Virtual Multiple Mini-Interviews for Veterinary Admissions
Bibliographic record
Abstract
Admissions teams are challenged to select the best applicants for their college. There is a growing emphasis on selecting applicants with personal attributes important for success in a variety of veterinary careers, but there is no clear consensus on how to best identify these individuals. A number of veterinary colleges are utilizing multiple mini-interviews (MMIs), a highly structured type of interview in this selection process. However, due to travel restrictions currently associated with COVID-19, many are now considering virtual MMIs. Long Island University (LIU) took the step to conduct MMIs virtually for its inaugural class before the pandemic restrictions occurred, largely because it hoped to reduce the cost of admission by eliminating travel costs. In this process, we encountered a unique set of challenges, the resolution of which we believe constitutes best practices for virtual MMIs. This report describes the design and execution of an MMI for LIU. We were able to interview 340 applicants in 7 days. Based on feedback from applicants as well as raters, most considered it an acceptable means of interviewing students. Both raters and applicants expressed a high degree of satisfaction with the process, and we were able to separate applicants based on MMI scores with 88% reliability.
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 | 0.001 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".