The prism model: advancing a theory of practice for arts and humanities in medical education
Bibliographic record
Abstract
INTRODUCTION: The arts and humanities have transformative potential for medical education. Realizing this potential requires an understanding of what arts and humanities teaching is and what it aims to do. A 2016 review of exclusively quantitative studies mapped three discursive positions (art as intrinsic to, additive to or curative for medicine) and three epistemic functions (art for mastering skills, perspective taking, and personal growth and activism). A more inclusive sample might offer new insights into the position and function of arts and humanities teaching in medical education. METHODS: Informed by this 2016 framework, we conducted discursive and conceptual analyses of 769 citations from a database created in a recent scoping review. We also analyzed the 15 stakeholder interviews from this review for recurring themes. These three analyses were iteratively compared and combined to produce a model representing the complex relationship among discursive functions and learning domains. RESULTS: The literature largely positioned arts and humanities as additive to medicine and focused on the functions of mastering skills and perspective taking. Stakeholders emphasized the intrinsic value of arts and humanities and advocated their utility for social critique and change. We offer a refined theory of practice-the Prism Model of four functions (mastering skills, perspective taking, personal insight and social advocacy)-to support more strategic use of arts and humanities in medical education across all learning domains. DISCUSSION: The Prism Model encourages greater pedagogical flexibility and critical reflection in arts and humanities teaching, offering a foundation for achieving its transformative potential.
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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 | 0.094 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".