Evidence map›Paper›PMID 36737442›Full record

ArticleScientific data2023

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

Tessa Rusch, Yanting Han, Dehua Liang, Amber R Hopkins, Caroline V Lawrence, Uri Maoz, Lynn K Paul, Damian A Stanley, COVID-Dynamic Team

Open access · goldAbstract readDataset
In one paragraph

Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.8field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 18 citations in OpenAlex.

  1. Trial
  2. Article
  3. Collective events and individual affect shape autobiographical memory.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  4. COVID-19 Patterns in Araraquara, Brazil: A Multimodal Analysis.International journal of environmental research and public health · 2023
    Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 6 institutions in 1 country.

Tessa Rusch *Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-5445-6885
Yanting Han *Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0003-3381-2059
Dehua Liang *Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.ORCID 0000-0003-4794-0270
Amber R HopkinsInstitute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
Caroline V LawrenceYale Law School, New Haven, CT, USA.
Uri MaozDivision of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
Lynn K PaulDivision of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-3128-8313
Damian A StanleyDivision of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, USA. dstanley@adelphi.edu.
COVID-Dynamic Team
California Institute of Technology · USDartmouth College · USChapman University · USRutgers, The State University of New Jersey · USYale University · USCity College of New York · US

Funding

The Neurobiology of Social Decision-Making: Social Inference and ContextP50MH094258 · NIMH · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI ADOLPHS, RALPH · 2012 to 2021
$18.6M
NIMH NIH HHS P50 MH094258U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) 2P50MH094258
6 · The paper itself

Abstract

The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of >1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants' responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities.

Indexed as

COVID-19Health BehaviorHumansLongitudinal StudiesPandemicsPublic HealthSARS-CoV-2

Identifiers

PMID36737442
PMCPMC9897616
OpenAlexW4319077815

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.