Bibliographic record
Abstract
Since around 2000, there has been a growing body of work which tells medical stories using the form of comics. This genre has come to be called “Graphic Medicine” and has been organized and supported with annual (since 2007) global “Comics & Medicine” conferences and a publication series at The Pennsylvania State University Press. The seminal "Graphic Medicine Manifesto" was published by that press in 2015. In my presentation I would like to begin with a survey of the genre’s history and to transmit a sense of its scope. Graphic Medicine publications are composed and illustrated by physicians, nurse practitioners, patients, patients’ family members and other creator/observers. Sometimes they are testimonies of actual cases and other times they are imagined and constructed. The perspectives offered and illustrative styles are wildly variable. The scope of vision might be broad and sociologically dense, displaying the patient’s lifeworld or only show the experience of an isolated figure. I believe that Graphic Medicine can make claims to offer a unique instrument of mediation. As such, it can be used to facilitate the relation between doctors, students, patients and families. Comics can illustrate the imaginative and symbolic representations of figures and their dynamic relationships.Furthermore, I propose that its features are sympathetic to and supportive of the goals and methods of Whole Person Care. Graphic Medicine is holistic as it presents a contextualizing situation, while subjective draughtsmanship enhances the affective dimension. And they are also anxiety reducing - for after all, they are still comics.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".