Promoting Wellness in Orthopaedic Surgery Residency
Bibliographic record
Abstract
The challenges associated with surgical residency have been well documented and described. Despite implementation of work-hour restrictions, residency remains a balancing act between patient care, surgical and clinical education, medical record documentation, and research endeavors. The added challenge of balancing these duties with life outside of the hospital further complicates the situation. Multiple studies have documented the stress associated with residency training, highlighting the prevalence of emotional exhaustion, detachment from people both in and out of the hospital, and a concerning rate of clinical depression among residents. Moreover, this emotional exhaustion has been shown to directly correlate with residents' clinical performance and abilities to carry out academic duties. More recently, feelings of isolation and detachment have been exacerbated by the necessity of COVID-19 precautions and change in clinical duties. The Accreditation for Graduate Medical Education (ACGM) now requires all residencies to include programming that focuses on resident well-being. Programs have implemented various strategies in an effort to help their trainees cope with the stress associated with residency and beyond. Despite the variety in approaches of programs, these initiatives have the similar objective of teaching resilience and the ability to navigate stressors in a healthy and effective manner. The programming can also serve to promote resident interaction and develop camaraderie in an effort to minimize feelings of emotional exhaustion and isolation. In this article, we discuss the importance of sustained physician wellness and describe approaches from various professions that can be implemented into the wellness curriculum for residency programs across the country. By promoting a culture of wellness and incorporating effective wellness programming, our aim is that residents will be able to succeed not only in their training but also in their personal lives and professional endeavors after graduation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".