Curriculum Redesign in Veterinary Medicine: Part II
Bibliographic record
Abstract
Curricular review is considered a necessary component for growth and enhancement of academic programs and requires time, energy, creativity, and persistence from both faculty and administration. On a larger scale, a comprehensive redesign effort involves forming a dedicated faculty redesign team, developing program learning outcomes, mapping the existing curriculum, and reviewing the curriculum in light of collected stakeholder data. The faculty of the Texas A&M University College of Veterinary Medicine & Biomedical Sciences (TAMU) recently embarked on a comprehensive curriculum redesign effort through partnership with the university's Center for Teaching Excellence. Using a previously developed evidence-based model of program redesign, TAMU created a process for use in veterinary medical education, which is described in detail in the first part of this article series. An additional component of the redesign process that is understated, yet vital for success, is faculty buy-in and support. Without faculty engagement, implementation of data-driven curricular changes stemming from program evaluation may be challenging. This second part of the article series describes the methodology for encouraging faculty engagement through the final steps of the redesign initiative and the lessons learned by TAMU through the redesign process.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".