Reading Patients: Our Story of Narrative Medicine
Bibliographic record
Abstract
As Dr. Rita Charon, pioneer of the field of narrative medicine, said “Literary accounts of illness can teach physicians concrete and powerful lessons about the lives of sick people” but also “enable physicians to recognize the power and implications of what they do” (Charon et al, 1995).Through various narrative medicine exercises, we have explored the benefits of narrative medicine for health care professionals. More specifically, we have created a reading club for medical students and developed a reading module as part of the Physician Apprenticeship Course for medical students at McGill University. Moreover, we led short writing workshops based on prompts from short stories and poems for health care professionals at Anna-Laberge Hospital.During our workshop, we will briefly review our narrative medicine initiatives and then dive into a narrative medicine exercise with the group to demonstrate its potential benefits among health care professionals. We hope that by providing concrete examples of narrative medicine projects we have developed and implemented, we will facilitate the integration of narrative medicine into participants’ own practices.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.027 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.012 | 0.032 |
| 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".