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Study claims ‘anti-Asian racism’ is a problem in med school, based on small sample size

A group of Yale doctors and other healthcare researchers recently published a small study that stated there is “anti-Asian racism” in medical school programs and concluded that Asian students are “invisible.”

However, the researchers who conducted the study rejected the idea that a small sample size and biased sampling methods made the study inapplicable.

The study, funded by grants from the Society for Academic Emergency Medicine, interviewed 25 Asian medical students who were recruited through the Asian Pacific American Medical Students Association.

Snowball sampling was also used in the study to identify participants, meaning some people were referred to the study by students who were already participating in it.

“In this qualitative study, Asian American medical students reported feeling invisible within medical school while a target of anti-Asian racism,” the study, published in JAMA Network Open concluded.

Dr. David Yang, one of the authors of the study, pushed back on the notion that the results of the study were inapplicable based on its research methods. “Since this was a qualitative research project, our focus from a data perspective was on theoretical sufficiency,” Yang told The College Fix via email.

“Therefore, we focused on making sure our dataset was comprehensive enough and conducted sufficient interviews to identify recurrent ideas and themes while also accounting for discrepancies in our examples,” Yang said.

Yang stood behind the results of the study, saying that the methods used to obtain them were “rigorous.”

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“I think our paper is applicable in that it helps us gain a better understanding of the experiences of the Asian American medical student population using rigorous qualitative methods,” Yang said.

He further elaborated on his support of the research methods used in the study stating his belief that the sample size was appropriate.

“From a qualitative methodology perspective, I would not have preferred a larger sample size,” Yang said. “We collected data until we reached theoretical sufficiency… I think subsequent studies can examine the emergent themes described in our study using larger sample sizes required for rigorous quantitative studies,” he said.

The study found that there are five common themes of discrimination experienced by Asian American medical school students including “invisibility as racial aggression” and “absence of the Asian American experience in medical school.”

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A spokesperson for Do No Harm, a nonprofit group fighting wokeness in healthcare, raised concerns about how the study was conducted including the sample size and participant selection methods.

“The sample size is an issue. So is their opaqueness over recruitment practices,” Ian Kingsbury, director of research at Do No Harm, told The Fix via a media statement.

Kingsbury said:

All they say is ‘Eligible participants were recruited through the Asian Pacific American Medical Students Association and snowball sampling, and the sample represented a disaggregated population of Asian Americans and all 4 medical school years.’ So one wonders: How many individuals declined to participate? And are we sure that students affiliated with the Asian Pacific American Medical Students Association are representative of Asian students?

He also raised concerns about the fact that the study was published through the American Medical Association, a publication he says is at the forefront of discrimination towards Asian medical students.

“Truthfully, the bigger problem here is that JAMA is at the front of the pack when it comes to anti-Asian discrimination in medical school,” Kingsbury said.

The American Medical Association, which publishes the journal, supports affirmative action.

“The race-based admissions for which they advocate places the greatest penalty on Asian students. Until they change their stance, it’s very hard to take seriously their ‘concerns’ about anti-Asian discrimination,” Kingsbury said.

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IMAGE: IU School of Medicine

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