefits such as employersponsored health insurance are rare, rises. Lack of access to dental care may lead to substantial reductions in quality of life due to both the discomfort of oral pain, and the embarrassment associated with having bad breath or bad teeth. Furthermore, there is research to suggest that poor oral health may be a disadvantage in the labour market and also that there may be a link between oral health on the one hand, and heart disease, strokes, and certain forms of cancer, on the other. Untreated oral health problems also are responsible for a not insignificant amount of visits to primary-care physicians and hospital emergency rooms. We believe provincial governments should take inspiration from other countries and start moving toward some form of universal dental insurance coverage; in doing so they should also consider ways in which the dental services sector could become more competitive and efficient. Policy initiatives along those lines could yield major payoffs, in terms of both equity and efficiency. A straightforward way of creating universality would be to gradually expand existing public plans until they covered everyone in the population. However, universality does not necessarily mean that everyone must be insured through the same plan. As an alternative, we explore a mixed model with competition between private and public insurance. In our proposals to improve public dental coverage in Canada, we further scope out possible stumbling blocks in developing a broader public insurance plan, for example, controversies over what should be covered, and how public payment models and regulation could encourage more efficient service delivery.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".