Uptake of the Interim Canada Dental Benefit: An investigation of data from the first 18 months of the program
Bibliographic record
Abstract
Abstract Introduction In 2022, the Government of Canada introduced the Interim Canada Dental Benefit (CDB) to support Canadian families with children < 12 years of age. This program operated from October 1, 2022, to June 30, 2024, with two application periods. The purpose of this study was to analyze data on applications accepted by the Canada Revenue Agency (CRA) during the first 18 months of the program. Methods This study used available data sourced from the CRA for applicants as of March 29, 2024, and assessed as of April 5, 2024. Data covered the entirety of the first period (October 1, 2022–June 30, 2023) of the Interim CDB and the first nine months of the second period (July 1, 2023–March 29, 2024). The rate of child participation was calculated using population data from Statistics Canada (2021). Results Over the first 18 months of the Interim CDB, a total of 410,920 applications were submitted and $403M distributed; $197M for 204,270 applications in period 1 and $175M for 173,160 applications in the first nine months of period 2. A total of 91.8% of applicants had a net family income < $70,000, receiving the maximum benefit amount. The provinces with the highest rate of child participation were Manitoba (77.1/1,000 period 1; 74.9/1,000 period 2), Ontario (82.5/1,000 period 1; 72.2/1,000 period 2), Nova Scotia (73.4/1,000 period 1; 71.1/1,000 period 2), and Saskatchewan (72.3/1,000 period 1; 68.2/1,000 period 2). Overall, there was an increase in the number of applications approved in period 2 compared to period 1. Conclusions Uptake in the first three quarters of period 2 remained consistent and in many instances, revealed higher rates of applications by parents for the Interim CDB. Analyzing this data will aid in policy recommendation for enhancement of the Canadian Dental Care Program.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".