Contemporary Challenges for Veterinary Medical Education: Examining the State of Inter-Professional Education in Veterinary Medicine
Bibliographic record
Abstract
Educational training in professional programs forms the foundation for how a person problem-solves throughout their career. However, training focused on only one profession ignores the value realized through collaborations among multiple professions for solving health-related problems. This is at the core of inter-professional education (IPE). Effective IPE programs can result in inter-professional collaboration and translation science endeavors across the health sciences and beyond. Recent events such as the COVID-19 pandemic and the opioid crisis highlight the importance of veterinary medicine in advancing One Health through IPE. The prevalence of IPE programs in veterinary curricula, and the process by which these have been established, has not been widely described in the literature. Through an 18-question survey sent to associate deans (ADs) of 50 veterinary schools, we sought to determine the status of IPE in the veterinary curriculum. Thirty-nine schools agreed to participate, representing primarily United States Doctor of Veterinary Medicine public and private programs with some representation from Canadian, United Kingdom, and Australasian programs. Schools that provide IPE courses developed their programs in collaboration with other health sciences programs across the 4-year curriculum. The perceived barriers for IPE offerings were no different between schools with or without opportunities; however, a lack of faculty and student-perceived value and lack of adequate space in the academic schedule were common threads. Our findings provide a snapshot of the current state of IPE in veterinary medical curricula, with a particular emphasis on the United States, and point to areas of programmatic need for the field.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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 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".