Bibliographic record
Abstract
Residency training is a challenging period in a physician's career owing to a multitude of stressors perhaps not previously encountered. In some cases, these stressors may culminate in a state of burnout. In response, much has been written about the issues of personal wellness during residency training. Recently, duty hours reform has been the major focus of addressing resident wellness; however, this intervention has established little benefit and has created unintended negative consequences. Alternatively, an emerging solution may be the implementation of resident wellness programs into residency training. Such programs are defined by a combination of active and passive initiatives targeting the various domains of physical, mental, social, and intellectual wellness. In contrast to duty hours reform, resident wellness programs are generally free from controversy and have been shown to improve resident wellness and enhance empathy.This article highlights the salient causes of burnout as it applies to present-day resident physicians and the patient care they provide. Moreover, in the wake of the controversy surrounding duty hours reform, a novel approach to resident wellness involving structured resident wellness programs is discussed. Specifically included are the fundamental components of a wellness program, the advantages held over duty hours reform, methods to evaluate program efficacy, and the current evidence to support these initiatives. Formal wellness curricula, including an evaluative process, should be an integral component of physician training. These programs represent a new hope in the solution to the long-debated issue of burnout and wellness during residency training.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.176 | 0.048 |
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".