Academic Year SIEPR Undergraduate Research Fellows Program
The SIEPR Undergraduate Research Fellows (UGRF) program offers faculty-mentored research opportunities for 乐鱼体育 undergraduates during the academic year. The program focuses on enriching the student research experience and offers additional opportunities for students to participate in the intellectual life of SIEPR.
Program Structure
Each quarter students and their faculty mentor will meet prior to the start of the quarter to agree on project goals and learning outcomes for a quarter-long research experience.
- The Spring Quarter program runs from March 31 to June 4, 2025.
- Students and faculty should meet prior to the start of the quarter to agree on project goals and learning outcomes for a quarter-long research experience.
- UGRFs will have the opportunity to participate in SIEPR policy forums and Summit.
- Students will be paid via payroll for up to 15 hours a week.
Eligibility
- UGRFs must be enrolled as full-time undergraduate students at 乐鱼体育. Coterm students who are interested in the program will need to hold undergraduate standing to be eligible for the SIEPR UGRF program.
- 乐鱼体育 students on hourly pay may not exceed 15 hours per week for all campus employment. Students with other hourly-paid commitments on campus may have limited hours of SIEPR-funded research. Time commitment should be discussed with faculty mentors prior to accepting the UGRF offer.
How to Apply
To apply for the SIEPR UGRF program, fill out the online application using the link below. Please follow the instructions in the application portal. The application will ask you to answer a few general questions about your academic interests.
Please prepare to upload the materials listed below:
- A list of the current courses that you are enrolled in for the upcoming quarter.
- Resume
- A cover letter that addresses the following:
- Why are you interested in a SIEPR UGRF position?
- What is your previous experience, if any, with research?
- What are your personal research interests?
Questions?
If you have questions, please email siepr-fellowships@stanford.edu.
Applications for 2025 Spring quarter are open.
Birth Centers in the United States
Faculty Mentor: Petra Persson & Maya Rossin-Slater
The number of births at birth centers鈥攆reestanding medical facilities equipped to perform low-risk deliveries鈥攈as more than doubled over the past decade in the US. Interest in their usage increased during the COVID-19 pandemic, and has continued to grow since. For example, a 2024 Florida law permitted advanced birth centers to perform C-sections outside of hospitals for the first time. Moreover, large racial disparities in maternal health outcomes have prompted advocates to encourage Black women in particular to consider birth centers for their perinatal care. To date, however, there is limited evidence on the causal impacts of access to birth centers on maternal and infant health outcomes, or how they interact with existent disparities. In this project, we aim to provide novel evidence on the impact of access to birth centers on maternal and infant health outcomes. We collect new data on birth centers from several industry organizations as well as the National Plan & Provider Enumeration System of the Centers for Medicare & Medicaid Services and use nearly two decades of restricted-use individual-level birth records data from the National Vital Statistics System to study these questions. We examine the impacts of county-level access to birth centers on pregnancy, labor/delivery, and infant health outcomes.
Responsibilities: The research fellow will primarily be responsible for both automated and manual data collection on birth center characteristics. Anecdotal evidence suggests that birth centers have positioned themselves to attract different types of patients, with potential impacts on selection into this type of care facility (versus a hospital or other setting). In this project, we plan to systematize evidence to support this, with the potential for analysis of birth center websites, including their text content and images.
Qualifications: Experience with Python or similar tools for automated data collection from websites, as well as text analysis, is preferred but not necessary. Interest in learning about maternal and infant health in the United States is also encouraged!