AnArtomy: Arts, Anatomy and Medicine - Human Beings Being Human
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background The study of anatomy underpins medical education and is an important facet of clinical practice in various diverse disciplines. We explored the dynamic relationship between arts, anatomy and medicine, along the continuum axis of anatomy, medicine, healthcare and art. Aims 1. to foster and gauge artistic and creative expression within the context of medical science and practice. 2. to generate representative artwork examining the relationship between art, medicine and healthcare. Methods Two purposefully open and expressive creative workshops were held within the cadaveric dissection laboratory of the Queen's University Department of Anatomy; with awide variety of artistic substrates available for faculty and student participants. Themes included: the relationship between art and medicine, the impact art and science have upon each other and the effects of creativity on wellbeing. Accompanying questionnaires included a quantification of perceived relationships between art and medicine; with an estimation of connectedness to feelings. Qualitative items within each questionnaire also addressed key humanistic questions. Comparative analysis of quantitative results was by Student's t-testing; statistical significance being p values <0.05. Results, Summary and Conclusions There was a statistically significant increase in "connectedness to feelings" amongst participants over the course of the workshop. There was a trend for participants to agree or strongly agree that art and medicine were important to each other. Qualitative responses changed from specific, task-oriented hopes to responses more aligned with social/gregarious themes and those related to higher order functioning. Humanistic responses changed across the entire group from a largely fixed inclusion of the concept of emotions to broader, more altruistic visions; inclusive of communal, social views. There was a noticeable shift in emphasis from succinctly defined descriptive terms to more expressive terms; reflective and inclusive of caring, holistic practice. The most arresting and compelling results were those of the resulting representative artwork.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".