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Record W3024916919 · doi:10.5489/cuaj.6696

Urology education in the time of COVID-19

2020· article· en· W3024916919 on OpenAlexaffvenue
Maylynn Ding, Yuding Wang, Luis H. Braga, Edward D. Matsumoto

Bibliographic record

VenueCanadian Urological Association Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsMcMaster Children's HospitalMcMaster University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMedicineUrologyVirologyInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0030.003
Open science0.0010.011
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0330.004

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.

Opus teacher head0.025
GPT teacher head0.274
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations14
Published2020
Admission routes2
Has abstractyes

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