Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility

2025-02-15

AI Summary

This study investigates inequalities in perceived healthcare access across demographic and geographic groups in the U.S. during the COVID-19 pandemic, and their underlying social determinants. Leveraging large-scale geotagged Google Maps reviews, we integrate a fine-tuned ModernBERT model—optimized for sentiment and accessibility-intent classification—with partial least squares (PLS) regression to enable nonlinear association analysis between unstructured geographic text and structured sociodemographic variables. Results reveal peak disparities in perceived access during pandemic surges; political affiliation, racial composition, and educational attainment exert significant, nonlinear effects on accessibility perceptions, collectively explaining substantial variance. The proposed framework advances scalable, empirically grounded identification of vulnerable areas, supports equitable resource allocation, and establishes a novel analytical paradigm for resilient public health policy design.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: AI for Accessibility
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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, and educational attainment. 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.

Problem

• Analyzes health resource accessibility perceptions
• Uses crowdsourced reviews and NLP techniques
• Identifies key socioeconomic influencing factors

Innovation

• Crowdsourced Google Maps reviews
• ModernBERT NLP techniques
• Partial Least Squares regression