S1494 Gastroenterology Fellowship Virtual Interviews: Applicant and Faculty Perceptions on Virtual Interview Advantages and Barriers
Bibliographic record
Abstract
Introduction: During the COVID-19 pandemic, virtual interviews for resident and fellowship applicants became the standard. However, studies evaluating the experience of virtual interviews format are lacking. Accordingly, we sought to survey both gastroenterology fellowship applicants and interviewing faculty members about their experiences with the virtual interview process. Methods: Interviewees and faculty at 13 different gastroenterology fellowship programs at academic medical centers across the United States completed a post-interview survey. The online survey was conducted during the 2020 ERAS fellowship interview season via Google Forms. The survey responses were anonymously collected and reported. Results: A total of 177 gastroenterology fellowship applicants and 83 faculty members completed the electronic surveys. Most participants reported a positive experience with 91% and 84% of applicants and faculty respectively, scoring at least 4 points on a 5-point scale. Eighty-8 percent and 85% of applicants and faculty respectively, reported that they had enough insight about the applicant or the fellowship program during the interview. Over 67% of applicants reported cost-savings of greater than $1,000 per interview. Thirty-6 percent of applicants reported that they missed the personal interaction with the current gastroenterology fellows in the respective programs and the experience of physically touring the facility. Twenty-7 percent and 25% of applicants and faculty experienced technical difficulties during the interview process, respectively. Thirty-one percent and 22% of applicants and faculty would like for the virtual interviews to be the standard of future fellowship interviews, while 35% and 42% of applicants and faculty would consider it in the future, respectively. Figure 1 shows the ranking process for both applicants and faculty. Conclusion: Virtual interviews were perceived as effective and cost-saving by both gastroenterology fellowship applicants and faculty members. The virtual experience was widely accepted by most applicants and faculty, with high potential to become the standard of fellowship interview process in the future. However, a substantial portion experienced technical difficulty. Further improvements in technology are needed to optimize the process and increase the acceptance of the virtual interview experience.Figure 1.: Applicants and faculty perspectives on the ranking process. LOR, letters of recommendation.
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 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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".