How an Arts-Based Clinical Skills Set Can Be Assessed During OSCEs
Bibliographic record
Abstract
Background: Arts-based activities' roles in medical education is to challenge students to cultivate clinical skills using ART (aesthetics, reflection, time).ART activities offer opportunities for students to cultivate creative dimensions of their clinical skills and to reflect on their responses to uncertainty and ambiguity.Faculty, however, are challenged to structure these learning activities in diverse, sometimes unfamiliar, health care settings.Methods: This study explored preclerkship medical students' responses to participating in ART activities presented in the common medical educational format of an objective structured clinical exam (OSCE).Activities included interpreting fine art (eg, images and poetry) and drawing a simulated patient.The discussion section transcript and student sketchbooks were analyzed to identify themes related to participating in the study.Results: Use of arts-based activities elicited behaviors similar to those observed in students' responses to formal summative OSCEs, although students also wrestled with challenges and expressed their subjective impressions.Conclusions: This study offers an arts-based tool set capable of being delivered within the familiar medical education setting and established structure of the OSCE.The American Medical Association designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 Credit™ available through the AMA Ed Hub TM .Physicians should claim only the credit commensurate with the extent of their participation in the activity.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".