Examining the geographic distribution of French-speaking physicians in Ontario.
Bibliographic record
Abstract
OBJECTIVE: To determine how many physicians in Ontario express a proficiency in providing services in the French language, and to assess the geographic distribution of such physicians. DESIGN: Population-based analysis of the 2007 College of Physicians and Surgeons of Ontario Annual Membership Renewal Survey. SETTING: Ontario. PARTICIPANTS: A total of 22 688 GPs, FPs, and other specialists certified by the College of Family Physicians of Canada and the Royal College of Physicians and Surgeons of Canada who responded to the survey. MAIN OUTCOME MEASURES: First official language spoken and languages of competency to conduct practice. RESULTS: The physician-to-patient ratio by first official language spoken is 1 physician per 138 Francophone patients in Ontario. There is 1 French-speaking GP or FP for every 297 Francophone patients, and most French-speaking physicians are located in southern Ontario (91.4%), at a ratio of 1 physician per 111 Francophone patients. The most promising French-speaking physician-to-Francophone patient ratios are found in southern Ontario (1:248 for GPs and FPs, and 1:202 for other specialists) and in urban Ontario (1:266 for GPs and FPs, and 1:209 for other specialists). CONCLUSION: Clearly, there is a promising number of physicians, relative to the amount of French-speaking residents in Ontario, who identified a competency in offering services in French. However, while the number of physicians who indicated a self-assessed competency to deliver health services in French is promising, it is the maldistribution of such services that is of concern. Thus, efforts must be made to attract French-speaking physicians to areas where there is the greatest demand, particularly in the northern part of the province.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".