Department of Mathematics and Computer Science

How can journalists report scientific medical information in a way, that we would avoid readers’ confusions seen during the COVID-19 pandemic? A crucial step is presenting scientific medical data, also called risk information, in a clear and transparent way. Yet, in the pressure of daily deadlines, many journalists may lack the time, tools, or training to spot common pitfalls associated with risk communication. That’s where the low-fidelity prototype Science X Media Risk Booster comes in. The tool is meant to help journalists to quickly assess the clarity of risk-related scientific press releases, identify missing data, and revise their own writing for better transparency. By making it easier for journalists to communicate scientific numbers accurately, the tool should support smarter, more responsible science reporting. Improving Science X Media Risk Booster’s usability and appeal could make a big difference—because it will invite more journalist to use it and hopefully result in more better reporting and more better-informed readers.

Now, we invite you to join us in advancing this mission. The goal of this master’s thesis is to improve the user experience and enhance both the objective and subjective understanding of the Science X Media Risk Booster.

General Research Process

• Understand the Research Context and Objectives
  Conduct a thorough review of the research domain, focusing on risk communication and its challenges. Clearly define the goals and scope of the thesis.
• Conduct a Thinking Aloud Study
  Using the existing low-fidelity prototype, conduct a thinking-aloud study with at least 15 participants to identify current issues in the tool’s design and functionality.
• Create a High-Fidelity Prototype
  Develop a high-fidelity prototype based on insights gathered. Utilize JavaScript frameworks such as React or Vue.js to build the new prototype.
• Conduct a Between-Subjects Design Study
  Design and execute an empirical study using a between-subjects design (comparing the old and new UI) to evaluate user experience (e.g., using the User Experience Questionnaire - UEQ) and understanding of the Science X Media Risk Booster. Recruit at least 15 participants per condition.
• Analyze, Present, and Discuss Results
  Perform both qualitative and quantitative analyses of the study results. Assess the impact of the revised user interface on users’ understanding and identify key insights and implications for future research.

By following this process, we aim to create a more effective and user-friendly Science X Media Risk Booster that empowers journalists to deliver clearer, more accurate scientific reporting.

References

1. Current Tool: https://riskbooster.shinyapps.io/risk_booster/ and Code on Github https://github.com/nigradwohl/risk_booster
2. Project Description: https://anaesthesieintensivmedizin.charite.de/forschung/arbeitsgruppen/heisenberg_professur_fuer_medizinische_risikokompetenz_evidenzbasiertes_entscheiden/science_x_media
3. Lühnen, J., Albrecht, M., Mühlhauser, I., & Steckelberg, A. (2017). Leitlinie Evidenzbasierte Gesundheitsinformation. Retrieved from https://www.leitlinie-gesundheitsinformation.de
4. Wegwarth, O. (2024). "Wir brauchen eine Revolution zur Förderung transparenter Informationen" [Editorial]. InFo Hämatologie + Onkologie, 27(9), 3. https://doi.org/10.1007/s15004-024-0697-8
5. Wegwarth, O., & Gigerenzer, G. (2014). Improving evidence-based practices through health literacy—in reply. JAMA Internal Medicine, 174(8), 1413–1414.
6. Wegwarth, O., Kendel, F., Tomsic, I., von Lengerke, T., & Härter, M. (2020). Die COVID-19-Pandemie: Risikokommunikation unter Unsicherheit [The COVID-19 pandemic: Communicating risk under uncertainty]. Umweltmedizin, Hygiene, Arbeitsmedizin, 25(4), 141–146.
7. Schneider, C. R., Kerr, J. R., Dryhurst, S., & Aston, J. A. D. (2024). Communication of statistics and evidence in times of crisis. Annual Review of Statistics and Its Application, 11(1), 1–26. https://doi.org/10.1146/annurev-statistics-040722-052011
8. Spiegelhalter, D. (2017). Risk and uncertainty communication. Annual Review of Statistics and Its Application, 4(1), 31–60. https://doi.org/10.1146/annurev-statistics-010814-020148