Outcomes Assessment in Veterinary Medical Education
Bibliographic record
Abstract
The Virginia-Maryland Regional College of Veterinary Medicine (VMRCVM) agreed to perform outcomes assessment (OA) as part of the accreditation review process for the American Veterinary Medical Association (AVMA). Nine OA instruments were developed and validated by a 20-member accreditation committee. The instruments were also pre-tested by a subset of the target population. The instruments were for alumni one to five years post-graduate, alumni 6-15 years post-graduate, faculty, staff, DVM students, employers of veterinarians, referring veterinarians using the Blacksburg campus, and referring veterinarians using the Leesburg campus. In addition, data were used from OA surveys previously done for the Office of Research and Graduate Studies. Data from the surveys were incorporated into each of the 11 Essentials for Accreditation required by the AVMA. The process of OA provided a comprehensive assessment of the many aspects of the operation of the college. An important follow-up to the OA process is use of data to enhance and/or re-prioritize existing programs.
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 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 | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".