Virtual interviewing for graduate medical education recruitment and selection: A BEME systematic review: BEME Guide No. 80
Bibliographic record
Abstract
The COVID-19 pandemic caused graduate medical education (GME) programs to pivot to virtual interviews (VIs) for recruitment and selection. This systematic review synthesizes the rapidly expanding evidence base on VIs, providing insights into preferred formats, strengths, and weaknesses. PubMed/MEDLINE, Scopus, ERIC, PsycINFO, MedEdPublish, and Google Scholar were searched from 1 January 2012 to 21 February 2022. Two authors independently screened titles, abstracts, full texts, performed data extraction, and assessed risk of bias using the Medical Education Research Quality Instrument. Findings were reported according to Best Evidence in Medical Education guidance. One hundred ten studies were included. The majority (97%) were from North America. Fourteen were conducted before COVID-19 and 96 during the pandemic. Studies involved both medical students applying to residencies (61%) and residents applying to fellowships (39%). Surgical specialties were more represented than other specialties. Applicants preferred VI days that lasted 4–6 h, with three to five individual interviews (15–20 min each), with virtual tours and opportunities to connect with current faculty and trainees. Satisfaction with VIs was high, though both applicants and programs found VIs inferior to in-person interviews for assessing ‘fit.’ Confidence in ranking applicants and programs was decreased. Stakeholders universally noted significant cost and time savings with VIs, as well as equity gains and reduced carbon footprint due to eliminating travel. The use of VIs for GME recruitment and selection has accelerated rapidly. The findings of this review offer early insights that can guide future practice, policy, and research.
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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 | 0.103 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.009 |
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".