Bibliographic record
Abstract
It was 7 AM, and we were clamoring onto the back of an open pickup truck. We were two medical students, spending the summer in Pierre Payen, a small town on the west coast of Haiti. It had not taken us long to become accustomed to the stifling heat, the Haitian Creole, and the dark nights filled with barking dogs and confused roosters. Traveling into town that morning, we stopped to pick up Dr. Joseph, a young, confident doctor who had graciously taken us under his wing. That day, we were heading into the mountains to a rural village, where international donors had established a small clinic. Dr. Joseph made the two-and-a-half-hour drive to this village once a week to see patients. Heavy rains the night before had turned the “road” into a dense muddy swamp. Along the way, we stopped outside modest homes to pick up our colleagues for the day—two nurses, a pharmacy technician, and two groundskeepers. The morning clinic was quieter than usual—The rains had deterred patients who normally would have had no qualms about a two-hour walk from their villages to see the doctor. As the clinic rooms filled, we moved between patients, watching Dr. Joseph uncover subtle physical findings with no more than his expertise and a stethoscope. Through our care passed febrile babies to be tested for typhoid and malaria, pregnant women with urinary tract infections, and elderly men with arthritis. They all left the clinic that day with reassurance, basic medications, and instructions to return should their condition not improve. By mid-afternoon, we were tired and hungry, but we watched as Dr. Joseph continued to approach each new patient with tireless empathy, curiosity, and equanimity. Not once did we hear him complain about his working environment or speak negatively of a patient or a colleague. During our ride back to town that evening, we listened to Dr. Joseph talk about what awaited him upon his return. In watching him perform expertly and compassionately as a general practitioner in a small rural clinic, we had forgotten that he was returning to town to continue his work as a general surgeon in an operating room. Humbled by the breadth of his medical knowledge and inspired by the compassionate care he provided, we reflected quietly as we rumbled along the muddy road. We were sweaty, tired, and a little carsick, but watching him work had rekindled the same fire and excitement about medicine that we felt only a few years ago when we received our acceptance letters to medical school. We had traveled to Haiti in search of an exciting clinical experience. What we learned from Dr. Joseph, though, extended far beyond the clinical symptoms of typhoid fever and malaria. He reminded us of the core values that we needed to practice good medicine. Throughout the remainder of our training, we will attempt to model the way he treated each patient with compassion, despite challenging surroundings. We will resist the urge to complain about minor delays or gaps in our health care system, instead focusing on its strengths. Finally, we will remember that it is a great privilege to be a medical student. As we proceed with our training, we can only hope that the lessons we learned during our time in Haiti—how to stay grounded and the importance of empathy, compassion, and optimism—will stay with us in the years ahead. Authors’ note: The name in this essay has been changed to protect the identity of the physician. Emily C. Wilson, MSc, and André R. Maddison, MSc
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.019 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.070 | 0.036 |
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".