Abstract
Access to health resources is a critical determinant of public well-being and societal resilience, particularly during public health crises when demand for medical services and preventive care surges. However, disparities in accessibility persist across demographic and geographic groups, raising concerns about equity. Traditional survey methods often fall short due to limitations in coverage, cost, and timeliness. This study leverages crowdsourced data from Google Maps reviews, applying advanced natural language processing techniques, specifically ModernBERT, to extract insights on public perceptions of health resource accessibility in the United States during the COVID-19 pandemic. Additionally, we employ Partial Least Squares regression to examine the relationship between accessibility perceptions and key socioeconomic and demographic factors—including political affiliation, racial composition, educational attainment and so on. Our findings reveal that public perceptions of health resource accessibility varied significantly across the U.S., with disparities peaking during the pandemic and slightly easing post-crisis. Political affiliation, racial demographics, and education levels emerged as key factors shaping these perceptions. These findings underscore the need for targeted interventions and policy measures to address inequities, fostering a more inclusive healthcare infrastructure that can better withstand future public health challenges.
| Original language | English |
|---|---|
| Pages | 1-12 |
| Number of pages | 12 |
| Publication status | In preparation - 3 Dec 2025 |
| Event | 6th International Conference on Social Computing: ICSC - Fudan University (Jiangwan Campus), Shanghai, China Duration: 12 Dec 2025 → 13 Dec 2025 https://icsc-conf.github.io/2025/ |
Conference
| Conference | 6th International Conference on Social Computing |
|---|---|
| Country/Territory | China |
| City | Shanghai |
| Period | 12/12/25 → 13/12/25 |
| Internet address |