Health Humanities as a New Rapidly Growing Transdisciplinary Academic Field
Bibliographic record
Abstract
Health or medical humanities is a rapidly growing transdisciplinary academic field that incorporates aspects of the arts and humanities to health and health care. It embraces various branches of humanities and social sciences such as religious studies, cultural and language studies, history, philosophy, anthropology and sociology and it has a wide-ranging application to medical education and health practices. Health humanities seeks novel ways of understanding health and illness in society, and how methods from the humanities and social studies may be brought to bear on biomedicine, clinical practice, and the politics of healthcare. Baccalaureate and Masters programmes in health humanities have been developed in the US, Canada and UK. Graduates of this programme are prepared for employment and success in many areas including, research technician, nonprofit facilitator, pharmacy manager, environmental law and policy, public health service, public administration, clinical research, marketing, media, PR, publishing & journalism, consumer & retail …etc. Therefore, this field should be studied at our universities and that should be encouraged through workshops, conferences and lectures on this issue. This study aims to improve our universities in order for them to follow suit and start to climb the world university rankings. Keywords: Medical humanities - health humanities - Anglophonic universities
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".