An investigation of the reach of the Interim Canada Dental Benefit for children under 12 years of age
Bibliographic record
Abstract
Abstract Introduction The Interim Canada Dental Benefit (CDB), introduced in 2022, provided financial assistance to families with children < 12 years. This study analyzed data from the Canada Revenue Agency (CRA) during the program’s entirety. Methods Data were accessed from the CRA for applicants and covered both the first (October 1, 2022–June 30, 2023) and second (July 1, 2023–June 30, 2024) periods. Rates of participation were calculated using population data from Statistics Canada. Adjusted rates were calculated based on the proportion of children without private dental insurance, and without private or public insurance. Results Over the 21 months of the Interim CDB, 408,240 regular applications were made and $401M distributed to Canadian families. More applications were made during period 1 (P1) than period 2 (P2), but more funding distributed in P2; $197M for 204,270 applications in P1 and $203M for 203,970 applications in P2. Overall, 321,000 children received the Interim CDB in P1 and 328,040 in P2. Provinces with highest rates of child participation included Manitoba, Ontario, Nova Scotia, and Saskatchewan. The highest adjusted rates based on the proportion of children without private or public insurance were Nova Scotia (673.3/1000 P1 and 717.8/1000 P2), Northwest Territories (618.4/1000 P1 and 573.2/1000 P2), and Saskatchewan (495.1/1000 P1 and 528.3/1000 P2) Conclusions Regions with access to care challenges had higher rates uptake of the Interim CDB when adjusting for the lack of private or public insurance. Findings from this study may help inform policy decisions and reach of the CDCP.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".