用地图评论分析美国医疗资源感知差距，发现疫情期差异最大且未完全恢复。
Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility
- 通过谷歌地图评论与DeBERTa模型，构建时空分辨率高的公众感知指数。
- 疫情高峰期感知不平等达峰值，后续仅部分缓解，城乡差异显著。
- 政治倾向、种族构成和教育水平是影响感知的主要因素，适合政策制定者参考。
在公共卫生危机中监测医疗资源不平等至关重要，但传统调查方法缺乏速度和空间精度。本研究提出一种新框架，利用2018-2021年谷歌地图的众包评论与先进NLP技术（DeBERTa），构建美国公众对医疗资源可及性的高分辨率时空感知指数。随后采用偏最小二乘回归（PLS）将该指数与社会经济及人口统计变量关联。结果量化了感知可及性在时空上的显著变化，证实疫情高峰期不平等达到顶峰，且疫情后仅部分恢复。政治归属、种族构成和教育水平是主要驱动因素。研究验证了一种可扩展的实时健康公平监测方法，并为构建更具韧性的医疗体系提供可操作证据。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。