Reorganizing Small Animal Gross Anatomy: Improving the Faculty and Student Experience and Incorporating Non-technical Competency Development
Bibliographic record
Abstract
The organization of the small animal gross anatomy course at Washington State University's College of Veterinary Medicine was modified in several ways over a two-year period. These modifications were motivated in part by the need to accommodate a larger number of students, but also by our desire to make more efficient use of faculty and student time as well as physical resources and to give our students opportunities to develop non-technical competencies such as communication and teamwork skills. The four major changes were (1) increasing dissection group size, (2) assigning specimens to dissection groups on a rotating basis, (3) reducing the amount of dissection time relative to time spent studying prepared specimens, and (4) introducing ''transition reviews,'' a limited form of peer teaching meant to emphasize peer interaction and communication skills rather than conveyance of specific and detailed subject matter. This article describes the details of these changes and evaluates their effectiveness based on faculty assessments, results from surveys of participating students, and comparison of examination performance and formal end-of-course student evaluations before and after reorganization of the course. Increasing the dissection group size and reducing per-student dissection time did not adversely affect student performance and were viewed at least neutrally or favorably by most students. Notably, the rotation of specimens and the transition reviews elicited strongly favorable responses from a large majority of students and had several beneficial effects of which students may not have been aware but which were apparent to participating faculty. Overall, the changes were well received by both faculty and students and were, in our view, of sufficient value that this course organization plan would be used regardless of class size.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".