Mapping Veterinary Curricula to Enhance World Organisation for Animal Health (OIE) Day 1 Competence of Veterinary Graduates
Bibliographic record
Abstract
Curriculum mapping provides a systematic approach for analyzing the conformity of an educational program with a given set of standards. The Chiang Mai University Faculty of Veterinary Medicine and the University of Minnesota College of Veterinary Medicine joined together in an educational twinning project to map their Doctor of Veterinary Medicine curricula against core competencies identified by the World Organisation for Animal Health (OIE) as critically important for Day 1 veterinary graduates to meet the needs for global public good services. Details of curriculum coverage for each specific and advanced competency were collected through a review of syllabi and course descriptions, followed by in-depth interviews of key faculty members. The depth of coverage of each competency was estimated by the tabulating the number of hours assigned. The teaching methods and levels of learning were also captured. While the overall design of the curricula conformed to the OIE Guidelines for Veterinary Education Core Curricula, the mapping process identified variability in the depth and breadth of coverage on individual competencies. Coverage of the Day 1 Specific Competencies was greater early in the curricula. More gaps existed in terms of the Advanced Competencies than the specific core competencies. Discussion of the identified gaps with faculty members led to opportunities for strengthening the curricula by adjustments of individual courses throughout the curricula. Documentation of teaching methods also led to professional development of new pedagogical skills and redesign of the teaching methods for particular subjects.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".