Graphic Medicine as Physician Tool to Understand Their Patient’s Experience of a Medical Condition
Bibliographic record
Abstract
BackgroundEngaging patients in their healthcare, listening to their stories, and improving the quality of their experience also depends on physicians understanding their patient’s experiences of a medical condition.Physicians have little time to converse with patients about this in the visit. Graphic Medicine – Comics – pictures and words together in sequence to tell a story – is a way to gain insight into a patient’s experience of what it’s like. MethodsA small, mixed-method study to test the effect on physicians of reading a comic book, “My Degeneration: Parkinson’s Disease.” The 13 participants, including 11 physician-editors (representing 10 disciplines), answered a 7-question pre-survey before receiving and reading the book, and a 10-question post survey. Also, the 12 participants present at the recent Permanente Journal Editorial meeting commented on their experience of reading the book, its attributes, and their recommendations for the comic book as an educational tool for residents and patients. ResultsGreatest Improvements were: “know patients’ wants,” (54%), “know treatments” (37%), “know patients’ needs” (34%) and “know patients’ experience” (30%). 82% recommended the comic book for resident education, and 73% for patients. Comments included: “My patients say:‘Doc, you guys really need to understand what’s going on for me. It’s really hard for me.’” “The things that people do to deal with their condition are remarkable!” “For a patient to have a conversation with his disease, as in the book, is a wonderful idea. “Combining pictures with words has triple the educational value for millennial residents who demand high yield.”
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".