Applicants to neurology residencies submit personal statements via the Electronic Residency Application System (ERAS). Textual analysis of personal statements has been performed in internal medicine, but never before in neurology. By using computational linguistics software, key words can be assessed to study motivations, expectations, and themes present amongst neurology applicants.

We sought to understand medical students’ motivations for choosing neurology, specifically investigating how applicants of different backgrounds conceptualize this field. This information can be used to foster further interest in neurology and develop educational programs to help future neurologists find gratifying career paths.

2,405 personal statements submitted over five years to our institution were de-identified and compiled into a database for evaluation through two computational linguistics software programs. We searched for term frequency (TF), utilized term weighting models, and calculated Term Frequency-Inverse Document Frequency (TF-IDF), to evaluate statistical differences among subgroups.

This first computational linguistic analysis of neurology personal statements provides an initial characterization of medical student motivations, highlighting interest in specific disease states and subspecialties. In gender subgroup analysis, there was large variation in language used by males and females. Ongoing subgroup and thematic analyses will inform neuro-educational strategies and enhance recruitment to our field.