Envisioning the Future of Veterinary Medical Education: The Association of American Veterinary Medical Colleges Foresight Project, Final Report
Bibliographic record
Abstract
This report of the Association of American Veterinary Medical Colleges’ 2006 Foresight Project, developed under the leadership of an AAVMC Steering Committee, drew on the experience, imagination, and energetic participation of more than 95 participants from across the United States and Canada. The environment of veterinary medicine is one of profound change. The current number of veterinarians is inadequate to address the present and future needs of society. To remain relevant, academic veterinary medicine must prepare veterinarians for what may come in the future. In order to be recognized and remunerated for their knowledge, compassion, integrity, and judgment, veterinarians must first demonstrate their relevance to new societal trends. The objective of the study reported here was to determine a future direction for academic veterinary medicine using Foresight technology. The tools employed were challenge questions and the development of eight future possible scenarios. The study supported the need for change. This report recommends an adaptive and responsive system of veterinary medical education, achieved by defining those areas of professional focus that would address all the anticipated needs of society. An area of professional focus signifies a pathway leading to a DVM degree. Colleges would choose to offer selected areas of professional focus most appropriate to their capabilities, according to a binational plan. Veterinary medicine is integral to the well-being of any future society. This is a pivotal moment for the veterinary profession and for veterinary medical education. Leadership, collaboration, and a shared vision will determine the destiny of the profession.
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 | 0.064 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".