Veterinary Curriculum Transformation at the University of Illinois, 2006–2016
Bibliographic record
Abstract
The organization and delivery of a curriculum is the responsibility of the faculty in educational institutions. Curricular revision is often a hotly debated topic in any college faculty. At the University of Illinois, a 2006 mandate for curriculum modernization from the American Veterinary Medical Association Council on Education provided impetus for a long-discussed curricular revision. After two iterations and a lengthy development process, a new curriculum was gradually implemented at Illinois with the August 2009 matriculation of the Class of 2013. The goals of the revision included earlier clinical exposure for veterinary students through introductions to clinical rotations in years 1 to 3 and an integrated body systems approach in lecture/laboratory courses. A new Clinical Skills Learning Center facilitates development of clinical skills earlier in the curriculum and promotes the development of those skills throughout all 4 years of the curriculum. New outcomes assessments include comprehensive written examinations and Objective Structured Clinical Examinations (OSCEs) in years 2 and 3. Curriculum management, including grading of clinical rotations in all 4 years, is achieved through a commercially available software package. For the past 5 years, when candidates were asked why they chose to apply to Illinois, the new curriculum (27.4%) was the most common answer given during interviews. The Illinois revision has resulted in measurably increased veterinary student self-confidence (p<.001) at graduation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".