Bibliographic record
Abstract
OBJECTIVE: Canada’s national system of health insurance facilitates equitable access to health care; however, since dental care is generally privately financed and delivered, access to oral health care remains uneven and inequitable. To avoid the upfront costs, many argue that socially marginalized groups should seek oral health care from medical providers. This study therefore explored the rates and numbers of visits to physicians for oral health-related diagnoses in Ontario, Canada’s most populated province. METHODS: A retrospective secondary data analysis of health system utilization in Ontario was conducted for visits to physicians for oral health-related diagnoses. Data for all Ontario Health Insurance Plan (OHIP) approved billing claims were accessed over 11 fiscal years (2001–2011). Age- and sex-adjusted rates were calculated. RESULTS: Approximately 208,375 visits per year, with an average of 1,298/100,000 persons, were made to physicians for oral health-related diagnoses. Women, irrespective of the year, made more visits, and there was an increasing trend in visits made by elderly people. CONCLUSION: The number of people visiting physicians for oral health reasons is arguably high. The public health system is being billed for services for oral health issues that the provider is not appropriately trained to treat. Provision of timely and accessible oral health care for socially marginalized populations needs to be prioritized in health care policy. KEY WORDS: Medical billing; health services; health policy; access to oral health care La traduction du résumé se trouve à la fin de l’article. Can J Public Health 2015;106(3):e127–e131
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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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.014 |
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".