Using the visual arts to teach clinical excellence
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Introduction: The authors conducted a review of the literature to identify curricula that incorporate the visual arts into undergraduate, graduate, and continuing medical education to facilitate the teaching of clinical excellence. Methods: The authors searched the PubMed and ERIC electronic databases in May 2017, using search terms such as "paintings," "visual arts," and "medical education," along with terms corresponding to previously defined domains of clinical excellence. Search results were reviewed to select articles published in the highest impact general medicine and medical education journals describing the use of visual arts to teach clinical excellence to all levels of medical trainees and practicing physicians. Results: Fifteen articles met inclusion criteria. Each article addressed at least one of the following clinical excellence domains: communication and interpersonal skills, humanism and professionalism, diagnostic acumen, and knowledge. No articles described the use of the visual arts to teach the skillful negotiation of the health care system, a scholarly approach to clinical practice, or a passion for patient care. Conclusions: This review supports the use of visual arts in medical education to facilitate the teaching of clinical excellence. However, research designed specifically to evaluate the impact of the visual arts on clinical excellence outcomes is needed.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".