IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i8p4858-d795499.html
   My bibliography  Save this article

Low-Carbon Action in Full Swing: A Study on Satisfaction with Wise Medical Development

Author

Listed:
  • Hailin Li

    (Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Fengxiao Fan

    (Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Yan Sun

    (Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Weigang Wang

    (Department of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China)

Abstract

The development of “wise medical” is crucial to global carbon reduction. The medical sector not only has the moral obligation to reduce carbon emissions, but also has the responsibility to provide high-quality services to patients. Existing research mostly focuses on the relationship between low-carbon and wise medical, while ignoring the transformation of wise medical and patients’ opinions in the context of low-carbon transition. The paper crawls the text data of comments on the Zhihu platform (a Chinese platform similar to Quora), explores the focus of patients on wise medical through the co-occurrence analysis of high-frequency words, with a focus directly related to the role of wise medical treatment in carbon reduction, and designed a questionnaire accordingly. Using 837 valid questionnaires collected in Zhejiang Province, an XGBoost model was constructed to discuss the main factors affecting patient satisfaction, and the regional heterogeneity among the coastal area of eastern Zhejiang, the plain area of northern Zhejiang and the mountainous area of southwestern Zhejiang is discussed. The results show that patients’ focus on wise medical lies mainly in the convenience brought by digitalization and the actual medical effect, and the main factors affecting satisfaction with medical treatment are the flow of people in hospitals, optimization of the medical treatment process, the application of digital platforms, the quality of telemedicine services and the appropriate quality of treatment. In terms of regional differences in Zhejiang Province, wise medical is more developed in the plain area of northern Zhejiang, with better simplified medical treatment processes and the construction of a digital platform, while the mountainous areas of southwestern Zhejiang have better quality in telemedicine services despite the geographical environment. Eastern Zhejiang is somewhere in between.

Suggested Citation

  • Hailin Li & Fengxiao Fan & Yan Sun & Weigang Wang, 2022. "Low-Carbon Action in Full Swing: A Study on Satisfaction with Wise Medical Development," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4858-:d:795499
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/8/4858/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/8/4858/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Audrey Boruvka & Daniel Almirall & Katie Witkiewitz & Susan A. Murphy, 2018. "Assessing Time-Varying Causal Effect Moderation in Mobile Health," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1112-1121, July.
    2. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    3. Wanchun Xu & Zijing Pan & Shan Lu & Liang Zhang, 2020. "Regional Heterogeneity of Application and Effect of Telemedicine in the Primary Care Centres in Rural China," IJERPH, MDPI, vol. 17(12), pages 1-15, June.
    4. Chang-Ping Hu & Ji-Ming Hu & Sheng-Li Deng & Yong Liu, 2013. "A co-word analysis of library and information science in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 369-382, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    2. María Pinto & Rosaura Fernández-Pascual & David Caballero-Mariscal & Dora Sales, 2020. "Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1479-1510, August.
    3. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
    4. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    5. Ijaz Ul Haq & Amin Ullah & Samee Ullah Khan & Noman Khan & Mi Young Lee & Seungmin Rho & Sung Wook Baik, 2021. "Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors," Mathematics, MDPI, vol. 9(6), pages 1-17, March.
    6. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    7. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    8. Oreshkin, Boris N. & Dudek, Grzegorz & Pełka, Paweł & Turkina, Ekaterina, 2021. "N-BEATS neural network for mid-term electricity load forecasting," Applied Energy, Elsevier, vol. 293(C).
    9. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    10. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    11. Chen Zhao & Xia Zhao & Zhao Li & Qiong Zhang, 2022. "XGBoost-DNN Mixed Model for Predicting Driver’s Estimation on the Relative Motion States during Lane-Changing Decisions: A Real Driving Study on the Highway," Sustainability, MDPI, vol. 14(11), pages 1-23, June.
    12. Shouxiang Wang & Pengfei Dong & Yingjie Tian, 2017. "A Novel Method of Statistical Line Loss Estimation for Distribution Feeders Based on Feeder Cluster and Modified XGBoost," Energies, MDPI, vol. 10(12), pages 1-17, December.
    13. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    14. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    15. Alexandros Menelaos Tzortzis & Sotiris Pelekis & Evangelos Spiliotis & Evangelos Karakolis & Spiros Mouzakitis & John Psarras & Dimitris Askounis, 2023. "Transfer Learning for Day-Ahead Load Forecasting: A Case Study on European National Electricity Demand Time Series," Mathematics, MDPI, vol. 12(1), pages 1-24, December.
    16. Marie Katsurai & Shunsuke Ono, 2019. "TrendNets: mapping emerging research trends from dynamic co-word networks via sparse representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1583-1598, December.
    17. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
    18. Zhao, Yin & Gong, Mingju & Sun, Jiawang & Han, Cuitian & Jing, Lei & Li, Bo & Zhao, Zhixuan, 2023. "A new hybrid optimization prediction strategy based on SH-Informer for district heating system," Energy, Elsevier, vol. 282(C).
    19. Hao Wang & Sanhong Deng & Xinning Su, 2016. "A study on construction and analysis of discipline knowledge structure of Chinese LIS based on CSSCI," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1725-1759, December.
    20. Xiaoguang Wang & Hongyu Wang & Han Huang, 2021. "Evolutionary exploration and comparative analysis of the research topic networks in information disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4991-5017, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4858-:d:795499. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.