Milestone Educational Planning Initiatives in Veterinary Medical Education: Progress and Pitfalls
Bibliographic record
Abstract
Three milestone educational planning initiatives engaged the veterinary medical profession in the United States and Canada between 1987 and 2011, namely the Pew National Veterinary Education Program, the Foresight Project, and the North American Veterinary Medical Education Consortium. In a quantitative study, we investigated the impact of these initiatives on veterinary medical education through a survey of academic leaders (deans, previous deans, and associate deans for academics from veterinary medical schools that are members of the Association of American Veterinary Medical Colleges) to assess their perspectives on the initiatives and eight recommendations that were common to all three initiatives. Two of the recommendations have in effect been implemented: enable students to elect in-depth instruction and experience within a practice theme or discipline area (tracking), and increase the number of graduating veterinarians. For three of the recommendations, awareness of the issues has increased but substantial progress has not been made: promote diversity in the veterinary profession, develop a plan to reduce student debt, and develop a North American strategic plan. Lastly, three recommendations have not been accomplished: emphasize use of information more than fact recall, share educational resources to enable a cost-effective education, and standardize core admissions requirements. The educational planning initiatives did provide collaborative opportunities to discuss and determine what needs to change within veterinary medical education. Future initiatives should explore how to avoid and overcome obstacles to successful implementation.
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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 | 0.092 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".