Half As Sad: A Plea for Narrative Medicine in Pediatric Residency Training
Bibliographic record
Abstract
During my third year of general pediatrics residency training, a patient well known to me and my fellow residents died unexpectedly. The child was an extremely bright and mature 8-year-old who had spent much of the last year of her life in our hospital being treated for cancer. Seemingly overnight, she became “too” sick (ICU sick, intubated and ventilated, on dialysis sick). She began to have intractable seizures, and with her family by her side, she died. The next morning, a colleague in his first year of residency asked how I was. I said that it had been a difficult week and I felt profoundly sad about the death of our patient. A look of relief came over his face. He told me that he was feeling despair. This was the first child he had cared for that had died. He had never felt such sadness before and felt guilty for feeling sad. He said that in medical school he had been told by a senior physician that it was the professional responsibility of a physician to “never feel more than 50% as sad as a patient’s family.” Faced with the reality of the death of a child, my colleague was left feeling confused, distressed, and disenfranchised from his own emotional response. “You put on the waterworks and it makes the parents feel better,” my genetics attending said. She seemed proud to have arrived at this simple remedy. She had just finished counseling a couple whose child had been diagnosed with a lethal genetic condition. Were the tears an act simulating empathy for … Address correspondence to Malgorzata Nowaczyk, MD, FRCPC, FCCMG, FACMG, Department of Pediatrics, McMaster Children’s Hospital, 1200 Main St W, Hamilton, ON L8N 3Z5, Canada. E-mail: nowaczyk{at}hhsc.ca
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.013 | 0.040 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".