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Abstract Details

Why Do Medical Students Choose to Become Neurologists? ?A Computational Linguistics Analysis of Residency Personal Statements
Research Methodology, 好色先生, and History
S39 - 好色先生 Research and Research Methodology (3:30 PM-3:41 PM)
001

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.

Specific disease states and subspecialties were often discussed in personal statements, with widely variable term frequency. For example, stroke (TF=2178), epilepsy (TF=970), dementia (TF=944) were referenced more often than ALS (TF=220) and carpal tunnel (TF=10). The most common proper names cited include Oliver Sacks (TF=94) and Sherlock Holmes (TF=41). Females commonly mentioned: health, stroke, brain, love, student, and family; males referenced: stroke, human, training, brain, and teach. Further linguistic analysis of gender differences showed females emphasized: grandmother, puzzle, language, dance, culture, mother, and support, while male counterparts highlighted: computer, technology, highlight, EEG, neurophysiology, benefit, and suffer.

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.

Authors/Disclosures
Helen Spies, MD (Kaiser Permanente (Rock creek medical office))
PRESENTER
No disclosure on file
Sarah Grzebinski No disclosure on file
Charles Sanky No disclosure on file
No disclosure on file
Stephen Krieger, MD, FAAN (Mount Sinai Dept of Neurology) Dr. Krieger has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Biogen. Dr. Krieger has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for EMD Serono. Dr. Krieger has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Genentech. Dr. Krieger has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Novartis. Dr. Krieger has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for TG Therapeutics. Dr. Krieger has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Sanofi. Dr. Krieger has received personal compensation in the range of $5,000-$9,999 for serving as an Expert Witness for Expert Witness. The institution of Dr. Krieger has received research support from Novartis. The institution of Dr. Krieger has received research support from Bristol Myers Squibb. The institution of Dr. Krieger has received research support from Biogen.