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

arXiv 2502.10641 ↗First posted 15 Feb 2025cs.CLcited by 0

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Abstract (arXiv)

Monitoring health resource disparities during public health crises is critical, yet traditional methods, like surveys, lack the requisite speed and spatial granularity. This study introduces a novel framework that leverages: 1) crowdsourced Google Maps reviews (2018-2021) and 2) advanced NLP (DeBERTa) to create a high-resolution, spatial-temporal index of public perception of health resource accessibility in the United States. We then employ Partial Least Squares (PLS) regression to link this perception index to a range of socioeconomic and demographic drivers. Our results quantify significant spatial-temporal shifts in perceived access, confirming that disparities peaked during the COVID-19 crisis and only partially recovered post-peak. We identify political affiliation, racial composition, and educational attainment as primary determinants of these perceptions. This study validates a scalable method for real-time health equity monitoring and provides actionable evidence for interventions to build a more resilient healthcare infrastructure.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 63 references that are in the map. First posted on arXiv 15 Feb 2025.

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References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,157
  4. 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 527
  5. 2020Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcited by 420
  6. 2022UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Miningcited by 33
  7. 2022Personalized Showcases: Generating Multi-Modal Explanations for Recommendationscited by 30
  8. 2019Demographic Inference and Representative Population Estimates from Multilingual Social Media Datacited by 24

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