Bibliographic record
Abstract
To the Editor: “Residents are miserable,” I was told uniformly by peers and residents when I was in medical school. Instead, I found that I was happier as a resident than I ever was as a medical student, contributing more to the care of my patients and finally being able to concentrate on the field of medicine that I found most inspiring. The way we talk about resident wellness is primarily through a lens of resilience (against the hardship of residency) and of work–life balance (which suggests that we are happy only when we optimize our time away from work). I was recently asked to give a talk to senior medical residents entitled “Senior Burnout,” which struck me as a very negative title on a topic that I do not believe needs to be negative. Burnout certainly is a significant problem, and sharing stories on the topic helps to normalize a condition that has approximately 50% prevalence among residents,1 but by failing to highlight those aspects of our profession that inspire us, we ignore the very aspects that stimulate us to return to work after a bout of burnout. We also risk priming trainees to anticipate burnout, instilling a dread of residency that may create a “nocebo effect.” I propose that we integrate “inspiration repletion” into our arsenal of tools for maintaining wellness in residency. Appreciative inquiry (AI) may be one way to achieve this. AI is a practice that highlights the positive factors that motivate individuals to do something well, galvanizing change in individuals by identifying areas of strength and personal value.2 Nurses have used AI extensively to improve morale3 and care,4 but AI is scarcely mentioned in the literature on resident wellness. We focus on reducing resident duty hours, but a loss of inspiration at work plays a significant role in burnout. In my first year of residency, I unexpectedly experienced burnout on a “light” rotation because I felt I was not making a positive impact. As chief medical resident, I try to normalize the hardships every resident faces, but I also tell my trainees stories of patients I connected to, diagnoses I made, and concepts that excite me. Wellness depends on remembering what we wrote in our personal letter in our application to residency, and carrying it with us every day. Ariel Lefkowitz, MD, CMFourth-year resident and chief medical resident, Mount Sinai Hospital, Toronto, Ontario, Canada; [email protected]; ORCID: http://orcid.org/0000-0002-0773-1346.
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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".