A Longitudinal Study of Differences in Canadian and US Medical Student Preparation for Family Medicine
Bibliographic record
Abstract
Background and Objectives: Previous research has found differences in preparation for entry into family medicine training between graduates of US and Canadian medical schools. However, this research was limited in that it utilized cross-sectional data to examine a longitudinal issue. This study aimed to examine these differences with a longitudinal data set. Methods: A comparison of the performance on the American Board of Family Medicine (ABFM) In-Training Exam (ITE) between 2014 and 2016 was conducted by examining the performance of Canadian medical school graduates and US medical school graduates longitudinally, as well as cross-sectionally, using independent t tests. Results: For first-year residents (PGY1), the Canadian 2014/2015 cohort showed significantly higher mean scores than US medical school graduates (USMG) and international medical school graduates (IMG). The Canadian 2015/2016 cohort showed no statistical difference from the USMGs, but did have a significantly higher mean than the IMGs. For second-year residents (PGY2), the Canadian 2014/2015 cohort showed a significantly lower mean than the USMG cohort, but had a significantly higher mean than the IMG cohort. The Canadian 2015/2016 cohort showed a statistically lower mean than the USMG cohort and no difference compared to the IMG cohort. Conclusions: Based on a comparison of ABFM ITE scores between 2014 and 2016, the Canadian medical school graduates performed as well as or better than the US graduates upon entry into residency, but performance was reversed for the second year of training. Our results also suggest an equity value of ACGME residency training independent of location of undergraduate medical training.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".