Urology education in the time of COVID-19
Bibliographic record
Abstract
We are living in unprecedented times.The emergence of coronavirus disease 2019 (COVID-19) and its rapid spread throughout the world has propelled the medical community into a time of accelerated innovation and change.Although the necessary focus has been on disease containment and delivery of care, the pandemic has had a profound impact on medical education at both the undergraduate and post-graduate levels.Didactic and team-based teaching have long been integral to post-graduate education in any specialty, including urology [1,2].Traditionally, these teaching sessions have taken place in physical spaces with "face to face" interactions.However, during the COVID-19 pandemic, social distancing has been adopted as a recommended preventative strategy pending development of an effective vaccine or treatment [3].By definition, social distancing precludes residents and staff from gathering in lecture halls or classrooms.In response, many institutions have leveraged technology to adapt portions of curricula to online formats.This is enabled by the availability of user-friendly Voice over Internet Protocol (VoIP) software.Since the introduction of Skype, one of the first VoIP software with video chat, in 2003, the technology has matured and become more widespread [4].There are now a variety of software with multi-video conferencing and screensharing capabilities.Some have end-to-end encryption.Many offer a free trial or free tier, with the option to upgrade for additional features.The availability of assessment technologies such as ExamSoft also enable administration of low-stakes tests from home.Perhaps the most significant shift that has occurred in urology education during the COVID-19 pandemic is the rise of massive open online courses (MOOCs).MOOCs are openaccess, online courses aimed at unlimited participation [5,6].They gained traction in 2012 with the emergence of well-funded course providers associated with top universities such as Coursera, Udacity, and EdX.Prior to the COVID-19 pandemic, MOOCs in urology are limited.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".