Prebunking messaging to inoculate against COVID-19 vaccine misinformation: an effective strategy for public health

Abstract

Background
Vaccination coverage needs to reach more than 80% to resolve the COVID-19 pandemic, but vaccine hesitancy, fuelled by misinformation, may jeopardize this goal. Unvaccinated older adults are not only at risk of COVID-19 complications but may also be misled by false information. Prebunking, based on inoculation theory, involves ‘forewarning people [of] and refuting information that challenges their existing belief or behavior’.

Objective
To assess the effectiveness of inoculation communication strategies in countering disinformation about COVID-19 vaccines among Canadians aged 50 years and older, as measured by their COVID-19 vaccine intentions.

Method
Applying an online experiment with a mixed pre–post design and a sample size of 2500 participants, we conducted a national randomized survey among English and French-speaking Canadians aged 50 years and older in March 2021. Responses to two different disinformation messages were evaluated. Our primary outcome was the intention to receive a COVID-19 vaccine, with attitudes toward COVID-19 vaccine a secondary outcome. The McNemar test and multivariate logistic regression analysis on paired data were conducted when the outcome was dichotomized. Wilcoxon sign rank test and Kruskal–Wallis were used to test difference scores between pre- and post-tests by condition.

Results
Group comparisons between those who received only disinformation and those who received the inoculation message show that prebunking messages may safeguard intention to get vaccinated and have a protective effect against disinformation.

Conclusion
Prebunking messages should be considered as one strategy for public health communication to combat misinformation.

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Prebunking messaging to inoculate against
COVID-19 vaccine misinformation: an effective
strategy for public health
Maryline Vivion, Elhadji Anassour Laouan Sidi, Cornelia Betsch, Maude
Dionne, Eve Dubé, S. Michelle Driedger, Dominique Gagnon, Janice Graham,
Devon Greyson, Denis Hamel, Stephan Lewandowsky, Noni MacDonald,
Benjamin Malo, Samantha B. Meyer, Philipp Schmid, Audrey Steenbeek,
Sander van der Linden, Pierre Verger, Holly O. Witteman, Mushin Yesilada &
Canadian Immunization Research Network (CIRN)
To cite this article: Maryline Vivion, Elhadji Anassour Laouan Sidi, Cornelia Betsch, Maude
Dionne, Eve Dubé, S. Michelle Driedger, Dominique Gagnon, Janice Graham, Devon Greyson,
Denis Hamel, Stephan Lewandowsky, Noni MacDonald, Benjamin Malo, Samantha B. Meyer,
Philipp Schmid, Audrey Steenbeek, Sander van der Linden, Pierre Verger, Holly O. Witteman,
Mushin Yesilada & Canadian Immunization Research Network (CIRN) (2022): Prebunking
messaging to inoculate against COVID-19 vaccine misinformation: an effective strategy for public
health, Journal of Communication in Healthcare, DOI: 10.1080/17538068.2022.2044606
To link to this article: https://doi.org/10.1080/17538068.2022.2044606
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Prebunking messaging to inoculate against COVID-19 vaccine misinformation:
an effective strategy for public health
Maryline Vivion
a
, Elhadji Anassour Laouan Sidi
b
, Cornelia Betsch
c
, Maude Dionne
b
, Eve Dubé
b,d
,
S. Michelle Driedger
e
, Dominique Gagnon
b
, Janice Graham
f
, Devon Greyson
g
, Denis Hamel
b
,
Stephan Lewandowsky
h
, Noni MacDonald
i
,Benjamin Malo
d,j
,Samantha B. Meyer
k
,
Philipp Schmid
c
,Audrey Steenbeek
l
,Sander van der Linden
m
,Pierre Verger
n
,Holly O. Witteman
o
,
Mushin Yesilada
h
and Canadian Immunization Research Network (CIRN)
p
a
Department of Social and Preventive medicine, Laval University, Quebec, Canada;
b
Institut national de santé publique du Québec
(INSPQ), Quebec, Canada;
c
Media and Communication Science, University of Erfurt, Erfurt, Germany;
d
Department of Anthropology, Laval
University, Quebec, Canada;
e
Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba,
Winnipeg, Canada;
f
Pediatrics (Infectious Diseases) and Social Anthropology, Dalhousie University, Halifax, Canada;
g
School of Population
and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, Canada;
h
University of Bristol, Bristol, UK;
i
Department
of Paediatrics, Dalhousie University, Canadian Centre for Vaccinology, IWK Health Centre, Haliffax, Canada;
j
CHU de Quebec Research
Center, Quebec, Canada;
k
School of Public Health Sciences, University of Waterloo, Waterloo, Canada;
l
School of Nursing, Dalhousie
University, Halifax, Canada;
m
School of Biology, Department of Psychology, University of Cambridge, Cambridge, UK;
n
ORS PACA, Regional
Health Observatory Provence-Alpes-Côte d’Azur, Marseille, France;
o
Department of Family & Emergency, Université Laval, Quebec City,
Canada;
p
https://cirnetwork.ca/
ABSTRACT
Background: Vaccination coverage needs to reach more than 80% to resolve the COVID-19
pandemic, but vaccine hesitancy, fuelled by misinformation, may jeopardize this goal.
Unvaccinated older adults are not only at risk of COVID-19 complications but may also be
misled by false information. Prebunking, based on inoculation theory, involves ‘forewarning
people [of] and refuting information that challenges their existing belief or behavior’.
Objective: To assess the effectiveness of inoculation communication strategies in countering
disinformation about COVID-19 vaccines among Canadians aged 50 years and older, as
measured by their COVID-19 vaccine intentions.
Method: Applying an online experiment with a mixed pre–post design and a sample size of
2500 participants, we conducted a national randomized survey among English and French-
speaking Canadians aged 50 years and older in March 2021. Responses to two
different disinformation messages were evaluated. Our primary outcome was the intention
to receive a COVID-19 vaccine, with attitudes toward COVID-19 vaccine a secondary
outcome. The McNemar test and multivariate logistic regression analysis on paired data
were conducted when the outcome was dichotomized. Wilcoxon sign rank test and Kruskal–
Wallis were used to test difference scores between pre- and post-tests by condition.
Results: Group comparisons between those who received only disinformation and those who
received the inoculation message show that prebunking messages may safeguard intention to
get vaccinated and have a protective effect against disinformation.
Conclusion: Prebunking messages should be considered as one strategy for public health
communication to combat misinformation.
KEYWORDS
Prebunking; inoculation
theory; COVID-19; vaccine;
misinformation;
disinformation; public health
communication
Background
COVID-19 vaccines infodemic and
misinformation
Effective vaccines against COVID-19, combined with
vaccination campaigns that successfully target all
communities are critical to ending the pandemic.
More than 80% of the population needs to be vacci-
nated against COVID-19 to potentially achieve herd
immunity, depending on vaccine efficacy, the local
reproduction rate of the virus and its variants, and
the level of transmission (estimates are still under
debate) [1–3]. A recent review of data from Canada
suggests that the intention to get vaccinated
against COVID-19 varied between 68% and 80%,
decreasing from May to October 2020 [4–7] but
increasing again in December [8]. By June 2021,
75% of Canadians had received at least one dose,
and 20% had both doses [9]. Vaccine hesitancy,
defined as ‘the decision to delay vaccination or the
refusal to vaccinate despite available vaccination ser-
vices’, could challenge COVID-19 vaccine acceptance.
Consistent with previous research [10], safety con-
cerns surrounding new vaccines generate greater
vaccine hesitancy and influence intention to receive
a COVID-19 vaccine [11–13]. Safety concerns are
© 2022 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Maryline Vivion maryline.vivion@fmed.ulaval.ca
Supplemental data for this article can be accessed at https://doi.org/10.1080/17538068.2022.2044606
JOURNAL OF COMMUNICATION IN HEALTHCARE
https://doi.org/10.1080/17538068.2022.2044606

one of the main reasons cited for being reluctant to
vaccinate [8,14–16].
While acquiring and promoting vaccines to manage
the pandemic response, governments have faced an
infodemic, an overabundance of information including
misinformation (false information, often shared
unknowingly) and disinformation (deliberately false or
misconstrued information) [17]. It has been hard for
people to find trustworthy sources and reliable gui-
dance when they need it [17]. Vaccine opponents
were prepared to resist and refuse COVID-19 vaccination
[18]. By the time the first two COVID-19 vaccines [Pfizer-
BioNTech and Moderna] were authorized by Health
Canada (December 2020), false information about the
vaccines was already circulating on social media. Mul-
tiple circumstances contributed to uncertainties about
the safety of these newly developed vaccines. First,
the vaccine development process was exceptionally
rapid, amplifying safety concerns [19]. Second, the first
vaccines available in Canada used mRNA technology
that had not been previously approved for use in
human vaccines. Elevated safety concerns over the
novel vaccine technology created ideal circumstances
for false information to abound, such as claims that ‘
mRNA vaccines will alter your DNA’circulating on the
Internet [18]. Third, vaccine acceptance is not static; it
is linked to information published in traditional media
[18]. Therefore, the way news media covers the antici-
pation and hype around the vaccine development and
campaign could easily worsen the pandemic by amplify-
ing the uncertainties [20].
Several studies indicate that misinformation can
decrease vaccine intention [18,21–23]. A study con-
ducted in France before any approval by health auth-
orities, for example, showed that COVID-19 conspiracy
beliefs were substantially and negatively related to
both positive attitudes toward vaccination science and
intention to be vaccinated [22]. A study conducted in
the United States (US) and United Kingdom (UK)
showed that misinformation around COVID-19 vaccines
resulted in a decrease in vaccination intent among
those who would otherwise ‘definitely’want to be vac-
cinated by 6.4% in the UK and 2.4% in the USA [18].
Certain groups are more vulnerable to misinforma-
tion due to various factors, including cognitive reflec-
tion, which refers to the capacity to verify
information and not rely solely on the first idea that
first comes to mind, older age and lower media or
digital literacy [24]. Older adults, for example, are
more susceptible to being influenced by online misin-
formation [25], possibly associated with lower digital
literacy, social isolation, and a decline in cognitive
reflection among some individuals [26,27]. As well as
being more susceptible to misinformation, unvacci-
nated older adults were at greater risk for severe out-
comes than healthy younger people, leading to the
prioritization of older adults for COVID-19 vaccination
[28]. In Canada, vaccination started in December
2020, and each province was responsible for its priori-
tization scheme, schedule, and vaccine administration.
Recognizing that COVID-19 vaccine acceptance by
older adults is important to protect them from the
virus and ending the COVID-19 pandemic, tailored
communication strategies addressing misinformation
around COVID-19 vaccines are therefore needed
more than ever.
Prebunking: inoculation theory
Several strategies have been developed to combat the
effect of COVID-19 misinformation [29,30]. For
example, debunking provides detailed and clear refu-
tations of false information after people have been
exposed to a falsehood [31]. Similarly, fact-checking
aims to ensure the accuracy of information and
correct misinformation if necessary [32]. Even if those
strategies are effective, due to the enormous quantity
of COVID-19 vaccine misinformation, identifying and
debunking each message takes time and resources.
Moreover, once people are exposed to falsehood,
they often continue to retrieve false details from
memory despite acknowledging factual corrections, a
phenomenon known as the continued influence of
misinformation [31]. Accordingly, research has looked
at ways to preempt misinformation from taking root
in the first place. Prebunking is an approach based
on inoculation theory, originating from social psychol-
ogy and following a biomedical analogy [29,33,34].
The theory suggests that just as vaccines trigger the
production of antibodies by exposing people to a wea-
kened dose of a pathogen, the same can be achieved
with information. Inoculation consists of introducing
a sense of threat by forewarning people that they
may be exposed to information that challenges their
existing beliefs or behaviors. Then, one or more (wea-
kened) examples of that information are presented
and directly refuted, which is the process called ‘pre-
bunking’[35,36]. By exposing people to a weakened
dose of misinformation, it becomes possible to ‘pre-
emptively confer psychological resistance against
unwanted persuasion’[25,37,38] and cultivate
‘mental antibodies’[25]. A prebunking message
needs two elements to be effective: an explicit
warning of an impending threat of being misled and
a refutation of the misinformation’s argument [29].
This technique, of ‘[f]orewarning people that they
may be exposed to information that challenges their
existing belief or behavior’, has been shown to
reduce the impact of misinformation [33,39]. The
effectiveness of inoculation has been demonstrated
across many different topics and can reduce suscepti-
bility to misinformation across cultures [25,29,40,41].
The objective of this study was to assess the effec-
tiveness of inoculation communication strategies in
2M. VIVION ET AL.

countering disinformation about COVID-19 vaccines
among French and English Canadians aged 50 years
and older, as measured by their intention to get a
COVID-19 vaccine.
Methods
First, a pilot test was conducted to assess if the impact
of different disinformation messages on vaccine inten-
tion varies in accordance with province or context (i.e.
French vs. English participants) and to adjust the
online experiment accordingly. The pilot-test result
had the intended effect of decreasing vaccine inten-
tion (Pilot-test methodology and results are presented
in supplementary file). Then the online experiment was
conducted. This study was approved by the Laval Uni-
versity Research Ethics Board (Comité d’éthique en
recherche de l’Université Laval, CERUL).
Data collection
First, a pilot test (n= 603) was conducted from 19th
February to 22nd February, and then the online exper-
iment (n= 2500) from 8 March to 17th March 2021.
Both were conducted using a sample from Leger Mar-
keting, a market research and polling firm that main-
tains a national panel of 400,000 individuals across
the 10 Canadian provinces (northern territories were
excluded). Their panel is benchmarked to known
Census targets, such as age, region, income, primary
language, and education, to ensure a representative
sample of the Canadian population [42]. Participants
aged 50 years and older, understanding French or
English, and with internet access who had not yet
received any COVID-19 vaccination were eligible to
participate in the pilot test and the online experiment.
An estimated 9.2% of the Canadian population had
received at least one dose of COVID-19 vaccine by
20th March 2021, at the time of the online experiment,
and most were aged 70 years and more [9]. Vaccination
coverage varied across provinces, from 4.2% in Nova
Scotia to 52.7% in Yukon [9](Table 1).
Outcome measure
Our primary outcome measure was the intention to
receive a COVID-19 vaccine, based on previous research
about intentions to vaccinate [43,44]. Intention was
measured using a 5-point Likert scale (from very likely
to very unlikely) to the question: How likely are you to
get vaccinated against COVID-19 when you will be eli-
gible to receive the vaccine? Our secondary outcome
was vaccine attitudes which we measured using the
short 5C psychological scale developed by Betsch and
collaborators and adapted to address COVID-19 vac-
cines [45,46].
Attitude was measured as a score of 5 items scaled
from strongly agree to strongly disagree. For each
scale, a numerical value was attributed (see sup-
plementary for details).
Pilot-testing messages’development
The message development was inspired by the study
of van der Linden et al. [25,33]. Disinformation mess-
ages were adapted from real posts found on social
media (Facebook and VKontakte). Three specific mess-
ages were pretested, and the two most influential
messages that decrease vaccine intention were used
for the online experiment. Message A targeted mRNA
vaccine safety, while Message B concerned the fast
approval of a COVID-19 vaccine by a federal agency
(Figure 1). The third, subsequently unused message
alleged that health care professionals and older
people are guinea pigs for the COVID-19 vaccine.
Prebunking message development
The inoculation intervention consisted of a warning
message of an impending threat/attack on one’s prior
belief/attitude (affective component). For example:
‘False claims rely on different techniques such as scaring
people with shocking claims: for example, ‘mRNA vaccines
can change your DNA forever!’’ Previous studies on vac-
cination and 9/11 conspiracy theories have shown the
effectiveness of these techniques [47,48]. Prebunking
messages used for the study are shown in Figure 2.
Sample size and study design
Our sample size was calculated based on a statistical
threshold of 5% and a baseline intention to vaccinate
of 70%. We estimated a sample size of 2500 partici-
pants would be required to detect a statistically signifi-
cant difference of 5% in intention between the pre-
and post-tests for all groups (n= 500 for each group),
and a difference of 10% between groups (mRNA
versus Quick Approval). Respondents’quotas were
set according to demographic province distribution
in terms of the population.
Table 1. Cumulative percent of the population who have
received at least one dose of COVID-19 vaccine as of 20th
March 2021 [1].
Provinces Vaccination coverage
Yukon 52.66%
British Columbia 10.46%
Alberta 8.78%
Saskatchewan 8.58%
Manitoba 6.57%
Ontario 8.32%
Quebec 11.03%
Newfoundland and Labrador 7.45%
New Brunswick 6.35%
Prince Edward Island 8.15%
Nova Scotia 4.17%
JOURNAL OF COMMUNICATION IN HEALTHCARE 3

Before receiving the intervention, each participant
had to answer a set of questions about their intention
and attitude toward COVID-19 vaccination and socio-
demographic questions (age, gender, income, and
their primary language). Then, participants were ran-
domly assigned to three groups: Disinformation
Figure 1. Disinformation and control messages used for the online experiment.
Figure 2. Prebunking messages used for the online experiment.
4M. VIVION ET AL.

group (Group D), Inoculation group (Group I), and the
control group. Participants in group D and I were
further randomly split to receive Message A (mRNA)
or Message B (quick approval). The control group
received a message about flowers. Participants in the
disinformation group received only a disinformation
message (A1 disinformation only on mRNA; B1 disin-
formation only on quick approval).
Participants in the inoculation group received pre-
bunking message before receiving a disinformation
message (A2 prebunking message followed by disin-
formation on mRNA and B2 prebunking message fol-
lowed by disinformation on quick approval).
Grouping was as follows:
.Disinformation group (D): Group A1 (mRNA) +
Group B1 (Quick Approval)
.Inoculation group (I): Group A2 (mRNA) + Group B2
(Quick Approval)
Immediately after receiving one of the three mess-
ages, participants had to complete the same questions
answered before the intervention. A final debriefing
message which debunked the myths was presented
to participants who were exposed to disinformation
to mitigate the potential harms of the study. The
study design is shown in Figure 3.
Data analysis
The primary outcome of interest, the intention of
getting vaccinated, was categorized into two groups,
i.e. those who express the intention to get vaccinated
(likely-somewhat likely) vs. those who are uncertain or
unlikely to get vaccinated (unlikely–somewhat unli-
kely). We decided to dichotomize the outcome to
avoid sparsity, as most responses favor vaccination,
(more than 80% of individuals are somewhat-to-very-
likely to get the vaccine so we have enough infor-
mation per cell. The approach of dichotomizing the
outcome was favored to a mixed ANOVA, as the distri-
bution of the outcome variable is heavily skewed to
the left (see Figure 1 in the supplementary file),
which potentially violates the ANOVA normality
assumption. Nonetheless, as a robustness check, we
also conducted ANOVA tests of difference-in-differ-
ence scores (the pre–post score in the treatment con-
dition minus the pre–post score in the control
condition). The results are similar and presented in
the supplementary file.
Thus, to measure how many participants changed
their minds in the course of the online experiment, a
McNemar 2 × 2 table test (yes/no in pre–post setting)
was conducted for each group. To allow comparison
between groups and measure the variation of inten-
tion while globally assessing the intervention’seffec-
tiveness, logistic regression models on paired data
were performed in the repeated measures setting, i.e.
with group by time interaction (pre and post). General-
ized Estimating Equations (GEE) type estimates were
produced to account for the within-subject variation.
To be more precise, regression models were adjusted
by age, education level, sex, and income. When the
outcome of interest is considered in continuum
fashion, a Wilcoxon’s signed-rank test, which is based
on the score differences between pre and post, was
conducted by conditions.
Further, we explored which prebunking message
(mRNA or Quick Approval) was more effective in shifting
vaccination intention, dichotomized as likely and some-
what likely vs. the others using McNemar’stestasdiffer-
ence scores using Wilcoxon’s signed-rank test. For the
Figure 3. Study design.
JOURNAL OF COMMUNICATION IN HEALTHCARE 5

secondary outcome, the attitude score based on the 5c
scale was also evaluated using the Kruskal–Wallis test.
This non-parametric approach was used as an alterna-
tive to analysis of variance (ANOVA), assuming data
come from a free distribution. Multiple comparison
tests were conducted among groups.
All analyses were based on two-sided p-values, with
statistical significance defined by p< .05. Data were ana-
lyzed using SAS version 9.4. We considered p-values and
confidence intervals to interpret the findings [49].
Results
Participants’characteristics
The distribution of participants’characteristics is
shown in Table 2. Participant characteristics are repre-
sentative of the Canadian population in terms of sex,
age, income, language, education level, and location
as per the Canadian census result [50]. Also, participant
characteristics were equally distributed across the five
groups, suggesting that these groups are comparable
for the remainder of the analyses.
Intention to get vaccinated
Figure 4 presents the proportion of participants who
likely express their intention to get vaccinated
against COVID-9 pre–post by conditions. From the
McNemar test, for the control group, there is a slightly
marginal increase in proportion for those who change
their mind toward getting vaccinated, from pre- to
post-test d= 1.45% [−0.10–3.005.70], p-value =
0.0673. At the same time, there is a noticeable
decrease in the intention to get vaccinated for group
Dbyd= 3.90% [2.40–5.406.96], p-value = 0.0001 as,
well a decrease for group I, but marginal d = 1.23%
[−0.05–2.52], p-value = 0.0603.
Inoculation effect on vaccine intention
Intention to get vaccinated against COVID-19 among
groups was evaluated via pairwise comparison in logis-
tic regression. Compared to the control group, the like-
lihood of intending to get vaccinated was lower OR =
0.63 [0.50–0.80] for group D, and OR = 0.80 [0.65–0.99]
for Group I. Comparison between group D and I also
supports that the participants in the D group have a
Table 2. Descriptive statistics of the respondents’demographic
information (%) (n= 2500).
Overall
Characteristics %
Gender Male 47
Female 53
Age group 50–59 40
60–69 35
70+ 25
Income <10,000$–29,999$ 13
30,000$–59,999$ 27
60,000$–89,999$ 18
+ 90,000$ 30
No answer 12
First language learn at home French 22
English 71
Neither English nor French 6
English and French 2
Education Elementary/High school 33
College 42
University 24
Location Western/ Prairies†30
Ontario 39
Quebec 22
Maritimes
‡
7
Others* 1
†
Western/ Prairies: British Columbia; Alberta, Saskatchewan and Manitoba.
‡
Maritimes: New Brunswick; Nova-Scotia and Prince Edward Island.
*Other Newfoundland and Labrador; Northwest Territories; Yukon;
Nunavut.
Figure 4. Pre–post test comparison of intent to get vaccinated by condition.
6M. VIVION ET AL.

lower likelihood of getting vaccinated, compared to I
OR = 0.79 [0.66–0.94] P-value = 0.0089). This indicates
that even though both changes in messages are
lower than in the control group, the likelihood of
getting vaccinated is much more pronounced in
group I than in group D. This suggests that prebunking
messages may have a protective effect against disin-
formation on intention to vaccinate. The ANOVA
tests of difference in difference converge essentially
in the same direction as the pairwise group compari-
son obtained from a logistic regression model (see
supplementary file) (Table 3).
Finally, we tested simple inoculation effects
between groups based on message type (message A
about mRNA versus message B about quick approval)
(Figure 5).
The influence of disinformation messages differed
between groups. When compared to the control
group, participants who received message A1
(mRNA) express less intention of getting vaccinated
against COVID-19 (OR = 0.57 [0.42–0.77], p-value =
0.0002) than participants who received message B1
Quick Approval, (OR = 0.69[0.53–0.90], p-value =
0.0061). On the other hand, compared to C, both
inoculation messages A2 mRNA (OR = 0.84[0.66–1.07],
p-value = 0.1670) and B2 Quick approval (OR = 0.76
[0.61–0.96], p-value = 0.0203) were shown to have
some protective effect on vaccine intention. This pro-
tective effect was also higher in participants who
received message A2 (mRNA) than those who received
message B2 (Quick Approval). In addition, looking at
the result from the Wilcoxon signed-rank test pre-
sented in Figure 5, there was no change in group A2
(mRNA) between pre and post period intention to vac-
cination, as the difference is nearly zero. There was a
significant change for groups A1 (mRNA), B1 (Quick
Approval), and even B2 (Quick Approval). This indicates
that there is a greater inoculation effect (protective
factor) with message A (mRNA) compared to
message B (Quick Approval).
Changes in vaccine attitude
Changes in attitude(s) toward COVID-19 vaccination
was measured with the 5C scale. No significant differ-
ence between pre–post surveys was found based on
the Kruskal–Wallis test. There was no meaningful
result from the multiple comparisons (methods
Dwass, Steel, Critchlow–Fligner) (Table 4).
Figure 5. Difference in % pre-post vaccination intention based on message type.
Table 3. Likelihood of getting vaccinated among groups.
Unadjusted Model Adjusted Model for Age, Education, Sex and Income
Label
Odds
ratio
Lower
Confidence
Upper
Confidence
p-value between
Disinformation group and
inoculation group
Odds
ratio
Lower
Confidence
Upper
Confidence
p-value between
Disinformation group and
inoculation groupLimit Limit Limit Limit
Group D vs
Group C
0.64 0.51 0.81 0.0086 0.63 0.50 0.80 0.0089
Group I vs
Group C
0.81 0.66 0.99 0.80 0.65 0.99
Group
control
(post-
pre)
1.13 0.95 1.35 1.14 0.95 1.37
D = Disinformation; I = Inoculation; C = Control.
JOURNAL OF COMMUNICATION IN HEALTHCARE 7

Discussion
Studies indicate that misinformation may negatively
affect vaccine decisions [18,22,23]. In this infodemic
era, with the increasing scourge of dis- and misinfor-
mation, it is important to use all available effective
strategies to counter COVID-19 vaccine misinforma-
tion. In our online experiment, we tested the effect
of prebunking messages on intention and attitude
toward vaccination. Our results show that prebunking
messages based on inoculation theory can reduce the
impact of misinformation and safeguard positive
intentions to vaccinate. Still, that effectiveness may
vary depending on the message content. This is con-
sistent with other studies that have shown that inocu-
lation theory can reduce the impact of misinformation
[40,41,51,52].
Two types of disinformation messages were used.
The message alleging that mRNA vaccines can
change DNA had more effect in decreasing vaccine
intention compared to the message alleging that
vaccine approval was too fast. Also, while both pre-
bunking messages were framed the same way, the
inoculation message A (mRNA) had more impact on
protecting vaccine intention than message B (Quick
Approval). This difference might be explained by the
fact that the perceived risk of the COVID-19 vaccine
was different since changes in the DNA could be per-
ceived as a greater risk than fast approval. Alterna-
tively, since mRNA vaccine technology was a new
concept for most of the general public in the past
year, and attitudes about government and pharma-
ceutical industry are more longstanding, we may see
a greater impact of both disinformation and inocu-
lation of newer information, about which views are
less solidified. It would be interesting for further
studies to explore disinformation and inoculation on
new information in a crisis context. This aspect is inter-
esting because the threat is an important mechanism
in the inoculation process by involving various cogni-
tive and affective processes [41,53–55]. Therefore, it
is more important to target messages that can affect
vaccine intention. Otherwise, prebunking effectiveness
might be more limited [29].
This online experiment had several strengths,
including a large number of respondents, inclusion of
all provinces, and a sample demographically similar
in sex, language, income, education, and location to
the general Canadian population 50 years and over.
A limitation was that the post-test was performed
right after the intervention. Repeated post-test
measures over time would have been interesting to
determine the length of the effect of inoculation on
vaccine intention and identify if a ‘’booster shot’’ of
information correction would be required [35,52,55].
Additionally, the pre–post test design, on intention
and attitude, may lead to participants constructing or
solidifying their preference, which could limit the
effect of inoculation. While we noted differences in
intention to be vaccinated against COVID-19, our
study did not show meaningful results in overall
vaccine attitudes. This may be explained by the fact
that it is more difficult to shift vaccine attitudes [21,
29]. However, prebunking messages can change atti-
tudes toward the way messages are evaluated by indi-
viduals as inoculation aims to train the ability to detect
disinformation [56]. And while this was not tested in
this online experiment, prebunking messages may
heighten awareness in general that misinformation is
circulating. This objective of inoculation is essential
since, along with low literacy levels, the capacity to
detect fake news is associated with the risk of being ‘
anti-vaccine’or ‘vaccine-hesitant’[57].
Implications for public health communication
While vaccine hesitancy was identified as one of the
ten threats to global health in 2019 by the WHO
[58], the end of the COVID-19 pandemic depends
on high worldwide vaccine acceptance to reach
global herd immunity. The results of this study
provide new insights for public health communi-
cation for COVID vaccines and likely for other vac-
cines as well. Providing prebunking messages
based on inoculation theory is an effective strategy
to help counter misinformation. As recent studies
indicate, simply providing clear and transparent
information on safety and efficacy does not increase
COVID-19 vaccine intention [59], as public health
authorities should include this strategy in their com-
munication practices. In a pandemic and infodemic
context where misinformation flows fast, it could be
interesting to use social media as it offers the oppor-
tunity to quickly spread prebunking messages, as in
this study that replicated social media platforms.
Social media use would provide the opportunity to
Table 4. Paired multiple bilateral comparison analysis for
attitude score
Group
Wilcoxon
Z
DSCF
value
Pr >
DSCF
Inoculation on Quick approval vs.
Control group
−0.1340 0.1896 0.9999
Inoculation on Quick approval vs
Inoculation on mRNA
−0.3642 0.5151 0.9963
Inoculation on Quick Approval vs.
Disinformation on mRNA
−0.9667 1.3671 0.8702
Inoculation on Quick approval vs.
Disinformation on mRNA
−0.0758 0.1073 1.0000
Control vs. Inoculation on mRNA −0.2281 0.3226 0.9994
Control vs Disinformation on mRNA −0.7814 1.1051 0.9361
Control vs. Disinformation on Quick
Approval
0.0627 0.0887 1.0000
Inoculation on mRNA vs.
Disinformation on mRNA
−0.5678 0.8030 0.9797
Inoculation on mRNA vs.
Disinformation on Quick Approval
0.2985 0.4221 0.9983
Disinformation on mRNA vs.
Disinformation on Quick Approval
0.9024 1.2763 0.8961
8M. VIVION ET AL.

share prebunking messageswithtargetgroupswhile
on the same ground of misinformation. To be
effective, however, prebunking messages must
quickly detect misinformation messages that could
be harmful and lead to dangerous behaviors. There-
fore, to frame prebunking messages, it is crucial for
public health authorities to integrate infodemiology
and infoveillance expertise to target the more
impactful misinformation messages [29,60].
Conclusion
The ability to identify misinformation is challenging for
most, especially in the context of a public health info-
demic [61]. Increasing health and digital literacy skills,
and thus the capacity to recognize misinformation is
one avenue to mitigate the impact of such information
[62]. As sustained literacy interventions may be difficult
to provide for those long out of school, prebunking
messages could be an interesting avenue to help indi-
viduals detect and question misinformation, especially
in times of crisis.
Acknowledgement
We are grateful to Bobbi Rotolo for the manuscript technical
and language editing.
Disclosure statement
No potential conflict of interest was reported by the author(s),
but Cornelia Betsch, Philipp Schmid, Sander L van der Linden,
Stephan Lewandowsky received funding from the European
Union’s Horizon 2020 research and innovation programme
under grant agreement No 964728 (JITSUVAX)”and Holly Wit-
teman is funded by a Canada Research Chair in Human-
Centred Digital Health.
Funding
This study was supported by the Public Health Agency of
Canada and the Canadian Institutes of Health Research,
through the Canadian Immunization Research network:
(Award Number: FRN: 173622).
Ethical approval
This study was approved by the Laval University
Research Ethics Board (Comité d’éthique en recherche
de l’Université Laval, CERUL). 2021-011/02-02-2021.
Notes on contributors
Maryline Vivion,Eve Dubé,Michelle Driedger,Devon
Greyson,Janice Graham,Noni MacDonald,Samantha
B. Meyer,Audrey Steenbeck and Holly Witteman are
members of the Canadian Immunization Research Network
(CIRN) a national network of vaccine researchers.
Cornelia Betsch is Professor of Health Communication with a
background in vaccine decision-making.
Stephan Lewandowsky is a cognitive scientist doing research
to examine people’s memory, decision-making and knowl-
edge structures.
Philipp Schmid is a postdoctoral researcher studying the psy-
chology of misinformation and science denialism.
Sander L van der Linden is Professor of Social Psychology in
Society. His research examines how people form judgments
and decisions about societal issues.
Pierre Verger is a physician, epidemiologist and senior
researcher doing research on vaccine hesitancy.
Elhadji Anassour Laouan Sidi,Denis Hamel,Maude Dionne
and Dominique Gagnon are research professionals and stat-
isticians at the National Institute of public health in Quebec.
Benjamin Malo is a PhD student in anthropology. Mushin,
Yesilada, is a PhD student in cognitive science.
ORCID
Maryline Vivion http://orcid.org/0000-0002-6400-1024
Cornelia Betsch http://orcid.org/0000-0002-2856-7303
Maude Dionne http://orcid.org/0000-0003-4989-1556
Eve Dubé http://orcid.org/0000-0003-1336-1510
S. Michelle Driedger http://orcid.org/0000-0003-3769-5785
Janice Graham http://orcid.org/0000-0002-6326-8122
Devon Greyson http://orcid.org/0000-0003-4860-384X
Stephan Lewandowsky http://orcid.org/0000-0003-1655-
2013
Benjamin Malo http://orcid.org/0000-0003-3826-4215
Samantha B. Meyer http://orcid.org/0000-0002-2098-2828
Philipp Schmid http://orcid.org/0000-0003-2966-0806
Audrey Steenbeek http://orcid.org/0000-0003-0409-158X
Sander van der Linden http://orcid.org/0000-0002-0269-
1744
Holly O. Witteman http://orcid.org/0000-0003-4192-0682
Mushin Yesilada http://orcid.org/0000-0003-1596-9976
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