Applying the Prism Model to design arts and humanities medical curricula
Bibliographic record
Abstract
The arts and humanities (A&H) play a fundamental role in medical education by supporting medical learners' development of core competencies. Like all medical curricula, those integrating the A&H are more likely to achieve the desired outcomes when the learning domains, goals, objectives, activities, and evaluation strategies are well-aligned. Few faculty development programs focus on helping medical educators design A&H curricula in a scholarly manner. The Prism Model, an evidence-based tool, supports educators developing A&H medical curricula in a rigorous way for maximum impact. The model posits that the A&H can serve four pedagogical functions for medical learners: 1) skill mastery, 2) perspective taking, 3) personal insight, and 4) social advocacy. Although this model has been described in the literature, no practical guidance exists for medical educators seeking to apply it to the development of a specific curriculum. This paper provides a step-by-step demonstration of how to use the Prism Model to design an A&H curriculum. Beginning with the first step of selecting a learning domain through the final step of curriculum evaluation, this paper helps medical educators apply the Prism Model to develop A&H curricula with intentionality and rigour to achieve the desired learning outcomes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".