My Heart, My Art: A novel Nepali medical student art project and the link to learning
Bibliographic record
Abstract
Background: Humanities programs in South Asian medical schools are slowly growing in popularity. Art-making opportunities within those programs, however, are limited despite their potential benefits including solidification and integration of learning. Aim: to examine art created by medical students for the breadth and depth of conceptual understanding that formed the foundation for its creation. Settings and design: Medical school in Nepal; qualitative study. Material and methods: First year medical students at the Patan Academy of Health Sciences in Nepal, in 2011, 2012, 2014 and 2016, were asked to volunteer and submit artistic interpretations of “cardiac science” during five weeks of learning about the cardiovascular system using any media. Submissions were digitally recorded. This art repository was used as the data set for the present study. Data analysis: curatorial analysis of a repository of art pieces using Rose’s criteria for critical visual analysis. Results: Four main categories were generated: Anatomy Literal Representation, Artistic Representation, Tactile Renderings, and Linked to Health/Nepal. Conclusions: From literal to artistic/fanciful representation, student’s art revealed a strong conceptual understanding of the cardiac science topic. A subset of tactile art highlighted the student’s manual dexterity and propensity for kinesthetic learning. The links made by their art to socially relevant health issues, illustrated the student’s ability to connect science to the needs of their patient population, and the important role for education in disease prevention. This is the first study that has explored art-making in the context of Nepali medical education and its potential role as an adjunct to science learning.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".