The Impact of COVID-19 on Surgical Education: A Monocentric Survey of Residents Training in Surgical Specialties
Bibliographic record
Abstract
Abstract BACKGROUND: This study aims to identify the effects of the COVID-19 on surgical resident education at University Laval during first wave of the pandemic of spring 2020.METHODS: We conducted a web-based survey study to all residents training within one of the ten surgical specialties at University Laval, Quebec City. The survey focused on clinical teaching hours, appreciation of activities and novelties experienced and the impacts of virtual teaching. Descriptive statistical analysis was performed to summarize the data.RESULTS: There were 48 surgical residents who responded to our survey. There were participants from ten specialties. During the pandemic the mean number of weekly teaching hours dropped from 4.31 to 3.69 hours. The most appreciated activity was teaching sessions lead by a staff surgeon. More than 80% of respondents reported having partaken in other activities at some time during an online class while over 70% expressed retaining less when material was taught online rather than in person.CONCLUSION: Our survey provides insight for surgical programs to improve resident teaching and illustrates the necessity to optimize teaching schedules rapidly in times of pandemic. Even though the appreciation of virtual learning seems unsatisfactory by certain residents, trainees still require and appreciate teaching by their mentors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".