Impact of Applicants' Characteristics and Geographic Connections to Residency Programs on Preference Signaling Outcomes in the Match

Purpose To assess the impact of applicant and residency program characteristics on preference signaling outcomes in the Match during the first 2 years of implementation across 6 specialties. Method Data were obtained from the Texas Seeking Transparency in Application to Residency survey for applicants applying into otolaryngology during the 2020-2021 and 2021-2022 application cycles and into dermatology, internal medicine (categorical and preliminary year), general surgery, and urology during the 2021-2022 application cycle. The primary outcome was signal yield, defined as the number of interviews at signaled programs divided by the total number of signals sent. Associations with applicant-reported characteristics and geographic connections to residency programs were assessed using Wilcoxon rank sum testing, Spearman's rank correlation testing, and ordinary least squares regression. Results 1,749 applicants with preference signaling data were included from internal medicine (n = 884), general surgery (n = 291), otolaryngology (n = 217), dermatology (n = 147), urology (n = 124), and internal medicine preliminary year (n = 86). On average 60.9% (standard deviation 32.3%) of signals resulted in an interview (signal yield). There was a stepwise increase in signal yield with the percentage of signals sent to programs with a geographic connection (57.3% for no signals vs. 68.9% for 5 signals, P <.01). Signal yield was positively associated with applicant characteristics, such as United States Medical Licensing Exam Step 1 and 2 scores, honors society membership, and number of publications (P <.01). Applicants reporting a lower class rank quartile were significantly more likely to have a higher percentage of their interviews come from signaled programs (P <.01). Conclusions Signal yield is significantly associated with geographic connections to residency programs and applicant competitiveness based on traditional metrics. These findings can inform applicants, programs, and specialties as preference signaling grows.