Design and Application of an Evaluation Tool to Assess World Organization for Animal Health Competencies for Graduating (Day One) Veterinarians
Bibliographic record
Abstract
Graduating competent veterinarians with the appropriate knowledge and skills to support and strengthen their country's National Veterinary Services is a key priority for veterinary institutions globally. The World Organisation for Animal Health (WOAH) developed a set of Day One Competencies that should be expected of every veterinary graduate. Veterinary schools need to be able to assess the coverage of these competencies in their curriculum and determine the level of proficiency of their graduates. This article describes the iterative design and development process used to create a semi-quantitative, competency-based assessment survey. The Evaluation Tool for WOAH Day One Graduating Veterinarian Competencies is used as part of a stepwise process to systematically assess a veterinary curriculum regarding these competencies. This tool was developed and tested at the University of Gondar College of Veterinary Medicine and Animal Sciences in Ethiopia. The Evaluation Tool was successful in systematically collecting, measuring, and analyzing the perceptions of faculty, senior veterinary students, recent graduates, and external stakeholders about the level of proficiency of graduates in all 19 WOAH Day One Competencies. It was specifically designed to be used in conjunction with curriculum mapping to provide a full picture of how effectively these competencies were taught and identify gaps that needed to be addressed. This tool, the supporting resources, and the methodology are now globally accessible to all veterinary institutions, enabling them to revise and update their curricula and, ultimately, improve the training of the future veterinary workforce to support veterinary services in their respective countries.
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 | 0.079 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".