Veterinary School Admission Interviews, Part 3: Strategies for Increasing Interview Validity
Bibliographic record
Abstract
The veterinary school admission interview is a widely used selection tool, yet concerns persist about its reliability, validity, and cost. Relative to medicine, optometry, and dentistry schools, veterinary schools have been more likely to conduct panel interviews and to fix the interview's weight in selection decisions, strategies that increase interview validity. This article provides strategies for further increasing the veterinary school interview's validity. Interview reliability and validity studies point to key strategies the veterinary school admissions committee can implement before the interview: (1) establishing the interview's purpose(s); (2) conducting a "job" analysis to identify desirable candidate skills, knowledge, and attributes; (3) developing a structured and panel interview where interviewers, if possible, are blind to other admission data; (4) training interviewers; (5) setting a reasonable interview schedule; and (6) determining methods for analyzing applicant data. During the interview, interviewers should proceed through a structured series of steps: (1) open the interview with a specified agenda; (2) probe for information using structured questions and anchored rating scales; (3) close the interview to allow for candidate questions; and (4) evaluate the interview data. After the interview, the admissions committee should (1) analyze the interview data within and across interviewers and (2) analyze the data across all selection tools in order to assign relative weights to the selection tools.
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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 | 0.376 | 0.567 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".