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.