The Hidden Curriculum of Veterinary Education: Mediators and Moderators of Its Effects
Bibliographic record
Abstract
The "hidden curriculum" has long been supposed to have an effect on students' learning during their clinical education, and in particular in shaping their ideas of what it means to be a professional. Despite this, there has been little evidence linking specific changes in professional attitudes to the individual components of the hidden curriculum. This study aimed to recognize those components that led to a change in students' professional attitudes at a UK veterinary school, as well as to identify the attitudes most affected. Observations were made of 11 student groups across five clinical rotations, followed by semi-structured interviews with 23 students at the end of their rotation experience. Data were combined and analyzed thematically, taking both an inductive and deductive approach. Views about the importance of technical competence and communication skills were promoted as a result of students' interaction with the hidden curriculum, and tensions were revealed in relation to their attitudes toward compassion and empathy, autonomy and responsibility, and lifestyle ethic. The assessment processes of rotations and the clinical service organization served to communicate the messages of the hidden curriculum, bringing about changes in student professional attitudes, while student-selected role models and the student rotation groups moderated the effects of these influences.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".