ArticlePloS one2025
The 3N Model and collective support for extreme measures to combat COVID-19.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding resistance to COVID-19 measures is crucial, since it undermines public health efforts during crises. Building on prior research showing the crucial role of individual psychological factors in shaping responses to such efforts, we focus on psychological factors involved. Specifically, we examine such psychological factors that shape support for COVID-19 measures by conceptualizing them as extreme measures-restrictions that significantly alter daily life-thus enabling the application of the 3N model of extreme behavior. Drawing on data from the multinational dataset (N = 62,983) across 114 countries, we tested the role of two types of needs/losses: collective loss and personal loss. The results reveal a differential pattern: collective loss is associated with lower support for extreme measures, while personal loss is associated with higher support. Both effects are mediated by perceived social network norms, though the strength of mediation differs. These findings extend the 3N model by highlighting how distinct types of loss shape responses to extreme measures, and they offer implications for designing public policies that address both individual and collective concerns.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.