Application of Admissions Criteria to Applicants with Practice versus Non-Practice Career Goals in North American Schools/Colleges of Veterinary Medicine
Bibliographic record
Abstract
PURPOSE: The study was intended to determine whether North American veterinary schools/colleges apply admissions criteria differently to applicants with practice versus non-practice career goals. METHODOLOGY: A written questionnaire with seven queries on admissions criteria was sent to the associate deans for academic affairs at each of the 31 North American veterinary schools/colleges. RESULTS: Questionnaires were completed and returned by 25 of the 31 institutions. The responses were summarized and individual comments were compiled. For veterinary and animal experience, similar amounts but different types of experiences were acceptable to most institutions for applicants with practice versus non-practice career goals. The required pre-veterinary course work was not different for the two groups of applicants. The backgrounds of mentors providing written evaluations were often allowed to be different for the two groups of applicants. The responses expected in applicant interviews were different for the two groups for queries related to veterinary and career experiences and knowledge of specific career areas but were similar for various basic qualities and skills expected of all applicants. CONCLUSION: Although institutions vary, North American veterinary schools/colleges tend to apply admissions criteria differently to applicants with practice versus non-practice goals, except for pre-veterinary course requirements.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".