The Veterinary Identity: A Time and Context Model
Bibliographic record
Abstract
The nature of professionalism teaching is a current issue in veterinary education, with an individual’s identity as a professional having implications for one’s values and behaviors, as well as for his or her career satisfaction and psychological well-being. An appropriately formed professional identity imparts competence in making complex decisions—those that involve multiple perspectives and are complicated by contextual challenges. It enables an individual to act in a way that aligns with his or her professional values and priorities, and imparts resilience to situations in which one’s actions are dissonant to these personal beliefs. There are challenges in professionalism teaching that relate to student engagement and faculty confidence in this area. However, these cannot be addressed without first defining the veterinary professional identity—in effect, the aim of professionalism teaching. In this article, existing identity models from the wider literature have been analyzed through a veterinary lens. This analysis was then used to construct a model of veterinary professional identity that incorporates the self (personal morals and values), social development (learning from the workplace environment), and professional behaviors. Individuals who form what we have termed self–environment–behavior connections are proposed to be able to use workplace learning opportunities to inform their identity development, such that environmental complexity does not obstruct the link between values and behaviors. Those who fail to connect with the environment in this way may perceive that environmental influences (e.g., the client, financial limitations) are obstructive to enacting their desired identity, and they may struggle with decision making in complex scenarios.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".