Evidence map›Paper›PMID 36995753›Full record

Trial reportJournal of medical Internet research2023

Using Normative Language When Describing Scientific Findings: Randomized Controlled Trial of Effects on Trust and Credibility.

Jon Agley, Yunyu Xiao, Esi E Thompson, Lilian Golzarri-Arroyo

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2023. 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

4 authors.

Jon AgleyPrevention Insights, Department of Applied Health Science, School of Public Health - Bloomington, Indiana University Bloomington, Bloomington, IN, United States.ORCID 0000-0003-2345-8850
Yunyu XiaoDepartment of Population Health Sciences, Weill Cornell Medical College, New York, NY, United States.ORCID 0000-0002-0479-1781
Esi E ThompsonThe Media School, Indiana University Bloomington, Bloomington, IN, United States.ORCID 0000-0002-0772-0997
Lilian Golzarri-ArroyoBiostatistics Consulting Center, School of Public Health - Bloomington, Indiana University Bloomington, Bloomington, IN, United States.ORCID 0000-0002-1221-6701

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundScientists often make cognitive claims (eg, the results of their work) and normative claims (eg, what should be done based on those results). Yet, these types of statements contain very different information and implications. This randomized controlled trial sought to characterize the granular effects of using normative language in science communication.

objectiveOur study examined whether viewing a social media post containing scientific claims about face masks for COVID-19 using both normative and cognitive language (intervention arm) would reduce perceptions of trust and credibility in science and scientists compared with an identical post using only cognitive language (control arm). We also examined whether effects were mediated by political orientation.

methodsThis was a 2-arm, parallel group, randomized controlled trial. We aimed to recruit 1500 US adults (age 18+) from the Prolific platform who were representative of the US population census by cross sections of age, race/ethnicity, and gender. Participants were randomly assigned to view 1 of 2 images of a social media post about face masks to prevent COVID-19. The control image described the results of a real study (cognitive language), and the intervention image was identical, but also included recommendations from the same study about what people should do based on the results (normative language). Primary outcomes were trust in science and scientists (21-item scale) and 4 individual items related to trust and credibility; 9 additional covariates (eg, sociodemographics, political orientation) were measured and included in analyses.

resultsFrom September 4, 2022, to September 6, 2022, 1526 individuals completed the study. For the sample as a whole (eg, without interaction terms), there was no evidence that a single exposure to normative language affected perceptions of trust or credibility in science or scientists. When including the interaction term (study arm × political orientation), there was some evidence of differential effects, such that individuals with liberal political orientation were more likely to trust scientific information from the social media post's author if the post included normative language, and political conservatives were more likely to trust scientific information from the post's author if the post included only cognitive language (β=0.05, 95% CI 0.00 to 0.10; P=.04).

conclusionsThis study does not support the authors' original hypotheses that single exposures to normative language can reduce perceptions of trust or credibility in science or scientists for all people. However, the secondary preregistered analyses indicate the possibility that political orientation may differentially mediate the effect of normative and cognitive language from scientists on people's perceptions. We do not submit this paper as definitive evidence thereof but do believe that there is sufficient evidence to support additional research into this topic, which may have implications for effective scientific communication.

trial registrationOSF Registries osf.io/kb3yh; https://osf.io/kb3yh. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/41747.

Indexed as

CommunicationCOVID-19TrustAdultHumansLanguageMasksSocial Mediameta-scienceRCTscientific communicationtrusttrust in science

Identifiers

PMID36995753
PMCPMC10131812

What OpenQuestion holds

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Registered trials

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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.