Scholarly activity in Canadian Residency Matching Service criteria: do Canadian programs really care about applicant research?
Bibliographic record
Abstract
Background: Residency programs across Canada evaluate applicants based on written applications, reference letters, and interviews. One key factor many institutions consider is "scholarly activity." To improve transparency, the Canadian Residency Matching Service (CaRMS) recently revised its program description pages, adding a dedicated section outlining expectations for research and academic work. This study examines whether these changes have made program criteria clearer for applicants. Methods: For all 17 Canadian faculties of medicine, 2023 R1 entry, -internal medicine, family medicine, pediatrics, general surgery, psychiatry and anesthesiology-program descriptions were reviewed on the CaRMS website, looking for keywords related to scholarly activity. Results: Although most residency programs now include scholarly activity in their CaRMS descriptions, several programs provide vague descriptions of this requirement. In 2023, nearly all family medicine (94%), internal medicine (100%), and pediatrics (100%) program descriptions referenced requiring or considering scholarly work as part of their selection process-up from 41%, 65%, and 71% in 2019. Programs commonly mentioned scholarly activity in two or three sections of their selection criteria, with key themes including active scholarly work, scholarly deliverables, and future scholarly potential. Conclusion: Canadian medical schools should set clearer expectations for scholarly activity in residency applications to ensure transparency and equal opportunities for all applicants. Programs could also explain why research matters-whether as a core component of training or to develop critical thinking and initiative. Greater clarity would help applicants see research as more than just a "check-the-box" requirement, fostering genuine engagement in scholarly work.
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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 | 0.058 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".