Veterinary School Admission Interviews, Part 1: Literature Overview
Bibliographic record
Abstract
An analysis of the admission interview used by schools in four health professions (veterinary medicine, allopathic medicine, optometry, and dentistry) portrays a largely similar approach to selection interviews: INTERVIEW USE: At least 80% of schools interview applicants. For schools that offer interviews, at least 40% of candidates are interviewed (a strong academic profile is the number one determinant of receiving an interview offer). The interview is one of the three most important selection tools used by schools. Less than 26% of schools fix the interview's weight in the selection process (fixed weights range from 31% to 35%). INTERVIEW PURPOSE AND CONTENT: The most common purposes of the interview are to (1) gather information, (2) make decisions, (3) verify information provided in other parts of the application, (4) recruit candidates, and/or (5) promote public relations. The most common characteristics and skills interviewers are interested in assessing are motivation for the profession, interpersonal skills, and communication skills. The desire to assess cognitive ability with the interview (>25% of schools) is surprising in view of the use of other selection tools (e.g., GPA). INTERVIEW FORMAT: Medical schools are more likely to offer two interviews per candidate, while optometry schools are more likely to offer one interview per candidate. Individual interviews (one interviewer, one candidate) are the predominant format among medical schools, while panel interviews (more than one interviewer, one candidate) are the most common format among optometry schools. The duration of the interview is 30 to 45 minutes. Interview questions most often address facts and knowledge, hypothetical situations, and the ability to meet program requirements. Most interviews do not meet the criteria for a structured interview, which has demonstrated greater validity and reliability than semi-structured or unstructured interviews. INTERVIEWERS: Interviewers are most likely to be health care faculty members (e.g., veterinarians at a veterinary school). Interviewers receive limited training. RECOMMENDATIONS FOR INCREASING INTERVIEW RELIABILITY AND VALIDITY: The purpose(s) of the interview must be clearly articulated so that the interview and interviewer training can be designed to achieve that purpose. Interview structure should be increased by developing a "job analysis" set of questions that is posed to all candidates and scored using behavioral anchors. Interviewers should receive more training in rater bias, listening skills, and interview structure. Panel interviews should be used to increase reliability. Interviewers should not have access to the candidate's application unless the interview is used to verify information. To increase the utility of the interview in the selection process, the weight of the interview in relation to other selection components should be determined.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.039 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".