Prioritizing resident and patient safety while maintaining educational value: emergency restructuring of a Canadian surgical residency program during COVID19
Bibliographic record
Abstract
Summary: Surgical programs are facing major and fluctuating changes to the resident workforce because of decreased elective volumes and high exposure risk during the coronavirus disease 2019 pandemic. Rapid restructuring of a residency program to protect its workforce while maintaining educational value is imperative. We describe the experience of the Division of General Surgery at the University of Ottawa in Ontario, Canada. The residency program was restructured to feature alternating "on" and "off" weeks, maintaining a healthy resident cohort in case of exposure. Teams were restructured and subdivided to maximize physical distancing and minimize resident exposure to pathogens. Educational initiatives doubled, with virtual sessions targeting every resident year and incorporating intraoperative teaching. The divisional research day and oral exams proceeded uninterrupted, virtually. A small leadership team enabled fast and flexible restructuring of a system for patient care while prioritizing resident safety and maintaining a commitment to resident education in a pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".