Developing a Professional Studies Curriculum to Support Veterinary Professional Identity Formation
Bibliographic record
Abstract
Professional studies teaching in medical and veterinary education is undergoing a period of change. Traditional approaches, aiming to teach students professional values and behaviors, are being enhanced by curricula designed to support students' professional identity formation. This development offers the potential for improving student engagement and graduates' mental well-being. The veterinary professional identity associated with emotional resilience and success in practice incorporates complexity in professional decision making and the importance of context on behaviors and actions. The veterinarian must make decisions that balance the sometimes conflicting needs of patient, clients, veterinarian, and practice; their subsequent actions are influenced by environmental challenges such as financial limitations, or stress and fatigue caused by a heavy workload. This article aims to describe how curricula can be designed to support the development of such an identity in students. We will review relevant literature from medical education and the veterinary profession to describe current best practices for supporting professional identity formation, and then present the application of these principles using the curriculum at the Royal Veterinary College (RVC) as a case study. Design of a "best practice" curriculum includes sequential development of complex thinking rather than notions of a single best solution to a problem. It requires managing a hidden curriculum that tends to reinforce a professional identity conceived solely on clinical diagnosis and treatment. It includes exposure to veterinary professionals with different sets of professional priorities, and those who work in different environments. It also includes the contextualization of taught content through reflection on workplace-learning opportunities.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".