Successful Applicant and Program Director Perspectives on the Virtual Residency Selection Process for Canadian Surgical Subspecialties
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic imparted an important shift in strategies postgraduate surgical programs use to recruit, interact with, and select medical students applying through the Canadian Resident Matching Service (CaRMS). With this unprecedented shift toward virtual applicant selection, this study sought to explore and analyze perspectives of the first cohort of program directors (PDs) and applicants who participated in this process. Methods: A cross-sectional survey study was designed using Google Forms for both PDs and applicants participating in the 2021 CaRMS surgical subspecialty selection process. Questions pertained to format and content of virtual engagement methods, the interview itself, as well as advice for future applicants. Results: Thirty-five PDs and 40 successful applicants (n = 75) participated in the study. Cost reduction was the most commonly reported benefit of online interviewing by PDs (85%), followed by efficiency (71%), enhanced resource management (49%), and ability to conduct more interviews (23%). Strong letters of reference (80%) and interview performance (74%) remained the most significant factors in virtual applicant selection. Attendance to virtual recruitment events did not increase the likelihood of offering interviews (n = 24, 69% of PDs), although the ability to perform in-person electives held tremendous value. Most applicants (90%) reported on virtual information sessions as the best method for learning about programs; work culture and environment were topics most valued as discussion points (90%). Successful applicants provided an average confidence of 76% regarding their suitability with their matched programs. Seventy-three percent of applicants (n = 29) had either a preference for virtual interviews or were equivocal, while 51.4% of PDs (n = 18) preferred interviews to be conducted virtually for future cohorts. Conclusion: Trainees are entering residency with confidence following a virtual selection process, and PDs feel confident in their selections. Although no clear consensus exists regarding preference for virtual or in-person interviews, several advantages for virtual resident selection exist. The influence of an in-person elective was found difficult to replace, regardless of interview format. The importance of applicant engagement with programs prior to interviews is highlighted and discussed with recommendations provided for best practices.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".