Dental Insurance and Treatment Patterns at a Not-For-Profit Community Dental Clinic.
Bibliographic record
Abstract
OBJECTIVES: To examine patient demographics, distance traveled and dental-related treatment provided according to type of dental insurance at a large, not-for-profit community dental clinic (CDC) in Vancouver, Canada. METHODS: Using electronic dental records, we assessed the use of private and government-sponsored (public) dental insurance at the CDC in 2014 and 2015 at the appointment and procedure levels. Study variables included patient demographics, distance traveled, type of treatment provided, type of dental insurance and cost of treatment. RESULTS: Examination of records from 9524 appointments involving 16 639 procedures revealed that 44% (4190 appointments) were made by patients with private insurance and 31.4% (2995) by those with public insurance. Patients with private dental insurance were 1.27 times more likely (p < 0.001) to have restorative treatment than those with public-sponsored dental insurance. Procedures involving tooth extraction were 14.2 times more likely (p < 0.001) to be performed in patients with public insurance than those with private insurance. CONCLUSIONS: Access does not equal equity; although the CDC enables access by various populations, its ability to provide equitable treatment is compromised by external factors. CDCs may have a vital role in oral health equity; however, dental treatment continues to be dictated by financial reimbursement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".