Applicant Perspectives on Virtual Otolaryngology Residency Interviews
Bibliographic record
Abstract
OBJECTIVE: Residency interviews serve as an opportunity for prospective applicants to evaluate programs and to determine their potential fit within them. The 2019 SARS-CoV2 pandemic mandated programs conduct interviews virtually for the first time. The purpose of this study was to assess applicant perspectives on the virtual interview. METHODS: A Qualtrics survey assessing applicant characteristics and attitudes toward the virtual interview was designed and disseminated to otorhinolaryngology applicants from 3 large academic institutions in the 2020 to 2021 application cycle. RESULTS: A total of 33% of survey applicants responded. Most applicants were satisfied with the virtual interview process. Applicants reported relatively poor quality of interactions with residents and an inability to assess the "feel" of a geographic area. Most applicants received at least 11 interviews with over a third of applicants receiving >16 interviews. Only 5% of applicants completed >20 interviews. Most applicants believed interviews should be capped between 15 and 20 interviews. Most applicants reported saving >$5000, with over a quarter of applicants saving >$8000, and roughly one-third of applicants saving at least 2 weeks of time with virtual versus in-person interviews. CONCLUSIONS: While virtual interviews have limitations, applicants are generally satisfied with the experience. Advantages include cost and time savings for both applicants and programs, as well as easy use of technology. Continuation of the virtual interview format could be considered in future application cycles; geographical limitations may be overcome with in-person second looks, and increased emphasis should be placed on resident interactions during and prior to interview day.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".