Using visual art and collaborative reflection to explore medical attitudes toward vulnerable persons
Bibliographic record
Abstract
BACKGROUND: Vulnerable persons often face stigma-related barriers while seeking health care. Innovative education and professional development methods are needed to help change this. METHOD: We describe an interdisciplinary group workshop designed around a discomfiting oil portrait, intended to trigger provocative conversations among health care students and practitioners, and we present our mixed methods analysis of participant reflections. RESULTS: After the workshop, participants were significantly more likely to endorse the statements that the observation and interpretive skills involved in viewing visual art are relevant to patient care and that visual art should be used in medical education to improve students' observational skills, narrative skills, and empathy with their patients. Subsequent to the workshop, significantly more participants agreed that art interpretation should be required curriculum for health care students. Qualitative comments from two groups from two different education and professional contexts were examined for themes; conversations focused on issues of power, body image/self-esteem, and lessons for clinical practice. CONCLUSIONS: We argue that difficult conversations about affective responses to vulnerable persons are possible in a collaborative context using well-chosen works of visual art that can stand in for a patient.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".