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Usage Information

A study of MD-PhD pre-health advising identifies challenges to building a robust MD-PhD applicant pool
Amara L. Plaza-Jennings, Christie B. Ryba, Jessica Tan, Jennifer E.L. Diaz, Grace E. Mosley, Talia H. Swartz, Margaret H. Baron, Robert Fallar, Valerie Parkas
Amara L. Plaza-Jennings, Christie B. Ryba, Jessica Tan, Jennifer E.L. Diaz, Grace E. Mosley, Talia H. Swartz, Margaret H. Baron, Robert Fallar, Valerie Parkas
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Physician-Scientist Development

A study of MD-PhD pre-health advising identifies challenges to building a robust MD-PhD applicant pool

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Abstract

MD-PhD programs provide interdisciplinary training in medicine and research. Undergraduate pre-health advisors (PHAs) play a critical role in counseling prospective applicants, yet there have been no studies to our knowledge of MD-PhD pre-health advising. Here we surveyed 280 PHAs from US colleges and universities using both qualitative and quantitative measures that assessed their real-world advising behaviors as well as standardized evaluation of 1 of 2 fictional MD-PhD applicants, identical except for gender. We identified 3 factors that influenced advising behaviors: experience advising MD-PhD applicants, attitudes toward MD-PhD programs, and gender bias. Those PHAs with less experience and who held negative attitudes toward MD-PhD programs were less likely to initiate discussions about MD-PhD programs with qualified applicants and less likely to recommend the fictional applicants apply to MD-PhD programs. Finally, there was subtle gender bias that favored the male applicant. PHAs face challenges in advising MD-PhD applicants because there are relatively few MD-PHD applicants overall and there is a lack of resources to guide them. Addressing these challenges by strengthening collaborations with PHAs and providing comprehensive information about the value of and applicant qualifications for MD-PhD programs is crucial to enhancing MD-PhD advising, mitigating effects of bias, and expanding the pool of qualified applicants.

Authors

Amara L. Plaza-Jennings, Christie B. Ryba, Jessica Tan, Jennifer E.L. Diaz, Grace E. Mosley, Talia H. Swartz, Margaret H. Baron, Robert Fallar, Valerie Parkas

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Usage data is cumulative from April 2025 through December 2025.

Usage JCI PMC
Text version 1,709 72
PDF 386 5
Figure 429 0
Table 137 0
Supplemental data 163 0
Citation downloads 171 0
Totals 2,995 77
Total Views 3,072

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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