A Systematic Review of Wellness in Plastic Surgery Training
Bibliographic record
Abstract
BACKGROUND: Physician and resident wellness has been increasingly emphasized as a means of improving patient outcomes and preventing physician burnout. Few studies have been performed with a focus on wellness in plastic surgery training. OBJECTIVES: The aim of this study was to systematically review what literature exists on the topic of wellness in plastic surgery training and critically appraise it. METHODS: A PubMed search was performed to identify journal articles related to wellness in plastic surgery residency. Seventeen studies (6 cohort and 11 cross-sectional) met inclusion criteria and were appraised with the Newcastle-Ottawa Quality Assessment Scale (NOQAS) to determine the quality of the studies based on selection, comparability, and outcome metrics. RESULTS: Critical assessment showed that the studies were highly variable in focus. Overall, the quality of the data was low, with an average NOQAS score of 4.1. Only 2 studies focused on plastic surgery residents, examining work hours and social wellness, respectively; they were awarded NOQAS scores of 3 and 4 out of 10. CONCLUSIONS: The results of this systematic review suggest that little research has been devoted to wellness in surgery training, especially in regard to plastic surgery residents, and what research that has been performed is of relatively low quality. The available research suggests a relatively high prevalence of burnout among plastic surgery residents. Evidence suggests some organization-level interventions to improve trainee wellness. Because outcomes-based data on the effects of such interventions are particularly lacking, further investigation is warranted.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".