Incorporating Inter-Professional Education into a Veterinary Medical Curriculum
Bibliographic record
Abstract
Inter-professional education (IPE) is identified as an important component of health profession training and is listed in the accreditation requirements for many fields, including veterinary medicine. The goals of IPE are to develop inter-professional skills and to improve patient-oriented care and community health outcomes. To meet these goals, IPE relies on enhanced teamwork, a high level of communication, mutual planning, collective decision making, and shared responsibilities. One Health initiatives have also become integral parts of core competencies for veterinary curricular development. While the overall objectives of an IPE program are similar to those of a One Health initiative, they are not identical. There are unique differences in expectations and outcomes for an IPE program. The purpose of this study was to explore veterinary medical students' perceptions of their interprofessional experiences following participation in a required IPE course that brought together beginning health profession students from the colleges of medicine, dentistry, nursing, pharmacy, nutrition, public health and health professions, and veterinary medicine. Using qualitative research methods, we found that there is powerful experiential learning that occurs for both the veterinary students and the other health profession students when they work together at the beginning of their curriculum as an inter-professional team.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".