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Trust in science meaningfully contributes to our understanding of people’s belief in misinformation and their intentions to take actions to prevent COVID-19. However, no experimental research has sought to intervene on this variable to develop a scalable response to the COVID-19 infodemic.
Our study examined whether brief exposure to an infographic about the scientific process might increase trust in science and thereby affect belief in misinformation and intention to take preventive actions for COVID-19.
This two-arm, parallel-group, randomized controlled trial aimed to recruit a US representative sample of 1000 adults by age, race/ethnicity, and gender using the Prolific platform. Participants were randomly assigned to view either an intervention infographic about the scientific process or a control infographic. The intervention infographic was designed through a separate pilot study. Primary outcomes were trust in science, COVID-19 narrative belief profile, and COVID-19 preventive behavioral intentions. We also collected 12 covariates and incorporated them into all analyses. All outcomes were collected using web-based assessment.
From January 22, 2021 to January 24, 2021, 1017 participants completed the study. The intervention slightly improved trust in science (difference-in-difference 0.03, SE 0.01, t1000=2.16,
Briefly viewing an infographic about science appeared to cause a small aggregate increase in trust in science, which may have, in turn, reduced the believability of COVID-19 misinformation. The effect sizes were small but commensurate with our 60-second, highly scalable intervention approach. Researchers should study the potential for truthful messaging about how science works to serve as misinformation inoculation and test how best to do so.
NCT04557241; https://clinicaltrials.gov/ct2/show/NCT04557241
RR2-10.2196/24383
The COVID-19 pandemic has been accompanied by a substantive, pervasive outpouring of misinformation about the disease [
Unfortunately, far from tapering more than a year into the pandemic, the volume of misinformation has remained high; representatives of multiple organizations, including the World Health Organization and US Food and Drug Administration, recently warned that misinformation poses a global concern and may drive pandemic-related harms [
In May 2020, in response to growing concern about the COVID-19 infodemic, our team conducted one of the first studies of COVID-19 misinformation believability and the factors associated therewith [
Then, we found that—controlling for race/ethnicity, gender, age, and education level—trust in science and scientists, a scale variable computed from 21 Likert-type questions of the Trust in Science and Scientists Inventory [
Based on our findings and research described subsequently, we speculated that the strong association between COVID-19 narrative belief profile and trust in science might mean that (1) if a brief, inexpensive intervention could increase trust in science, it might possibly (2) affect individuals’ COVID-19 narrative belief profile membership. We also wondered whether this effect, mediated by belief profile, might (3) influence behavioral intentions to undertake COVID-19 preventive behaviors. Much of our rationale for these ideas is laid out in the published protocol for the present study [
Trust is highly complex [
Importantly, though, the prior formulation only pertains to claims about research findings, which make assertions about reality with varying degrees of certainty (eg, face mask use can reduce community transmission of COVID-19 [
These concepts can help illustrate how the Trust in Science and Scientists Inventory [
In addition to our own identification of associations between trust in science and belief in misinformation [
One prominent approach to addressing misinformation is debunking (eg, fact checking). Despite some initial concerns, fact checking appears unlikely to backfire [
A promising additional approach is prebunking (eg, inoculation) to confer resistance to the potential influence of misinformation before it is encountered [
In our prior study of COVID-19 misinformation, around 70% of respondents were classified as belonging to the “scientific profile,” and classification therein was strongly associated with trust in science. Such an association is also supported both by theoretical and scientific literature. Separately, research on COVID-19 misinformation has suggested the value of scalable, universal prophylaxis that can support people in resisting the influence of misinformation. Therefore, our current study combines those ideas to examine an inoculation approach to COVID-19 misinformation using trust in science as a scalable intervention target.
In this preregistered, randomized controlled trial, we examined the effects of a brief prophylactic intervention (viewing a single infographic about the scientific process for at least 60 seconds). The study had 3 aims with corresponding hypotheses, which we copied verbatim from the study protocol [
We aim to assess the effect of a brief informational infographic about the scientific process on trust in science. We hypothesize that exposure to such an intervention will have a moderate, positive effect on trust in science.
We aim to assess the effect of a brief informational infographic about the scientific process on the likelihood of believing scientifically implausible narratives about COVID-19. We hypothesize that exposure to such an intervention will have a small, negative effect on the likelihood of believing implausible narratives, as evidenced by profile membership, and that this will be partly mediated by trust in science.
We aim to assess the effect of a brief informational infographic about the scientific process on behavioral intentions to engage in recommended COVID-19 [nonpharmaceutical preventive behaviors (NPBs)]. We hypothesize that exposure to such an intervention will have a small, positive effect on behavioral intentions to engage in recommended COVID-19 NPBs that will be partly mediated by misinformation profile membership.
This study of COVID-19 misinformation prophylaxis was a single-stage, two-arm, parallel-group, randomized superiority trial with a 1:1 allocation ratio. Participants were a US-based nationally representative population sample by age, sex, race, and ethnicity recruited using the online data collection platform Prolific [
After providing sociodemographic information and passing quality control checks, participants were randomly assigned to 1 of 2 study arms: (1) a control group that viewed an infographic about how hunting dogs point at targets (
Control infographic.
Intervention infographic.
Enrollment was managed by Prolific, entirely independent of the study team. Enrolled subjects accessed a link to Qualtrics (QualtricsXM, Seattle, WA) to participate in the study. Eligible participants were randomized to study arms using the randomizer procedure in Qualtrics with a 1:1 allocation ratio, ensuring no involvement by study personnel. To prevent expectancy biases, study hypotheses and intentions were masked to participants. The summary statement indicated only that “we are interested in understanding how people perceive and think about messages and images.”
As prespecified [
Eligible participants who passed quality control checks were randomized (no indication of this was provided to participants) and then proceeded to the Trust in Science and Scientists Inventory [
This study had 3 primary prespecified outcome measures corresponding with 3 aims.
Aim One investigated the effect of the intervention on participants’ trust in science and scientists. That construct was measured using the 21-item Trust in Science Inventory [
Aim Two investigated the effect of the intervention on participants’ classification into misinformation believability profiles [
The rollout of 5G cellphone networks caused the spread of COVID-19.
Bill Gates caused (or helped cause) the spread of COVID-19 in order to expand his vaccination programs.
COVID-19 was developed as a military weapon (by China, the United States, or some other country).
The number of deaths from COVID-19 has been exaggerated as a way to restrict liberties in the United States.
A fifth statement referenced the explanation that is currently considered most plausible by much of the scientific community [
SARS-Cov-2, the virus that causes COVID-19, likely originated in animals [like bats] and then spread to humans.
Finally, 2 additional misinformed statements about face masks were added for this study [
Wearing a face mask for COVID-19 prevention can cause oxygen deficiency or carbon dioxide intoxication.
Face masks are probably not helpful in reducing COVID-19 spread in a community.
Statistical and logical classification of participants into latent profiles based on the believability of misinformation was demonstrated in our prior research [
Aim Three targeted the intervention’s effect on participants’ behavioral intentions to engage in the COVID-19 preventive behaviors recommended by the CDC at the time of study administration [
Wash your hands often (or use a hand sanitizer that contains at least 60% alcohol).
Avoid close contact (stay at least 6 feet from other people).
Cover your mouth and nose with a mask when around others.
Cover coughs and sneezes.
Clean and disinfect frequently touched surfaces daily.
Monitor your health daily.
Intention to get vaccinated was not prespecified in the protocol but was added as the seventh behavioral intention prior to administration in response to availability of vaccination for some US residents.
As planned, overall preventive behavioral intentions were assessed using exploratory factor analysis (see the Statistical Analysis section) to determine the number of factors present and then by computing mean scores for each factor to serve as outcomes. Intention to get vaccinated was analyzed as an isolated outcome of interest in a separate study [
Because they already had received at least one shot of the vaccine, 49 participants were not asked to respond to the question about intention to get vaccinated for COVID-19; data for those individuals were imputed as a 7 (likely). Sensitivity analyses were performed without imputing data for those 49 participants, which led to similar results and conclusions. Therefore, imputed results were used in analyses throughout the manuscript.
Additional measures were added as covariates for analysis, as prespecified, including political orientation and religious commitment [
Due to evolving circumstances in the United States during this study, a question about COVID-19 vaccination status was added after the protocol was published. It read, “Vaccines to prevent COVID-19 have been approved by the Food and Drug Administration for use in the United States. The vaccines will be available to different people at different times. Did you already get a COVID-19 vaccine (at least one shot)?”
We planned to recruit 1000 participants, which would allow detection of small differences (Cohen d=0.18) with 80% power and would be sufficient for both types of planned analysis, linear mixed models (LMM) and path analyses [
The primary outcome for Aim One, the effect of the infographic intervention on trust in science, was analyzed using an LMM controlling for all covariates (see the Outcomes section) with a random intercept for the individual participant. The interaction between study condition (intervention/control) and time (pre/postintervention) was estimated using contrasts to obtain the difference-in-difference using Kenward-Roger degrees of freedom approximation.
For the first component of this aim, we examined believability profiles for narrative statements about COVID-19 using latent profile analysis. To select the number of classes, we reviewed the Akaike information criterion (AIC), Bayesian information criterion (BIC) and adjusted BIC, class size, entropy, and results from the Vuong-Lo-Mendell-Rubin likelihood ratio test (LMR) to examine improvements in model fit for
Next, we assigned a “profile” value to each participant based on the profile to which they most closely belonged. That variable was used as an outcome in the prespecified path analysis for this aim, which investigated adjusted odds of being a member of a less-scientific profile by examining the direct effect of the intervention and the indirect effect of the intervention mediated by trust in science, controlling for all other covariates. Finally, we presented results in parallel, treating profile as a multinomial variable (single model) and treating it as a dummy variable (one model per identified profile).
To elucidate other potentially interesting connections between the study variables, we conducted an exploratory, unplanned multivariate logistic regression analysis using profile membership as the outcome variable. All other variables served as dependent predictors except pre-intervention trust in science and having a professional diagnosis of COVID-19, which were highly associated with postintervention trust and believing one had been infected by COVID-19, respectively.
To determine the format of the outcome variable for this aim, we first conducted exploratory factor analysis (maximum likelihood with varimax rotation) to decide whether it was appropriate to treat the behavioral intentions regarding preventive behaviors as a monotonic scale [
We computed exploratory path analyses to assess the influence of trust in science on preventive behaviors, with a mediation pathway through believability profile membership, with other variables serving as covariates. These analyses were for informative purposes only and were not used to generate any causal inferences.
The funders of the study had no role in data collection, analysis, interpretation, or writing of the report. As reported in the protocol, grant reviewers made suggestions to improve study rigor that were incorporated prior to study initiation. Grant reviews were published alongside the protocol [
A representative panel of 1000 paid US respondents by gender, age, and race/ethnicity was solicited from Prolific on January 22, 2021 [
CONSORT flow diagram.
Sample characteristics by study arm.
Variable | Intervention (n=511) | Control (n=503) | |
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Male | 251 (49.1) | 238 (47.3) |
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Female | 254 (49.7) | 261 (51.9) |
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Nonbinary | 3 (0.6) | 3 (0.6) |
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Transgender | 3 (0.6) | 1 (0.2) |
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White | 388 (75.9) | 394 (78.3) |
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Black or African American | 74 (14.5) | 58 (11.5) |
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American Indian or Alaska Native | 3 (0.6) | 2 (0.4) |
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Asian | 35 (6.8) | 37 (7.4) |
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Native Hawaiian or Pacific Islander | 0 (0.0) | 1 (0.2) |
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Other | 11 (2.2) | 11 (2.2) |
Hispanic or Latino/a (Yes), n (%) | 28 (5.5) | 35 (7.0) | |
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Yes | 15 (2.9) | 27 (5.4) |
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No/unsure | 496 (97.1) | 476 (94.5) |
Religious commitment (1=low to 10=high), mean (SD) | 4.15 (3.45) | 4.06 (3.35) | |
Political orientation (1=liberal to 10=conservative), mean (SD) | 4.27 (2.78) | 4.15 (2.71) | |
Vaccination intentiona (1=unlikely to 7=likely), mean (SD) | 5.48 (2.14) | 5.50 (2.10) | |
Seriousness of contracting COVID-19 (1=not at all serious to 10=very serious), mean (SD) | 6.60 (2.72) | 6.41 (2.61) | |
Confidence avoiding COVID-19 (1=not very confident to 5=very confident), mean (SD) | 3.25 (0.98) | 3.27 (0.97) | |
Family/friends COVID-19 avoidance (1=strongly disagree to 7=strongly agree), mean (SD) | 5.56 (1.51) | 5.72 (1.40) | |
Age (years), mean (SD) | 45.50 (16.61) | 45.28 (16.19) |
aData do not include imputed values of “7” for vaccinated individuals.
We hypothesized that exposure to the infographic intervention would have a moderate, positive effect on trust in science. This hypothesis was partly upheld. Our difference-in-difference analysis suggested that, controlling for all covariates, viewing the intervention infographic had a small, positive effect (0.03, SE 0.01, t1000=2.16,
Contrast estimates for aim one.
Contrast | Estimate | SE | df |
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Control pre vs intervention pre | 0.00 | 0.03 | 1048.17 | 0.10 | >.999 |
Control pre vs control post | –0.04 | 0.01 | 1000.00 | –5.11 | <.001 |
Intervention pre vs intervention post | –0.07 | 0.01 | 1000.00 | –8.23 | <.001 |
Control post vs intervention post | –0.02 | 0.03 | 1048.17 | –0.69 | .90 |
Difference-in-difference (control pre-post) vs (intervention pre-post) | 0.03 | 0.01 | 1000.00 | 2.16 | .031 |
Trust in science scores and 95% CIs.
Based on fit statistics (see
Standardized means for latent profiles of narrative believabilitya for aim two.
Statement | Profile One (828/1017, 81.42%) | Profile Two (42/1017, 4.13%) | Profile Three (147/1017, 14.45%) |
5G | 1.06 | 4.17 | 1.26 |
Gates/vaccine | 1.14 | 3.54 | 2.75 |
Masks—CO2 or O2 concerns | 1.49 | 4.38 | 3.86 |
Military weapon | 2.19 | 4.38 | 4.48 |
Restrict liberty | 1.39 | 4.39 | 5.51 |
Masks—not prevent spread | 1.47 | 3.51 | 4.31 |
Zoonotic | 5.55 | 4.63 | 4.15 |
aBelievability scores ranged from 1 (Extremely unbelievable) to 7 (Extremely believable).
Believability of narrative statements by latent profile. Believability scores range from 1 (Extremely unbelievable) to 7 (Extremely believable).
Profile One (828/1017, 81.42%), the largest class, was most likely to believe the zoonotic narrative (mean 5.55) and found most other narratives to be extremely unbelievable (mean <1.50), with the exception of the military weapon narrative (mean 2.19).
Profile Two (42/1017, 4.13%) was the smallest class and considered all the narratives to be moderately plausible, within a narrow band of believability scores (mean >3.50 and
Profile Three (147/1017, 14.45%) reported differential believability across narratives. Members reported that the 5G theory (mean 1.26) and Bill Gates/vaccine narrative (mean 2.75) were extremely or mostly unbelievable. The misinformed idea that face masks can cause carbon dioxide intoxication or oxygen deficiency was perceived to be somewhat more believable (mean 3.86), as were the scientifically implausible statements that masks are not helpful in reducing COVID-19 spread (mean 4.31) or that COVID-19 was developed as a military weapon (mean 4.48). Believability of the zoonotic narrative also fell within this range (mean 4.15). For this profile, the most believable narrative was that the number of deaths from COVID-19 was exaggerated as a way to restrict liberties in the United States (mean 5.51).
We hypothesized that exposure to the intervention would have a small, negative effect on the likelihood of belonging to a profile that believed misinformed or implausible narratives and that it would be partially mediated by trust in science. This hypothesis was partly upheld, as there was no evidence of a direct effect, but some evidence of a mediated effect (
Path analysis of the effects of the intervention on the believability profile.
Dependent variables | Odds ratio | SE | Lower CI | Upper CI | z | AICa | ||
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416.88 | |
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Direct effect | 0.96 | 0.35 | 0.28 | 1.64 | –0.10 | .92 | |
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Indirect effect | 0.92 | 0.04 | 0.84 | 1.00 | –1.95 | .051 | |
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Total effect | 0.89 | 0.32 | 0.25 | 1.52 | –0.34 | .74 | |
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Direct effect | 0.98 | 0.22 | 0.54 | 1.41 | –0.11 | .91 | |
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Indirect effect | 0.95 | 0.02 | 0.90 | 1.00 | –1.82 | .07 | |
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Total effect | 0.93 | 0.21 | 0.52 | 1.35 | –0.32 | .75 | |
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Direct effect | 1.03 | 0.21 | 0.61 | 1.45 | 0.16 | .88 | 214.38 |
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Indirect effect | 1.06 | 0.03 | 1.00 | 1.12 | 2.01 | .045 | |
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Total effect | 1.10 | 0.23 | 0.65 | 1.54 | 0.44 | .66 | |
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Direct effect | 1.00 | 0.35 | 0.31 | 1.70 | 0.01 | .99 | –147.42 |
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Indirect effect | 0.93 | 0.04 | 0.86 | 1.00 | –1.84 | .07 | |
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Total effect | 0.94 | 0.33 | 0.28 | 1.59 | –0.19 | .85 | |
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Direct effect | 1.00 | 0.22 | 0.56 | 1.43 | 0.00 | 1.00 | 146.46 |
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Indirect effect | 0.97 | 0.02 | 0.93 | 1.01 | –1.56 | .12 | |
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Total effect | 0.97 | 0.22 | 0.55 | 1.39 | –0.15 | .88 |
aAIC: Akaike information criterion.
bReference is Profile One.
cEach profile is a dummy variable.
Influence of the intervention on the likelihood of being classified in Profile One, adjusted for age, gender, race, vaccination status, political orientation, perceived severity, perceived susceptibility, family behavior, prior diagnosis, prior infection, and pre-intervention trust. OR: odds ratio.
In the multinomial analysis, controlling for all covariates, the direct effect of viewing the intervention on belonging to Profile Two (versus Profile One) was nonsignificant (adjusted odds ratio [AOR] 0.96, SE 0.35, 95% CI 0.28-1.64,
To support disambiguation of the indirect effect, we also conducted binomial path analyses using each profile as a dummy variable. The direct effect of viewing the intervention on belonging to Profile One was nonsignificant (AOR 1.03, SE 0.21, 95% CI 0.61-1.45,
Exploratory factor analysis did not clearly indicate whether the 7 preventive behaviors formed a monotonic or 2-factor scale. Discrimination based on eigenvalues favored a 2-factor solution, which cumulatively explained 46% of the variance (χ28=124.1,
In the 2-factor solution, handwashing, cleaning and disinfecting surfaces daily, and monitoring one’s health daily cleanly loaded on factor 1, while avoiding close contact, covering one’s mouth and nose with a mask when around others, and getting vaccinated for COVID-19 loaded on factor 2, with covering coughs and sneezes loading weakly on both factors, but more strongly (0.41) on factor 1. The 95% CIs for the Cronbach alpha were 0.68-0.73 for factor 1 and 0.64-0.71 for factor 2. In the 1-factor solution, variable loadings ranged from 0.48 to 0.71, and the 95% CIs for the Cronbach alpha was 0.74-0.79.
As prespecified [
We hypothesized that exposure to the intervention would have a small, positive effect on behavioral intentions that would be partially mediated by believability profile membership (
Hypothesized causal pathway of the intervention (not supported), adjusted for age, gender, race, vaccination status, political orientation, perceived severity, perceived susceptibility, family behavior, prior diagnosis, prior infection, and pre-intervention trust.
Path analysis of effects of the intervention on preventive behaviors (1-factor solution).
Mediators | Coefficient | SE | Lower CI | Upper CI | z | AICa | ||
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Direct effect | –0.04 | 0.04 | –0.12 | 0.05 | –0.85 | .39 | 3018.13 |
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Indirect effect | 0.05 | 0.22 | –0.38 | 0.49 | 0.23 | .82 | |
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Total effect | 0.01 | 0.23 | –0.43 | 0.46 | 0.06 | .95 | |
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Direct effect | –0.04 | 0.04 | –0.12 | 0.05 | –0.85 | .39 | |
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Indirect effect | 0.04 | 0.15 | –0.26 | 0.33 | 0.24 | .82 | |
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Total effect | 0.00 | 0.16 | –0.30 | 0.30 | 0.00 | 1.00 | |
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Direct effect | –0.04 | 0.04 | –0.12 | 0.05 | –0.86 | .39 | 2814.37 |
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Indirect effect | 0.05 | 0.13 | –0.22 | 0.31 | 0.35 | .73 | |
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Total effect | 0.01 | 0.14 | –0.27 | 0.29 | 0.07 | .94 | |
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Direct effect | –0.03 | 0.05 | –0.12 | 0.06 | –0.70 | .48 | 2528.63 |
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Indirect effect | 0.01 | 0.15 | –0.28 | 0.31 | 0.10 | .92 | |
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Total effect | –0.02 | 0.16 | –0.32 | 0.29 | –0.11 | .91 | |
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Direct effect | –0.03 | 0.04 | –0.12 | 0.05 | –0.76 | .45 | 2763.44 |
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Indirect effect | 0.01 | 0.13 | –0.24 | 0.27 | 0.08 | .93 | |
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Total effect | –0.02 | 0.14 | –0.29 | 0.25 | –0.17 | .87 |
aAIC: Akaike information criterion.
bReference is Profile One.
cEach profile is a dummy variable.
Multinomial path analysis of the effects of the intervention on preventive behaviors (2-factor solution), in which the reference is Profile One.
Mediator | Coefficient | SE | Lower CI | Upper CI | z | AICa | ||
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3376.55 | |
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Direct effect | –0.03 | 0.05 | –0.13 | 0.07 | –0.62 | .53 | |
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Indirect effect | 0.03 | 0.13 | –0.22 | 0.28 | 0.23 | .82 | |
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Total effect | 0.00 | 0.14 | –0.27 | 0.26 | –0.02 | .98 | |
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Direct effect | –0.03 | 0.05 | –0.13 | 0.07 | –0.62 | .53 | |
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Indirect effect | 0.02 | 0.07 | –0.12 | 0.15 | 0.24 | .81 | |
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Total effect | –0.02 | 0.09 | –0.18 | 0.15 | –0.18 | .86 | |
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3440.75 | |
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Direct effect | –0.04 | 0.05 | –0.15 | 0.06 | –0.81 | .42 | |
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Indirect effect | 0.08 | 0.35 | –0.61 | 0.77 | 0.23 | .82 | |
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Total effect | 0.04 | 0.35 | –0.66 | 0.73 | 0.11 | .92 | |
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Direct effect | –0.04 | 0.05 | –0.15 | 0.06 | –0.81 | .42 | |
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Indirect effect | 0.06 | 0.26 | –0.44 | 0.57 | 0.24 | .81 | |
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Total effect | 0.02 | 0.26 | –0.49 | 0.53 | 0.07 | .94 |
aAIC: Akaike information criterion.
bPreventive behaviors 1, 4, 5, and 6.
cPreventive behaviors 2, 3, and 7.
Binomial path analysis of the effects of the intervention on preventive behaviors (2-factor solution), in which each profile is a dummy variable.
Mediator | Coefficient | SE | Lower CI | Upper CI | z | AICa | |||||||||
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Direct effect | –0.03 | 0.05 | –0.13 | 0.07 | –0.62 | .53 | 3172.83 | |||||||
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Indirect effect | 0.02 | 0.06 | –0.10 | 0.15 | 0.35 | .73 | ||||||||
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Total effect | –0.01 | 0.08 | –0.17 | 0.15 | –0.12 | .91 | ||||||||
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Direct effect | –0.03 | 0.05 | –0.13 | 0.07 | –0.57 | .57 | 2820.14 | |||||||
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Indirect effect | 0.01 | 0.09 | –0.17 | 0.19 | 0.09 | .92 | ||||||||
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Total effect | –0.02 | 0.11 | –0.23 | 0.19 | –0.20 | .84 | ||||||||
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Direct effect | –0.03 | 0.05 | –0.13 | 0.07 | –0.58 | .56 | 3100.05 | |||||||
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Indirect effect | 0.00 | 0.06 | –0.11 | 0.12 | 0.08 | .93 | ||||||||
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Total effect | –0.03 | 0.08 | –0.18 | 0.13 | –0.33 | .74 | ||||||||
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Direct effect | –0.04 | 0.05 | –0.15 | 0.06 | –0.81 | .42 | 3237.87 | |||||||
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Indirect effect | 0.08 | 0.23 | –0.36 | 0.52 | 0.35 | .73 | ||||||||
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Total effect | 0.04 | 0.23 | –0.42 | 0.49 | 0.15 | .88 | ||||||||
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Direct effect | –0.03 | 0.06 | –0.15 | 0.08 | –0.59 | .56 | 3020.59 | |||||||
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Indirect effect | 0.02 | 0.22 | –0.42 | 0.46 | 0.10 | .92 | ||||||||
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Total effect | –0.01 | 0.23 | –0.47 | 0.44 | –0.05 | .96 | ||||||||
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Direct effect | –0.04 | 0.05 | –0.15 | 0.07 | –0.69 | .49 | 3203.54 | |||||||
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Indirect effect | 0.02 | 0.23 | –0.42 | 0.46 | 0.08 | .93 | ||||||||
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Total effect | –0.02 | 0.23 | –0.48 | 0.44 | –0.08 | .94 |
aAIC: Akaike information criterion.
bPreventive behaviors 1, 4, 5, and 6.
cPreventive behaviors 2, 3, and 7.
Independent of the path analysis, we investigated what factors were associated with classification in each of the 3 belief profiles. Each 1-point movement toward political conservatism on a 10-point scale was associated with 1.39 adjusted odds of belonging to Profile Three versus Profile One (χ21=40.52, 95% CI 1.25-1.53,
Finally, each 1-point increase in trust in science was associated with substantially lower adjusted odds of belonging to Profile Three (0.21) or Profile Two (0.14) compared with Profile One (Profile Three: χ21=55.57, 95% CI 0.14-0.31,
We computed exploratory path analyses to assess the influence of trust in science on preventive behaviors, with a mediation pathway through believability profile membership. In the 1-factor preventive behavior model treating believability Profile One as a dummy variable, there was a significant direct effect (0.46, SE 0.11, 95% CI 0.24-0.69,
This study provides preliminary evidence and a proof-of-concept for using infographics that truthfully address underlying reasons why a person might not trust science or scientists to (1) improve trust in science and (2) provide inoculation against COVID-19 misinformation. However, observed effects were small, as expected for a short, passive, and inexpensive intervention. Much remains to be learned in this area of research. Here, we discuss the main findings separately by study aim, provide additional interpretation of exploratory analyses, and make recommendations for future work.
This study found that viewing an infographic designed to truthfully address underlying reasons why a person might not trust science or scientists [
The scale used in this study measured trust in science as a composite from 21 questions to yield a score from 1 to 5. We posit that the small difference-in-difference improvement estimate (+0.03) is meaningful due to the simplicity of the intervention and the ease with which such an intervention could be deployed to large numbers of people. Especially given recent research indicating that aggregate social trust in science may affect variables like vaccine confidence beyond individual-level trust [
We found some evidence that viewing an infographic designed to truthfully address underlying reasons why a person might not trust science or scientists [
There is ongoing discussion among methodologists and metascientists as to how to interpret mediation effects in terms of causal attribution, especially when the direct and total effects are nonsignificant. In general, we can think of a direct effect as proposing that “
Because this study used a randomized, controlled experimental design and included numerous covariates, endogeneity bias was not a substantive concern in interpretation [
That noted, we encourage such research to be undertaken with some urgency. This work has meaningful, practical application if the findings hold true. While fact checking can reduce belief in misinformation, it is not likely feasible to respond to the amount and variability that is produced, even for a single topic like COVID-19 [
Viewing an infographic designed to truthfully address underlying reasons why a person might not trust science or scientists [
We hypothesize, but cannot be certain, that this null finding emerged because this specific infographic addressed a component of rational epistemic trust (eg, why it makes sense to trust scientific findings even when they change over time) [
This study also identified potentially valuable information about how beliefs about COVID-19 may cluster. In May 2020, we identified a single latent profile that endorsed the zoonotic narrative and generally found other narratives unbelievable and 3 profiles that believed misinformed narratives to varying degrees but also believed the zoonotic narrative [
Two major findings about profiles were consistent between our studies: There was a single profile endorsing the zoonotic narrative and generally disbelieving other narratives, and no narrative profile rejected the zoonotic explanation. However, there were only 2 nonscientific profiles in this study rather than 3, and interpretation of their meaning was clearer than in the original study. The smallest profile (Profile Two) found all narratives to be at least somewhat believable. In contrast, Profile Three was comparatively less likely to endorse narratives that were subjectively less political in the United States (eg, 5G, Gates/vaccine) and more likely to believe other narratives (eg, restrict liberty, masks don’t prevent spread). It is unclear whether this difference was due to the addition of the face mask narratives, a change in the information ecosystem, the use of a nationally representative sample, or a different reason altogether.
Notably, in our unplanned regression analysis, trust in science remained the most substantive predictor of profile membership, as in May 2020. However, unlike our previous study, in which political orientation was not associated with profile membership, here we found that conservative political orientation was associated with classification in Profile Three versus Profile One, but not with classification in Profile Two. Along with the profile analysis itself, this suggests the possibility of 2 “typologies” of misinformation belief, 1 that is apolitical and may believe even scientifically impossible narratives (eg, finding all narratives to be plausible) and 1 that is associated with political orientation and that believes misinformation somewhat selectively, applying an alternate decision heuristic in determining what is plausible.
This study investigated multiple outcomes and so there was some increased risk of Type 1 error. For this reason, we interpreted the outcomes cautiously and recommend replication prior to any definitive determination about these findings. At the same time, the primary outcomes were prespecified and were assessed using a limited number of models. A limitation specific to the third aim is that behavioral intentions are not behaviors, so this study should not be interpreted to assess the effect of the intervention on actual behavior. In addition, we opted to limit the allowable content in the intervention. As we note in our pilot study [
There were numerous decisions made in the course of developing the single image used as the intervention in this study, as well as the structure of the intervention. Given this proof-of-concept, there is much room to explore alternative approaches, including, but not limited to, investigating whether a brief video would be more efficacious than a static image, the art style or amount of wording matters, embedding the image as an ad in social media (eg, repeated natural exposures) over a period of time affects the results, and comparison to real negative messages about science would produce similar results to this study, which used an active placebo about dogs.
All analytic code and instructions for its use to replicate results and generate additional tables.
CSV datasets used with Appendix 1.
CONSORT-eHEALTH checklist (V 1.6.1).
Akaike information criterion
adjusted odds ratio
Bayesian information criterion
Centers for Disease Control and Prevention
linear mixed model
Vuong-Lo-Mendel-Rubin Likelihood Ratio Test
nonpharmaceutical preventive behaviors
odds ratio
The infographics and art were produced by Ms. Amanda Goehlert, a Designer on the Creative Team at Indiana University Studios.
This study was funded by the Indiana Clinical and Translational Sciences Institute funded, in part by Award Number UL1TR002529 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.
JA was the lead investigator and authored the first draft of the paper. JA, YX, EET, and LG contributed to the design, prepared the protocol and analytic plan, and conducted the randomized pilot test. EET and AG (see Acknowledgments) led the process to develop infographics from our working documents. XC and YX conducted statistical analyses with support from LG and JA, all of whom had access to the data and take responsibility for the integrity of the data and the analytic outcomes. JA, YX, and EET secured funding for the project. All authors contributed to the manuscript and had joint responsibility for the decision to submit for publication.
None declared.