National Workshop on Core Competencies for Success in the Veterinary Profession
Bibliographic record
Abstract
A workshop was designed to (1) present results of the Core Competencies for Veterinary Medicine project conducted by Personnel Decisions International (PDI); (2) discuss and analyze the implications of the PDI study results for academia, private practice, and industry; (3) identify actionable items-discuss opportunities and barriers; and (4) develop appropriate recommendations-devise specific actions for implementation as next steps. In total, 25 veterinary colleges were represented at the workshop and a total of 110 attendees participated, a broad cross-section of the veterinary profession (both academic and non-academic). Through an orchestrated combination of general sessions and facilitated, small group discussions, prioritized recommendations for implementation and initial action plans for next steps were developed. Recommendations included publicizing results of the PDI study, reconsidering current admissions policies and processes, evaluating the applicant pool and current recruitment programs, developing structured mentoring programs, enhancing DVM/VMD training programs, coordinating the development of continuing education programs, and overcoming existing barriers to change. Next steps should involve collaborative efforts across all sectors of the veterinary profession to develop plans for implementing the workshop's recommendations. Leadership for follow-up might reasonably come from the Association of American Veterinary Medical Colleges (AAVMC), the American Veterinary Medical Association (AVMA), and the American Animal Hospital Association (AAHA), either individually or collectively, through the National Commission on Veterinary Economic Issues (NCVEI). Partnerships with industry are also possible and should be strongly considered.
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 | 0.023 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".