Open AccessLinear Modeling to Reduce Bias in Plastic Surgery Residency Selection
Author(s) -
Shady Elmaraghi,
Venkat K. Rao,
Bruce Christie
Publication year - 2021
Publication title -
plastic and reconstructive surgery/psef cd journals
Language(s) - English
Resource type - Journals
eISSN - 1076-5751
pISSN - 0032-1052
DOI - 10.1097/prs.0000000000007684
Subject(s) - ranking (information retrieval) , united states medical licensing examination , consistency (knowledge bases) , medicine , rank (graph theory) , scrutiny , selection (genetic algorithm) , ordinal regression , rank correlation , process (computing) , statistics , medical education , computer science , information retrieval , medical school , mathematics , machine learning , artificial intelligence , combinatorics , political science , law , operating system
Consistently selecting successful, productive applicants from an annual candidate pool is the goal of all resident selection practices. Efforts to routinely identify high-quality applicants involve scrutiny of multiple factors and formulation of an ordinal rank list. Linear modeling offers a quantified approach to applicant selection that is strongly supported by decades of psychological research.