Why Resident Duty Hours Regulations Must Address Attending Physicians’ Workload
Bibliographic record
Abstract
For much of the past decade, the duty hours debate has focused primarily on the intended and unintended effects of resident duty hours restrictions on resident well-being and fatigue, patient safety, discontinuity and handoffs, and opportunities for teaching. On the other hand, attending physicians' needs and perspectives generally have been ignored. The authors of this commentary discuss the report by Roshetsky and colleagues in this issue of Academic Medicine, which found that attending physicians reported a greater clinical workload since the implementation of the 2003 Accreditation Council for Graduate Medical Education resident duty hours regulations, and that this increase in attending physicians' workload appears to correlate with decreased time for teaching. However, attending physicians not having time to teach may be but the tip of the iceberg. Other potential implications include faculty members' decreased availability for direct supervision of residents, their reduced emphasis on resident assessment, and their burnout and dissatisfaction, which ultimately also negatively affect patient care. Therefore, deliberate efforts to address attending physicians' workload must receive greater attention if the duty hours movement is to achieve its ultimate goal of improving patient outcomes. In this commentary, the authors advocate that duty hours regulations require programs to seek creative solutions to address these issues, just as the 2011 regulations require programs to address patient handoff training.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.050 | 0.038 |
| Insufficient payload (model declined to judge) | 0.003 | 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".