Being Human: Envisioning the Future of Museum-Based Education for Health Professionals
Bibliographic record
Abstract
ABSTRACT: The arts and humanities are fundamental to health professions education and can substantially enhance clinician empathy, tolerance for ambiguity, and openness to new perspectives. Museum-based education for health professionals (MBE-HP) has particular potential to achieve these aims but remains a nascent field of practice and study. To catalyze the growth of MBE-HP, a convening of 50 individuals from the United States and Canada, including museum educators and health professions educators, administrators, researchers, and learners, as well as patrons of the arts, was held for 3 days in December 2023 in Washington, DC. Activities included 7 distinct MBE-HP experiences at 5 national museums as well as facilitated discussions. This article describes the purpose and design of this unique gathering and summarizes its key outcomes. Through their shared experiences and discussions, participants developed a definition of MBE-HP as an approach that can occur in museums, other in-person settings, and/or online, which is informed by museum-based education adapted for health professional learners and involves exploring and/or creating visual and other forms of art, as well as individual and group reflection on these activities. Participants also developed strategies to advance MBE-HP and steps that can be taken by individuals, programs, and institutions involved in health professions education, as well as changes that may influence health professions education systems, such as professional organizations' sponsorship, private and public funding, evidence generation through research, and changes to regulations and policymaking. Museum-based education for health professionals is reaching critical mass, backed by science and supported by an increasing collection of resources for health professions educators; the definitions and strategies outlined here may be of value to stakeholders seeking to move MBE-HP into the mainstream to realize its potential to benefit learners, educators, patients, and communities.
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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 | 0.020 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".