Parent’s Views on Access to Dental Care and the Interim Canadian Dental Benefit
Bibliographic record
Abstract
Abstract Introduction This study investigated parents’ perspectives on access to oral health care and the Interim Canada Dental Benefit (CDB). In the context of Canada’s national health insurance, which historically excluded dental care, the introduction of the Interim CDB in October 2022 represented a paradigm shift towards enhancing dental care accessibility for children under 12 years of age from lower-income families. Methods This study analyzed aggregate and de-identified data from the comprehensive online survey conducted by The Strategic Counsel for Health Canada, involving 2,203 parents from across Canada. The survey was administered in March of 2023. Paired/overlap t-test for means and paired/overlap z-test for percentages were performed, with statistical significance at p ≤ 0.05. Results The majority of participants expressed concerns regarding the costs (90.9%) and accessibility (80.9%) of dental care, indicated that regular dental visits for children is important (97.2%), and would take their children more frequently to dental appointments if had extra money (79.9%). Some of the barriers preventing regular dental visits for children included costs of service and transportation and lack of insurance. The majority of parents showed support for the Interim CDB (87%), with the greatest support coming from the provinces of Manitoba and Saskatchewan (90.4%). Conclusion This research underscores the imperative for ongoing evaluation and policy refinement to ensure the CDCP effectively addresses the nuanced needs of Canadian families, fostering a more inclusive and accessible dental care system. Parents’ concerns regarding dental care and their support for the Interim CDB signal a clear mandate for improving program outreach and accessibility through the Canadian Dental Care Plan (CDCP). Knowledge Transfer Statement Findings from this study highlight the significant concern among parents regarding dental care affordability in Canada, reinforcing the necessity of programs like the Interim Canada Dental Benefit (CDB) and Canadian Dental Care Plan. High rates of support suggest a positive public reception of the CDB, which is crucial for policy intervention’s success. Concern about accessing dental services, despite the availability of the insurance, indicates ongoing barriers to dental care, suggesting areas for future policy refinements.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".