An investigation of the reach of the interim Canada dental benefit for children under 12 years of age
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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 and 95% confidence intervals (CIs) 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 (673.3/1,000, 95% CI: 658.5-688.4 P1 and 717.8/1,000, 95% CI: 702.5-733.3 P2), Northwest Territories (618.4/1,000, 95% CI: 560-681.3 P1 and 573.2/1,000, 95% CI: 517-633.8 P2), and Saskatchewan (495.1/1,000 P1, 95% CI: 486.5-503.7 and 528.3/1,000, 95% CI: 519.5-537.2 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it