Assessment Structure, Culture and Resources at AAVMC affiliated Colleges of Veterinary Medicine in the USA
Bibliographic record
Abstract
While outcomes assessment is commonplace in colleges of veterinary medicine, no information is published on how veterinary colleges resource, administer, and view assessment. Consequently, this article has the following objectives: (a) to determine the current level of resources (personnel, committees, software) allocated toward education assessment and program evaluation in colleges of veterinary medicine, (b) to characterize any common organizational structures within colleges of veterinary medicine for assessment, (c) to determine assessment personnel (faculty and staff) perceptions regarding whether existing assessment resources and structures are sufficient, and (d) to examine the perceived strength of the culture of assessment. Our survey found that most assessment professionals had been in their position for 4 years or less and over 50% did not have formal assessment training. A majority of respondents agreed that assessment was encouraged and supported at their institution, but there was much less agreement on items related to formal plans and structures. For example, only one quarter of respondents reported that assessment was connected to planning and budgeting, and only one third reported having a formal assessment plan. We hope that our survey will be a resource tracking the development of assessment resources and climate at American colleges of veterinary medicine.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".