Developing Inter-Professional Education Initiatives to Aid Working and Learning Between Veterinarians and Veterinary Nurses/Vet Techs
Bibliographic record
Abstract
The veterinary workplace consists of different professionals working together in inter-professional teams. Previous work has explored the benefits of effective veterinary teamwork for multiple stakeholders. In this teaching tip article, we outline the underlying educational theories and tips for developing inter-professional teaching to foster students’ appreciation of the different roles and responsibilities of veterinarians and veterinary nurses/vet techs. Inter-professional education (IPE) requires students to learn with, about, and from each other and implies recognition of social learning as an underpinning approach. It involves developing learning opportunities to address students’ potential misunderstandings of each other’s motivations, to allow them to explore issues present in the other profession’s practice, and to clarify sometimes overlapping roles and responsibilities. Students are given opportunities to explore the complexity of inter-professional teamwork in a safe environment using real-life topics as context for their collaboration. Two veterinary examples of IPE at the Royal Veterinary College (RVC) are provided to explore different teaching methods and topics that have proved successful in our context: dentistry and directed learning scenarios. We describe how RVC has developed an IPE team consisting of faculty members who champion IPE, which has, in turn, inspired students to create a student-led IPE club, hosting extracurricular educational events. This is an example of an effective student–teacher partnership. A number of challenges exist in embedding IPE, but the benefits it offers in integrating clinical and professional elements of the curricula make it worthy of consideration.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".