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- Creator:
- Irani, Sanaya , Tolia, Sangini, Finks, Jonathan, and Sandhu, Gurjit
- Description:
- Program Description DoT was founded in 2012 with a mission to increase diversity amongst medical professionals by preparing students from underrepresented communities in Detroit to successfully pursue careers in healthcare. Our program builds on a partnership between Cass Technical High School (CTHS) and the University of Michigan Medical School (UMMS). The CTHS student body is reflective of the Detroit population with more than 80% of students identifying with racial and ethnic minority backgrounds. Students with an interest in healthcare apply for the program as ninth graders. In recent years, the program has received over 60 applications for approximately 30 positions in each grade. DoT’s unique strength lies in its longitudinal structure. There are three branches of the program – Foundations (ninth and tenth grade), Rising (eleventh and twelfth grade) and Succeed (undergraduate). Ninth graders start out in DoT Foundations. Each student is paired with a first-year medical student mentor at UMMS for the entire academic year. DoT students travel to UMMS every month for a visit day, with activities designed to give students hands-on experiences in healthcare, such as suturing and ultrasound skills in the simulation center, and clinical shadowing. Students then meet with their medical student mentor over lunch. The latter part of the day is dedicated to working on their capstone projects. For the capstone projects, students work in small teams led by medical student leaders to identify a community health issue, partner with a local organization, and present their proposed solutions at a formal symposium at the end of the year. , Transition to Virtual Programming In light of the recent COVID-19 pandemic, a growing number of universities cancelled all campus events including those of pipeline programs. We felt that our programming offered an important service to our students that would be greatly missed, so our team worked to quickly create and implement a virtual program. We ensured that each of our students had access to technology at home and those who did not were offered scholarships. During our introductory student session and new parent meeting, our leadership team discussed how to set up a Gmail email address for weekly communications and taught the students how to use Zoom, Google Drive, Google Docs and Google Sheets for online learning collaboration. For the virtual Foundations program, we offered 1-hour seminars each month, where a physician was invited to give a 30-minute presentation about different organ systems, followed by a 30-minute case-based session where students worked with medical student mentors to apply their new knowledge. We also created novel sessions such as “The Path to College and Medical School” and collaborated with members of the Black Medical Association (BMA) and Latin American and Native American Medical Association (LANAMA) to host a panel session where students could learn from medical students who identified as URiM. For the mentorship aspect, we created “pods” of Foundations, Rising, and Succeed students along with medical student and physician mentors. The Foundations students and mentors met every month for an hour on Zoom, a virtual communication platform, to work on their Capstone project. Rising and Succeed students joined the group for three full-pod meetings. The goal was to increase near-peer mentorship and connections between DoT students at all levels. , and Study Population Due to the virtual nature of the 2020-2021 program, we accepted 100% of 9th grade applicants from CTHS. We also expanded our reach to a new school, The School at Marygrove (TSM), which is also located in Detroit, Michigan. TSM is involved in the Detroit-20 Partnership with the University of Michigan College of Education and includes a novel three-year residency program for novice teachers. During the 2020-2021 school year, 108 students participated in the Foundations programming with 72 of them being 9th graders and 36 being 10th graders. The students were mostly from CTHS with 12 students out of the 108 total being from TSM. Students were predominantly from an African American/Black racial background (68.4% from N=98 respondents). The students were representative of their respective schools. The majority of students at CTHS identify as black, come from low-income homes, and have variable levels of parental education.
- Keyword:
- pipeline program, Underrepresented in medicine, Mentorship, Medical education, and COVID-19
- Discipline:
- Health Sciences
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- Creator:
- Thomer, Andrea K. , Starks, Joseph R. , Rayburn, Alexandria, and Lenard, Michael
- Description:
- This dataset is the results from qualitatively coding the 76 articles that represent the dataset from our literature review. We categorized papers according to their approach (case study, other research project, position paper), setting (library, museum, research lab), and publication domain (library information science, computer science, domain publication, other). We also coded for the focus of the paper, and whether motivating needs were listed as a reason for migration.
- Citation to related publication:
- Thomer, AK, Starks, JS, Rayburn, A, Lenard, M. Maintaining repositories, databases, and digital collections in memory institutions: an integrative review. Accepted at the 85th Annual Association for Information Science and Technology.
- Discipline:
- Social Sciences
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- Creator:
- Jiao, Zhenbang, Chen, Yang, and Manchester, Ward
- Description:
- GOES_flare_list: contains a list of more than 12,013 flare events. The list has 6 columns, flare classification, active region number, date, start time end time, emission peak time. SHARP_data.hdf5 files contain time series of 20 physical variables derived from the SDO/HMI SHARP data files. These data are saved at a 12 minute cadence and are used to train the LSTM model.
- Keyword:
- Solar Flare Prediction and Machine Learning
- Citation to related publication:
- Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., & Chen, Y. (2020). Solar Flare Intensity Prediction With Machine Learning Models. Space Weather, 18(7), e2020SW002440. https://doi.org/10.1029/2020SW002440 and Chen, Y., & Manchester, W. (2019). Data and Data products for machine learning applied to solar flares [Data set], University of Michigan - Deep Blue. https://doi.org/10.7302/qnsq-cs38
- Discipline:
- Engineering and Science