Objective: To investigate the perception and value of virtual open houses for urology applicants in the COVID-19 era, since students can no longer attend subinternships and all interviews will be conducted virtually.
Methods: A Twitter survey was sent to 230 likely urology applicants connected through the UroResidency platform. It asked about the relative value of components of the virtual open house and areas for suggested improvement.
Results: Seventy responded. Most potential applicants valued virtual open houses that discussed strengths and weaknesses of the program, had time to interact directly with the faculty, and included resident led presentations or discussions. Most agreed programs needed to have more direct time with residents to better understand the culture of the program.
Conclusion: In this first virtual interview season for urology, likely applicants generally engage in virtual open houses and strongly prefer time to interact directly with residents to assess the program culture.
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http://dx.doi.org/10.1016/j.urology.2020.08.077 | DOI Listing |
Am J Surg
January 2025
Association of Women Surgeons, Chicago, IL, USA.
Background: The COVID-19 pandemic brought additional challenges to the 2020-2021 application cycle. The objective of this study was to explore how such challenges altered the perceptions/motivations/concerns of applicants to surgical fields, particularly those self-identifying as women underrepresented-in-medicine (UiM).
Methods: An anonymous mixed-methods survey was electronically distributed to all medical student members of the Association of Women Surgeons between 10/1/2020-12/31/2020.
Curr Urol Rep
January 2025
Department of Urology, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Purpose Of Review: The evaluation and selection process of similarly qualified applicants for surgical residency positions in the United States (US) is challenging. Technical aptitude assessments may provide an opportunity to improve the selection process by offering insight into a candidate's technical skills. The use of these assessments prompts consideration of the degree to which technical aptitude in surgery is innate versus acquired.
View Article and Find Full Text PDFPLoS One
January 2025
Carrera de Medicina Humana, Universidad Científica del Sur, Lima, Perú.
Objective: To investigate gender disparities in applications and admissions to the medical residency programs in Peru, focusing on differences in application and admission proportions between male and female.
Methods: We conducted a cross-sectional study to assess the proportions of female applicants and admissions to medical residency programs in Peru from 2016 to 2023. Bayesian multilevel linear models were employed, incorporating random intercepts and slopes by specialty to account for variability across specialties.
Urology
December 2024
University of Kansas, Department of Urology, Kansas City, KS.
Objective: To provide a cross-sectional view of the current opinions surrounding the urology match by analyzing data from the annual Society of Academic Urologists Program Director Surveys conducted between 2022 and 2024.
Methods: Data collected through surveys distributed to all urology program directors (PD) consisting of questions covering program demographics, applicant selection criteria, preference signals (PS), virtual interviews (VI), and other relevant topics.
Results: 89, 90, and 89 PD participated in the surveys for the years 2022, 2023, and 2024, respectively.
BMJ Open
December 2024
School of Nursing and Health Management, Shanghai University of Medicine and Health Sciences, Shanghai, People's Republic of China
Objective: The aim of this study is to develop, implement the precise reservation path (PRP) and investigate its prediction function for scheduling shunting patients for specialist appointment registration in Shanghai, China.
Design: The PRP system was built on the hospital's existing information system, integrated with WeChat (WeCom) for user convenience. The outcome analysis employed a mixed-methods approach, integrating quantitative analysis with statistical and machine learning techniques, including multivariate logistic regression, random forest (RF) and artificial neural network (ANN) analysis.
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