Impact of the COVID-19 Pandemic on the Education of Plastic Surgery Trainees in the United States
Bibliographic record
Abstract
The current COVID-19 pandemic has vastly impacted the health care system in the United States, and it is continuing to dictate its unprecedented influence on the education systems, especially the residency and fellowship training programs. The impact of COVID-19 on these training programs has not been uniform across the board, with plastic surgery residency and fellowship programs among the hardest hit specialties. Implementation of social distancing regulations has affected departmental educational activities, including preoperative, morbidity and mortality conferences and journal clubs; operating room educational activities; as well as the overall education of plastic surgery trainees in the United States. Almost all elective and semielective surgeries across the United States were suspended for a few months during the COVID-19 pandemic; this constitutes a significant portion of plastic surgery cases. Considering the current staged reopening policies, it may be a long time, if ever, before restrictions are completely lifted. In this paper, we review the multidimensional impact of the current COVID-19 pandemic on the training programs of plastic surgery residents and fellows in the United States and worldwide, along with some potential solutions on how to address existing challenges.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".