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A US National Study of Barriers to Science Training Experienced by Undergraduate Students during COVID-19.

Sara E GrineskiDanielle Xiaodan MoralesTimothy W CollinsShawna NadybalShaylynn Trego
Published in: International journal of environmental research and public health (2022)
Undergraduate research is a high-impact practice on college campuses. How the COVID-19 pandemic has affected undergraduate researchers' progress is poorly understood. We examine how demographics, academic characteristics, research disruptions and faculty mentorship are associated with four barriers to research progress. Data are drawn from a survey of over 1000 undergraduate student researchers across the US. We examine students who actively continued to conduct faculty-mentored research during mid-March/April 2020 ( n = 485). Using generalized estimating equations that control clustering by institution, we found economic hardship, discomfort teleconferencing, lower quality mentors, sexual minority status and higher grade point averages were associated with motivation problems. Economic hardship, serious illness, Internet connection issues, a lack of face-to-face meetings and lower a frequency of mentor-mentee communication were associated with a time crunch with regard to conducting research. Discomfort teleconferencing, Internet connection issues, a lack of face-to-face meetings and decrease in research workload were associated with task uncertainty. Economic hardship, serious illness and being an engineering major were associated with lacking needed tools for the research. In sum, economic hardship was an important correlate of research barriers, as were communication challenges and sexual minority status. Results can inform practical actions by research program directors and faculty undergraduate research mentors.
Keyphrases
  • medical education
  • medical students
  • nursing students
  • quality improvement
  • mental health
  • healthcare
  • primary care
  • health information
  • public health
  • life cycle
  • high school
  • single cell
  • social media
  • machine learning