Practice Profiles and Patterns of Ontario Family Medicine Residents 5 Years After Residency Examinations: An Exploratory Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The primary goal of family medicine residency training is for graduates to provide high-quality, safe, and effective patient care for the population they serve when they enter practice. This study explores (a) the practice profiles, 5 years into practice, of residents who completed family medicine training in Ontario, Canada; and (b) relationships between performance on the College of Family Physicians of Canada's (CFPC) Certification Examination in Family Medicine and quality of care provided 5 years into practice. METHODS: We performed a retrospective study with secondary data analysis. We merged CFPC examination data sets with the ICES (Institute for Clinical Evaluative Sciences) administrative database. We included physicians who passed the examination between the years 2000 and 2010 and practiced in Ontario after graduation. Practice profile indicators included practice type, continuity and comprehensiveness of care, patient rostering and panel size, and rurality index. We explored 11 indicators related to management of diabetes and cancer screening. RESULTS: We included a total of 1,983 physicians in the analyses. Five years after the examinations, 74.3% of the physicians were working in major urban centers, and 67.3% of the physicians were providing comprehensive primary care. We noted significant differences across the six medical schools in multiple practice profile indicators, and three indicators showed significant differences across the examination score quintiles. CONCLUSIONS: Graduates of Ontario family medicine residency programs were providing care to a broad spectrum of the population 5 years after passing the examination, and they performed similarly across quality-of-care indicators regardless of examination scores.
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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 | 0.002 | 0.003 |
| 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) | 0.001 | 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".