Bibliographic record
Abstract
During my first week of medical school a professor told me that, by the end of their third year, medical students feel less empathy for their patients than they did when they started on their journey to becoming a physician. At the time, this didn’t make sense to me. How could a greater understanding of the underpinnings of illness and almost daily contact with others’ suffering lead students to feel more distance from the experience of those they were treating? After only two years of medical school I have begun to see how this might happen. The volume of information presented to medical students throughout their training is staggering. Every detail seems more important than the last, and there are always more details. To help learners commit the minutiae of a disease to memory, textbooks often feature photographs of real patients. The ubiquitous image of a person presenting with illness—eyes censored—is familiar to anyone who has ever opened a medical textbook and is an invaluable tool; these impersonal images allow students to gain a more informed understanding of a particular disease state. If the image serves its purpose, students will be better at identifying and diagnosing that same disease in one of their future patients after seeing it represented in their textbook; maybe that information will even help save someone’s life. But how does this educational approach affect the perception that physicians (and physicians-in-training) have of their patients? What do doctors see when they look at their patients—the disease those people are inflicted with, or a person full of emotion and idiosyncrasy, interests and personality, who is experiencing illness?The Changing Face of MedicineThe images presented here are a selection from a series of photographs I took of people I knew who were struggling with illness. (The individuals pictured gave their consent to be featured on this month’s cover.) The images on the left show the diseased patients, eyes censored, their sole purpose to be impersonally displayed as an illustration of how their bodies have “failed” them. A trained eye will instantly begin to pick apart the images: What lesions or deformities, if any, are present? Is there asymmetry, atrophy, swelling? What is wrong with these people? The images on the right side, of people smiling and eyes glinting with personality, are meant to inspire a second set of questions: What happened that made them smile? What are their names? Who do they love, and who loves them? When they wake up in the morning, what do they most look forward to doing that day? What is most important to them, and how can I help them address their needs? While taking these series of photographs I kept thinking back to the loss of empathy that students experience as they progress through medical school. This project is one approach I have used to counteract that loss. By training my eye to view my future patients in a more holistic light I am attempting to understand how they are experiencing their lives and illnesses from their own viewpoints—to empathize with them, and ultimately to help skillfully navigate them through their period of illness. It is my hope that other health care providers who come across this project will also be inspired to reflect on how they view their patients. Linn Milligan L. Milligan is a third-year student, Dalhousie University, Halifax, Nova Scotia, Canada; e-mail: linn. [email protected]
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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.344 | 0.176 |
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".