ESRA 2027 sessions by theme

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Cancer screening participation is critical for early detection, yet women with disabilities often face physical, communicative, and structural barriers that limit their access. This study analyzes responses from 1,032 women with disabilities in Sweden’s Rivkraft panel, using a mixed-methods approach that integrates AI-assisted thematic analysis (Vibe, formerly Le Chat) with human validation to process open-ended survey data efficiently.
The survey included six open-ended questions on barriers to and facilitators of cervical and breast cancer screening. AI was used to identify preliminary themes, which were then refined iteratively by researchers to ensure accuracy and depth. This hybrid method enabled scalable, yet nuanced analysis of qualitative data, preserving the respondent-centered perspective essential for marginalized groups.
Key barriers identified include inaccessible facilities, psychological distress, unclear information, and administrative hurdles. Participants proposed practical solutions, such as flexible screening options, improved accessibility, and person-centered care. The findings directly informed two national reports by the Swedish Public Health Agency, providing actionable recommendations to healthcare providers and policymakers to reduce disparities in screening participation.
By demonstrating how AI can enhance traditional survey analysis, this study aligns with the conference theme of "Moving forward: integrating survey methods, statistics, and social data science with new data sources, AI, and machine learning." It also highlights the practical impact of inclusive survey research in shaping real-world health policies.

This study will be presented by Katrin Bjerhag and Sara Scholtens from the Swedish Public Health Agency.