Gender Differences in Letters of Recommendations and Personal Statements for Neurotology Fellowship over 10 Years: A Deep Learning Linguistic Analysis

Abstract

Objective Personal statements (PSs) and letters of recommendation (LORs) are critical components of the neurotology fellowship application process but can be subject to implicit biases. This study evaluated general and deep learning linguistic differences between the applicant genders over a 10-year span. Study Design Retrospective cohort. Setting Two institutions. Main Outcome Measures PSs and LORs were collected from 2014 to 2023 from two institutions. The Valence Aware Dictionary and Sentiment Reasoner (VADER) natural language processing (NLP) package was used to compare the positive or negative sentiment in LORs and PSs. Next, the deep learning tool, Empath, categorized the text into scores, and Wilcoxon rank sum tests were performed for comparisons between applicant gender. Results Among 177 applicants over 10 years, 120 were males and 57 were females. There were no differences in word count or VADER sentiment scores between genders for both LORs and PSs. However, among Empath sentiment categories, male applicants had more words of trust (p = 0.03) and leadership (p = 0.002) in LORs. Temporally, the trends show a consistently higher VADER sentiment and Empath “trust” and “leader” in male LORs from 2014 to 2019, after which there was no statistical significance in sentiment scores between genders, and females even have higher scores of trust and leadership in 2023. Conclusions Linguistic content overall favored male applicants because they were more frequently described as trustworthy and leaders. However, the temporal analysis of linguistic differences between male and female applicants found an encouraging trend suggesting a reduction of gender bias in recent years, mirroring an increased composition of women in neurotology over time.