A Visiting Professorship in Undergraduate Medical Education at the University of Alberta: Reflections on possibilities for medical humanities in China, and elsewhere
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Enhancing humanities in medical education is a pressing concern in China. Similar to other countries, medical education in China evolved over the past century to emphasize bioscience and technology in treating illness and disease. Increasing recognition of the limitations of biomedical technology led to emergence of the medical humanities in the West in the latter half of the 20 th century, an interdisciplinary area that has continued to expand and grow. In China and elsewhere, activity in this area developed somewhat later. Ongoing patient-doctor disputes and decline in public trust in the medical profession in China has led many to advocate for enhanced emphasis on humanism and medical humanities. In 2017, the Chinese government introduced new healthcare reforms which included an education and training plan that promotes medical humanities teaching. Global developments have led to a wide variety of models and approaches that may be considered in cultivating medical humanities and humanism in China. With the support of China Medical University in Shenyang, Liaoning Province, PRC, Professor Wei visited the Faculty of Medicine & Dentistry at the University of Alberta through the 2019/20 academic year. This article provides an overview of a wide array of medical humanities teaching and learning opportunities associated with the undergraduate medical education program at the University of Alberta. Professor Wei reflects on possibilities for medical humanities in medical education in China given all she learned and experienced as a visiting professor at the University of Alberta, which may be of interest to others who are also developing new approaches to introducing medical humanities as part of their health professions education program. Additional reflections regarding possibilities for global medical humanities are also offered.
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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 | 0.012 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.028 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".