Undergraduate Veterinary Education at University College Dublin: A Time of Change
Bibliographic record
Abstract
The final-year Bachelor of Veterinary Medicine (MVB) class of 2005 were the first cohort of students to complete the new curriculum at the Faculty of Veterinary Medicine, University College Dublin (UCD). The new curriculum is a fundamental departure from the traditional curriculum that had served the veterinary profession in Ireland over many years. The change was not a precipitate action but the outcome of a prolonged and thorough examination of the realities of veterinary medicine, its science and its art, in the first decade of a new millennium. Over recent decades, rapid and fundamental changes have been witnessed in the economic, cultural, and ethical environment in which the veterinary profession operates, and these changes, coupled with the "information explosion," dictated an examination of the educational paradigm. The new curriculum exposes the first-year class to veterinary information technology and problem-based learning (PBL). In the second year, students are instructed in clinical examination, history taking, and client communication skills, in addition to further exposure to PBL. The third and fourth years are now systems-based, with coordinated input from microbiologists, parasitologists, pathologists, and clinicians in teaching each body system. The first lecture-free final year in the 104-year history of veterinary education in Ireland consists of clinical rotations and a four-week elective pursued within the faculty or at other recognized institutions. Students must also complete a minimum of 24 weeks' extramural studies (EMS). Critically, the development and assessment of all courses in the new undergraduate degree program has been driven by carefully thought out learning outcomes. The new curriculum will provide graduates with the essential knowledge and skills required for entry into the veterinary profession. Society expects these qualities from veterinarians in the interests of the communities they serve during their professional careers. In addition, the curriculum should foster the ability to adapt to changing circumstances, instill the desire and ability to work in teams, and develop life skills. It is hoped that the academic innovations will arouse the intellectual curiosity and commitment to lifelong learning that future graduates will require if they are to retain the confidence of the society in which they work in the future.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.025 | 0.009 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".