Teaching Professionalism: Using Role-Play Simulations to Generate Professionalism Learning Outcomes
Bibliographic record
Abstract
For a constructively aligned curriculum in veterinary professionalism, there is a need for well-designed higher order learning outcomes to support students' professional identity formation. A lack of uniformly accepted definitions of veterinary professionalism necessitates the defining and refining of current concepts of professionalism to inform teaching and assessment. A potential method for achieving such learning outcomes is to generate these from simulated professionalism teaching scenarios. A workshop was designed in which veterinary educators used role play to resolve a professional dilemma. Following discussion of the appropriate management approach, participants were asked to reflect on the learning outcomes that were required to resolve the scenario and that students would achieve by going through the same classroom-based process. Workshop participants identified several professionalism learning outcomes that are not currently defined in the literature: realizing that there is not a single correct answer to a professional dilemma, making a decision despite this uncertainty, communicating differences of opinion, and understanding the effect of differences in professional identity. Although the process described runs counter to traditional curricular design, it may offer a valuable contribution to the discourse surrounding professionalism learning outcomes. Furthermore, it has generated higher level learning outcomes than have been obtained through other methods.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".