Evaluating diversity in neurosurgery through the use of a multidimensional statistical model: a pilot study

Area of Science:

• Medical Education
• Surgical Specialties
• Diversity and Inclusion

Background:

• Neurosurgery lags in diversity compared to other medical fields.
• Measuring and tracking diversity in neurosurgery is challenging due to its complex nature.
• A multidimensional statistical model can quantify and assess diversity within the neurosurgery workforce.

Purpose of the Study:

• To assess neurosurgery resident diversity using Simpson's Diversity Index and Sullivan's Composite Diversity Index (CDI).
• To determine if top-ranked medical schools (US News & World Report) produce more neurosurgery residents.
• To evaluate the utility of a multidimensional statistical model for measuring neurosurgery diversity.

Main Methods:

• Cross-sectional study of neurosurgery residents and medical students.
• Biographical diversity data (gender, matriculation) collected from institutional websites.
• Calculated Composite Diversity Index (CDI) for residents and students.

Main Results:

• Neurosurgery residency CDI (0.21) was significantly lower than medical school CDI (0.42).
• No significant CDI difference between top-ranked and non-top-ranked neurosurgery programs.
• Top-40 ranked medical schools produced significantly more neurosurgery residents (11.9 vs. 5.6).

Conclusions:

• A multidimensional statistical model is feasible for assessing neurosurgery diversity.
• Lower diversity in neurosurgery may stem from uneven attraction and recruitment of medical students.
• Utilizing diversity measures can help neurosurgery monitor progress in achieving workforce diversity.