Development of an OIE Harmonized Day 1 Competency-Based Veterinary School Curriculum in Ethiopia: A Partnership Model for Curriculum Evaluation and Implementation
Bibliographic record
Abstract
The University of Gondar College of Veterinary Medicine and Animal Sciences (UoG-CVMASc) and the Ohio State University College of Veterinary Medicine (OSU-CVM) developed an objective methodology to assess the curriculum of veterinary institutions and implement changes to create a curriculum that is harmonized with OIE standards while also covering the needs and realities of Gondar and Ethiopia. The process, developed under the sponsorship of the World Organisation for Animal Health (OIE) Veterinary Education Twinning Programme, is outlined in this article with the hope that it can be applied by other countries wishing to improve national veterinary services (VS) through the improvement of their academic programs. The plan created by the UoG-OSU Twinning team consisted of an in-depth curriculum assessment and development process, which entailed three consecutive stages. Stage 1 (Curriculum Assessment) included the design and development of an Evaluation Tool for OIE Day 1 Graduating Veterinarian Competencies in recent graduates, and the mapping and evaluation of the current UoG-CVMASc curriculum based on the OIE Veterinary Education Core Curriculum. Stage 2 (Curriculum Development) consisted of the identification and prioritization of possible solutions to address identified curriculum gaps as well as the development of an action plan to revise and update the curriculum. Finally, Stage 3 (Curriculum Implementation) focused on the process to launch the new curriculum. In September 2017, 53 first-year students started the professional program at the UoG-CVMASc as the first cohort to be accepted into the newly developed OIE Harmonized Curriculum, the first of its kind in Africa.
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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 | 0.105 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".