Academics’ Experiences in Veterinary Educational Research: Results of an International Survey
Bibliographic record
Abstract
Research conducted by the veterinary education community is critical to continual improvement of educational outcomes. Additionally, research productivity is one metric in promotion and tenure decisions. We sought to identify challenges encountered or anticipated when undertaking or planning veterinary educational research (VER), to learn how these challenges might be overcome, and to synthesize tips for success from those who have performed VER. A branching survey was developed and deployed along the authors' worldwide veterinary education contacts in a cascading manner. The survey collected quantitative and qualitative information from participants who had performed VER and those who planned to perform VER in the future. The 258 participants represented 41 countries. Of the participants, 204 had performed VER (79%) and 54 planned to in the future (21%). The median time spent teaching was 14 years, and median time performing VER was 5 years. The most commonly reported challenges in performing VER were lack of funding, lack of time, and difficulties encountered when undertaking a study, including data collection, analysis, and publishing. When asked about overcoming the challenges, a major theme emerged around people, who provided expertise and mentoring. The most commonly reported tip for success was collaboration; 73% of experienced researchers reported people as most helpful upon beginning VER. Collaborators provided diverse help with ideas, study design, statistics, and other aspects. These results suggest that institutions can offer support to academics in the form of small grants, protected research time, writing workshops, and mentorship to assist with the production of meaningful VER.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".