Attitudes toward Virtual Admissions Interviews among Applicants to a Veterinary School
Bibliographic record
Abstract
Virtual interviews have gradually begun to be utilized in health professions education; however, the COVID-19 pandemic resulted in virtual interviews rapidly becoming commonplace for the 2020–2021 admissions cycle. This study aimed to evaluate attitudes toward and experience with virtual interviews of applicants to a veterinary medical college. All applicants to the Midwestern University College of Veterinary Medicine (MWU-CVM) were provided with a link to a voluntary, anonymous survey after completing a virtual interview with the program. A 27.5% response rate (114/415) was obtained. Responses indicate widespread acceptance of virtual interviews, with respondents noting they would be more likely to interview for an out-of-state program with a virtual interview option and most feeling more positively about the program after their virtual interview. In-person interviews were preferred by 62.3% of applicants, while 32.5% favored a virtual option. Most applicants (58.8%) applied to more than six schools, indicating a major burden of cost and time associated with veterinary college applications. Students who experienced technical difficulties were less likely to feel positively about the interview (p = .01). Overall, virtual interviews were viewed favorably by applicants, although many indicated a preference for an in-person interview when possible. Prioritizing an accessible technology platform and high-quality sound input/output for interviewers may help foster a more positive virtual interview for applicants. Virtual interviews are a viable option for veterinary admissions interviews associated with a positive applicant experience.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 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".