Bibliographic record
Abstract
Dec 25, 2013—Christmas day evening, snow is silently falling outside my window, decorating the bare branches as the precious daylight slowly recedes, bringing this holiday to a climactic close for most people in the small city. I curl up in my tiny one-bedroom apartment, sigh as I sip the last mouthful of lukewarm tea after waking from a long slumber, embracing the looseness of my limbs and the fogginess of my mind that usually accompanied days like these. It is just another postcall day in the life of a clerk. It was a psychiatry call on that Christmas Eve. I felt bitter and discontented with being trapped in the emergency department as I worked through the endless number of admissions. I met a recently engaged young man, accompanied by his fiancé with newly diagnosed bipolar disorder, who was completely oblivious to the distress of his significant other and what this diagnosis might mean for their future. I assessed a man with chronic schizophrenia who had been sent from the shelter for acting violently but told me all he wanted was some sandwiches and sleep. I cared for a middle-aged woman whose life had fallen apart after a nasty divorce and who had stopped caring for herself. During the darkest and toughest hour of these nights, I am plagued with self-doubt, questioning whether I am capable of doing anything to help my patients. I count the minutes until the end of a call when I will be able to escape a place that is filled with so much pain and misery, all the while feeling my life forces draining out of me as I mechanically take in the patients’ stories, remaining unsure that I have accomplished anything. But as dawn approaches—as the grumpy patients I saw the night before who were just as exhausted as I begin to smile at me when I visit them in the morning—it all seemed worth it. I have survived another call; I have grown this much more.The Persistence of Clerkship MemoryThe postcall days often make me feel that time no longer progresses in a linear fashion, and the world becomes somewhat distorted and surreal. My long, deep slumbers are interrupted by the faces of those whose lives I have touched in significant and insignificant ways during these exhausting nights. I know that it is because of them that I am able to carry on learning, carry on doing what I do. Inspired by the surrealist masterpiece of Salvador Dali, the artwork on the cover of this issue represents the nature of time during challenging clerkships. Our clerkships are measured by moments of contaminating the sterile field in the operating room in the dead of the night, napping in a textbook in the corner of the emergency room, trembling while taking that first suture, and, of course, surviving every one of those dreaded call nights—all while time ruthlessly ticks on, as we grow and mature from clueless students to competent physicians. Joyce Chenzi Zhang, MD J.C. Zhang was a third-year student, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada, at the time this was written; e-mail: [email protected]
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.738 | 0.509 |
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".