Prevalence of Medical Humanities Teaching in Medical Schools: Review of Curricula in the United States, Canada, and the United Kingdom
Bibliographic record
Abstract
Rationale and Objectives Medical humanities are becoming increasingly popular, required, and recognized as positively impacting medical education and medical practice. However, the extent of medical humanities teaching in medical schools is largely unknown. We aimed to review medical school curricula in Canada, the UK, and the US. Our secondary objective was to compare the inclusion of medical humanities in the curricula with rankings of medical schools. Methods We searched the curriculum websites of all accredited medical schools in Canada, the UK, and the US to check which medical humanities topics were taught, and whether they were mandatory or optional. We then noted rankings both by Times Higher Education and U.S. News and World Report and calculated the average rank. We formally explored whether there was an association between average medical school ranking and medical humanities offerings using Spearman’s correlation and inverse variance weighting meta-analysis. Results We identified 18 accredited medical school programmes in Canada, 41 in the UK, and 156 in the US. Of these, 9 (56%) in Canada, 34 (73%) in the UK and 124 (79%) in the US offered at least one medical humanity that was not ethics. The most common medical humanities were Unspecified Medical Humanities, History, and Literature (Canada), Sociology and Social Medicine, Unspecified Medical Humanities, and Art (UK), and Unspecified Medical Humanities, Literature, and History (US). There was a negative relationship between the ranking of the medical school and whether they offered medical humanities. Conclusions The extent and content of medical humanities offerings at accredited medical schools in Canada, the UK, and the US varies. The quality of our analysis was limited by the data provided on the Universities’ curriculum websites. Given the potential for medical humanities to improve medical education and medical practice, this variation should be investigated further.
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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 | 0.011 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.042 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".