Bibliographic record
Abstract
Medicine magnifies monstrosity. A fundamental decay is created by objective reductionism, by the focus on the critical values, labs, and exams. What results is often a failure to recognize the individual. Numbers substitute. Beings evaporate. And there is a condensation of personhood onto disease, rather than the other way around. The story “Panacea” heightens the making of a monster. It is the writer who becomes tortured, unrecognizable, a fright to his past self. Meanwhile, the patient is untouched and unhealed. Only sheer life and death awaken the writer to the person in front of him, a person who no longer wants to be as such. In this way, the story suggests a means of increasing empathy that is supported by research: stories. 1,2 Previously, Gramelspacher and Cummins 3 have shown that the ability to share patients’ narratives is a powerful way for both patient and provider to bear witness to human suffering and to navigate the uncertainty of treatment. Much of the academic space has translated these ideas of stories into narrative medicine. As a whole, narrative medicine is about how to engage with practitioners so that they feel heard, told, listened to, and invested. They regain themselves through themselves. Championed by literary historian and physician Rita Charon, the practice bears witness to the narrative of doctor–patient relationships, the language employed by caregivers and care-receivers alike, and the way such linguistic comparisons can better health care ethically and effectively. 4 Recent systematic reviews have shown narrative medicine to be an effective pedagogic tool, 5 using creative writing workshops where participants write and share their stories. 6 Such a focus on stories has been further shown to develop cultural competence, 7 increase empathy, 8 and improve patient and physician relationships. 9 What “Panacea” suggests, however, is that knowledge of literary training or its elements alone is not enough. True care requires more than cold analysis or beautiful sentences reflecting rarefied air. It requires more than rigorous study, more than wonderful words alone. It is not enough to be a vessel and simply “retain all that [one has] learned.” One must recall the life behind the lessons, the beings who are faulty, frail, and failing, who themselves may one day forget the very aphorisms and meaning that they provide. The storytelling this protagonist has done has helped him not just to emptily reflect but, instead, to place into action a new outlook on other patients. He sits. He listens whole. A bad story has ended in hopes of a good one beginning.
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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.075 | 0.085 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".