Graduating resident and fellow readiness for general urologic practice during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: Our goal was to compare the perceived readiness of graduating urologic residents and fellows to program directors (PDs) in U.S.-based postgraduate training programs. Additionally, we set out to assess the impact of COVID-19 on postgraduation plans to pursue fellowship training. METHODS: Graduating residents, fellows, and PDs of accredited residency/fellowship programs in the U.S. were surveyed. The ranked preparedness of trainees to perform common urologic procedures was measured using a Likert scale from 1 (not comfortable) to 5 (fully proficient). The impact of COVID-19 was measured using a three-point Likert scale. Chi-squared and Kruskal-Wallis analyses were used to compare the groups. RESULTS: From 93 responders, 21 were residents, 19 were fellows, 24 were residency PDs, and 29 were fellowship PDs. The median levels of comfort for trans-urethral resection of the prostate, hydrocelectomy, vasectomy, and urethral sling were at or above (≥3) moderate for both PDs and trainees. PDs were more likely to report underperformance for hypospadias repair (60% vs. 39%), penile prosthesis implantation (39% vs. 26%), and orthotopic neobladder formation (57% vs. 18%) than the trainees. Fifty-three (57.0%) of the surveyors felt that COVID-19 did not impact the trainees' comfort in performing general urologic procedures. COVID-19 influenced trainees' decision to pursue a fellowship or opt to practice as general urologists (p=0.002). CONCLUSIONS: Our study suggests there may be a self-reported discrepancy between graduating trainees and their PDs regarding trainees' comfort levels performing general urologic procedures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".