Bibliographic record
Abstract
Over the last 30 years medicine has undergone a significant paradigm shift. Due to the tremendous advances of modern medicine more and more people are living longer with their illnesses. These people have stories to tell, and they want these stories to be heard: They are reclaiming their voices. As clinical geneticists we need to hear what these voices are telling us, especially so in our area of clinical care where cures are rare, and disease states can be permanent. Narrative medicine is an important new skill set that hones abilities to do just that.This article highlights how integral narrative medicine is to clinical genetics practice, how geneticists already employ many of its tools and how they practice it diligently every day. I will show how geneticists can further improve their abilities to hear and honor patients' stories by writing and sharing stories with patients and with each other as doctors, counselors, and nurses, social workers and chaplains. The review presents the skills of close reading and how they improve patient care and illustrates how geneticists can, by using reflective writing, reshape their emotions in order to understand them, to let them go, and to make room for more. It presents the major types of illness narratives whose recognition allows us to hear and understand patients' stories. When used, the tools of narrative medicine can result in better patient care.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".