Urology Residency Training During the Pandemic: A Review of the Current Literature
Bibliographic record
Abstract
Background Since COVID–19 was declared a pandemic on March 11, 2020, health care systems worldwide have been under significant strain. Although urology is not on the frontline of care for patients with COVID-19, every practicing urologist has been affected by the global outbreak. The objective of this review is to evaluate the impact of COVID–19 pandemic on urology residency training programs. Methods We reviewed the current evidence on urology residency training during the COVID-19 pandemic. Relevant databases (MEDLINE, Scopus, Cochrane Library) were searched for articles published to June 2021 that included residents’ or directors’ opinions on their residency training programs during the COVID-19 crisis. Results The literature search identified 72 articles. Fifteen studies including more than 2500 residents were eligible for inclusion in the analysis. During the pandemic, learning activities carried out by urology residents have been extensively affected. Worldwide, operation volume has decreased, especially for procedures in which residents were directly involved. Similarly, there has been a decline in most academic activities, and many studies have reported the negative impact on residents’ mental well-being and lifestyle. On the other hand, the lockdown provided an opportunity to review the current training system and to increase the implementation of tools such as telemedicine and smart-learning surgical skill training programs. Conclusion The COVID-19 pandemic has resulted in significant changes in urology residency programs worldwide, which have had a negative impact on surgical training and academic activities. Residents’ well-being and mental health have also been put at risk. However, this unprecedented situation has also generated new online learning modalities and technological innovations in the field of training in urology.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".