Bibliographic record
Abstract
The teaching and assessment of professional behaviors and attitudes are important components of veterinary curricula. This article aims to outline some important considerations and concepts which will be useful for veterinary educators reviewing or developing this topic. A definition or framework of veterinary professionalism must be decided upon before educators can develop relevant learning outcomes. The interface between ethics and professionalism should be considered, and both clinicians and ethicists should deliver professionalism teaching. The influence of the hidden curriculum on student development as professionals should also be discussed during curriculum planning because it has the potential to undermine a formal curriculum of professionalism. There are several learning theories that have relevance to the teaching and learning of professionalism; situated learning theory, social cognitive theory, adult learning theory, reflective practice and experiential learning, and social constructivism must all be considered as a curriculum is designed. Delivery methods to teach professionalism are diverse, but the teaching of reflective skills and the use of early clinical experience to deliver valid learning opportunities are essential. Curricula should be longitudinal and integrated with other aspects of teaching and learning. Professionalism should also be assessed, and a wide range of methods have the potential to do so, including multisource feedback and portfolios. Validity, reliability, and feasibility are all important considerations. The above outlined approach to the teaching and assessment of professionalism will help ensure that institutions produce graduates who are ready for the workplace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".