Bibliographic record
Abstract
To the Editor: My pager goes off for the sixth time in the last 15 minutes, and by now I haven’t slept for 21 hours. The hairs on the back of my neck literally stand on end. My hands unconsciously fumble to press a button on the pager dangling from the drawstring of my oversized scrubs—anything to make the beeping stop. I return all six pages, yielding to a request to write a prescription (at 4 am!), answering a question from a nurse regarding new bruising on a patient’s penis (I’m not joking), and seeing the two new patients who were awaiting me in the ER. With the hairs on my neck gradually settling back to a resting position, I tuck my pen into my pocket and head for the meditation room. Excuse me? Of course I didn’t go to the meditation room. I immediately went to see the sick patient on the ward, and then to the ER where I did two consults. But what if there were a meditation room? Would it have done me any good? Unfortunately, the evidence is scant. Only a few small studies have attempted to bring meditation to the attention of the medical education community. In 2015, a group at Duke University looked at the impact of mindfulness training on residents’ stress levels and their ability to cope with the cognitive demands of residency.1 In the same year, another group tried to implement a meditation program for residents on call.2 Results from these underpowered studies showed no clear benefit; stress tallies remained fairly constant despite mindfulness training. Yet we know from the medical literature that mindfulness provides a cornucopia of health benefits, and it is even as effective as selective serotonin reuptake inhibitors in the treatment of mild clinical depression.3 We are at a turning point in the history of medical education, where for the first time trainee resilience has become a major theme in curriculum development. Mindfulness meditation deserves appropriately powered research projects to answer questions about its utility in medical education. Conor LavelleResident physician, Department of Emergency Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada; [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 | 0.041 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.031 | 0.052 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".