An investigation of data from the first year of the interim Canada Dental Benefit for children <12 years of age
Bibliographic record
Abstract
Introduction: In 2022, the federal government announced a commitment of $5.3B to provide dental care for the uninsured, beginning with children <12 years of age. Now referred to as the Interim Canada Dental Benefit (CDB), the program targets those <12 years of age from families with annual incomes <$90,000 without private dental insurance. The purpose of this study was to review federal data from the Government of Canada on public uptake and applications made to the Canada Revenue Agency (CRA) during the first year of the Interim CDB. Methods: Data for the first year of the Interim CDB (up to June 30, 2023) were accessed from the Government of Canada Open Data Portal through Open Government Licence-Canada. Rates of children receiving the Interim CDB per 1,000 were calculated by dividing the number of beneficiaries by the total number of children 0-11 years by province or territory, available from Statistics Canada for the year 2021. Results: During the first year of the program, a total of 204,270 applications were approved, which were made by 188,510 unique applicants for 321,000 children <12 years of age. Over $197M was distributed by the CRA. Overall, the national rate for receiving the Interim CDB was 67.8/1,000 children. Ontario (82.5/1,000), Manitoba (77.1/1,000), Nova Scotia (73.4/1,000), and Saskatchewan (72.3%), all had rates of children with the Interim CDB above the national rate. Conclusions: Data from the first year of the Interim CDB suggests that this federal funding is increasing access to care for children <12 years by addressing the affordability of dental care. Governments and the oral health professions need to address other dimensions of access to care including accessibility, availability, accommodation, awareness, and acceptability of oral health care.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".