Abstract

To evaluate if there are gender differences in the letters of recommendation (LOR) written for residents applying to gynecology surgical fellowships. Retrospective study. Single, academic institution. LOR for applicants to gynecology oncology, urogynecology, and minimally invasive gynecology fellowships during the 2019-2020 application cycle. Not applicable. We analyzed the linguistic content of the letters for the presence of 4 summary variables and 21-word categories based on previous studies using a validated computerized text analysis software. We used multivariable analysis using linear mixed models to compare letter linguistic characteristics by applicant gender. We performed qualitative content on letters and compared the frequency of code themes by gender. The mixed-method design was planned to analyze domains not captured in text analysis. Among 680 letters written for 186 applicants, 124(18.2%) were written for men and 556 (81.8%) were written for women. There were no differences in the least square mean (SE) word counts for LOR written for men and women applicants, 465(20.0) v. 458(9.4) words, p=.74. On multivariable analysis, LOR written for men had higher authentic tone and more risk words (p=.005 and p=.03, respectively). LOR written for women contained more communal (relationship-oriented) words (p=.006). The qualitative analysis demonstrated ability, interpersonal traits, surgical skills, and research were the most mentioned themes. Comments about compassion/empathy, leadership potential, teaching, interpersonal skills, and patient rapport were found more in letters for men. More doubt raisers (words that raise doubt or concern) were present in letters for men, but letters for both genders had similar levels of negative criticism. In contrast, comments on ability, being "drama-free", and self-awareness were found more in letters for women. There were gender differences in LOR written for obstetrics and gynecology surgical sub-specialty fellowship applicants indicating presence of gender bias.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call