Anatomy in Practice: How Do Equine and Production Animal Veterinarians Apply Anatomy in Primary Care Settings?
Bibliographic record
Abstract
To successfully prepare veterinary undergraduates for the workplace, it is critical that anatomy educators consider the context in which developing knowledge and skills will be applied. This study aimed to establish how farm animal and equine general practitioners use anatomy and related skills within their daily work. Qualitative ethnographic data in the form of observations and semi-structured interviews were collected from 12 veterinarians working in equine or farm animal first-opinion practice. Data underwent thematic analysis using a grounded theory approach. The five themes identified were relevant to both equine and farm animal veterinarians and represented the breadth and complexity of anatomy, its importance for professional and practical competence, as well as the requirement for continuous learning. The centrality and broad and multifaceted nature of anatomy was found to challenge equine and farm animal veterinarians, highlighting that essential anatomy knowledge and related skills are vital for their professional and practical competence. This aligns with the previously described experiences of companion animal clinicians. In equine practice, the complexity of anatomical knowledge required was particularly high, especially in relation to diagnostic imaging and assessing normal variation. This resulted in greater importance being placed on formal and informal professional development opportunities. For farm animal clinicians, anatomy application in the context of necropsy and euthanasia was particularly noted. Our findings allow anatomy educators to design appropriate and effective learning opportunities to ensure that veterinary graduates are equipped with the skills, knowledge, and resources required to succeed in first-opinion veterinary practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".