P.177 The Effect of the COVID 19 Pandemic on theTraining of Surgical Residents in Canada. A Survey of Residents and Program Directors.
Bibliographic record
Abstract
Background: The coronavirus 2019 pandemic has led to restructuring of health care systems and has subsequently had secondary effects on medical education. This study examines the impact on training of surgical residents in Canada. Methods: The study consisted of a 25-question survey for residents and a 22-question survey for program directors, which were distributed electronically through program administrators on July 3rd- July 11th, 2020. Questions sought to elicit residents’ current experiences and gain insight into methods by which to enhance future training. Results: 108 residents and 21 program directors, from various surgical specialties across Canada, completed the survey. Operative exposures were reported to be reduced by 25-100% and 39% of residents were redeployed. However, 89% of residents reported accessing academic half days virtually and 57% had additional online modules. Despite lost time, 100% of program directors confirmed that residents did not require training extensions. Concerns regarding training, personal health, employability and fellowships were raised. 55-70% of residents and program directors advocated for alternative educational courses, increasing elective time, utilizing simulation for assessment and flexibility in crediting different training experiences. Conclusions: Canadian surgical residents had a significant reduction in operative experiences during the pandemic. Moving forward, it will be important to find alternative educational experiences.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".