Is private insurance enough to address barriers to accessing dental care? Findings from a Canadian population-based study
Bibliographic record
Abstract
Abstract Background In Canada, as in many other countries, insurance plays a crucial role in facilitating access to dental care. Private dental insurance, though it greatly reduces financial barriers to dental care, does not guarantee affordability, as there are issues with the quality and level of coverage of insurance plans. Individuals experiencing barriers to dental care are expected to have poorer oral health. It is important to examine access to dental care and the oral health status of individuals with private insurance. Methods Our study is a secondary data analysis of the most recent available cycle (2017-18) of the Canadian Community Health Survey (CCHS), a national cross-sectional survey. Univariate analysis was conducted to determine the characteristics of Ontarians with private insurance (n = 17,678 representing 6919,814 Ontarians)—bivariate analysis to explore their financial barriers to dental care, and how they perceive their oral health. Additionally, logistic regressions were conducted to identify relationships between covariates and each outcome variable in our study (cost barriers to dental care, visiting the dentist only for emergency, perceived their oral health as “fair to poor” and “dissatisfied and very dissatisfied” with their teeth/denture appearance). Results We found that the majority of those with private insurance did not experience cost barriers to dental care and perceived their oral health as good to excellent. However, specific populations, including those aged 20–39 years, and those earning less than $40,000, despite having private dental insurance, faced significantly more cost barriers to access to care. Additionally, those with the lowest income (earning less than $20,000) perceived their oral health as “fair to poor” more than their counterparts. Adjusted estimates revealed that respondents aged 20–39 were six times more likely to report cost barriers to dental care and ten times more likely to visit the dentist only for emergencies than those aged 12–19. Additionally, those aged 40–59 were two times more likely to report poorer oral health status compared to those aged 12–19. Conclusion Given the upcoming implementation of the Canadian Dental Care Plan, the results of this study have implications for identifying vulnerable populations who currently are ineligible for insurance coverage.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".