How Are the Arts and Humanities Used in Medical Education? Results of a Scoping Review
Bibliographic record
Abstract
PURPOSE: Although focused reviews have characterized subsets of the literature on the arts and humanities in medical education, a large-scale overview of the field is needed to inform efforts to strengthen these approaches in medicine. METHOD: The authors conducted a scoping review in 2019 to identify how the arts and humanities are used to educate physicians and interprofessional learners across the medical education continuum in Canada and the United States. A search strategy involving 7 databases identified 21,985 citations. Five reviewers independently screened the titles and abstracts. Full-text screening followed (n = 4,649). Of these, 769 records met the inclusion criteria. The authors performed descriptive and statistical analyses and conducted semistructured interviews with 15 stakeholders. RESULTS: The literature is dominated by conceptual works (n = 294) that critically engaged with arts and humanities approaches or generally called for their use in medical education, followed by program descriptions (n = 255). The literary arts (n = 197) were most common. Less than a third of records explicitly engaged theory as a strong component (n = 230). Of descriptive and empirical records (n = 424), more than half concerned undergraduate medical education (n = 245). There were gaps in the literature on interprofessional education, program evaluation, and learner assessment. Programming was most often taught by medical faculty who published their initiatives (n = 236). Absent were voices of contributing artists, docents, and other arts and humanities practitioners from outside medicine. Stakeholders confirmed that these findings resonated with their experiences. CONCLUSIONS: This literature is characterized by brief, episodic installments, privileging a biomedical orientation and largely lacking a theoretical frame to weave the installments into a larger story that accumulates over time and across subfields. These findings should inform efforts to promote, integrate, and study uses of the arts and humanities in medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.254 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.051 | 0.053 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".