Impact of virtual education on urology education during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The coronavirus pandemic changed the way urology education was delivered. At Dalhousie University, third-year medical students (clinical clerks) undergoing a two-week urology elective had the historic in-person seminars changed to virtual seminars with pre-recorded lectures by staff. The academic abilities of the clerks were measured via a standardized written exam and clinical score assigned by a staff preceptor. This study aimed to measure the impact of virtual education on student performance. METHODS: Clerk clinical and exam scores have been recorded since 2014. The in-person seminar (pre-COVID) cohort included students from January 2014 to March 2020 (n=109), while the virtual seminar (post-COVID) cohort was recorded from April 2020 to August 2022 (n=60). Independent t-test was used to compare clinical, exam, and total scores between the pre-COVID student groups after ensuring normality. RESULTS: Students in the virtual seminar group (mean ± standard deviation 88.69±6.50%) performed better than the in-person seminar student groups (86.32±6.33%) in terms of clinical performance gradings (p=0.02). There was no statistically significant difference in written exam scores between the in-person seminar and virtual seminar cohorts (77.34±10.94% vs. 78.75±11.37%, p=0.43). Cumulative scores were higher for virtual seminar student groups vs. in-person seminar cohort (86.70±5.40% vs. 84.52±5.44%, p=0.01). CONCLUSIONS: Clinical clerks undergoing virtual education during a two-week urology elective had improved clinical and cumulative score performances when compared to the in-personal seminar cohort; virtual seminars did not statistically negatively impact exam scores.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".