Effects of income and dental insurance coverage on need for dental care in Canada.
Bibliographic record
Abstract
OBJECTIVE: To estimate the strength of the associations among income, dental insurance coverage and need for dental care (both urgent and nonurgent) in Canada. METHODS: Multinomial logistic models were fit to data from the 2009 Canadian Health Measures Survey to test unadjusted associations among household income, dental insurance coverage and the need for urgent and nonurgent dental care. Adjusted associations, controlling for socio-demographic variables (age, sex, immigration status, education and province of residence) and oral health habits (brushing, flossing and visits to the dentist) were also evaluated. RESULTS: In the unadjusted model, need for treatment was lower among people with dental insurance than among those without insurance coverage (for urgent treatment: odds ratio [OR] 0.76, 95% confidence interval [CI] 0.66-0.89; for nonurgent treatment: OR 0.59, 95% CI 0.50-0.70). In addition, there was an income gradient, whereby people with higher income had less need for dental treatment (for urgent treatment: OR 0.99, 95% CI 0.99-1.00; for nonurgent treatment: OR 0.99, 95% CI 0.98-0.99). Controlling for socio-demographic and oral health variables decreased the magnitude of the association between dental insurance coverage and need for treatment (for urgent treatment: OR 0.80, 95% CI 0.68-0.95; for nonurgent treatment: OR 0.76, 95% CI 0.63-0.92). An interaction term between dental coverage and income was significant in relation to the need for nonurgent treatment: among lower-income individuals, having insurance slightly decreased the odds of needing nonurgent treatment, with this decrease in odds becoming greater for middle-income earners and even greater for high-income earners. CONCLUSION: Income-related inequality in need for dental care exists even in the presence of dental insurance coverage and good dental hygiene habits. These findings highlight the need for increased access to dental care for low-income populations and families living in poverty.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".