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Collectivism predicts mask use during COVID-19
Open accessAbstract
Since its outbreak, COVID-19 has impacted world regions differentially. Whereas some regions still record tens of thousands of new infections daily, other regions have contained the virus. What explains these striking regional differences? We advance a cultural psychological perspective on mask usage, a precautionary measure vital for curbing the pandemic. Four large-scale studies provide evidence that collectivism (versus individualism) positively predicts mask usage-both within the United States and across the world. Analyzing a dataset of all 3,141 counties of the 50 US states (based on 248,941 individuals), Study 1a revealed that mask usage was higher in more collectivistic US states. Study 1b replicated this finding in another dataset of 16,737 individuals in the 50 US states. Analyzing a dataset of 367,109 individuals in 29 countries, Study 2 revealed that mask usage was higher in more collectivistic countries. Study 3 replicated this finding in a dataset of 277,219 Facebook users in 67 countries. The link between collectivism and mask usage was robust to a host of control variables, including cultural tightness-looseness, political affiliation, demographics, population density, socioeconomic indicators, universal health coverage, government response stringency, and time. Our research suggests that culture fundamentally shapes how people respond to crises like the COVID-19 pandemic. Understanding cultural differences not only provides insight into the current pandemic, but also helps the world prepare for future crises.
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“Indeed, levels of collectivism were associated with higher intentions to engage in social distancing behaviours and mask wearing during the COVID-19 pandemic [44, 45].”
From Adaptive function and correlates of anxiety during a pandemic (2021) · cites this paper as [45]
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Metadata
- Authors
- Lu, J. G., Jin, P., & English, A. S.
- Affiliations
- Massachusetts Institute of Technology (US)
- Shanghai International Studies University (CN)
- Corresponding author
- Lu, Jackson G.
- English, Alexander S.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-05-20
- Volume
- 118
- Issue
- 23
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8201834
- OpenAlex
- W3160951844
- Cited by
- 342
- References
- 57
- Citations by year
- 2026: 20 · 2025: 49 · 2024: 57 · 2023: 73 · 2022: 118 · 2021: 23
- FWCI
- 67.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- Massachusetts Institute of Technology
- Topics
- Social Sciences › Psychology › Social Psychology › Cultural Differences and Values
- Social Sciences › Sociology and Political Science › COVID-19 Prevention and Impact
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- collectivism, individualism, masking, COVID-19 pandemic, cultural values, cross-national comparison, cultural tightness-looseness, crisis response, cultural psychology, public health behavior
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, Individuality, Masks, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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