ArticlePloS one2022
The new normal: Covid-19 risk perceptions and support for continuing restrictions past vaccinations.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed, 14 citations in OpenAlex.
- Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI.Journal of medical Internet research · 2025Article
- COVID-19 risk perceptions in Japan: a cross-sectional study.Scientific reports · 2024Article
- Aggressive measures, rising inequalities, and mass formation during the COVID-19 crisis: An overview and proposed way forward.Frontiers in public health · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
I test the possibility that over-estimating negative consequences of COVID-19 (e.g., hospitalizations, deaths, and threats to children) will be associated with stronger support the 'new normal' (i.e., continuation of restrictions for an undefined period starting with wide-spread access to vaccines and completed vaccinations of vulnerable people). The new normal was assessed by endorsing practices such as vaccine passports, travel restrictions, mandatory masking, continuing contact tracing, and pursuing elimination. Results are based on five samples (N = 1,233 from April 2021 and N = 264 from January 2022) and suggest that people over-estimate COVID-19 risks to children and healthy people, as evidenced by median estimates that 5% of all global deaths were children, 29% were generally healthy people under 65, and that a healthy person under the age of 65 has 5% chance of dying from COVID-19. Over-estimates observed in this study align with those based on representative samples, and they were consistently related to stronger support for the new normal. This relationship emerged when participants estimated risks with percentages (core indicators) and indicated the extent to which risk-based statements are true/supported with evidence or false/unsupported (alternative indicators). People were notably more likely to support continuing restrictions if they believed that COVID-19 risk and risk mitigation tactics are true, even when they are not (e.g., children need to be prioritized for boosters). These relationships persisted when considering competing explanations (political ideology, statistics literacy, belief in conspiracy theories). I trace these effects to well-meaning efforts to prevent under-estimation. Public policy and people's perceptions of risks are intertwined, where even inaccurate judgments may influence decisions. Failure to combat all misinformation with equal rigor may jeopardize the restoration of the social and economic life essential for building adaptive post-pandemic societies.
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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.