Connect with us

Health

COVID-Dynamic: A large-scale longitudinal study of socioemotional and behavioral change across the pandemic

COVID-Dynamic: A large-scale longitudinal study of socioemotional and behavioral change across the pandemic

 


  • Shiller, R. Why we can’t foresee the pandemic’s long-term effects. The New York Times https://www.nytimes.com/2020/05/29/business/coronavirus-economic-forecast-shiller.html (2020).

  • Bureau of Labor and Statistics. https://www.bls.gov/data/ (2020).

  • Eisenberg, I. W. et al. Uncovering the structure of self-regulation through data-driven ontology discovery. Nat. Commun. 10, 2319 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Raifman, M. A. & Raifman, J. R. Disparities in the population at risk of severe illness from COVID-19 by race/ethnicity and income. Am. J. Prev. Med. 59, 137–139 (2020).

    Article 

    Google Scholar
     

  • Health Equity Considerations & Racial & Ethnic Minority Groups. Cleaning and Disinfecting: Everyday steps, when someone is sick, and considerations for employers. Centers for Disease Control and Prevention https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html (2020).

  • The New York Times. How George Floyd died, and what happened next. The New York Times https://www.nytimes.com/article/george-floyd.html.

  • FACT SHEET: Anti-Asian Prejudice March 2021 & Center for the Study of Hate & Extremism. FACT SHEET: Anti-Asian Prejudice March 2021. https://www.csusb.edu/sites/default/files/FACT%20SHEET-%20Anti-Asian%20Hate%202020%20rev%203.21.21.pdf (2021).

  • Pavlovia. https://pavlovia.org/.

  • Lawrence, C. & COVID-Dynamic Team. Masking up: a COVID-19 face-off between anti-mask laws and mandatory mask orders for Black Americans. 11 Calif. L. Rev. Online 479 Available at SSRN: https://ssrn.com/abstract=3695257 or https://doi.org/10.2139/ssrn.3695257 (2020).

  • Grasso, D. J., Briggs-Gowan, M. J., Ford, J. D. & Carter, A. S. The Epidemic—Pandemic Impacts Inventory (EPII). (2020).

  • Conway, L. G. III, Woodard, S. R. & Zubrod, A. Social psychological measurements of COVID-19: coronavirus perceived threat, government response, impacts, and experiences questionnaires. PsyArXiv. Available from: https://psyarxiv.com/z2x9a/ (2020).

  • Wolff, N. & Shi, J. Screening for substance use disorder among incarcerated men with the Alcohol, Smoking, Substance Involvement Screening Test (ASSIST): a comparative analysis of computer-administered and interviewer-administered modalities. J. Subst. Abuse Treat. 53, 22–32 (2015).

    Article 

    Google Scholar
     

  • Beck, A. T., Steer, R. A. & Brown, G. K. BDI-II, Beck Depression Inventory: Manual. (Psychological Corporation, 1996).

  • Connor, K. M. & Davidson, J. R. T. Development of a new resilience scale: The Connor-Davidson resilience scale (CD-RISC). Depress. Anxiety 18, 76–82 (2003).

    Article 

    Google Scholar
     

  • Haidt, J., McCauley, C. & Rozin, P. Individual differences in sensitivity to disgust: a scale sampling seven domains of disgust elicitors. Pers. Indiv. Differ. 16, 701–713 (1994).

    Article 

    Google Scholar
     

  • Olatunji, B. O. et al. The Disgust Scale: item analysis, factor structure, and suggestions for refinement. Psychol. Assessment 19, 281–297 (2007).

    Article 

    Google Scholar
     

  • Plant, E. A. & Devine, P. G. Internal and external motivation to respond without prejudice. J. Pers. Soc. Psychol. 75, 811–832 (1998).

    Article 

    Google Scholar
     

  • Williams, D. R., Yu, Y., Jackson, J. S. & Anderson, N. B. Racial differences in physical and mental health: socio-economic status, stress and discrimination. J. Health Psychol. 2, 335–351 (1997).

    Article 
    CAS 

    Google Scholar
     

  • Nadelhoffer, T., Shepard, J., Nahmias, E., Sripada, C. & Ross, L. T. The free will inventory: measuring beliefs about agency and responsibility. Conscious. Cogn. 25, 27–41 (2014).

    Article 

    Google Scholar
     

  • Katz, I. & Hass, R. G. Racial ambivalence and American value conflict: forrelational and priming studies of dual cognitive structures. J. Pers. Soc. Psychol. 55, 893–905 (1988).

    Article 

    Google Scholar
     

  • Langbehn, D. R. et al. The Iowa Personality Disorder Screen: development and preliminary validation of a brief screening interview. J. Pers. Disord. 13, 75–89 (1999).

    Article 
    CAS 

    Google Scholar
     

  • Trull, T. J. & Amdur, M. Diagnostic efficiency of the Iowa Personality Disorder Screen items in a nonclinical sample. J. Pers. Disord. 15, 351–357 (2001).

    Article 
    CAS 

    Google Scholar
     

  • Weathers, F. W. et al. The life events checklist for DSM-5 (LEC-5). Instrument available from the National Center for PTSD at www.ptsd.va.gov (2013).

  • Sternthal, M. J., Slopen, N. & Williams, D. R. Racial disparities in health. Du. Bois. Rev. 8, 95–113 (2011).

    Article 

    Google Scholar
     

  • Costa, P. T. & McCrae, R. R. The NEO-PI/NEO-FFI manual supplement. Odessa, FL: Psychological Assessment Resources.(1989).

  • NEO-FFI subcomponents: Norms and scoring. https://pages.uoregon.edu/gsaucier/NEO-FFI%20subcomponent%20norms%20and%20scoring.htm.

  • Gershon, R. C. et al. NIH toolbox for assessment of neurological and behavioral function. Neurology 80, S2–6 (2013).

    Article 

    Google Scholar
     

  • Cohen, S., Kamarck, T. & Mermelstein, R. A global measure of perceived stress. J. Health Soc. Behav. 24, 385–396 (1983).

    Article 
    CAS 

    Google Scholar
     

  • Watson, D., Clark, L. A. & Carey, G. Positive and negative affectivity and their relation to anxiety and depressive disorders. J. Abnorm. Psychol. 97, 346–353 (1988).

    Article 
    CAS 

    Google Scholar
     

  • Weathers, F. W. et al. The PTSD checklist for DSM-5 (PCL-5). Scale available from the National Center for PTSD at www.ptsd.va.gov (2013).

  • Prins, A. et al. The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5): development and evaluation within a veteran primary care sample. J. Gen. Intern. Med. 31, 1206–1211 (2016).

    Article 

    Google Scholar
     

  • Wang, Y.-W. et al. The Scale of Ethnocultural Empathy: development, validation, and reliability. J. Couns. Psychol. 50, 221–234 (2003).

  • Ho, A. K. et al. The nature of social dominance orientation: theorizing and measuring preferences for intergroup inequality using the new SDO7 scale. J. Pers. Soc. Psychol. 109, 1003–1028 (2015).

    Article 

    Google Scholar
     

  • Cohen, S., Doyle, W. J., Skoner, D. P., Rabin, B. S. & Gwaltney, J. M. Jr. Social ties and susceptibility to the common cold. JAMA 277, 1940–1944 (1997).

    Article 
    CAS 

    Google Scholar
     

  • Spielberger, C. D. Manual for the State-trait Anxiety Inventory (form Y) (“self-evaluation Questionnaire”). (Consulting Psychologists Press, 1983).

  • Bizumic, B. & Duckitt, J. Investigating right wing authoritarianism with a Very Short Authoritarianism Scale. J. Soc. Political Psychol. 6, 129–150 (2018).

    Article 

    Google Scholar
     

  • Nizzi, M.-C. et al. From armchair to wheelchair: how patients with a locked-in syndrome integrate bodily changes in experienced identity. Conscious. Cogn. 21, 431–437 (2012).

    Article 

    Google Scholar
     

  • Bellet, B. W. et al. Identity confusion in complicated grief: a closer look. J. Abnorm. Psychol. 129, 397–407 (2020).

    Article 

    Google Scholar
     

  • Nizzi, M.-C. Assessing the sense of self (Unpublished doctoral dissertation). Department of Psychology, Harvard University, Cambridge, MA. (2018).

  • Greenwald, A. G., McGhee, D. E. & Schwartz, J. L. Measuring individual differences in implicit cognition: the implicit association test. J. Pers. Soc. Psychol. 74, 1464–1480 (1998).

    Article 
    CAS 

    Google Scholar
     

  • Marvel, J. D. & Resh, W. D. An unconscious drive to help others? Using the implicit association test to measure prosocial motivation. Int. Public Manag. J. 22, 29–70 (2019).

    Article 

    Google Scholar
     

  • Sriram, N. & Greenwald, A. G. The Brief Implicit Association Test. Exp. Psychol. 56, 283–294 (2009).

    Article 
    CAS 

    Google Scholar
     

  • Nosek, B. A., Bar-Anan, Y., Sriram, N., Axt, J. & Greenwald, A. G. Understanding and using the Brief Implicit Association Test: recommended scoring procedures. PLoS One 9, e110938 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Payne, B. K., Cheng, C. M., Govorun, O. & Stewart, B. D. An inkblot for attitudes: affect misattribution as implicit measurement. J. Pers. Soc. Psychol. 89, 277–293 (2005).

    Article 

    Google Scholar
     

  • Stanley, D. A., Sokol-Hessner, P., Banaji, M. R. & Phelps, E. A. Implicit race attitudes predict trustworthiness judgments and economic trust decisions. P. Natl. Acad. Sci. USA 108, 7710–7715 (2011).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Hutcherson, C. A., Bushong, B. & Rangel, A. A neurocomputational model of altruistic choice and its implications. Neuron 87, 451–462 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Ledyard, J. O. Public goods: a survey of research. Social Science Working Paper, 861. California Institute of Technology, Pasadena, CA. (Unpublished) (https://resolver.caltech.edu/CaltechAUTHORS:20170823-1607360111994).

  • Wills, J. et al. Dissociable contributions of the prefrontal cortex in group-based cooperation. Soc. Cogn. Affect. Neur. 13, 349–356 (2018).

    Article 

    Google Scholar
     

  • The New York Times. Coronavirus in the U.S.: latest map and case count. The New York Times https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html (2020).

  • CDC, COVID-19 Community Intervention and Critical Populations Task Force, Monitoring and Evaluation Team, & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program. State and Territorial COVID-19 Orders and Proclamations Banning Gatherings. (2021).

  • CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, & the CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program. State and Territorial COVID-19 Orders and Proclamations Closing and Reopening Bars. (2021).

  • CDC, COVID-19 Community Intervention & Critical Populations Task Force, Monitoring & Evaluation Team, Mitigation Policy Analysis Unit, & the CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program. State and Territorial COVID-19 Orders and Proclamations Closing and Reopening Restaurants. (2021).

  • CDC, COVID-19 Community Intervention and At-Risk Task Force, Monitoring and Evaluation Team, & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program. State and Territorial COVID-19 Orders and Proclamations for Individuals to Stay Home. (2021).

  • CDC et al. U.S. State and Territorial Orders Requiring Masks in Public. (2021).

  • The New York Times. See coronavirus restrictions and mask mandates for all 50 states. The New York Times https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html.

  • Treisman, R. West: Coronavirus-Related Restrictions By State. NPR.org https://www.npr.org/2020/05/01/847416108/west-coronavirus-related-restrictions-by-state (2020).

  • MultiState. COVID-19 State Reopening Guide. MultiState https://www.multistate.us/issues/covid-19-state-reopening-guide.

  • Crowd Counting Consortium. https://sites.google.com/view/crowdcountingconsortium/view-download-the-data.

  • Rusch, T. et al. Introduction to the COVID Dynamic dataset. OSF https://doi.org/10.17605/OSF.IO/KEX8Y (2022).

  • Rocher, L. & Hendrickx, J. M. & De Montjoye, Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10, 1–9 (2019).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Bureau, U. S. C. Selected housing characteristics: 2013–2017 American community survey 5-year estimates (2017).

  • Gallup. Gallup historical trends: party affiliation. Gallup.com https://news.gallup.com/poll/15370/Party-Affiliation.aspx (2007).

  • Team, R. C. R: A language and environment for statistical computing. (Vienna, Austria, 2013).

  • Pasek, J. anesrake: ANES raking implementation. R package version 0. 80 (2019).

  • Battaglia, M. P., Hoaglin, D. C. & Frankel, M. R. Practical considerations in raking survey data. Surv. Pract. 2, 2953 (2009).

    Article 

    Google Scholar
     

  • Atkeson, L. R. & Alvarez, R. M. The Oxford Handbook of Polling and Survey Methods. (Oxford University Press, 2018).

  • Pew Research Center. The political typology: in polarized era, deep divisions persist within coalitions of both Democrats and Republicans. https://www.pewresearch.org/politics/2021/11/09/beyond-red-vs-blue-the-political-typology-2/.

  • Lane, K. A., Banaji, M. R., Nosek, B. A. & Greenwald, A. G. Understanding and using the implicit association test: IV: what we know (so far) about the method. in Implicit Measures of Attitudes 59–102 (The Guilford Press, 2007).

  • Bird, S., Klein, E. & Loper, E. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. (“O’Reilly Media, Inc.”, 2009).

  • Qualitative Research & Evaluation Methods. SAGE Publications Inc https://us.sagepub.com/en-us/nam/qualitative-research-evaluation-methods/book232962 (2021).

  • R Studio. Shiny. https://shiny.rstudio.com/.

  • Murray, J. S. Multiple imputation: a review of practical and theoretical findings. Stat. Sci. 33, 142–159 (2018).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Huque, M. H., Carlin, J. B., Simpson, J. A. & Lee, K. J. A comparison of multiple imputation methods for missing data in longitudinal studies. BMC Med. Res. Methodol. 18, 168 (2018).

    Article 

    Google Scholar
     

  • Liu, X. Methods and Applications of Longitudinal Data Analysis. (Elsevier, 2015).

  • Fine, K. L., Suk, H. W. & Grimm, K. J. An examination of a functional mixed-effects modeling approach to the analysis of longitudinal data. Multivar. Behav. Res. 54, 475–491 (2019).

    Article 

    Google Scholar
     

  • Zorowitz, S., Niv, Y. & Bennett, D. Inattentive responding can induce spurious associations between task behavior and symptom measures. Preprint at https://doi.org/10.31234/osf.io/rynhk (2021).

  • Ward, M. K., Meade, A. W., Allred, C. M., Pappalardo, G. & Stoughton, J. W. Careless response and attrition as sources of bias in online survey assessments of personality traits and performance. Comput. Human Behav. 76, 417–430 (2017).

    Article 

    Google Scholar
     

  • Gadarian, S. K., Goodman, S. W. & Pepinsky, T. B. Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic. PLoS One 16, e0249596 (2021).

    Article 
    CAS 

    Google Scholar
     

  • Tung, H. H., Chang, T.-J. & Lin, M.-J. Political ideology predicts preventative behaviors and infections amid COVID-19 in democracies. Soc. Sci. Med 308, 115199 (2022).

    Article 

    Google Scholar
     

  • Chen, H.-F. & Karim, S. A. Relationship between political partisanship and COVID-19 deaths: future implications for public health. J. Public Health (Oxf.) (2021).

  • McCredie, M. N. & Morey, L. C. Who are the Turkers? a characterization of MTurk workers using the Personality Assessment Inventory. Assessment 26, 759–766 (2019).

    Article 

    Google Scholar
     

  • APS Global Collaboration on COVID-19. Association for Psychological Science – APS https://www.psychologicalscience.org/covid-initiative.

  • De Leeuw, J. R. jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behav. Res. Methods 47, 1–12 (2015).

    Article 
    ADS 

    Google Scholar
     

  • Van Rossum, G. & Drake, F. L. Python 3 Reference Manual: (Python Documentation Manual Part 2). (CreateSpace Independent Publishing Platform, 2009).

  • Van Rossum, G. & Drake, F. L. Introduction to Python 3: (Python Documentation Manual Part 1). (CreateSpace Independent Publishing Platform, 2009).

  • Sources

    1/ https://Google.com/

    2/ https://www.nature.com/articles/s41597-022-01901-6

    The mention sources can contact us to remove/changing this article

    What Are The Main Benefits Of Comparing Car Insurance Quotes Online

    LOS ANGELES, CA / ACCESSWIRE / June 24, 2020, / Compare-autoinsurance.Org has launched a new blog post that presents the main benefits of comparing multiple car insurance quotes. For more info and free online quotes, please visit https://compare-autoinsurance.Org/the-advantages-of-comparing-prices-with-car-insurance-quotes-online/ The modern society has numerous technological advantages. One important advantage is the speed at which information is sent and received. With the help of the internet, the shopping habits of many persons have drastically changed. The car insurance industry hasn't remained untouched by these changes. On the internet, drivers can compare insurance prices and find out which sellers have the best offers. View photos The advantages of comparing online car insurance quotes are the following: Online quotes can be obtained from anywhere and at any time. Unlike physical insurance agencies, websites don't have a specific schedule and they are available at any time. Drivers that have busy working schedules, can compare quotes from anywhere and at any time, even at midnight. Multiple choices. Almost all insurance providers, no matter if they are well-known brands or just local insurers, have an online presence. Online quotes will allow policyholders the chance to discover multiple insurance companies and check their prices. Drivers are no longer required to get quotes from just a few known insurance companies. Also, local and regional insurers can provide lower insurance rates for the same services. Accurate insurance estimates. Online quotes can only be accurate if the customers provide accurate and real info about their car models and driving history. Lying about past driving incidents can make the price estimates to be lower, but when dealing with an insurance company lying to them is useless. Usually, insurance companies will do research about a potential customer before granting him coverage. Online quotes can be sorted easily. Although drivers are recommended to not choose a policy just based on its price, drivers can easily sort quotes by insurance price. Using brokerage websites will allow drivers to get quotes from multiple insurers, thus making the comparison faster and easier. For additional info, money-saving tips, and free car insurance quotes, visit https://compare-autoinsurance.Org/ Compare-autoinsurance.Org is an online provider of life, home, health, and auto insurance quotes. This website is unique because it does not simply stick to one kind of insurance provider, but brings the clients the best deals from many different online insurance carriers. In this way, clients have access to offers from multiple carriers all in one place: this website. On this site, customers have access to quotes for insurance plans from various agencies, such as local or nationwide agencies, brand names insurance companies, etc. "Online quotes can easily help drivers obtain better car insurance deals. All they have to do is to complete an online form with accurate and real info, then compare prices", said Russell Rabichev, Marketing Director of Internet Marketing Company. CONTACT: Company Name: Internet Marketing CompanyPerson for contact Name: Gurgu CPhone Number: (818) 359-3898Email: [email protected]: https://compare-autoinsurance.Org/ SOURCE: Compare-autoinsurance.Org View source version on accesswire.Com:https://www.Accesswire.Com/595055/What-Are-The-Main-Benefits-Of-Comparing-Car-Insurance-Quotes-Online View photos

    ExBUlletin

    to request, modification Contact us at Here or [email protected]