Improvement of DVM Curriculum to Meet OIE Recommendations at Chattogram Veterinary and Animal Sciences University, Bangladesh
Bibliographic record
Abstract
A veterinary education twinning project between Chattogram Veterinary and Animal Sciences University (CVASU) and Tufts Cummings School of Veterinary Medicine (TCSVM) was supported by the World Organisation for Animal Health (OIE) to align CVASU's veterinary curriculum with OIE's recommended Core Curriculum and Day 1 Competencies. The major objectives were curriculum development with improvement to the internship program, introduction of problem-based learning (PBL), and implementation of continuing education (CE). Major activities to achieve these objectives involved several workshops and seminars at CVASU and establishing student exchange and CVASU faculty training programs. Major accomplishments were (a) implementation of a revised Doctor of Veterinary Medicine (DVM) curriculum at CVASU aligned with the OIE-recommended curriculum and Day 1 Competencies; (b) incorporation of PBL into the curriculum and development of 23 PBL cases relevant to Bangladesh-specific diseases; (c) improvement of the internship program by including Day 1 Competencies; (d) development and implementation of 11 structured CE sessions including hands-on training; (e) improvement of curriculum, teaching, and clinical training at CVASU following training of CVASU faculty and students at TCSVM; and (f) three peer-reviewed publications from summer research projects by TCSVM students at CVASU. The twinning project allowed CVASU to improve its DVM curriculum by aligning with OIE's recommended curriculum and Day 1 Competencies. The impact of the project went beyond CVASU as evidenced by other veterinary schools adopting the CVASU curriculum and PBL, veterinary school deans engaged in improving veterinary curriculum and clinical training, and implementation of a national CE program for veterinarians.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".