Bibliographic record
Abstract
In Reply: Advances in medical technology, pharmacotherapy, and knowledge translation have created an unprecedented era of rapidly changing, evidence-based medical care. A step behind this outward advancement in medical practice is a new introspective realization that physician health is a key factor in quality patient care. Dr. Rao correctly identifies the need for wellness role models and an atmosphere of physician wellness at the faculty level. Indeed, recent evidence demonstrates that faculty members are not immune to the alarming prevalence of burnout in medicine.1 Furthermore, it has been argued that “students are quick to notice the negative emotions of hostility, indifference, frustration, and impatience that supervisors express—as well as the positive emotions of caring, compassion, and kindness shown toward patients.”2 As such, a culture of wellness at the faculty level is critical for physician health, trainee education, and, ultimately, good medical practice. However, culture is a difficult thing to change. Dr. Rao highlights a pervasive culture of “workaholism” among physicians, a quality that is dear to some and repulsive to others. In some cases, cultures of “workaholism” may clash with those promoting wellness and work–life balance. This conflict may represent a generation gap in the perceptions of medical professionalism.3 Yet, mounting evidence continues to correlate personal wellness with physician health and improved patient care. I join Dr. Rao in urging residents and faculty alike to understand the importance of self-care, work satisfaction, and personal health in their professional role as physicians. Dennis C. Lefebvre, MD, PhD Emergency medicine physician, Royal Alexandra Hospital, Edmonton, Alberta, Canada; [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 | 0.011 | 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 | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.039 | 0.059 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".