Early Identification of Family Physicians Using Qualitative Admissions Data

The medical community has been concerned about the shortage of family physicians for decades. Identification of likely family medicine (FM) student matches early in medical school is an efficient recruitment tool. The objective of this study was to analyze qualitative data from medical school applications to establish themes that differentiate future family physicians from their non-FM counterparts.

We conducted a qualitative analysis of admissions essays from two groups of 2010-2019 medical school graduates: a study group of students who matched to FM (n=135) and a random sample comparison group of non-FM matches (n=136). We utilized a natural language modeling platform to recognize semantic patterns in the data. This platform generated keywords for each sample, which then guided a more traditional content analysis of the qualitative data for themes.

The two groups shared two themes: emotions and science/academics, but with some differences in thematic emphasis. The study group tended toward more positive emotions and the comparison group tended to utilize more specialized scientific language. The study group exhibited two unique themes: special interests in service and community/people. A secondary theme of religious faith was evident in the FM study group. The comparison group exhibited two unique themes: lab/clinical research and career aspirations.

Aided by machine learning, a novel analytical approach revealed key differences between FM and non-FM student application materials. Findings suggest qualitative application data may contain identifiable thematic differences when comparing students who eventually match into FM residency programs to those who match into other specialties. Assessing student potential for FM could help guide recruitment and mentorship activities.