The Relationship between Program and Applicant Characteristics with Applicant Program Signals in the 2022 Residency Recruitment Cycle: Findings from 3 Specialties

Purpose Continuing increases in application volume have driven a national dialogue to reform the residency recruitment process. Program signaling allows applicants to express interest in a program at the preinterview stage with the goal of helping programs identify applicants with more genuine interest in their programs. This study explored the relationship between program signals and program and applicant characteristics. Method Participating dermatology, general surgery, and categorical internal medicine (IM) programs and applicants of the 2022 supplemental ERAS application (SuppApp) were included. Data from the SuppApp, the MyERAS Application for Residency Applicants (MyERAS), and the 2020 GME Track Survey were used. Cohen's h was used to determine effect size, and chi-squared was used to determine statistical significance. Results There was an uneven distribution of signals to programs, with 25% of programs receiving about half of the signals across all 3 specialties. Programs with larger numbers of both residents and applicants received greater numbers of program signals relative to their program density, although this effect was small (h < 0.50, P <.001). No meaningful differences were seen across genders for any specialty. Only Hispanic applicants in IM sent a higher proportion of signals to programs with more underrepresented in medicine residents than White only applicants (40% vs 26%, h = 0.30, P <.001). Across all specialties, there was a small-to-moderate effect for international medical graduate (IMG) applicants sending a larger proportion of signals to programs with more IMG residents (h < 0.80, P <.001). Conclusions This first-year pilot study (i.e., SuppApp) provided initial evidence that supports the feasibility and fairness of program signals in residency selection. As program signals become more common across specialties, future research should continue to evaluate trends in where applicants send signals, and possible relationships between program and application characteristics.