Should the Medical Humanities Be Vital to Curricula?
Bibliographic record
Abstract
To the Editor: We find the recent appeal by Dr. Bleakley1 for early integration of medical humanities in educational curricula refreshingly warm. As medical students, we often notice the lens of skepticism through which our peers view the arts in medicine. After all, if material is unlikely to be tested on clinical rotations or standardized exams, it quickly falls to the bottom of a trainee’s list of priorities. As years progress, we learn that we are rewarded for memorizing differential diagnoses and clinical facts. Thus, spending precious time reflecting on a narrative poem or an abstract art piece becomes increasingly trivial. In doing so, however, our perspectives harden to the scientific mold of medicine. We begin seeing the body as a machine with faulty parts rather than a human being with empathetic needs. Although this is certainly natural and welcome during a physician’s professional development, we must not forget to acknowledge humanity along the process. By reminding us of our initial aspirations for pursuing medicine, Dr. Bleakley emphasizes the why, rather than the how, behind teaching the medical humanities. The author expresses that longitudinal, integrated curricula in the medical humanities can develop skills, such as navigating uncertainty as a basis to promoting patient–physician trust relations. His call for heightened awareness of this softer school of thought in the face of an often mechanical and traditionally patriarchal medical system is not new. Many have previously delineated and robustly studied the benefits of self-reflection using history, art, and literature in medicine. And yet, as these virtues have become more defined, incorporating the humanities into medical education has slowly morphed into an administrative, rather than a learner-centered, endeavor. We often ask: How do medical educators fit lessons of compassion into busy curriculum schedules when already pressed for time to teach the fundamentals of medicine? What long-term objective outcomes can be deciphered by exposing students to the arts early in their careers? Who will volunteer their time to influence medical trainees to embrace this seemingly subjective practice, when we are undeniably conditioned to achieve numerical accomplishments instead? We applaud Dr. Bleakley for ignoring these often-debated bureaucratic challenges and rather for approaching the topic with deeper meaning. Trainees in all aspects of medicine can profit from this philosophical outlook as motivation for learning lessons of compassion and humanity from a regularly forgotten but unquestionably vital piece of curriculum. Aleksandar RadonjicThird-year medical student, Faculty of Medicine, University of Ottawa, Ottawa, Canada; [email protected]; ORCID: https://orcid.org/0000-0003-0296-376X.Emily Louise EvansThird-year medical student, Faculty of Medicine, University of Ottawa, Ottawa, Canada.
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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".