Workload of French-speaking family physicians in francophone rural and northern communities in Ontario.
Bibliographic record
Abstract
INTRODUCTION: Previous studies have shown that French-speaking family physicians (FSPs) in Ontario are less numerous in areas with high proportions of francophones. The purpose of the current study was to assess whether the degree of concordance between physicians' language of competence and the linguistic profile of the community in which they practise is associated with workload and to explore variations in this relation in rural and northern regions of the province. METHODS: This was a secondary analysis of the 2013 College of Physicians and Surgeons of Ontario Annual Membership Renewal Survey. We analyzed the primary practice location and language of competence of family physicians/general practitioners. We compared the practice characteristics of FSPs and non-French-speaking physicians (NFSPs) by the proportion of the francophone population, geographic location (north vs. south) and community size (urban vs. rural). RESULTS: Data for 10 548 family physician/general practitioners were analyzed. In areas densely populated by francophones, FSPs worked more hours per week on average and had a greater mean number of patient visits than NFSPs. Non-French-speaking physicians working in areas densely populated by francophones had fewer patient visits per hour on average than FSPs. In most cases, the results were particularly accentuated in rural and northern communities. CONCLUSION: Our findings suggest that, compared to NFSPs, the demands placed on FSPs are disproportionately greater in communities where the need for French-language health care services is greatest and the supply of FSPs is the smallest. Our results underline the importance of properly preparing family physicians to work in areas densely populated by francophones.
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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 | 0.001 |
| 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".