Coronavirus Disease 2019 and the Yale Response: A Semistructured Interview Study on Plastic Surgery Resident Education and Departmental Adaptation to the Lockdown
Bibliographic record
Abstract
BACKGROUND: Sooner-than-expected progression to statewide lockdown at the height of the coronavirus disease 2019 pandemic left minimal time for medical specialty boards, including The American Board of Plastic Surgery, to issue guidance for their respective programs. As a result, programs were tasked with developing creative alternatives to their standard resident curricula and department schedules. OBJECTIVE: To capture attending and resident experience of the coronavirus disease 2019 lockdown in narrative form and to understand what specific changes enacted to maintain adequate education should be considered for continuation after the pandemic's conclusion. METHODS: Qualitative, semistructured interviews of residents, fellows, and faculty of the Section of Plastic and Reconstructive Surgery during 2019 to 2020 academic year were conducted on the following topics: (1) general reflection on lockdown, (2) resident maintenance of daily logs, (3) multi-institutional collaborative lectures, (4) modified didactic curriculum, (5) virtual 3-dimensional craniofacial planning sessions, (6) maintenance of department camaraderie, and (7) effect on preparation to become a surgeon. RESULTS: Twenty interviews (response rate 77%) were conducted between October 2020 and February 2021. Of residents, 100% felt observing the craniofacial planning sessions was beneficial, with many explicitly noting it provided a unique perspective into the surgeon's thought process behind planned manipulations, to which they usually are not privy. Of residents, 100% felt confident at the time of the interview that the lockdown would have no lasting effects on their preparation to become a surgeon. CONCLUSIONS: Rapid changes enacted at Yale enabled resident training to advance, and documentation of the success of these changes can inform future curriculum design.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".