Providing dental insurance can positively impact oral health outcomes in Ontario
Bibliographic record
Abstract
BACKGROUND: Universal coverage for dental care is a topical policy debate across Canada, but the impact of dental insurance on improving oral health-related outcomes remains empirically unexplored in this population. METHODS: We used data on individuals 12 years of age and older from the Canadian Community Health Survey 2013-2014 to estimate the marginal effects (ME) of having dental insurance in Ontario, Canada's most populated province (n = 42,553 representing 11,682,112 Ontarians). ME were derived from multi-variable logistic regression models for dental visiting behaviour and oral health status outcomes. We also investigated the ME of insurance across income, education and age subgroups. RESULTS: Having dental insurance increased the proportion of participants who visited the dentist in the past year (56.6 to 79.4%, ME: 22.8, 95% confidence interval (CI): 20.9-24.7) and who reported very good or excellent oral health (48.3 to 57.9%, ME: 9.6, 95%CI: 7.6-11.5). Compared to the highest income group, having dental insurance had a greater ME for the lowest income groups for dental visiting behaviour: dental visit in the past 12 months (ME highest: 17.9; 95% CI: 15.9-19.8 vs. ME lowest: 27.2; 95% CI: 25.0-29.3) and visiting a dentist only for emergencies (ME highest: -11.5; 95% CI: - 13.2 to - 9.9 vs. ME lowest: -27.2; 95% CI: - 29.5 to - 24.8). CONCLUSIONS: Findings suggest that dental insurance is associated with improved dental visiting behaviours and oral health status outcomes. Policymakers could consider universal dental coverage as a means to support financially vulnerable populations and to reduce oral health disparities between the rich and the poor.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".