Dental insurance, income and the use of dental care in Canada.
Bibliographic record
Abstract
Using recent Canadian health survey data, we investigated the effect of socioeconomic status on patients' use of dental services and dental insurance coverage. Our results point to an important socioeconomic gradient in the use of dental services. The probability of receiving any dental care over the course of a year increases markedly with dental insurance, household income, and level of education. Among those receiving at least some dental care, however, a person's general oral health--not financial factors--largely determined visit frequency. The insurance effect appears to operate through a reduction in price paid at point of service, not decisions by those with high anticipated need for dental care to selectively purchase insurance. Indeed, those with poorer self-assessed oral health, as well as those from Quebec (where dental benefits are subject to personal income tax) and those over 65 years of age (who have likely lost employer provided coverage) were less likely to be insured.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".