Disparities in the availability of dental care in metropolitan Toronto.
Bibliographic record
Abstract
OBJECTIVES: The availability of dentists as a barrier to access to care has not been thoroughly explored, particularly in large cities. In this study, we aimed to identify disparities in the availability of dentists in Canada's largest urban centre, Toronto, and explore whether distributional disparities are associated with underlying factors, such as affordability as measured by average household income. METHODS: Geocoded data on number of dentists and population estimates for metropolitan Toronto's forward sortation areas (FSA) were used to calculate dentists per 100,000 population. Dentist density and average annual household income by FSA were then mapped using geographic information system techniques. Pearson testing was used to identify associations of various factors with dentist density. Significance testing was performed to compare average dentist to population ratios in high (> $100,000) and low ($40,000-$60,000) income FSAs. RESULTS: Communities with high household incomes and high dentist density were clustered in central Toronto. Income-based disparities in dentist distribution were also observed. Compared with low-income FSAs, dentist density increased by a factor of 2.47 in the highest income FSAs. Dentist density also increased with income and education but decreased with immigrant level. CONCLUSIONS: Dentist availability may be linked to demographic factors, including affordability. The income-based disparity in availability in Toronto was as high as that observed elsewhere between rural and urban communities.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".