Twitter Engagement of Medical Students Applying to Urology Residency During COVID-19: A Mixed Methods Study

OBJECTIVE To determine how medical students' Twitter engagement impacted the urology residency match and overall student perception of Twitter. METHODS We utilized a mixed methods approach with (1) Twitter metrics data, (2) online student surveys, and (3) qualitative semi-structured interviews. Interviews were evaluated with iterative thematic content analysis, while quantitative data were analyzed with descriptive statistics, and univariate analyses. RESULTS We identified 245 Twitter accounts of Urology residency applicants from the 2021 cycle. Matched students were more likely to have a Twitter account (59% matched vs 28% unmatched, P = .002) and account creation increased following the COVID-19 pandemic announcement. Matched students' profiles were associated with more followers, bios mentioning Urology, home Urology residency programs, and no international flags and/or references. The online survey had a 16% response rate. A majority reported utilizing Twitter for residency information (95%), wanting to continue Twitter throughout residency (67%), and feeling uncomfortable tweeting about racial, political, or diversity issues (64%). Nine interviews revealed 4 themes: Twitter's opportunities for networking, Twitter's role in the application process, the burden of social media use, and professionalism. CONCLUSION Students applying to Urology residency increasingly utilized Twitter during the COVID-19 pandemic and having a Twitter account was associated with matching. While Twitter may not be necessary to succeed in the match and can pose an additional time burden, applicants view it as an opportunity for learning, networking, and personal branding. (C) 2022 Elsevier Inc.

Automated summary

This study aimed to determine the impact of medical students' Twitter engagement on the urology residency match and their overall perception of Twitter. The study used a mixed methods approach, including Twitter metrics data, online student surveys, and qualitative semi-structured interviews.