Library Collaboration with Medical Humanities in an American Medical College in Qatar
Bibliographic record
Abstract
The medical humanities, a cross-disciplinary field of practice and research that includes medicine, literature, art, history, philosophy, and sociology, is being increasingly incorporated into medical school curricula internationally. Medical humanities courses in Writing, Literature, Medical Ethics and History can teach physicians-in-training communication skills, doctor-patient relations, and medical ethics, as well as empathy and cross-cultural understanding. In addition to providing educational breadth and variety, the medical humanities can also play a practical role in teaching critical/analytical skills. These skills are utilized in differential diagnosis and problem-based learning, as well as in developing written and oral communications. Communication skills are a required medical competency for passing medical board exams in the U.S., Canada, the UK and elsewhere. The medical library is an integral part of medical humanities training efforts. This contribution provides a case study of the Distributed eLibrary at the Weill Cornell Medical College in Qatar in Doha, and its collaboration with the Writing Program in the Premedical Program to teach and develop the medical humanities. Programs and initiatives of the DeLib library include: developing an information literacy course, course guides for specific courses, the 100 Classic Books Project, collection development of 'doctors' stories' related to the practice of medicine (including medically-oriented movies and TV programs), and workshops to teach the analytical and critical thinking skills that form the basis of humanistic approaches to knowledge. This paper outlines a 'best practices' approach to developing the medical humanities in collaboration among the medical library, faculty and administrative stakeholders.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.060 | 0.007 |
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".