The Veterinary Education and Training Landscape Beyond Graduation: Where Is the Evidence?
Bibliographic record
Abstract
Abstract Veterinarians undergo several years of rigorous education in order to qualify in their chosen profession. As they enter clinical practice, or work within other areas of the profession, they embark upon a career-long journey of learning, whether that be formal or informal education and training, in order to develop themselves professionally and remain up to date. However, the vast majority of published educational literature within the veterinary sector relates to undergraduate programs. Research and scholarship relating to veterinary education and training beyond graduation is extremely sparse in comparison. This is somewhat different to what is seen in other health professions, including medical education, where a significant proportion of the literature focuses on education and training beyond graduation, from early career training and residencies through to continuing education. The advantages of publishing high-quality scholarship and research in any field are well known. Sharing more evidence and best practice in post-graduation education and training will inform international advances in this area. Although the specific educational challenges facing the profession at different career stages are distinct, evidence-informed approaches to educational interventions—whether that be supporting graduates’ transition into the workplace, specialty training, or continuing education—have the potential to have a positive impact on many levels, from improved patient outcomes and client satisfaction to enhancing veterinarians’ job satisfaction and retention in the workplace. This article discusses the gaps in evidence in veterinary education and training beyond graduation , identifying some of the current challenges that could be addressed through a greater focus in this area, and their importance. In relation to graduate transition into the workplace, further work is needed to understand the optimal design and effectiveness of support programs, including coaching and mentoring for graduates. For formal post-graduate education leading to a more advanced level of practice, there is a need to better understand which approaches to teaching and assessment promote high-quality, consistent learning experiences and outcomes. Further evidence regarding how continuing education is identified and undertaken by learners, and the corresponding impact on practice, would be valuable, and a greater understanding into feasible yet robust licensure assessments and mechanisms for revalidation are needed.
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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 | 0.066 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".