Exploring the association between dental insurance coverage and dental care utilization and oral health among elderly Ontarians
Bibliographic record
Abstract
OBJECTIVES: There has been an increasing interest in addressing the equity issue of accessing dental care for low-income elderly. This study aimed to estimate the marginal effects (ME) of dental insurance coverage for seniors on dental care utilization and oral health status outcomes. We also estimated the ME of dental insurance across income subgroups. METHODS: Data was sourced from the 2017/18 Canadian Community Health Survey (CCHS)-Annual component. The ME analysis included individuals aged ≥65 years residing in Ontario (n = 10,030). ME were derived from multivariate probit regression models for dental care utilization and oral health status outcomes. RESULTS: Dental insurance increased the likelihood of reporting excellent/very good oral health and never avoiding foods due to oral problems by 6.9% (ME:6.9, 95% CI: 5.4-8.3) and 3.5% (ME: 3.5, 95% CI: 1.9-5.1), respectively. Dental insurance increased the likelihood of dental visits within the past year by 11.3% (ME: 11.3, 95% CI: 9.8-12.8) and decreased the likelihood of dental visits only for emergencies by 11.2% (ME: -11.2, 95% CI: -12.5 to -9.9). Compared to low- and high-income groups, dental insurance had the highest ME for the middle-income groups for dental visits within the past year (ME middle: 13.1, 95% CI: 10.5-15.7) and dental visits only for emergencies (ME middle: -14.4, 95% CI: -16.0 to -12.8). CONCLUSION: Dental insurance can improve the utilization of dental care and can help mitigate the negative effects of poor oral health in elderly populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".