The Effects of the COVID-19 Pandemic on Resident Education and Wellness: A National Survey of Plastic Surgery Residents
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease of 2019 (COVID-19) pandemic has had a profound effect on surgical training programs, reflecting decreases in elective surgical cases and emergency restructuring of clinical teams. The effect of these measures on U.S. plastic surgery resident education and wellness has not been characterized. METHODS: An institutional review board-exempted anonymous survey was developed through expert panel discussion and pilot testing. All current U.S. plastic surgery trainees were invited to complete a cross-sectional 28-question survey in April of 2020. Respondents were queried regarding demographic information, educational experiences, and wellness during the COVID-19 pandemic. RESULTS: A total of 668 residents responded to the survey, corresponding to a 56.1 percent response rate. Sex, training program type, postgraduate year, and region were well represented within the sample. Nearly all trainees (97.1 percent) reported restructuring of their clinical teams. One-sixth of respondents were personally redeployed to assist with the care of COVID-19 patients. A considerable proportion of residents felt that the COVID-19 pandemic had a negative impact on their education (58.1 percent) and wellness (84.8 percent). Residents found virtual curriculum effective and meaningful, and viewed an average of 4.2 lectures weekly. Although most residents did not anticipate a change in career path, some reported negative consequences on job prospects or fellowship. CONCLUSIONS: The COVID-19 pandemic had a considerable impact on U.S. plastic surgery education and wellness. Although reductions in case volume may be temporary, this may represent a loss of critical, supervised clinical experience. Some effects may be positive, such as the development of impactful virtual lectures that allow for cross-institutional curriculum.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".