Student Involvement in Global Veterinary Education and Curricula: 7 Years of Progress (2013–2019)
Bibliographic record
Abstract
As central members of the veterinary education community, students are well placed to highlight current problems in veterinary education. Motivated by the lack of current formal student involvement, the largest global veterinary student association, the International Veterinary Students’ Association (IVSA), realized the necessity for students to express their opinions within the veterinary education field. Thus, two standing committees related to veterinary education were created: the Standing Committee on One Health in 2013 and the Standing Committee on Veterinary Education in 2014. For 7 years, veterinary students have been acting in a four-dimensional plane to involve students in (a) electronic educational resources and e-learning, (b) interdisciplinary collaboration and One Health, (c) curriculum involvement, and (d) vocational guidance. Through multiple projects, such as student and tutor interaction, idea exchanges, development of e-resources, and curriculum development campaigns, IVSA has managed to increase awareness to students and schools of the important role students play within veterinary education. This article highlights students’ ability to work together to help other students learn and succeed within their veterinary studies, as well as the necessity for student engagement in curricular renewal and development. Consequently, IVSA’s projects and achievements are described, highlighting a from students—to students approach to promote active student involvement in veterinary education and curricula globally.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".