Canadian Dentists' Opinions on Publicly Financed Dental Care
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to inform policy leaders of the opinions of Canada's major dental care service provider regarding publicly financed dental care. METHODS: Using provincial/territorial dental regulatory authority listings, a 26-item questionnaire was sent to a representative sample of Canadian dentists (n = 2219, response rate = 45.8 percent). Descriptive statistics were produced, and bivariate and multivariate logistic regressions were conducted to assess what predicts dentists' responses. RESULTS: Canadian dentists support governmental involvement in dental care, preferring investments in prevention to direct delivery. The majority of dentists have less than 10 percent of their practice represented by publicly insured patients, with a small minority having greater than 50 percent. The majority would accept new publicly insured patients, preferring fee for service remuneration. Dentists generally appear dissatisfied with public forms of third-party financing. CONCLUSIONS: Dentists prefer a targeted effort at meeting public needs and are influenced in their opinions largely in relation to ideology. In order to move forward, policy leaders will need to devote some attention to the influence and complexity of public and private tensions in dentistry At the very least, public and private practitioners must come to appreciate each other's challenges and balance public and private expectations in public programming.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".