Adaptations to general surgery resident education in response to COVID-19
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic led to many new provincial public health measures to reallocate resources in response to an impending surge of cases. These necessary decisions had several downstream effects on general surgery training. We surveyed the actions taken by Canadian general surgery training programs in response to the COVID-19 pandemic. METHOD: A mixed-methods survey was sent to all general surgery program directors to assess various domains in surgical education and modifications made because of the pandemic. Responses were quantified as proportions or qualitative narratives describing those changes. RESULTS: Most programs (13/15) recalled residents from planned rotations and redistributed them to rotations considered as core required services, including acute care surgery, trauma surgery and intensive care. Many programs also restructured their acute care surgery models to allow for a group of "reserve" residents to replace trainees who became infected with SARS-CoV-2. In terms of clinical experience, there was a reduction in both clinical and operative exposure among trainees. The reduction in clinical exposure disproportionately affected junior residents, whose involvement in COVID-19 cases was restricted. Formal educational sessions were maintained, but delivered virtually. Many programs instituted a program of increased frequency of communication with trainees. CONCLUSION: Many programs embraced using virtual platforms for teaching. The demonstrated utility of virtual teaching may lead to rethinking how training programs deliver didactic teaching and expand teaching opportunities. However, many programs also perceived a decrease in clinical and procedural exposure, primarily affecting junior residents. More information is needed to quantify the deficit in learning incurred as a result of the pandemic as well as its long-term effects on resident competency.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".