What affects medical students’ applications to five-year FRCPC emergency medicine programs?
Bibliographic record
Abstract
Purpose: To compare the impact of institutional and epidemiologic factors on differences in application trends of Canadian medical graduates (CMGs) from different medical schools to FRCPC emergency medicine (EM) residency programs. Methods: This was a retrospective cohort study. Data from 2013-2018 were obtained from the Canadian Resident Matching Service (CaRMS) database and standardized questionnaires sent to Canadian medical schools. Results: CaRMS data were available for all schools and survey data was available for 76% schools. Five schools yielded significantly higher rates of applications to FRCPC-EM programs (8.8-13.1%, p<0.05), and 5 schools had significantly lower rates compared to the national mean (2.9-5.1%, p<0.05). Increased exposure to EM (a core rotation and/or elective rotation in EM in the third year of medical school at home-school) yielded 28-55% higher application rates (p<0.001). The presence of an FRCPC-EM residency program at the applicant's home school, and a home school program with 5 or more CMG residency positions at a CMG’s increased the application rates by 39 and 17%, respectively (p<0.05). Conclusion: These data demonstrate a significant difference in application rates of CMGs graduating from Canadian medical schools and certain factors may affect application rates. This information could be used by medical schools to modify curricula, increase exposure to EM, and contribute towards addressing the forecasted national shortage of EM physicians.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".