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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.710 | 0.007 |
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; both teacher heads agree on what is shown here.
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".