Using Video-Conference Interviews in the Residency Application Process
Bibliographic record
Abstract
To the Editor: Video-conference interviews (VCIs) offer an opportunity for residency programs to decrease costs and streamline the experience of the interview process. In our recent article, “Residency Interviews in the 21st Century,” we outline several steps that programs can take to modernize their application process.1 Of these, we believe that VCI is the most underutilized. Multiple programs have introduced this modality of interviews with marked success. A urology program that randomized applicants to VCI or traditional on-site interviews found that both applicants and faculty favored using VCI as an adjunct to on-site interviews, and that using this approach resulted in applicants spending significantly less time away from school.2 Other studies have shown that using VCI saved both applicants and programs over $500 per applicant and that 80% of applicants felt it should be offered as an option.3,4 VCI has the opportunity to benefit all parties involved, yet its adoption has been slow and circumspect. While we recognize that implementing VCI is not without challenges or costs, other industries with similarly complex recruitment processes have implemented this modality successfully and with overall reduced recruitment costs. VCI can be implemented in a variety of ways based on program preference: as an alternative to on-site interviews, decoupling the interview and tour process; or as an adjunct to on-site interviews, reducing the number of applicants that are invited to travel on-site. Furthermore, it can help level the playing field for applicants of low socioeconomic status who cannot afford to travel to multiple interviews. While more research is needed to understand how to best implement this interview modality, we encourage the academic community to experiment and disseminate their institutional experience with this technology. VCI may be one of the most impactful ways to improve and modernize the residency application process. Eduardo Hariton, MD, MBAClinical fellow in obstetrics, gynecology, and reproductive biology, Department of Obstetrics and Gynecology, Massachusetts General Hospital, and Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts; [email protected] Pietro Bortoletto, MDClinical fellow in obstetrics, gynecology, and reproductive biology, Department of Obstetrics and Gynecology, Massachusetts General Hospital, and Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts. Nworah Ayogu, MD, MBAResident, Department of Internal Medicine, Columbia College of Physicians and Surgeons, New York, New York.
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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 | 0.018 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".