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University student-led public engagement event: increasing audience diversity and impact in a non-science space.

Melissa M LaceyKelly Capper-ParkinRachel Schwartz-NarbonneKate HargreavesCatherine HighamCatherine J DuckettSarah ForbesKatherine E Rawlinson
Published in: Access microbiology (2023)
There is a wealth of innovation in microbiology outreach events globally, including in the setting where the public engagement is hosted. Previous data indicate an underrepresentation of marginalized ethnic groups attending UK science-based public engagement events. This project engaged our student cohort, encompassing a diverse range of ethnic groups, to create an integrated art and science event within an existing series of adult education evenings. The study's objectives were to increase the proportion of visitors from marginalized ethnic groups and to gain a greater understanding of the impact of the event on the visitors' reported science capital. The participants' demographics, links to our students and University, and detailed impact on participants' science capital of the event were determined through analysis of exit questionnaires. There was an increase in the proportion of marginalized ethnic group visitors compared to similar previous events. A higher proportion of visitors from marginalized ethnic groups had links with our students and University compared to white/white British visitors. Elements of the exit questionnaire were mapped to the science capital framework and participants' science capital was determined. Both ethnically marginalized participants and white/white British visitors showed an increase in science capital, specifically dimensions of science-related social capital and science-related cultural capital, after the event. In conclusion, our study suggests that a student-led blended art and science public engagement can increase the ethnic diversity of those attending and can contribute towards creating more inclusive public engagement events.
Keyphrases
  • public health
  • healthcare
  • mental health
  • machine learning
  • electronic health record
  • cross sectional
  • young adults
  • deep learning
  • psychometric properties
  • infectious diseases