Evidence map›Paper›PMID 38198461›Full record

ArticlePloS one2024

Trumpism, climate and COVID: Social bases of the new science rejection.

Lawrence C Hamilton

Abstract read
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

1 author.

Lawrence C HamiltonDepartment of Sociology, University of New Hampshire, Durham, New Hampshire, United States of America.ORCID 0000-0003-1977-0649

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although the hazards posed by greenhouse warming and COVID-19 are quite different, diagnosis and mitigation prospects for both depend heavily on science. Unfortunately, the reality of both threats has been subject to politicized science rejection in the US, making these deadly problems less tractable. There are substantial parallels between the two cases of science rejection, including common rhetoric and conservative political leadership. Survey research has reached widely-replicated conclusions regarding the social bases of climate-change perceptions. Corresponding studies of COVID-19 perceptions have found some political commonalities, but less agreement on other details. Here, we address this gap using generalized structural equation modeling (GSEM) and 2021 US survey data to make direct comparisons between the social bases of rejecting the reality of anthropogenic climate change (ACC) and rejecting COVID-19 vaccination. Trumpism, operationalized from approval of ex-president Trump, is viewed as an intervening variable that influences both types of science rejection. Trumpism itself is predicted by age, race, evangelical religion, ideology, and receptivity to seemingly non-political conspiracy beliefs. Considering direct as well as indirect effects (through Trumpism), climate change and vaccine rejection are similarly predicted by white and evangelical identity, conspiracism, and by education×ideology and friends×party interactions. The finding that Trumpism exacerbates science rejection could also apply to other science- and expertise-related topics unrelated to climate and COVID. These results invite broader comparisons across topics, with analogous movements in other countries, and continued tracking as US Trumpism evolves beyond Trump.

Indexed as

COVID-19Climate ChangeCOVID-19 VaccinesDiagnosis, DifferentialEducational StatusHumansCOVID-19 Vaccines

Identifiers

PMID38198461
PMCPMC10781192

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.