Using the visual arts to teach clinical excellence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<ns4:p>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.</ns4:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it