Bibliographic record
Abstract
There are limited curricular options for medical students to engage in art-making during their training. Yet, it is known that art-making confers a variety of benefits related to learning. This qualitative study utilises a visual methodology to explore students' art-making in the context of the cardiovascular sciences. The existence of a multiyear repository of medical/dental student generated, cardiac-inspired art, collected over 6 years, provided the opportunity to explore the nature of the art made. The aim was to categorise the art produced, as well as the depth and breadth of understanding required to produce the art. The data set included a wide variety of titled art (paintings, photographs, sketches, sculptures, collages, poetry and music/dance). Systematic curation of the collection, across all media, yielded three main categories: anatomical renderings, physiology/pathophysiology renderings and kinesthetic creations (music/dance/tactile). Overall (medical and dental) student-generated art suggested a high level of content/process understanding, as illustrated by attention to scientific detail, integration of form and function as well as the sophisticated use of visual metaphor and word play. Dental students preferentially expressed their understanding of anatomy and physiology kinesthetically, creating art that required manual dexterity as well as through choreography and dance. Combining art-making with basic science curricular learning invited the medical and dentistry students to link their understanding to different modes of expression and a non-biomedical way of knowing. Subsequent incorporation of the student-generated cardiac art into lectures exposed the entire class to creative pictorial expressions of anatomy, physiology and pathophysiology.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".