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Leveraging artificial intelligence for resident recruitment

LIWC Research Series:

Candidates for competitive medical residencies are largely assessed on their academic performance scores, which limits the degree to which each candidate is evaluated on their overall strengths and weaknesses. Using text analytic methods, the current study examined the language of candidates to better inform who they are as an individual (e.g., their traits and motivations) and to help gauge their fit in a particular residency. Specifically, researchers assessed candidates’ personality and drives by analyzing the personal statements of medical students applying to a general surgery residency versus personal statements of current general surgery residents. Language from all personal statements were assessed using Receptiviti’s language-based measures for each of the “Big-Five” personality traits and facets as well as measures for various psychological drives. Results demonstrated that residency applicants came across as more self-assured and trusting but less stress-prone and impulsive in their language than the general population. However, current residents came across as more emotionally aware and organized but less self-assured and power-driven in their language than residency applicants. Such findings provide empirical insights and implications for facilitating candidate evaluation.

Read the paper


John, A. S., & Kavic, S. M. (2022). Leveraging artificial intelligence for resident recruitment: Can the dream of holistic review be realized? Artificial Intelligence Surgery,2(4), 195-206.

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