Life, Death, and Humanity in Veterinary Medicine: Is It Time to Embrace the Humanities in Veterinary Education?
Bibliographic record
Abstract
Medical humanities is a multidisciplinary, interdisciplinary field of study that has experienced explosive growth in the United States since the 1960s. Two key components of medical humanities include, first, the use of literature, poetry, and visual arts in the education of medical students, and second, the representation or examination of medical culture by scholars in the humanities, arts, and social sciences such as literary and film creators, sociologists, and anthropologists. The American Association of Medical Colleges recently reported that as of 2018, approximately 94% of medical schools had core or elective humanities offerings in their curricula. The examination of the medical milieu by scholars across the humanities has resulted in the emergence of important specialty fields such as end-of-life care, disability studies, and health disparities research. Veterinary medicine has been slow to embrace the humanities as relevant to our profession and to the education of our students. Only sporadic, isolated attempts to document the value of the arts and humanities can be found in the veterinary literature, and valuable observations on our profession made by scholars in diverse disciplines of the humanities are largely buried in publications not often accessed by veterinarians. Here a case is made that the time is right for the emergence of a more cohesive field of veterinary humanities. Embracing the observations of humanities scholars who engage with our profession, and appreciating the ways in which the humanities themselves are effective tools in the education of veterinary professionals, will bring many benefits to our evolving profession.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".