Impact Of Physician Age, Gender, And Race On Cosmetic Plastic Surgeon Selection
Darya Fadavi, BS, Helen Xun, BS, Pooja Yesantharao, MS, Justin M. Sacks, MD, MBA, FACS.
Johns Hopkins School of Medicine, Department of Plastic and Reconstructive Surgery, Baltimore, MD, USA.
Purpose: Implicit or conscious biases have a significant impact on surgeon selection. Especially in the field of Plastic Surgery and Cosmetic Surgery, patients wield power in selecting their surgeons, as they often have the opportunity to research and visit several surgeons, ultimately choosing who they want to perform their procedure. Gender biases have been shown to exist in many fields in medicine, impacting all levels of training, but age bias has not been studied to the same extent, nor has it been studied in the context of gender and race. This study aims to analyze the impact of physician age, gender, and race on surgeon selection in the field of cosmetic Plastic Surgery.
Methods: In an IRB exempt study, Amazon Mechanical Turk was used to identify 328 participants who responded to a Qualtrics generated survey. Participants were shown pictures of individuals of different ages, genders, and races and asked to rank the likelihood that they would see this surgeon for a cosmetic surgical procedure. All statistical analyses were completed using Stata v.13 (StataCorp, College Station, TX). Patient-level variables and survey responses were analyzed using two-tailed ANOVA and chi square analyses. The threshold for statistical significance was set at an alpha value of 0.05.
Results: 328 respondents consisted of 202 females (61.6%), 123 Males, and 3 Non-binary individuals, with a mean age of 36.9 ± 12.6 years. 76.8% were White, 7% Black, and 10.1% Asian. 12.5% self-identified as Hispanic or Latino. When asked to rank images of physicians most likely to be selected (1) to least likely to be selected (4), surprisingly the older female physician was ranked highest (1.8), followed by the younger male (2.4), younger female (2.5), and older male (2.9) (p<0.001) (Table 1). When asked to rank computer generated images of males and females of White, Black, Latino, and Asian races (from 1 to 8), the highest ranked image was the White male (2.9, p<0.001) and the lowest ranked was the Black female (6.6). For each race pair, the male was ranked higher than the female except for the Latino pair (p<0.001) (Table 1).
Conclusion: The Age and Gender photo set was composed of 4 images of White physicians, and showed the highest preference for the older female physician, contrasted by the results of the Race and Gender photo set, which showed a preference for male physicians in White, Black, and Asian race groups. While it is known that individual age, gender, and race biases exist, we reflect that when all three are tested, the biases are layered. This is demonstrated in that within most races, male physicians are ranked higher than female physicians, but within each age, gender was ranked differently. To mitigate these potential biases, we recommend promoting diversity in Plastic Surgery teams by including surgeons of varying ages, genders, and races.
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