Workplace Learning in Veterinary Education: A Sociocultural Perspective
Bibliographic record
Abstract
Veterinary practice is a broad sphere of professional activity encompassing clinical activity and other vocational opportunities conducted in rapidly changing contemporary social conditions. Workplace learning is an important but resource-intensive component of educating students for practice. This conceptual article argues that literature on workplace learning in the veterinary context is dominated by descriptive accounts and that there is a dearth of theoretically informed research on this topic. Framing veterinary practice as a social, relational, and discursive practice supports the use of workplace learning theories developed from a sociocultural perspective. Situated learning theory, with its associated concepts of communities of practice and legitimate peripheral participation, and workplace learning theory focused on workplace affordances and learner agency are discussed. Two composite examples of student feedback from veterinary clinical learning illustrate the concepts, drawing out such themes as the roles of teachers and learners and the assessment of integrated practice. The theoretical perspective described in this article can be used to inform development of models of workplace learning in veterinary clinical settings; relevant examples from medical education are presented.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".