Creating the Next Generation of Evidence-Based Veterinary Practitioners and Researchers: What are the Options for Globally Diverse Veterinary Curricula?
Bibliographic record
Abstract
Veterinary educators strive to prepare graduates for a variety of career options with the skills and knowledge to use and contribute to research as part of their lifelong practice of evidence-based veterinary medicine (EBVM). In the veterinary curriculum, students should receive a grounding in research and EBVM, as well as have the opportunity to consider research as a career. Seeing a lack of a cohesive body of information that identified the options and the challenges inherent to embedding such training in veterinary curricula, an international group was formed with the goal of synthesizing evidence to help curriculum designers, course leaders, and teachers implement educational approaches that will inspire future researchers and produce evidence-based practitioners. This article presents a literature review of the rationale, issues, and options for research and EBVM in veterinary curricula. Additionally, semi-structured interviews were conducted with 11 key stakeholders across the eight Council for International Veterinary Medical Education (CIVME) regions. Emergent themes from the literature and interviews for including research and EBVM skills into the curriculum included societal need, career development, and skills important to clinical professional life. Approaches included compulsory as well as optional learning opportunities. Barriers to incorporating these skills into the curriculum were grouped into student and faculty-/staff-related issues, time constraints in the curriculum, and financial barriers. Having motivated faculty and contextualizing the teaching were considered important to engage students. The information has been summarized in an online "toolbox" that is freely available for educators to inform curriculum development.
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 | 0.267 | 0.257 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.036 | 0.049 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".