A Retrospective Analysis of Veterinary Medical Curriculum Development in The Netherlands
Bibliographic record
Abstract
Over the past two decades, the Faculty of Veterinary Medicine of Utrecht University (FVMU) has introduced major curriculum changes to keep pace with modern veterinary educational developments worldwide. Changes to program outcomes have been proposed according to professional and societal demands, with more attention paid to generic competencies and electives and species/sector differentiation. Furthermore, changes in educational approaches and the educational organization have been proposed, aiming at a transition from teacher-centered education toward more student-centered education. Curriculum development is a complex and difficult process, with many elements interacting. For a new curriculum to become valid, curriculum elements and their interrelation-such as statements of intent (also called outcomes, goals, or objectives), content, teaching and learning strategies, assessment strategies, and context-need to be addressed in the educational philosophy (i.e., the intended curriculum). This paper describes a document analysis of the major curriculum reforms of the FVMU. Curriculum committee reports were critically analyzed to gain insight into the intentions of the curriculum designers and the match between the curriculum elements, as described by Prideaux. The results show that the reports paid considerable attention to generic competency training, especially to academic training, and to the introduction of more student-centered teaching and learning strategies. However, little attention was paid to assessment strategies and the statements of intent were defined rather broadly. Curriculum evaluation (i.e., what is delivered to the students and how is the curriculum experienced) is needed at all curriculum levels. Possible mismatches between levels need to be identified.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".