Do medical schools teach medical humanities? Review of curricula in the United States, Canada and the United Kingdom
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Medical humanities are becoming increasingly recognized as positively impacting medical education and medical practice. However, the extent of medical humanities teaching in medical schools is largely unknown. We reviewed medical school curricula in Canada, the UK and the US. We also explored the relationship between medical school ranking and the inclusion of medical humanities in the curricula. 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 154 in the US. Of these, nine (56%) in Canada, 34 (73%) in the UK and 124 (80%) in the US offered at least one medical humanity that was not ethics. The most common medical humanities were medical humanities (unspecified), history, and literature (Canada); sociology and social medicine, medical humanities (unspecified), and art (UK); and medical humanities (unspecified), literature and history (US). Higher ranked medical schools appeared less likely to offer medical humanities. CONCLUSIONS: The extent and content of medical humanities offerings at accredited medical schools in Canada, the UK and the US varies, and there appears to be an inverse relationship between medical school quality and medical humanities offerings. Our analysis was limited by the data provided on the Universities' websites. Given the potential for medical humanities to improve medical education and medical practice, opportunities to reduce this variation should be exploited.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.045 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 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".