Starting from Scratch: A Micro-Costing Analysis for Public Dental Care in Canada
Bibliographic record
Abstract
This paper presents a formulaic approach to micro-costing the expected direct clinical cost of a given public dental program in Canada. A micro-costing approach enables policy leaders to create or radically redesign existing public-sector dental programs by projecting the total cost generated by expected clinical demand. Current public dental plans employ heavy restrictions around the types of dental treatments offered, and the frequency by which a patient may seek treatment. These restrictions result in minimal allocations of public funds, and often result in patients being under-served and dental professionals being under-funded. Using the model described in the paper governments can project the costs of future dental care programming. The two novel programs costed in this paper were a universal dental care program for all Canadian residents (referred to as denticare), and a public dental insurance plan for all Canadian children and uninsured adults (denitcade). Details in the paper are laid out such that if a government was curious about the cost of programs other than the ones described in this study, such as funding preventative services for low-income seniors, then using this micro-costing model could generate a high- low- and baseline estimate for such a program’s annual clinical expenses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".