Examining Older Adults' Information Exposure, Wellbeing, and Adherence to Protective Measures During the COVID-19 Pandemic

• URL: http://arxiv.org/abs/2112.11215v1
• Date: Fri, 17 Dec 2021 15:33:13 GMT
• Title: Examining Older Adults' Information Exposure, Wellbeing, and Adherence to Protective Measures During the COVID-19 Pandemic
• Authors: Nurul Suhaimi, Nutchanon Yongsatianchot, Yixuan Zhang, Anisa Amiji, Shivani A. Patel, Stacy Marsella, Miso Kim, Jacqueline Griffin, Andrea Parker
• Abstract summary: Older adults are at greater risk of experiencing negative physical and psychological impacts of the novel coronavirus 2019 (COVID-19) pandemic. This work investigates the potential association between information exposure and wellbeing as well as adherence to COVID-19 protective measures.
• Score: 9.630865346003752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Older adults are at greater risk of experiencing negative physical and psychological impacts of the novel coronavirus 2019 (COVID-19) pandemic. Our ongoing study is assessing COVID-19 information exposure in adults aged 55 and above compared to other age groups living in Massachusetts and Georgia. This work investigates the potential association between information exposure and wellbeing as well as adherence to COVID-19 protective measures. Our initial results show that older adults received information related to COVID-19 less frequently than the middle-aged group, yet they feel more content and less stressed than the other age groups. Further analysis to identify other potential confounding variables is addressed.

Related papers

• Older adults' safety and security online: A post-pandemic exploration of attitudes and behaviors [0.0]
  The behaviors and attitudes of a group of older adults aged 60 years and older regarding different dimensions of online safety and cybersecurity are investigated. Results show that older adults report a discernible degree of concern about the security of their personal information. Support systems should include older adults in the development of protective measures and acknowledge their diversity.
  arXiv  Detail & Related papers  (2024-03-14T09:22:16Z)
• Population Age Group Sensitivity for COVID-19 Infections with Deep Learning [0.0]
  The COVID-19 pandemic has created unprecedented challenges for governments and healthcare systems worldwide. This study aimed to identify the most influential age groups in COVID-19 infection rates at the US county level.
  arXiv  Detail & Related papers  (2023-07-03T04:56:55Z)
• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• Exploring Social Media for Early Detection of Depression in COVID-19 Patients [44.76299288962596]
  Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. We managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
  arXiv  Detail & Related papers  (2023-02-23T14:13:52Z)
• "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation [60.268682953952506]
  We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models. We provide a strategy to combat social media posts that are likely to cause widespread harm.
  arXiv  Detail & Related papers  (2022-06-12T19:41:01Z)
• Assessing COVID-19 Impacts on College Students via Automated Processing of Free-form Text [3.1569066974597293]
  We process free-form texts generated by college students via an app specifically designed to assess and improve their mental health. Our analysis reveals that topics like Education became noticeably less important to students post COVID-19, while Health became much more trending. We expect our study to have an impact on policy-makers in higher education across several spectra, including college administrators, teachers, parents, and mental health counselors.
  arXiv  Detail & Related papers  (2020-12-17T02:46:48Z)
• A New Screening Method for COVID-19 based on Ocular Feature Recognition by Machine Learning Tools [66.20818586629278]
  Coronavirus disease 2019 (COVID-19) has affected several million people. New screening method of analyzing the eye-region images, captured by common CCD and CMOS cameras, could reliably make a rapid risk screening of COVID-19.
  arXiv  Detail & Related papers  (2020-09-04T00:50:27Z)
• COVID-19 Pandemic Outbreak in the Subcontinent: A data-driven analysis [0.8057708414390126]
  COVID-19 virus emerged in late December 2019 in Wuhan city, Hubei, China. Numerous studies claim that the subcontinent could remain in the worst affected region by the COVID-19. This paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers.
  arXiv  Detail & Related papers  (2020-08-22T10:40:17Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.