Best Practice in Supporting Professional Identity Formation: Use of a Professional Reasoning Framework
Bibliographic record
Abstract
Professional identity and professionalism education are increasingly important to veterinary education, but many of the concepts remain intangible to veterinary students, and engagement is a persistent challenge. While whole-curriculum integration is recommended for a successful professional studies program, this is complicated by clinical faculty’s discomfort with the content. Where professional studies education is centered around professional identity formation, a key element of this is the multi-perspective nature of veterinary work, with the veterinarian negotiating the needs of multiple stakeholders in animal care. Constructing teaching around a framework of professional reasoning, which incorporates the negotiation of different stakeholder needs, ethical decision making, communication, teamwork, and outcome monitoring, offers the potential to make professional identity a concept more visible to students in veterinary work, and guides students in the contextualization of taught material. A framework is presented for veterinary professional reasoning that signposts wider curriculum content and helps illustrate where material such as veterinary business studies, animal welfare, the human–animal bond, and professional responsibility, as well as attributes such as empathy and compassion, all integrate in the decisions and actions of the veterinary professional. The aims of this framework are to support students’ engagement in professional studies teaching and help them use workplace learning experiences to construct an appropriate professional identity for competence and resilience in the clinic. For faculty involved in curriculum design and clinical teaching, the framework provides a tool to support the integration of professional identity concepts across the extended curriculum.
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 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 | 0.118 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.014 | 0.049 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".