From Columbia University (S.G., J.O.); and Icahn School of Medicine at Mount Sinai (H.C., C.S., S.K.), New York, NY.
Neurology. 2021 Jul 6;97(1):e103-e108. doi: 10.1212/WNL.0000000000011753. Epub 2021 Mar 3.
To understand medical students' motivations for choosing neurology and how applicants conceptualize the field, as this information can be used to enhance interest in neurology and develop educational programs to help identify, support, and recruit future neurologists.
Applicants to neurology residencies submit personal statements describing themselves and their motivations. Textual analysis of personal statements has been performed in internal medicine and general surgery, but never before in neurology. We hypothesized that specific words and themes would be mentioned in residency personal statements with high frequencies indicating students' motivations.
We used computational linguistics software to assess key words and thereby study motivations, expectations, and themes present among neurology applicants. A total of 2,405 personal statements submitted over 5 years to our institution were de-identified and compiled into a database for evaluation through 3 computational linguistics software programs. We performed calculations of term frequencies (TF) and TF-inverse document frequencies and performed K-means clustering to identify unique words and common themes.
Specific disease states were discussed. For example, stroke (TF 2,178), epilepsy (TF 970), and dementia (TF 944) were referenced more often than amyotrophic lateral sclerosis (TF 220) and carpal tunnel (TF 10). The most common proper names cited were Oliver Sacks (TF 94) and Sherlock Holmes (TF 41). Common themes included fascination with the brain, interest in research, desire to help patients, early interests in neurology, continued pursuit of learning, appreciation for time with patients, family history with neurologic illness, and intellectual curiosity.
This first computational linguistic analysis of neurology personal statements provides understanding into medical students' motivations and interests. Ongoing subgroup and thematic analyses may inform educational strategies and enhance recruitment to our field.
了解医学生选择神经病学的动机，以及申请人对该领域的概念，因为这些信息可以用来提高对神经病学的兴趣，并制定教育计划，以帮助识别、支持和招募未来的神经病学家。
神经病学住院医师申请人提交个人陈述，描述自己和他们的动机。内科和普通外科的个人陈述已经进行了文本分析，但在神经病学中从未有过。我们假设在住院医师个人陈述中，会高频提到特定的词语和主题，这些主题表明学生的动机。
我们使用计算语言学软件来评估关键词，从而研究神经病学申请人的动机、期望和主题。我们机构在 5 年内收到的 2405 份个人陈述被去识别并汇编成一个数据库，通过 3 种计算语言学软件程序进行评估。我们计算了术语频率（TF）和 TF 逆文档频率，并进行了 K-均值聚类，以识别独特的词语和常见的主题。
具体的疾病状态被讨论。例如，中风（TF 2178）、癫痫（TF 970）和痴呆（TF 944）比肌萎缩侧索硬化症（TF 220）和腕管综合征（TF 10）被更多地提及。引用最多的专有名词是奥利弗·萨克斯（TF 94）和夏洛克·福尔摩斯（TF 41）。常见的主题包括对大脑的着迷、对研究的兴趣、帮助患者的愿望、对神经病学的早期兴趣、对学习的持续追求、对与患者相处时间的欣赏、家族神经病史和求知欲。
这是对神经病学个人陈述的第一次计算语言学分析，提供了对医学生动机和兴趣的理解。正在进行的亚组和主题分析可能为我们的领域提供教育策略和增强招聘信息。
