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Assessment on Distributional Fairness of Physical Rehabilitation Resource Allocation: Geographic Accessibility Analysis Integrating Google Rating Mechanism

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  • Hui-Ching Wu

    (Department of Medical Sociology and Social Work, Chung Shan Medical University, Taichung 402367, Taiwan
    Social Service Section, Chung Shan Medical University Hospital, Taichung 402367, Taiwan
    These authors contributed equally to this work.)

  • Ming-Hseng Tseng

    (Department of Medical Informatics, Chung Shan Medical University, Taichung 402367, Taiwan
    These authors contributed equally to this work.)

  • Chuan-Chao Lin

    (School of Medicine, Chung Shan Medical University, Taichung 402367, Taiwan
    Department of Physical Medicine and Rehabilitation, Chung Shan Medical University Hospital, Taichung 402367, Taiwan)

Abstract

Identifying and treating co-existing diseases are essential in healthcare for the elderly, while physical rehabilitation care teams can provide interdisciplinary geriatric care for the elderly. To evaluate the appropriateness of demand and supply between the population at demand and physical rehabilitation resources, a comparative analysis was carried out in this study. Our study applied seven statistical indices to assess five proposed methods those considered different factors for geographic accessibility analysis. Google ratings were included in the study as a crucial factor of choice probability in the equation for calculating the geographic accessibility scores, because people’s behavioral decisions are increasingly dependent on online rating information. The results showed that methods considering distances, the capacity of hospitals, and Google ratings’ integrally generated scores, are in better accordance with people’s decision-making behavior when they determine which resources of physical rehabilitation to use. It implies that concurrent considerations of non-spatial factors (online ratings and sizes of resource) are important. Our study proposed an integrated assessment method of geographical accessibility scores, which includes the spatial distribution, capacity of resources and online ratings in the mechanism. This research caters to countries that provide citizens with a higher degree of freedom in their medical choices and allows these countries to improve the fairness of resource allocation, raise the geographic accessibilities of physical rehabilitation resources, and promote aging in place.

Suggested Citation

  • Hui-Ching Wu & Ming-Hseng Tseng & Chuan-Chao Lin, 2020. "Assessment on Distributional Fairness of Physical Rehabilitation Resource Allocation: Geographic Accessibility Analysis Integrating Google Rating Mechanism," IJERPH, MDPI, vol. 17(20), pages 1-21, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:20:p:7576-:d:430799
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    References listed on IDEAS

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    1. Pan, Jay & Zhao, Hanqing & Wang, Xiuli & Shi, Xun, 2016. "Assessing spatial access to public and private hospitals in Sichuan, China: The influence of the private sector on the healthcare geography in China," Social Science & Medicine, Elsevier, vol. 170(C), pages 35-45.
    2. Hui-Ching Wu & Ming-Hseng Tseng, 2018. "Evaluating Disparities in Elderly Community Care Resources: Using a Geographic Accessibility and Inequality Index," IJERPH, MDPI, vol. 15(7), pages 1-18, June.
    3. Shlomo Yitzhaki, 1979. "Relative Deprivation and the Gini Coefficient," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 93(2), pages 321-324.
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    1. Ming-Hseng Tseng & Hui-Ching Wu, 2021. "Integrating Socioeconomic Status and Spatial Factors to Improve the Accessibility of Community Care Resources Using Maximum-Equity Optimization of Supply Capacity Allocation," IJERPH, MDPI, vol. 18(10), pages 1-25, May.
    2. Ming-Hseng Tseng & Hui-Ching Wu, 2021. "Accessibility Assessment of Community Care Resources Using Maximum-Equity Optimization of Supply Capacity Allocation," IJERPH, MDPI, vol. 18(3), pages 1-19, January.

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