Selecting the best and brightest: A comparison of residency match processes in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Selecting candidates for plastic surgery residency training remains a challenge. In the United States, academic measures (United States Medical Licensing Exam Step I scores, medical school class rank and publications) are used as primary criteria for candidate selection for residency. In contrast, Canadian medical education de-emphasizes academic measures by using a pass-fail grading system. As a result, choosing residents from many qualified applicants may pose a challenge for Canadian programs without objective measures of academic success. METHODS: A 25-question online survey was distributed to program directors of Canadian plastic surgery residency-training programs. Program directors commented on number of yearly residents and applicants; application sections (ranked in importance using a Likert scale); interview invitation and rank-order list determination; and their satisfaction with the selection process. RESULTS: Ten Canadian plastic surgery program directors responded (90.9% response rate). The most important application components determining invitation to interview were letters of reference from a plastic surgeon (mean importance of 5.0 on the Likert scale), clinical electives in plastic surgery (mean 4.6) and electives with their program (mean 4.5). Applicants invited for interview were assessed on the quality of their responses to questions, maturity and personality. The majority of program directors agreed that a clinical elective with their program was important for consideration on their rank-order list. Program directors were neutral on their satisfaction with the selection process. CONCLUSION: Canadian plastic surgery residency programs emphasize clinical electives with their program and letters of reference from colleagues when selecting applicants for interviews. In contrast to their American counterparts, Canadian program directors rely on clinical interactions with prospective residents in the absence of objective academic measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".