Case Study in Planning Revisions to a Veterinary Professional Curriculum: Opportunity, Motive, and Means
Bibliographic record
Abstract
The Ohio State University (OSU) recently responded to a mandate from the state to convert from a quarter-based to a semester-based academic calendar. The OSU College of Veterinary Medicine took this opportunity to review and revise the curriculum leading to the DVM degree. This mandate occurred at a time when the college was motivated to act on recommendations that had been made during a recent reaccreditation process, some of which had been under discussion for several years, and had the personnel in place to initiate the change process. This article describes the means by which the curriculum change was planned. A review of the literature on change in health-sciences-related programs suggested that the ability to conclude the planning of changes in a relatively short time period was facilitated by adopting practices shown to promote successful curricular change. Critical aspects of the process included engaging the faculty, establishing a collective vision that entails agreement on principles, having a clear mandate and time frame for change, providing resources and training to support and sustain the change effort, and managing the effort centrally with groups that are broadly representative of the faculty.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| 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".