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The Matthew Effect: Prevalence of Doctor and Physician Parents among Ophthalmology Applicants.

Diana KhairCody C BlanchardKevin K WangDaniel B Moore
Published in: Journal of academic ophthalmology (2017) (2023)
Objective  This article determines the prevalence of physician parents among ophthalmology residency applications. Design  Retrospective, single-center cohort study. Subjects  All applicants to the University of Kentucky Ophthalmology Residency between 2018 and 2023. Methods  Residency applications were reviewed, with data collection including applicant gender, self-identified Under-Represented in Medicine (URiM) status, United States Medical Licensing Examination (USMLE) Step 1 score, USMLE Step 2 score, and whether the application identified a doctor or physician as a parent. Doctor was defined as a profession requiring a doctorate degree, and similarly, physician as a profession requiring a medical degree. Results  A total of 2,057 applications were reviewed, representing 54% of all match participants during the study period. Fourteen percent (296) of applications indicated a parent was a doctor and 12% (253) a parent was a physician. There were no differences between gender, URiM, USMLE Step 1, and Step 2 scores between applicants indicating a doctor or physician as a parent and those that did not ( p all > 0.4 and Cohen's d all < 0.02). Of the type of doctors, 85% (253) were physicians, 6% (17) optometrists, 6% (17) Doctors of Philosophy, 3% (8) dentists, 1% (1) pharmacist, and 1% (1) veterinarian. Eighty-six percent (217) of applications with a physician parent provided the type of physician, with ophthalmologist the most common (93, 43%). Ninety-eight percent (249) of applications with a physician parent provided the gender of the parent, with father (168, 68%) more common than mother (42, 17%) or both parents (39, 16%). Conclusion  Physician parents are substantially overrepresented in ophthalmology residency applicants. This raises concerns regarding diversity and inclusion efforts for recruitment in medicine.
Keyphrases
  • primary care
  • emergency department
  • artificial intelligence
  • healthcare
  • medical students
  • risk factors
  • machine learning
  • quality improvement