Distribution and language abilities of primary care physicians in Ontario
Bibliographic record
Abstract
Context: Language-concordant healthcare is an important social determinant of health, associated with better patient outcomes and lower mortality in some settings. Understanding where Ontario’s family physicians practice and how many can provide language-concordant care to official language minority communities can inform future research and policy development. Objectives: To conduct an Ontario-wide geospatial analysis of the supply of French-speaking physicians and French-speaking Ontarians, identifying underserved regions based on counts and physician/patient ratios. Study Design and Analysis: A descriptive cross-sectional geospatial analysis. Setting or Dataset: Publicly available physician data was collected from the College of Physicians and Surgeons of Ontario’s (CPSO) website in January 2024. Regional geographic and census data were obtained from Statistics Canada’s public website. Population Studied: Population of Ontario, Canada Intervention/Instrument: R Language for Statistical Computing (R Core Team 2023) and RStudio (Posit team 2023). Outcomes Measured: Prevalence and distribution of community-based family physicians in Ontario (all, and French-speaking). Results: The study found n=41,814 physicians practicing in Ontario, with n=14,754 (35.3%) identified as community-based family physicians. Of these, n=1,678 (11.4%) reported speaking French. French-speaking family physicians, and family physicians in general, are not uniformly distributed across the province compared to the population. Northern and rural regions of Ontario are particularly under-served. French-speaking family physicians were more prevalent in Southern vs Northern Ontario (3.9 vs. 2.0 French-speaking physicians per 1,000 Francophone residents), and Urban vs. Rural Ontario (3.7 vs. 2.4 French-speaking physicians per 1,000 Francophone residents). However, Ontario Francophone residents are contrastingly more likely to live in Rural and Northern regions than the general population. Conclusion: Locating Ontario’s French-speaking family physicians is an important step towards measuring and understanding gaps in access to language-concordant healthcare for Francophone residents. However, more sophisticated measures of healthcare access, such as travel burden analyses to estimate local access to family physicians within a specific distance, and estimating competition for scarce resources should be considered.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".