Evaluation of the Bachelor of Veterinary Medicine (BVM) Curriculum at Sokoine University of Agriculture in Tanzania: Mapping to OIE Veterinary Graduate ‘Day 1 Competencies’
Bibliographic record
Abstract
The World Organisation for Animal Health (OIE) provides the requirements needed for graduating veterinary professionals to be competent in the delivery of animal health services. However, significant differences in veterinary curricula across countries-attributable to differing animal health priorities and predominant types of veterinary practice-provide a challenge for veterinary schools to address these competencies adequately. As part of the OIE's veterinary education establishment Twinning Project activities, the College of Veterinary Medicine and Biomedical Sciences (CVMBS) of Sokoine University of Agriculture (SUA) in Tanzania undertook a curriculum mapping and gap analysis to assess the extent to which the veterinary curriculum addresses OIE's 'Day 1 Competencies' for graduating veterinarians. Results of the analysis indicated that all the OIE's Day 1 Competencies (general, specific, and advanced) are addressed to some degree by the courses present in the curriculum. However, gaps in the depth and breadth of instruction were found for a number of competencies in all three categories. These findings indicate a need for addressing the gaps in the next curriculum review. This will allow the development of a stronger curriculum that will efficiently meet the national and international animal health requirements.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".