Don2 (Don1 Revised)
Contributor
As noted upthread, 90+ percent of NA doctors don't work on reservations.
The statistic about reservations is a red herring. Working on a reservation is merely one specific example of how different demographics might fulfill a public need. The broader and more rigorous data shows that NA doctors proportionally serve across all types of underserved areas at higher rates. Equating "underserved" exclusively with "reservations" artificially shrinks the success rate of the metric and erroneously manufactures an undeserved high failure rate.
If for the sake of getting one mission-fit doctor you rate nine premeds as mission-fit who aren't actually mission-fit, that looks a lot more like measuring race than measuring mission-fitness.
First, because NA doctors serve in other underserved areas at higher rates, the premise of a 9-in-10 false prediction rate doesn't hold up to the data.
More importantly, there seems to be a fundamental disconnect about the mechanism of how mission-fitness is plausibly applied. Let me try to clear that up:
Admissions committees do not look at just one or two metrics; they evaluate a multivariate set of data: MCAT, GPA, transcripts, community service, clinical work, essays, and interviews. They apply consistent weights to these different criteria to each individual. "Mission fitness" is an implied variable that emerges from assessing an individual's unique essays, past community service, and interview answers regarding their career goals. It is not a checkbox that blindly translates race into a bonus score--if it were, we wouldn't see such high absolute rejection rates for NA and Black applicants.
What happens when you aggregate these individual assessments? The demographics naturally stratify. If we assume a baseline level of honesty across all applicants, NA applicants as a group are going to provide answers and track records that align more frequently with a commitment to underserved areas.
Conversely, Asian applicants, in the aggregate, demonstrate lower rates of eventually working in these specific environments (for instance, White doctors end up serving in rural areas at higher rates than Asian doctors). If you expect Asian applicants to score equally well on essays and interviews regarding a commitment to underserved areas, you are essentially expecting them to lie at higher rates during the process. If we expect the same frequency of honesty across all racial demographics, the aggregate scores for this specific variable will naturally diverge.
To be clear: I am not claiming this is the absolute, guaranteed mechanism for every school. I am pointing out that this is a mathematically sound, plausible model of how weighted variables work. It explains the aggregate national data without requiring the assumption that committees are simply using race as a blind proxy for academic success.
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