Bibliographic record
Abstract
To the Editor: In proposing residency wellness programs as a way to reduce burnout, Dr. Lefebvre1 pushes this issue into the limelight. However, there is a second issue in many of our institutions that must be dealt with at the same time—faculty role modeling. Residents will look first to their supervisors and mentors during times of distress to determine how to react. I recognize from my current position as an academic pediatrician that we faculty are inconsistent role models of physician wellness. For wellness programs to work, I propose that we as academic faculty must show that we can follow the adage “Physician, heal thyself.” We have to promote the idea that our own self-care affects our trainees and ultimately patients. In the rest of this letter, I offer some suggestions of how to do this. In a survey of Quebec physicians, many respondents alluded to a culture of “workaholism” among physicians and a stigma attached to their seeking professional help.2 To remove this stigma, I suggest that wellness programs for both residents and faculty emphasize how such programs contribute to an environment of patient safety. An ideal way to do this is to publicize that mindfulness training in wellness programs reduces physician errors. In addition, we need to demonstrate repeatedly that an atmosphere of physician wellness leads to improved quality of patient care. Finally, a recent cross-sectional survey by Shanafelt et al3 emphasizes that time spent in a meaningful activity has a strong inverse relationship with risk of burnout. Perhaps in emphasizing and implementing self-care, we will become able to rediscover the meaning and satisfaction in our daily work. I applaud Dr. Lefebvre’s message and work. I recommend that all of us in academic medicine re-examine how to generate buy-in for wellness programs by physicians at all levels. Perhaps in the current climate of acceptance of quality improvement, those of us who are physicians can retrain ourselves to accept our own well-being as a part of effective patient service. Sheela Rao, MD Assistant professor of pediatrics, Keck School of Medicine at the University of Southern California, Children’s Hospital Los Angeles, Los Angeles, California; [email protected]
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 | 0.010 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.023 | 0.025 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".