Bibliographic record
Abstract
INTRODUCTION: The recently launched income-based Canadian Dental Care Plan (CDCP) may help to reduce oral health inequities and financial barriers for almost 9 million uninsured residents of Canada. The objective of this study was to critically appraise early CDCP data in terms of patient eligibility and provider enrolment and participation in the program. METHODS: This study was based on cross-sectional and retrospective publicly available data from the CDCP and from the Survey of Oral Health Care Providers (SOHCP) released by the Government of Canada, Health Canada and Statistics Canada in 2024 and 2025. The results of descriptive statistical analyses are reported as means, percentages and averages. RESULTS: The number of approved applicants (where eligibility was based on annual income tax records), the number of those receiving care and the number of oral health care providers (e.g., dentists, dental therapists, independent practising hygienists) participating in the CDCP increased by 77.2%, 344.6% and 34.0%, respectively, between August 2024 and May 2025. According to the providers participating in the SOHCP, more than 60% of practices would be able to accommodate the forecasted additional patient visit load associated with introduction of the CDCP. As wait times for existing patients increased, the likelihood of accommodating more patients decreased. At the time this study was conducted, no claims data on types of treatment provided or patients' perspectives about the program were available. CONCLUSION: The CDCP is a form of policy offering publicly funded oral health care, but potential enrolment is restricted to those who file their income tax returns. Most practices seem able to cope with the foreseen increase in patients seeking oral health care. Service utilization and oral health indicators are still emerging; comprehensive claims data and further research on patients' perspectives are needed to explore the extent to which the CDCP is addressing oral health inequities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.851 | 0.716 |
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".