Survey of Canadian urology residency programs: Perception of virtual education during the COVID-19 pandemic and beyond
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has caused many residency programs to pivot from traditional face-to-face to virtual teaching. The objective of this study was to assess the state of virtual education in Canadian urology programs and gauge interest in a national virtual curriculum. METHODS: An electronic 15-item survey was distributed to all 13 Canadian urology programs' directors and administrative assistants for circulation to residents. Data collection took place over six weeks from September to November 2020. A mixed-methods approach was used, including descriptive statistics and an inductive thematic analysis of responses to open-ended questions. RESULTS: Eleven program directors and 32 residents from all four geographic areas (Atlantic, Ontario, Quebec, Western [MB, AB, BC]) responded to the survey. Overall, 95.3% of respondents indicated a role for virtual education in their program during the pandemic. Most respondents (74.4%) believe there is a significant or very significant role for a virtual national urology curriculum. All program directors indicated they are at least somewhat likely to require resident participation in such a curriculum. Most (90.6%) resident respondents indicated they believe such a curriculum will be at least somewhat important to their learning. Commonly described benefits include exposure to subspecialties, expertise at other institutions, and standardization of teaching. Commonly described barriers include difficulty with engagement, time zone differences, and lack of dedicated time for attendance. CONCLUSIONS: During the COVID-19 pandemic, virtual education has become well-integrated in Canadian urology programs. This study highlights interest in the development of a national virtual urology curriculum and puts forth some key considerations to ensure its success.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".