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Prevailing research on individuals’ compliance with public health related behaviours during the COVID-19 pandemic tends to study composite measures of multiple types of behaviours, without distinguishing between different types of behaviours. However, measures taken by governments involve adjustments concerning a range of different daily behaviours. In this study, we seek to explain students’ public health related compliance behaviours during the COVID-19 pandemic by examining the underlying components of such behaviours. Subsequently, we investigate how these components relate to individual attitudes towards public health measures, descriptive norms among friends and family, and key demographics. We surveyed 7,403 university students in ten countries regarding these behaviours. Principal Components Analysis reveals that compliance related to hygiene (hand washing, coughing behaviours) is uniformly distinct from compliance related to social distancing behaviours. Regression analyses predicting Social Distancing and Hygiene lead to differences in explained variance and type of predictors. Our study shows that treating public health compliance as a sole construct obfuscates the dimensionality of compliance behaviours, which risks poorer prediction of individuals’ compliance behaviours and problems in generating valid public health recommendations. Affecting these distinct behaviours may require different types of interventions.
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Hygiene and Social Distancing as Distinct Public Health
Related Behaviours Among University Students During
the COVID-19 Pandemic
Annelot Wismansab, Srebrenka Letinac, Roy Thurikabd, Karl Wennbergc,
Ingmar Frankenbe, Rui Baptistaf, Jorge Barrientos Maríng, Joern Blockh, Andrew Burkei,
Marcus Dejardinjk, Frank Janssenj, Jinia Mukerjeed, Enrico Santarellil,
José María Millánm, Olivier Torrèsdn
[a] Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands. [b] Erasmus University
Rotterdam Institute for Behavior and Biology (EURIBEB), Rotterdam, The Netherlands. [c] Linköping University,
Linköping, Sweden. [d] Montpellier Business School, Montpellier, France. [e] Erasmus School of Social and Behavioural
Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands. [f] Instituto Superior Técnico, University of Lisbon,
Lisbon, Portugal. [g] University of Antioquia, Medellín, Colombia. [h] University of Trier, Trier, Germany. [i] Trinity
Business School, Trinity College Dublin, Dublin, Ireland. [j] Université catholique de Louvain, Louvain-la-Neuve,
Belgium. [k] Université de Namur, Namur, Belgium. [l] Department of Economics, University of Bologna, Bologna, Italy.
[m] Department of Economics, University of Huelva, Huelva, Spain. [n] University of Montpellier, Montpellier, France.
Social Psychological Bulletin, 2020, Vol. 15(4), Article e4383,
Received: 2020-09-15 • Accepted: 2020-11-06 • Published (VoR): 2020-12-23
Handling Editors: Katarzyna Cantarero, Social Behavior Research Center, Wroclaw Faculty of Psychology, SWPS
University of Social Sciences and Humanities, Wrocław, Poland; Department of Psychology, University of Essex,
Colchester, United Kingdom; Olga Białobrzeska, Faculty of Psychology, SWPS University of Social Sciences and
Humanities, Warsaw, Poland; Wijnand A. P. van Tilburg, Department of Psychology, University of Essex, Colchester,
United Kingdom
Corresponding Author: Annelot Wismans, Department of Applied Economics, Erasmus School of Economics,
Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands. E-mail:
Related: This article is part of the SPB Special Issue “Psychosocial Functioning During the COVID-19 Pandemic”,
Guest Editors: Katarzyna Cantarero, Olga Białobrzeska, & Wijnand A. P. van Tilburg, Social Psychological Bulletin,
Supplementary Materials: Data, Materials [see Index of Supplementary Materials]
Research Article
This is an open access article distributed under the terms of the Creative Commons Attribution
4.0 International License, CC BY 4.0, which permits unrestricted use, distribution, and
reproduction, provided the original work is properly cited.
Prevailing research on individuals’ compliance with public health related behaviours during the
COVID-19 pandemic tends to study composite measures of multiple types of behaviours, without
distinguishing between different types of behaviours. However, measures taken by governments
involve adjustments concerning a range of different daily behaviours. In this study, we seek to
explain students’ public health related compliance behaviours during the COVID-19 pandemic by
examining the underlying components of such behaviours. Subsequently, we investigate how these
components relate to individual attitudes towards public health measures, descriptive norms
among friends and family, and key demographics. We surveyed 7,403 university students in ten
countries regarding these behaviours. Principal Components Analysis reveals that compliance
related to hygiene (hand washing, coughing behaviours) is uniformly distinct from compliance
related to social distancing behaviours. Regression analyses predicting Social Distancing and
Hygiene lead to differences in explained variance and type of predictors. Our study shows that
treating public health compliance as a sole construct obfuscates the dimensionality of compliance
behaviours, which risks poorer prediction of individuals’ compliance behaviours and problems in
generating valid public health recommendations. Affecting these distinct behaviours may require
different types of interventions.
COVID-19, public health compliance, social distancing, hygiene, students, descriptive norms, attitude
Compliance with public health measures set by authorities during the
COVID-19 pandemic consists of two clearly distinct components: Social
Distancing and Hygiene.
There is significant variability among students in Social Distancing and
Hygiene across countries.
Attitudes towards regulations and descriptive norms are predictive of both
behaviours, but are more strongly related to Social Distancing.
Treating public health compliance as a simple construct obfuscates the
dimensionality of compliance.
To dampen the spread of COVID-191, public authorities have taken a range of measures
including recommendations or restrictions of behaviours, all of which require adjust‐
ments concerning different daily behaviours (Anderson et al., 2020; Hale et al., 2020;
Sebhatu et al., 2020). Scholars worldwide have sought to obtain more insights into
individuals’ compliance with such recommendations or restrictions. Current explanations
of individuals’ compliance stem from surveys using demographic characteristics such
1) In the paper and in our student survey we refer to ‘COVID-19’ and ‘COVID-19 health recommendations and
restrictions’ as synonymous with the SARS-CoV-2 virus for the sake of simplicity and readability.
Hygiene and Social Distancing Are Distinct Behaviours 2
Social Psychological Bulletin | 2569-653X
as gender, age, employment status and education (e.g., Farias & Pilati, 2020), sometimes
combined with political attitudes or personality scales (e.g., Allcott et al., 2020; Blagov,
2020; C. Clark et al., 2020; Farias & Pilati, 2020). Other studies highlight cognitive and
information processing factors as important for social distancing2 behaviour and compli‐
ance (Banerjee et al., 2020; Stanley et al., 2020; Wise et al., 2020).
Yet, most studies focus solely on composite measures assessing compliance with
multiple types of behaviours (C. Clark et al., 2020; Harper et al., 2020; Plohl & Musil,
2020) without distinguishing between different types of public health measures or behav‐
iours. This may be problematic since adjustment concerning a range of different daily
behaviours cannot simply be understood as a sole behavioural construct, as stressed in
a pre-COVID review of 26 papers on the determinants of compliance during pandemics
(Bish & Michie, 2010). Next to more novel behaviours that require learning (e.g., keeping
distance from others) there are established behaviours that only have to be changed
in intensity or frequency (e.g., improving hygiene behaviours). Where some behaviours
require conscious deliberation (e.g., deciding not to visit family), others are part of
natural routines for most people (e.g., hand washing). Some behaviours that need to be
stopped are so habitual that they are hard to change, like touching your face (Verplanken
& Wood, 2006). Other behaviours go against deep-rooted human desires such as avoiding
physical contact with others. There is also a distinction between the degree to which
compliance with certain measures can be affected individually. Keeping distance is not
independent of the behaviours of proximate others. It is thus likely that predictors of
compliance differ across different types of protective behaviours (Bish & Michie, 2010).
In sum, studies that focus on public health compliance as being a sole and coherent
construct may obfuscate the potential dimensionality of COVID-19 compliance, and as
a result lead to undertheorized models with poor prediction of individuals’ compliance,
and unvalidated public health recommendations. To address this, we examine the ex‐
tent to which compliance with key public health measures correlates with compliance
with other measures in a large cross-national study of university students’ self-repor‐
ted perception of and self-reported compliance regarding COVID-19 recommendations
and restrictions. The importance of cross-national studies was highlighted in a recent
review on how social and behavioural science can support COVID-19 pandemic response
(Van Bavel et al., 2020). We seek to explain students’ public health related compliance
behaviours during the COVID-19 pandemic by examining the underlying components of
such behaviours, then investigate how these components relate to individual attitudes
towards public health measures, descriptive norms among friends and family, and key
2) By social distancing behaviours we refer to “a constellation of behaviours that decrease close physical contact
among non-household members” (Bourassa et al., 2020; Koo et al., 2020). For details of how we measure social
distancing behaviours, see Method and Results sections.
Wismans, Letina, Thurik et al. 3
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Explaining Different Types of Health Behaviours
In research unrelated to pandemics, compliance or non-adherence behaviours have been
studied in connection to medical recommendations for the chronically ill (for a review,
see DiMatteo, 2004), while in health psychology, health-related recommendations and
required behavioural changes (e.g., physical activity, sex behaviour, drinking, smoking)
have been extensively studied. While compliance with COVID-19 measures revolves
around health behaviours, there are three important differences between the health-re‐
lated recommendations typically studied and COVID-19 recommendations. First, recom‐
mended COVID-19 related behaviours apply to everyone and not exclusively to specific
subpopulations, even though certain groups are at higher risk (Brandén et al., 2020; A.
Clark et al., 2020; Hashim et al., 2020; Mueller et al., 2020; Williamson et al., 2020; Zhou
et al., 2020). Second, studies of health-related behaviours usually focus on one type of
behaviour (e.g., smoking or drinking) or a range of closely related behaviours (e.g., eating
habits). COVID-19 related recommendations cover more diverse types of behaviours
not necessarily closely related, such as keeping physical distance and washing hands
frequently (Alwan et al., 2020; Chu et al., 2020; Ioannidis, 2020; Jones et al., 2020; Rundle
et al., 2020). Third, while previously studied behaviours have direct personal benefits,
this is not the case for COVID-19 recommendations. For students, following COVID-19
measures means potentially significant changes in daily behaviours entailing giving up
a lot in terms of social life, while they are in general less at risk of suffering from
negative health consequences of COVID-19 infection (Brandén et al., 2020; Götzinger et
al., 2020; Ioannidis et al., 2020; Swann et al., 2020; Zhou et al., 2020). Compliance with
such recommendations is thus more about protecting others than oneself, i.e., leading to
a social benefit instead of personal one.
The Importance of Attitudes and Descriptive Norms
The goal of COVID-19 recommendations is to bring about and maintain a change in
individual behaviours that will make people less likely to get infected and infect oth‐
ers. For this to happen, an underlying assumption is that people will perceive these
recommendations as appropriate and have favourable attitudes towards following them.
Recent studies on attitudes towards COVID-19 recommendations also suggest overall
high agreement and adherence with public health guidelines (Czeisler et al., 2020; Selby
et al., 2020). The notion that the attitudes towards recommendations influence compli‐
ance follows from the research in social and health psychology (e.g., Stroebe, 2011).
Eagly and Chaiken (1993, p. 1) define attitudes as “a psychological tendency that is
expressed by evaluating a particular entity with some degree of favour or disfavour”.
The concept of attitudes has been widely used in predicting different health related
behaviours (e.g., Doganis & Theodorakis, 1995), usually as an integral part of wider
theoretical frameworks such as the theories of Reasoned Action or Planned Behaviour
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Social Psychological Bulletin | 2569-653X
(Ajzen et al., 2007), or the Health Belief Model (Janz & Becker, 1984). We thus expect
more positive attitudes (e.g., the degree to which people take them seriously and think
they are appropriate) towards public policy to lead to higher compliance with COVID-19
In addition to an individual's attitude towards specific behaviours, another central
factor in psychological theories of health behaviours is the role of behavioural norms
in individuals' social context. Norms are powerful shapers of behaviour (Cialdini &
Goldstein, 2004; Sherif, 1936) and individuals are guided by norms in their understanding
of and response to situations, especially during times of uncertainty (Cialdini, 2009).
A distinction can be made between injunctive and descriptive norms: Injunctive norms
relate to what is seen as (dis)approved by others, i.e., what you perceive others think
you ought to do, whereas descriptive norms relate to what is typically done by others,
i.e., what you observe others to actually do (Deutsch & Gerard, 1955). Although the
two are often correlated, they are conceptually and motivationally different (Cialdini,
2007). Bicchieri and Xiao (2009) showed that injunctive norms are of importance when
in line with the descriptive norm. However, if the two contradict, descriptive norms are
more important: people do what they think others would do, even when they believe
this is not the behaviour that is approved (Bicchieri & Xiao, 2009; Kallgren et al., 2000;
Smith-McLallen & Fishbein, 2008; Stok et al., 2014). When it comes to health-risk behav‐
iours, descriptive norms have been indicated as particularly important (Rivis & Sheeran,
2003; Van Bavel et al., 2020). Further, descriptive norms tend to have the strongest effect
on behaviour if they stem from people with which an individual identifies, such as
family and friends (Abrams et al., 1990). Since non-compliance with COVID-19 measures
is a health-risk behaviour, we expect descriptive norms to play an important role for
the behaviours we examine. Since the COVID-19 pandemic requires behaviour change
from everyone, descriptive norms can easily be formed. Together, we expect descriptive
social norms, specifically the degree to which friends and family comply with COVID-19
measures, to play a role in explaining compliance with COVID-19 measures.
The Current Study
We examine the extent to which compliance with key public health measures correlates
with compliance with other measures, and if these behaviours differ across and within
student populations in distinct countries. We use Principal Components Analysis (PCA)
to examine underlying components of compliance behaviour. Moreover, using the inter‐
national setting of the dataset we examine how the different compliance components
acquired in step one vary across countries. Finally, we study whether a set of individual
attitudes towards public health measures, descriptive norms among friends and family,
and key demographics are differently related to the compliance components unearthed
using multiple regression analysis.
Wismans, Letina, Thurik et al. 5
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We surveyed 7,403 students from late April to the beginning of May 2020 (week 17
through 19) at twelve universities in ten countries: Belgium, Colombia, France, Germany,
India, Ireland, Italy, the Netherlands, Portugal, Spain and Sweden. We used an online
survey based on the Qualtrics software, approved in advance by the Internal Review
Board of the Erasmus University Rotterdam.
At the time of data collection, all countries had initiated various recommendations
and restrictions regarding health-related behaviour. Eight of the countries were in com‐
plete lockdown (India, Colombia, Spain, Italy, Portugal, Ireland, Belgium, France), mean‐
ing that inhabitants could only go outside if movements could be justified. However,
specific regulations differed across countries. Measures were least strict in Sweden,
followed by the Netherlands. For an overview of regulations applicable across countries
at the time of data collection see Supplementary Materials (Table S1).
Students have been shown to be a key group for studies on compliance behaviours for
several reasons: with former general lockdown measures across the world having been
relaxed, infection levels have started to rise in late summer of 2020 and in Europe as
well as the United States, new cases are mostly found among the younger generation and
have been linked to student gatherings and parties (Murillo-Llorente & Perez-Bermejo,
2020; The Economist, 2020; Wilson et al., 2020). Students are epidemiologically important
in respect to their demographics and social behaviours: most are young, live in shared
housing, and meet many others on a daily basis. This makes them susceptible to super‐
spreading events (Lau et al., 2017). The World Health Organisation highlights young
people’s compliance with COVID-19 related measures as ‘a priority’ (The Economist,
2020). Hence, scientific knowledge of students’ health behaviours is crucial, especially
given that universities around the world are partly or fully open for study in fall 2020 and
early 2021 (Liu et al., 2020).
The survey could be completed in English, Dutch, French or Spanish. Translations
were made by a native speaker, reviewed by another native speaker and if necessary
adapted after consultation between both translators. A pre-test was conducted with
Dutch students3. Only fully completed surveys were used for analyses. An informed
consent had to be signed at the start of the survey. 44 students did not sign the consent,
leading to a total of 7,359 completed questionnaires. The number of missing values was
low (.02%). Therefore, pairwise deletion was used depending on the analyses conducted.
Descriptive sample statistics are presented as Supplementary Materials (Table S2).
The sample consists of both undergraduate and graduate (but not postgraduate) students
3) When we refer to students from a specific country in this paper, we mean students studying in that country,
e.g., with Dutch students we refer to students that study in the Netherlands.
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across disciplines (e.g., economics, business, social sciences, humanities, science, engi‐
neering, and medicine). Response rates varied between 7% (Belgium) and 31% (France),
with an overall response rate of 8.5%, excluding Netherlands and India where exact
response rates could not be computed. Average age (Standard deviation, SD) is 22.8 (5.9).
More women (61.3%) than men (38.7%) participated in the survey, consistent with the
average rate of university studies in most of the countries studied (World Economic
Forum, 2020). 54.1% of the students were in a relationship at the time of completing the
survey. 12.9% had lived in the country of their university for less than five years; we infer
that these are international students. In the Netherlands and Ireland, the percentage of
international students was relatively high (NL: 30.5%, IRE: 30.0%).
In this section we describe all measures used for analyses. Descriptive statistics for
all variables and the anticipated outcome variables of the PCA are presented as
Supplementary Materials (Table S3) including mean, standard deviations and correla‐
Compliance was measured using nine items revolving around different behaviours rela‐
ted to the recommendations and restrictions by governments. The behaviours investiga‐
ted are listed in Table 1. Items were preceded by the following introductory text: ´In
the past two months, which of the following measures did you follow and to which extent?
Please indicate to what extent you disagree or agree with these statements.’ Answers were
given on a scale of 1 (completely disagree) to 5 (completely agree). Due to the novel
situation, we were not able to use existing validated questionnaires. The items were
constructed ad hoc and reviewed by all authors involved in the study. Simple scales
were used to reduce problems with cross-country translation equivalence (Steenkamp &
Baumgartner, 1998).
Pearson’s correlations of the compliance items are presented for the full dataset in
Table 1. Inter-item correlations are positive but mostly low, suggesting that investigated
compliance behaviours show relatively small covariation. In other words, knowing one
student’s compliance with one specific behaviour does not allow for a high prediction
of compliance with another specific behaviour. Item means in Table 1 are relatively high
and variability (standard deviations) is small, indicating negatively skewed distributions:
More students indicated (completely) agreeing than (completely) disagreeing to perform
the behaviours studied.
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Table 1
Correlation Table Compliance Items (Total Sample, N = 7,309)
Item M SD 1 2 3 4 5 6 7 8
1. I avoided touching my face 3.17 1.26
2. I coughed and sneezed into my elbow and/or
used a handkerchief 4.46 .84 .27
3. I washed my hands more often and longer 4.23 .86 .31 .25
4. When not at home I kept the advised distance
between myself and others 4.36 .87 .19 .18 .15
5. I did not meet with others unless it was strictly
necessary 4.13 1.07 .11 .03 .04 .31
6. I only went outside if it was strictly necessary 3.91 1.17 .15 .07 .03 .28 .59
7. I did not shake hands 4.76 .62 .09 .13 .11 .33 .25 .21
8. I did not visit others/have not had visitors 3.82 1.27 .13 .08 .05 .27 .63 .51 .22
9. I have not visited elderly people or people who
are vulnerable for health reasons 4.56 .92 .08 .11 .05 .11 .18 .17 .13 .29
Note. Compliance was measured at a scale from 1 (lowest agreement) to 5 (highest agreement). All correlations
are significant at 1% significance level.
Students report complying most with ‘not shaking hands’ and least with ‘avoiding
touching their face’. Most variation was present for ‘visiting others/having visitors’,
indicating that students differ most in their agreement with performing this behaviour.
The least variation was found for ‘not shaking hands’, meaning that students answered
relatively uniformly for this question.
Independent Variables
Attitudes — Attitudes to public health measures is captured by two individual items
revolving around the extent to which students report taking measures seriously and
how they feel about the amount of measures taken in their country. ‘Taking Measures
Seriously’ was captured by the following question: ‘To what extent do you take the Gov‐
ernment measures seriously?’. Students could answer on a 7-point scale (1: ‘Not at all’ to
7: ‘Extremely’). Opinions on the amount of measures taken was assessed by the following
question: ‘Do you think that the Government is taking too few or too many measures to
prevent the spread of the coronavirus?. Answers could be given on a 7-point Likert scale
(1: ‘Far too few’; 4: ‘Just the right amount’; 7: ‘Far too many’). With the initial scoring it
was not possible to capture the strength of the relationship of perceiving measures as too
few versus as too many. To allow for a different influence of the two (non-linear effects),
the variable was recoded to three dummy variables: ‘Too Few Measures’ (1-3 = 1; 4-7 =
0), ‘Right Amount’ (4 = 1; 1-3 = 0; 5-7 = 0), and ‘Too Many Measures’ (5-7 = 1; 1-4 = 0).
Hygiene and Social Distancing Are Distinct Behaviours 8
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42.75% of the students indicated too few measures were taken, 42.55% indicated the right
amount of measures were taken, and 14.70% indicated too many measures were taken.
Descriptive norm — The descriptive norm was captured using one item on the degree
to which friends and family of students have complied with the measures. The question
that had to be answered was as follows: ‘To what extent do your family and friends strictly
follow the measures related to the coronavirus?. Answers were given on a 7-point Likert
scale (1: ‘They do not follow the measures at all’; 7 ‘They strictly follow all measures’).
Demographic Variables
The following demographic variables were included: age (continuous), gender (0 = male,
1 = female) and relationship status (0 = not in a relationship, 1 = in a relationship).
Data Analysis
To study the dimensionality of compliance we investigate how the nine compliance
behaviours relate to each other and whether it is possible to create composite measures
of students’ public-health related behaviour. We use PCA to identify orthogonal compo‐
nents explaining most of the variance in the data by reducing dimensions of the original
set of items, while preserving as much information as possible. Parallel Analysis is
used to determine the number of components that should be retained (Horn, 1965), a
suitable method when 95th-percentile eigenvalues (EVs) are used (Glorfeld, 1995; Hayton
et al., 2004). The parallel analyses are based on O’Connor’s (2000) syntax, estimated with
Monte Carlo simulation, 100 iterations. Components with EVs greater than the randomly
generated 95th-percentile EVs are retained (Hayton et al., 2004). These analyses inform
which items underlie the extracted dimensions, and therefore these items can be used to
construct composite scores which capture the identified dimensions the best.
After obtaining the components of compliance by creating item-average scores, we
examine how they correlate and how they vary across countries by studying descriptive
statistics (mean and standard deviations).
Finally, we predict each compliance component using multiple regression analyses
and the predictors described. The models include country dummies to control for country
differences, a method recommended when the number of countries in a sample is low (<
50) (Möhring, 2012; Wooldridge, 2010, p. 132).
Principal Component Analysis
The Kaiser-Mayer-Olkin measure verified the sampling adequacy for the PCA,
KMO = .756 (Hutcheson & Sofroniou, 1999). Bartlett’s test of sphericity indicated that
Wismans, Letina, Thurik et al. 9
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correlations between items were sufficiently large for PCA, χ2(36) = 11983.94, p < .001.
Parallel analysis indicated that two components should be retained that together explain
47.06% of the variance. Table 2 shows the component loadings, those with an absolute
value greater than .40 (bold printed) are interpreted (Stevens, 2009).
Table 2
Component Matrix Principal Components Analysis of Compliance Behaviours
Component 1 Component 2
1. I avoided touching my face .37 .58
2. I coughed and sneezed into my elbow and/or used a handkerchief .30 .62
3. I washed my hands more often and longer .26 .67
4. When not at home I kept the advised distance between myself and others .59 .17
5. I did not meet with others unless it was strictly necessary .77 -.35
6. I only went outside if it was strictly necessary .72 -.29
7. I did not shake hands .50 .10
8. I did not visit others/have not had visitors .76 -.30
9. I have not visited elderly people or people who are vulnerable for health reasons .40 -.03
Looking at the items that cluster on the same components in Table 2, it is apparent that
component 1 represents types of behaviour that are all related to social distancing, e.g.,
being in physical contact with other people. This component thus seems to well capture
Social Distancing compliance4. Items that load on Component 2 all seem to be related
to hygiene behaviour (washing hands, coughing into the elbow and not touching the
face). Therefore, we suggest that this component captures Hygiene compliance. Social
Distancing comprises items 4-9 of Table 2, and Hygiene comprises Items 1-3. In the rest
of the paper we will refer to Social Distancing and Hygiene to indicate compliance with
behaviours that these components capture. It is important to note that by “Hygiene”
in this paper we refer only to compliance with the hygiene behaviours described in
the three items used to measure it, that is, ‘washing hands’, ‘touching one’s face’, and
‘coughing/sneezing into the elbow’.
We also conducted PCAs on the separate country samples. In eight out of ten coun‐
tries, parallel analysis confirms that two factors should be retained. In two countries,
the parallel analysis indicates that one component should be retained: Spain and Ireland.
Looking closely at these country sub-group samples, our interpretation is that the one-
factor structure arises in the Spanish sample due to Spanish students indicating high
4) It should be noted that Social distancing has and can be used interchangeably with Physical Distancing. In our
paper we refer to Social Distancing, because of its extensive use in literature and media and to avoid confusion that
physical distancing only refers to "keeping the advised distance between self and others".
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compliance on both social distancing and hygiene items, meaning that all items load
highly (> .40) on the first component. For Ireland, the interpretation is less clear since
all items except avoiding ‘touching one’s face’ and ‘washing hands’ load highly (> .40)
on the first component. These two hygiene-related items load highly on the second
component, which seems to hint at a two-factor structure. The somewhat divergent
pattern in the Irish sub-sample may be caused by the relatively small sample size of Irish
students (N = 100).
To check whether compliance behaviours can be understood as a similar two-dimen‐
sional construct across countries, we compared item loadings on the first two principal
components of each country with the pattern of loadings extracted for the whole sample.
This is done by following the procedure advised by researchers dealing with evaluation
of degree of cross-cultural replication (McCrae et al., 1996; Van de Vijver & Leung,
1997). The procedure involves orthogonal Procrustes rotation, followed by computation
of congruence coefficients which quantifies in which degree components are replicated.
Values on the diagonal of the resulting matrix are known as Tucker’s phi coefficient of
agreement (Van de Vijver & Leung, 1997). The results presented in Table 3 indicate high
cross-cultural equivalence.
Table 3
Tucker’s Phi Coefficients
Component 1
Social Distancing
Component 2
Belgium 1.00 1.00
Colombia 0.98 0.99
Spain 0.76 0.43
France 1.00 0.96
India 0.98 0.99
Ireland 0.97 0.90
Italy 0.99 0.99
Netherlands 0.99 0.99
Portugal 0.98 0.98
Sweden 0.99 0.99
The structure was equal for all countries (> .95, good similarity), except for the second
component in the Irish sample (> .85, fair similarity), and the loadings of both compo‐
nents in the Spanish sample (< .85, no similarity) (Lorenzo-Seva & ten Berge, 2006). The
latter finding is in line with the one-dimensional structure found in Spain using Horn’s
parallel analysis. Component matrices per country are presented as Supplementary
Materials (Table S4).
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Using the outcomes of the PCA, composite continuous scores can be created by
taking the average of the items that belong to each component. By doing so we created
two composite measures of different types of compliance: Social Distancing (Item 4-9)
and Hygiene (Item 1-3). Internal consistency of items included in the Social Distancing
construct was good (α = .73) while internal consistency of the Hygiene construct was
weaker (α = .52). This lower reliability likely results from the small number of items
related to Hygiene included in the survey.
Relating the item-average composite measures of Social Distancing and Hygiene to
each other strongly supports that these are two distinct behaviours that are only weakly
correlated (r = .21).
Social Distancing and Hygiene Across Countries
Using the measures of students’ average compliance with Social Distancing and Hygiene
obtained from the PCA, we examine how these behaviours vary between students in
different countries. Finally, we calculate how much of the variation in compliance is
dependent on the country that the student lives in.
To compare the extent to which students comply with measures in each country we
compare the average scores of Social Distancing and Hygiene among all students in a
country in Figure 1, with average Hygiene on the y-axis and average Social Distancing
on the x-axis, and country means and standard deviations provided in Table 4 below.
The figure reveals several groupings of countries with similar compliance. This suggests
that student populations across countries cannot simply be placed on a continuum of
compliance with both Social Distancing and Hygiene, but that compliance with each type
of behaviour is distinct across countries. The right corner of Figure 1 however shows that
for students in Spain, high levels of Social Distancing are correlated with high levels of
Hygiene, in line with the one-factor structure of the compliance measure found in this
sample. We observe a cluster of countries where students report similar scores on both
behaviours: Colombia, France, Ireland, India and Portugal. Sweden and the Netherlands
are both ‘outliers’ in terms of relatively lower Social Distancing. Students in Sweden
exhibit on average a higher level of Hygiene compared to students in all other countries
except Spain. Students in Italy and Belgium comply strictly with Social Distancing, but
more weakly with Hygiene. Results of one-way ANOVA tests of the mean differences
between countries are presented in the Supplementary Materials (Table S5).
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Figure 1
Visualization Average Social Distancing (Axis x) and Hygiene (Axis y) Across Countries
Table 4
Country Means and Standard Deviations of Social Distancing and Hygiene
Social Distancing Hygiene
Belgium 4.31 0.61 3.84 0.74
Colombia 4.41 0.59 4.06 0.71
Spain 4.61 0.53 4.24 0.71
France 4.27 0.69 4.09 0.69
India 4.47 0.54 4.10 0.72
Ireland 4.33 0.65 4.10 0.56
Italy 4.50 0.51 3.87 0.78
Netherlands 3.80 0.69 4.00 0.66
Portugal 4.44 0.57 4.10 0.65
Sweden 3.65 0.72 4.15 0.59
Total 4.26 0.66 3.96 0.72
Wismans, Letina, Thurik et al. 13
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We calculated the intraclass correlation coefficient (ICC) to gauge the variance in stu‐
dents’ self-reported behaviour that can be attributed to the different country clusters,
as opposed to variation between individual students regardless of country of residence5.
Using Maximum Likelihood, the ICC of countries for Hygiene is only .024. For Social
Distancing the ICC is much higher: .18. This indicates that country residence explains
more of the variation in compliance with Social Distancing than with Hygiene. Two
plausible reasons for this are (i) cross-national differences in regulations mainly differ
regarding Social Distancing, not regarding Hygiene, and (ii) in our data, items related to
Hygiene exhibited smaller variability and higher values in general.
Explaining Social Distancing and Hygiene
Table 5 presents results of multiple regression predicting Social Distancing (Models 1 and
2) and Hygiene (Models 3 and 46. Models 1 and 3 are based on all variables except compli‐
ance with the other type of behaviour, which is added in Models 2 and 4, respectively. All
models include country dummies (not displayed)7, with Dutch students as the reference
group. The coefficients for ‘Too Few Measures’ and ‘Too Many Measures’ are estimated
against the reference category ‘Right Amount of Measures’.
We find ‘Taking measures seriously’ to be positively related to both Social Distancing (B
= .26, p < .001) and Hygiene (B = .17, p < .001). Students that feel that ‘Too few measures’
are being taken to decrease the spread of COVID-19 are more likely to comply with both
Social Distancing (B = .12, p < .001) and Hygiene (B = .07, p < .001), compared to students
reporting ‘Right Amount of Measures’. Students that report ‘Too many measures’ have
been taken are slightly less compliant when it comes to Social Distancing (B = -.02,
p = .047), compared to students reporting ‘Right Amount of Measures’. However, this
result becomes insignificant when adding Hygiene as a control variable to the model
predicting Social Distancing (B = -.02, p = .062). With respect to Hygiene, perceiving that
too many measures are taken compared to the right amount of measures does not affect
We also find that students reporting higher descriptive social norms in one’s environ‐
ment (having friends and family more strictly following the measures) are more likely to
comply with Social Distancing (B = .15, p < .001) and Hygiene (B = .08, p < .001).
5) We note that ICC estimates may be unreliable due to the low number of countries in our sample (Bryan &
Jenkins, 2016). For this reason, we refrain from conducting further multilevel analyses.
6) The same models estimated without international students were all but identical, except for the coefficient
‘Too Many Measures’ in Model 2 (B = -.03, p = .025 when excluding international students, B = -.02, p = .062 in the full
7) While multilevel analysis is unreliable with only 10 countries included (Bryan & Jenkins, 2016; Maas & Hox,
2005), unreported robustness tests (available upon request) based on singular covariance matrices indicate that results
remain identical as a pooled OLS with country dummies presented here.
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Regarding the control variables, we find students’ Age to be positively related to both
Social Distancing (B = .11, p < .001) and Hygiene (B = .11, p < .001), as is Gender (being
female) (Social Distancing: B = .05, p < .001, Hygiene: B = .11, p < .001). Students in a
relationship are somewhat less likely to comply with Social Distancing (B = -.04, p < .001)
but more likely to comply with Hygiene (B = .09, p < .001).
Table 5
Multiple Regression Analyses Explaining Social Distancing and Hygiene
Dependent Variable
Social Distancing Hygiene
Model 1 Model 2 Model 3 Model 4
B SE p B SE p B SE p B SE p
Age 0.11 0.00 < .001 0.09 0.00 < .001 0.11 0.00 < .001 0.10 0.00 < .001
Gender (1 = female) 0.05 0.01 < .001 0.04 0.01 < .001 0.11 0.02 < .001 0.10 0.02 < .001
Relationship (1 = yes) -0.04 0.01 < .001 -0.05 0.01 < .001 0.09 0.02 < .001 0.10 0.02 < .001
Taking Measures Seriously 0.26 0.01 < .001 0.24 0.01 < .001 0.17 0.01 < .001 0.13 0.01 < .001
Too Few Measures (dummy) 0.12 0.01 < .001 0.11 0.01 < .001 0.07 0.02 < .001 0.05 0.02 < .001
Too Many Measures (dummy) -0.02 0.02 0.047 -0.02 0.02 0.062 -0.02 0.02 0.203 -0.01 0.03 0.305
Descriptive Norm 0.15 0.01 < .001 0.14 0.01 < .001 0.08 0.01 < .001 0.06 0.01 < .001
Social Distancing 0.15 0.01 < .001
Hygiene 0.13 0.01 < .001
Adjusted R20.273 0.287 0.116 0.134
N7217 7201 7221 7201
Note. Country dummies included but not shown. Dutch students that perceive the right amount of measures are
taken serve as a reference group. B is standardized beta.
By adding Hygiene and Social Distancing as control variables in Models 2 and 4 of
Table 5, we observe that both types of compliance are positive and significant predictors
of each other but that the direction and strength of the relationships of the other
predictor variables do not change much. Adjusted R2 shows only a small increase for
both models after adding the alternative type of compliance: from .273 to .287 for the
Social Distancing model, and from .116 to .134 for the Hygiene model. The small increase
in adjusted R2 again suggests that the two types of behaviours are distinct.
Summary of Findings
We used a continuous measure of compliance with multiple behaviours and showed
that compliance with public health measures set by authorities during the COVID-19
pandemic consists of two clearly distinct components: Social Distancing and Hygiene.
Despite the differences in the restrictive measures and prevalence of COVID-19 among
Wismans, Letina, Thurik et al. 15
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the ten studied countries, our findings point towards high commonalities in regard to
the dimensionality of compliance. The two types of behaviours are only weakly corre‐
lated with each other, and differently predicted by individual attitudes towards public
health measures, descriptive norms among friends and family, and key demographics.
In other words: Social Distancing does not necessarily go hand in hand with Hygiene.
This means that one cannot simply rank students as ‘more or less compliant with
COVID-19 measures’ (e.g., Harper et al., 2020; Plohl & Musil, 2020). Moreover, we reveal
significant variability among students in Social Distancing and Hygiene across countries.
Country-samples cannot be placed on a continuum of compliance with both measures
since high average levels of either Social Distancing or Hygiene do not necessarily imply
a high average level of the other type of behaviour. We also show that the country of
residence explains more of the variation in Social Distancing than in Hygiene. Finally, a
selection of commonly used variables – attitudes and descriptive norms – were predictive
of both behaviours, but more strongly related to Social Distancing. In line with previous
studies, being male and being younger is negatively related to Social Distancing and
especially Hygiene (Bish & Michie, 2010; Farias & Pilati, 2020). Finally, we found that
being in a relationship is negatively related to Social Distancing, but positively related
to Hygiene. These results indicate that compliance with public health related measures
during the COVID-19 pandemic cannot be reduced to one single composite measure, and
that doing so may lead to a poorer prediction of individuals’ compliance and problems in
generating valid public health recommendations.
Scientific Contributions
The contributions of this study are multiple. First, we show that Social Distancing and
Hygiene are two distinct types of behaviours during the COVID-19 pandemic, and poten‐
tially also during other infectious diseases. With this finding we hope to inspire future
research to study the behaviours separately and develop stronger predictive models for
each behaviour. Assuming that compliance is unidimensional and/or mostly composed
of behaviours related to “social” distancing is wrong and can result in a missed opportu‐
nity to correctly identify possibly different antecedents of these different behavioural
dimensions. Our findings show that compliance with public health measures is best
viewed as a multidimensional construct and this directly implies that both dimensions
should be taken into account to design effective strategies, and when investigating,
theorizing and modelling compliance (and pandemic related outcomes) (e.g., Aleta et al.,
2020; Bahl et al., 2020). Once identified, it is important to recognize that behaviours
captured by each dimension are likely different in many aspects: Social Distancing
behaviours require more conscious deliberation, while Hygiene behaviours are generally
more automatic. Further, our analyses show these behaviours to be differently related
to theoretically relevant predictors. While we show that Social Distancing and Hygiene
levels are independent, the combination of these behaviours on an individual level affects
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the individual exposure and infection risk differently. Ideally, both Social Distancing and
Hygiene should be high, and one cannot compensate for the lack of the other. High
Social Distancing but low Hygiene still puts a person at risk for an infection since it is
unrealistic that people can completely and absolutely distance themselves from others
for prolonged periods of time. Importantly, while we can assume individuals have a high
control over Hygiene by performing certain behaviours, their “social” distance depends
not only on their own behaviours but also on the behaviours of people they have contact
with. For example, if a student A with a high Social Distancing comes across a student
B with a low Social Distancing, this dyadic interaction will likely result in a less than
optimal “social” distance between the two. The co-dependent nature of “achieved” Social
Distancing as opposed to Hygiene – people do not affect each other's hygiene directly
– implies that while both behaviours will affect the spread of infection, their effect will
be different and argues for more nuanced models of infection spread. Therefore, showing
that compliance is “made up” by two behaviours gives important input for modelling the
spread of disease.
Second, we show that attitudes towards public policy and descriptive norms are
more predictive of Social Distancing than for Hygiene. Given that Hygiene related
behaviours are less salient (less visible) than behaviours related with Social Distancing,
more routinized (automatic), and less problematized and discussed in the media, it is
not surprising that they were shown to be less strongly connected with attitudes and
norms. It is highly possible that thinking about the recommendations and restrictions
related to COVID-19 is dominated by behaviours related with “social” distancing, and
therefore reported attitudes and descriptive norms are more closely related with these
behaviours than with Hygiene. Social distancing behaviours are more easily (and correct‐
ly) observable. In contrast, Dickie et al. (2018) for example showed that college students
consistently believed that they washed their hands more frequently than their peers.
However, higher predictability of Social Distancing could partly be a result of the more
reliable measurement of this construct in comparison with Hygiene (in terms of the
number of items and alpha coefficient). These differences underline the importance of
distinguishing between the types of compliance. Further research could study whether
injunctive social norms (the perception of what one ought to do) has a similar effect
on both types of compliance, since this is unaffected by the visibility of the behaviours
as performed by others. Further, our findings that Social Distancing and Hygiene are
distinct types of compliance motivates further research regarding the descriptive norms
about each type of compliance. Psychological models should seek to identify stronger
antecedents in terms of attitudes, behavioural norms towards these behaviours, for
example, by relying on established health psychological research examining attitudes,
behavioural norms and intentions related to, e.g., alcohol abstaining (Conner et al., 1999),
healthy eating (Conner et al., 2002) or condom use (Montanaro & Bryan, 2014). With the
need for compliance continuing to exist, attitudes and descriptive norms are likely to
Wismans, Letina, Thurik et al. 17
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shift over time; e.g., students become fatigued with the measures and see compliance of
their peers decreasing. For both future research and public authorities it would be fruitful
to monitor attitudes and descriptive norms towards the measures as an important proxy
and predictor of compliance. Public authorities should focus on creating interventions
to improve attitudes, e.g., by using attitudinal argumentation (Ajzen et al., 2007), and
descriptive norms, e.g., by stressing in their communication that the majority of the
population is compliant instead of focusing on non-compliant groups. Our results should
make public health authorities aware of the fact that they require inhabitants to change
multiple types of behaviour that may require distinct interventions (Michie et al., 2011;
Verplanken & Wood, 2006). Moreover, they tentatively suggest that interventions aimed
at enhancing Social Distancing benefit more from influencing attitudes and descriptive
norms than interventions aimed at enhancing Hygiene.
Third, our study is based on a rather large sample compared to existing samples
previously conducted on compliance during the COVID-19 pandemic. We found a stable
distinction between Social Distancing and Hygiene both in the overall sample as well
as when examining the specific country-samples. It should be mentioned that for two
countries (Ireland and Spain) one component emerged from the PCA, indicating that
Social Distancing and Hygiene are more related for students in these countries. This
is likely explained by high levels of both Social Distancing and Hygiene in Spain and
by a relatively small sample size (N = 100) in Ireland, as the component loadings of
the Irish sample do show fair similarity to that of the total sample. In general, also
on a country-level we can conclude that the Social Distancing-Hygiene distinction is
present and similar. Taken together, our findings provide cues to scholars and public
health officials interested in modelling the individual compliance and the spread of the
disease and devising applicable interventions to uphold prescribed recommendations and
Limitations and Future Research
Results of our study should be interpreted acknowledging the timing of data collection.
The end of April 2020 was still in the early phase of the COVID-19 pandemic. Public
health behaviours related to Hygiene and Social Distancing may change over time, while
we implicitly model Social Distancing and Hygiene in this study as stable traits. We
recognize that in reality these are dynamic behaviours, showing even daily fluctuations.
Future research should investigate the temporal stability of both dimensions, using not
only self-reported behaviours - which are likely affected by social desirability to a certain
degree - but also measures of actual behaviours. Such an approach would also reduce the
common method bias of a single survey being used to measure all variables of interest
self-reported by the participants at the same point in time (Podsakoff et al., 2003). Finally,
we did not collect data on the place of residence of students, e.g., whether they live in a
Hygiene and Social Distancing Are Distinct Behaviours 18
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large city or small town. Future research should investigate whether there are differences
in compliance between students living in rural versus urban areas.
A strength of this study comes from the fact that we collected data on samples of
students in ten different countries at a simultaneous relevant point in time. Yet, we were
not able to avoid self-selection bias, which probably led to low compliance students being
underrepresented. While we assume that their underrepresentation did not affect the
findings about the dimensionality of compliance in any substantial degree, it is possible
that due to the range restriction in our dependent variable the investigated predictor
variables could have been compromised. Future data collection efforts should try to
secure the participation of students such that those who are not complying highly are
incentivised to participate.
We identified two distinct dimensions of compliance and investigated them using
attitudes and descriptive norm variables. We hope that future research will build on our
findings and use more elaborate models of behaviours of interest distinguishing between
Social Distancing and Hygiene. A logical step would be to validate key constructs from
central theories of health behaviours such as perceived behavioural control as in the
Theory of Planned Behavior (Ajzen et al., 2007), belief in the compliance effectiveness
and beliefs about personal COVID-19 threat as in the Health Belief Model (Janz & Becker,
1984). Future research should also go beyond internal beliefs and intentions towards
also considering unconscious priming and situational cues in changing automatic and
habitual behaviours (Stroebe, 2011). Measuring the behaviour or attitudes of close social
contacts would also allow more precise insights about the mechanism of social influence
in compliance behaviours. Finally, there are opportunities in widening the theoretical
framework by incorporating other relevant theories from the field of social psychology
(e.g., social identity theory and COVID-19; Jetten et al., 2020) for psychological science to
make valuable contributions in understanding and addressing the challenges arising from
the pandemic.
Funding: Roy Thurik, Jinia Mukerjee and Olivier Torrès are members of LabEx Entreprendre of the Université de
Montpellier (Montpellier Management, MOMA) funded by the French government (LabEx Entreprendre, ANR-10-
Labex-11-01) as well as of the public research centre Montpellier Research in Management (EA 4557, Université de
Competing Interests: The authors have declared that no competing interests exist.
Acknowledgments: The authors have no support to report.
Data Availability: For this article, a dataset is freely available (Wismans et al., 2020).
Wismans, Letina, Thurik et al. 19
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Supplementary Materials
Supplementary Material 1 gives an overview of the COVID-19 regulations across countries at
the time data was collected. Supplementary Material 2 presents descriptive statistics for the full
sample. Supplementary Material 3 shows means, standard deviations and correlations for all varia‐
bles part of the regression analyses. Supplementary Material 4 consists of component matrices of
principal component analyses that were conducted using country samples. Supplementary Material
5 consists of the results of One-Way ANOVA’s testing the mean differences in compliance between
countries. Finally, research data that was used for the study and a codebook explaining all variables
in this data are part of the Supplementary Materials (for access see Index of Supplementary
Materials below).
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... Risk perception refers to individuals' perceived susceptibility to an external threat, which may be a potential mechanism of social factors affecting individual behaviors in uncertain situations (Sitkin and Pablo, 1992). Moreover, behavioral visibility involves the possibility of the behavior being observed by others and may be an indispensable condition for the functioning of descriptive norms (Wismans et al., 2020). Thus, we also considered risk perception and behavioral visibility to further analyze the mechanisms and applicable conditions of descriptive norms. ...
... Policy compliance in the field of public health is also a major focus of researchers and administrators, as individuals' active compliance with public health policies is key to controlling the spread of diseases (French, 2011). In a pandemic context, public health compliance behaviors refer not only to daily behaviors such as eating healthy and regularly exercising but also to a series of measures to prevent and control diseases, including washing hands, wearing masks, and maintaining physical distance (Wismans et al., 2020). These policy measures for disease prevention and control are the public health behaviors that we focused on in this study. ...
... In the context of COVID-19, the government has proposed more public health behaviors that individuals should comply with. Public health behaviors cannot simply be understood as a sole behavioral construct, because different characteristics of behaviors may have different effects on individual compliance (Wismans et al., 2020). Behavioral visibility is an important characteristic of public health behaviors, and it is defined as the performance of the individual behavior that can be observed by others through minimal effort (Leonardi and Treem, 2020). ...
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In a pandemic context, public health events are receiving unprecedented attention, and identifying ways to enhance individual public health compliance behaviors has become an urgent practical problem. Considering that individual decisions are susceptible to group members’ behaviors and that descriptive norms provide social information about the typical behaviors of others, we focused on the effects of the properties and reference groups of descriptive norms on public health compliance behaviors. We also investigated the mechanism with risk perception as a mediator and the applicable condition with behavioral visibility as a moderator. Through a 2 × 2 × 2 between-subject survey experiment with 529 subjects, we demonstrated that (1) compared with the negative norm, the positive norm was more effective in promoting public health compliance behaviors; (2) compared with the distal group norm, the proximal group norm more significantly promoted public health compliance behaviors; (3) the effect of the property of descriptive norms on public health compliance behaviors was weakened in the treatment of the proximal group norm; (4) risk perception partially mediated the association between the property of descriptive norms and public health compliance behaviors and fully mediated the effect of the interaction of the property and the reference group of descriptive norms on public health compliance behaviors; in the treatment of the negative-proximal group norm, individuals perceived more risk, thus effectively nudging their public health compliance behaviors; (5) compared with low-visibility behaviors, public health compliance behaviors were significantly stronger for high-visibility behaviors; (6) the property of descriptive norms had a weaker effect on public health compliance behaviors for low-visibility behaviors. In terms of theoretical significance, we refined the study of descriptive norms to promote the application of behavioral public policy. Moreover, the new model of public health compliance behaviors constructed in this study explains the mechanism and applicable conditions of public health compliance behaviors. In practical terms, this study has implications for designing intervention programs to nudge public health compliance behaviors.
... In this study we will analyze how macrolevel policies and individual-level factors independently and jointly associate with face mask use during the early stages of the global COVID-19 pandemic when regulations on face mask use were divergent. We use data from a large sample of approximately 7000 university students from ten countries (Belgium, Colombia, France, India, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden), collected between 23rd April-12th of May 2020, as part of the Erasmus University Rotterdam International COVID-19 Students Survey [38,57,58]. First, we study how (selfrelated and other-related) risk perception, (direct and indirect) experience with COVID-19, attitude towards government and policy stringency independently shape face mask use. ...
... We use data from the first wave of the Erasmus University Rotterdam International COVID-19 Student Survey [38,57,58]. The dataset consists of survey data from a large sample of university students from multiple countries. ...
... The survey was shared with students in Belgium, Colombia, France, India, Ireland, Italy, the Netherlands, Portugal, Spain, and Sweden, primarily using university e-mail addresses and online university platforms. Previous studies have already used this dataset [38,57,58]. The survey was completed online using survey software from Qualtrics. ...
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Background During the 2020 COVID-19 pandemic, governments imposed numerous regulations to protect public health, particularly the (mandatory) use of face masks. However, the appropriateness and effectiveness of face mask regulations have been widely discussed, as is apparent from the divergent measures taken across and within countries over time, including mandating, recommending, and discouraging their use. In this study, we analyse how country-level policy stringency and individual-level predictors associate with face mask use during the early stages of the global COVID-19 pandemic. Method First, we study how (self and other-related) risk perception, (direct and indirect) experience with COVID-19, attitude towards government and policy stringency shape face mask use. Second, we study whether there is an interaction between policy stringency and the individual-level variables. We conduct multilevel analyses exploiting variation in face mask regulations across countries and using data from approximately 7000 students collected in the beginning of the pandemic (weeks 17 through 19, 2020). Results We show that policy stringency is strongly positively associated with face mask use. We find a positive association between self-related risk perception and mask use, but no relationship of mask use with experience with COVID-19 and attitudes towards government. However, in the interaction analyses, we find that government trust and perceived clarity of communication moderate the link between stringency and mask use, with positive government perceptions relating to higher use in countries with regulations and to lower use in countries without regulations. Conclusions We highlight that those countries that aim for widespread use of face masks should set strict measures, stress self-related risks of COVID-19, and use clear communication.
... Auch mit Blick auf die Eindämmung der COVID-19-Pandemie sind Studierende als junge, aktive und mobile Teilpopulation immer wieder ins Zentrum der Aufmerksamkeit geraten (vgl. Barello et al. 2020;Ioannidis 2021;Rossmann et al. 2021;Schäfer et al. 2021;Wismans et al. 2020Wismans et al. , 2021, wenngleich ihre soziale und gesundheitliche Situation in Deutschland von der Politik lange Zeit vernachlässigt wurde (vgl. Dietz et al. 2021). ...
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Eine zu geringe Impfbereitschaft zählt zu den größten globalen Gesundheitsgefahren und war in der COVID-19-Pandemie auch in Deutschland eine der großen Herausforderungen der öffentlichen Gesundheit. Die Identifikation potenzieller Einflussfaktoren auf das Impfverhalten ist deshalb für eine zielgruppengerechte Gesundheitskommunikation von großer Bedeutung. Studierende sind eine besonders wichtige Zielgruppe der Prävention und Gesundheitsförderung. Der Beitrag geht mit Hilfe einer Online-Befragung der Studierenden einer westdeutschen Universität (n = 1398) im Sommersemester 2021 den Fragen nach, inwieweit sich geimpfte und ungeimpfte Studierende mit hoher bzw. niedrigerer Impfintention hinsichtlich a) ihrer Medien- und Informationsnutzung und b) ihres Vertrauens in Medien und Informationsquellen in der COVID-19-Pandemie unterschieden. Die Ergebnisse zeigen z. T. deutliche Differenzen. Während geimpfte Studierende sich intensiver informierten und hierfür auch stärker auf klassische Medienangebote zurückgriifen, vertrauten insbesondere ungeimpfte Studierende mit niedrigerer Impfintention u. a. mehr auf alternative Nachrichtenseiten und Blogs.
... Of course during the COVID-19 pandemic, many of these scenarios became a reality for workers. Many sectors faced, and continue to face, job insecurity (Wilson, et al., 2020), pay cuts (Gonzalez et al., 2020;Wilson, et al., 2020), and working conditions that were not compliant with social distancing rules (Wismans et al., 2020). Many of these factors further negatively influence the mental health of workers (Wilson, et al., 2020). ...
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This document was prepared in the context of scoping the Cyber Security Leadership and Culture theme of 2020/21 at the Research Institute for Sociotechnical Cyber Security (RISCS) sponsored by, and in cooperation with the UK’s National Cyber Security Centre (NCSC). The aim of the project is to understand the implications of mass remote/hybrid working arrangements due to the Covid-19 outbreak, that started in March 2020 and is still on-going at the time of writing this document. The research objectives focus on the psychological contract between employees and leadership from the perspective of cyber risk, specifically: • To understand how different organisations adjusted to new forms of working while maintaining/reducing their cyber risk exposure. • To explore strategies used by cyber security leaders to keep a positive cyber security culture front of mind. • To gather best practices used for maintaining trust, nurturing teamwork, safeguarding mental health of team members (reducing insider risk / human error). This document represents the first evidence-gathering phase of the project and formed the basis of the topics of interest to be discussed in the next stage, during the expert interviews. The document includes gathering evidence on fresh research carried out within the research scope, and also previous, non-Covid 19 related research on the dynamics of remote working, mental health and cyber security risk. The initial scoping of the research and the current literature review document was brought together by the broader RISCS community. It is an example of a much needed co-operation between academic researchers (Georgia Crossland and Amy Ertan, PhD researchers at the Information Security Group at Royal Holloway, University of London), small business owners (Berta Pappenheim, RISCS Research Fellow and Co-Founder at The CyberFish and Nadine Michaelides, Founder at Anima) working together with, and supported by the UK Government (Nico B, from the Economy and Society engagement team at the NCSC).
... As highlighted in previous studies [10,11,19], a lower risk perception was associated with a lower compliance with barrier gestures and could, therefore, explain a relaxation of their implementation. That said, this hypothesis should be confirmed, as a study realized in 10 universities around the world [28] showed that compliance with barrier gestures was not uniformly influenced by the same factors, given the underlying differences between hygiene measures and measures related to physical distancing. This decrease in compliance with greetings without contact during the two last periods could, therefore, be independent and not reflect a general decrease in compliance with other gestures. ...
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During the COVID-19 pandemic, barrier gestures such as mask wearing, physical distancing, greetings without contact, one-way circulation flow, and hand sanitization were major strategies to prevent the spread of SARS-CoV-2, but they were only useful if consistently applied. This survey was a follow-up of the first survey performed in 2020 at the University of Liège. We aim to evaluate the compliance with these gestures on campuses and examine differences in the extent of the compliance observed in different educational activities and contexts. During 3.5 months, the counting of compliant and non-compliant behaviors was performed each week in randomly selected rooms. Using data collected during both surveys (2020 and 2021), binomial negative regression models of compliance depending on periods (teaching periods and exam sessions), type of rooms, and campuses were conducted to evaluate prevalence ratios of compliance. The percentage of compliance in this second survey was the highest for mask wearing and physical distancing during educational activities (90% and 88%, respectively) and lowest for physical distancing outside educational activities and hand sanitization (45% and 52%, respectively). Multivariate analyses revealed that the compliance with most gestures was significantly higher in teaching rooms than in hallways and restaurants and during exam sessions. The compliance with physical distancing was significantly higher (from 66%) in auditoriums, where students had to remain seated, than during practical works that allowed or required free movement. Therefore, the compliance with barrier gestures was associated with contextual settings, which should be considered when communicating and managing barrier gestures. Further studies should specify and confirm the determining contextual characteristics regarding the compliance with barrier gestures in times of pandemic.
... In contrast, while respondents practiced hand washing frequently and experienced the behaviours to be automatic, their behaviours remained to be highly predicted by their intentions. These contrasting results corroborate a recent study that identified distinct predictors for physical distancing versus personal hygiene behaviours among university students in the Netherlands and other European countries [41]. ...
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Background: Since the outbreak of the COVID-19 pandemic, physical distancing and hand washing have been used as effective means to reduce virus transmission in the Netherlands. However, these measures pose a societal challenge as they require people to change their customary behaviours in various contexts. The science of habit formation is potentially useful for informing policy-making in public health, but the current literature largely overlooked the role of habit in predicting and explaining these preventive behaviours. Our research aimed to describe habit formation processes of physical distancing and hand washing and to estimate the influences of habit strength and intention on behavioural adherence. Methods: A longitudinal survey was conducted between July and November 2020 on a representative Dutch sample (n = 800). Respondents reported their intentions, habit strengths, and adherence regarding six context-specific preventive behaviours on a weekly basis. Temporal developments of the measured variables were visualized, quantified, and mapped onto five distinct phases of the pandemic. Regression models were used to test the effects of intention, habit strength, and their interaction on behavioural adherence. Results: Dutch respondents generally had strong intentions to adhere to all preventive measures and their adherence rates were between 70% and 90%. They also self-reported to experience their behaviours as more automatic over time, and this increasing trend in habit strength was more evident for physical-distancing than for hand washing behaviours. For all six behaviours, both intention and habit strength predicted subsequent adherence (all ps < 2e-16). In addition, the predictive power of intention decreased over time and was weaker for respondents with strong habits for physical distancing when visiting supermarkets (B = -0.63, p <.0001) and having guests at home (B = -0.54, p <.0001) in the later phases of the study, but not for hand washing. Conclusions: People's adaptations to physical-distancing and hand washing measures involve both intentional and habitual processes. For public health management, our findings highlight the importance of using contextual cues to promote habit formation, especially for maintaining physical-distancing practices. For habit theories, our study provides a unique dataset that covers multiple health behaviours in a critical real-world setting.
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Non-pharmaceutical interventions, including promotion of social distancing, have been applied extensively in managing the COVID-19 pandemic. Understanding cognitive and psychological factors regulating precautionary behavior is important for future management. The present study examines the importance of selected factors as predictors of having visited or intended to visit crowded places. Six online questionnaire-based waves of data collection were conducted in April–October 2020 in a Norwegian panel (≥18 years). Sample size at Wave 1 was 1,400. In the present study, “Visited or intended to visit crowded places” for different types of locations were the dependent variables. Predictors included the following categories of items: Perceived response effectiveness, Self-efficacy, Vulnerability, Facilitating factors and Barriers. Data were analyzed with frequency and percentage distributions, descriptives, correlations, principal components analysis, negative binomial-, binary logistic-, and multiple linear regression, and cross-lagged panel models. Analyses of dimensionality revealed that a distinction had to be made between Grocery stores, a location visited by most, and locations visited by few (e.g., “Pub,” “Restaurants,” “Sports event”). We merged the latter set of variables into a countscore denoted as “Crowded places.” On the predictor side, 25 items were reduced to eight meanscores. Analyses of data from Wave 1 revealed a rather strong prediction of “Crowded places” and weaker associations with “Supermarket or other store for food.” Across waves, in multiple negative binomial regression models, three meanscore predictors turned out to be consistently associated with “Crowded places.” These include “Response effectiveness of individual action,” “Self-efficacy with regard to avoiding people,” and “Barriers.” In a prospective cross-lagged model, a combined Response effectiveness and Self-efficacy score (Cognition) predicted behavior (“Visited or intended to visit crowded places”) prospectively and vice versa. The results of this study suggest some potential to reduce people's visits to crowded locations during the pandemic through health education and behavior change approaches that focus on strengthening individuals' perceived response effectiveness and self-efficacy.
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Humanity has faced different pandemics in history. The Covid-19 pandemic has made a new course in the world caused by SARS-CoV-2 that can be transmitted to humans. Finding alternative methods to prevent and control the disease through food and some micronutrients is important. This review summarizes effect of food and key micronutrients on Covid-19. There are currently no reports of the feasibility of transmission through the food sector. However, malnutrition and deficiency of some nutrients can lead to disorders of immune system. Coronavirus may be transferred through raw and uncooked foods; more safety and preventive measures are needed. Furthermore, sufficient intake of omega-3 fatty acids, minerals and vitamins are required for proper immune system function. Therefore, a healthy diet is required for prevent Covid-19. Personal hygiene and employee awareness is the two most important features in the prevention of Covid-19. Further studies are needed to confirm these results.
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Background: Housing characteristics and neighbourhood context are considered risk factors for COVID-19 mortality among older adults. The aim of this study was to investigate how individual-level housing and neighbourhood characteristics are associated with COVID-19 mortality in older adults. Methods: For this population-based, observational study, we used data from the cause-of-death register held by the Swedish National Board of Health and Welfare to identify recorded COVID-19 mortality and mortality from other causes among individuals (aged ≥70 years) in Stockholm county, Sweden, between March 12 and May 8, 2020. This information was linked to population-register data from December, 2019, including socioeconomic, demographic, and residential characteristics. We ran Cox proportional hazards regressions for the risk of dying from COVID-19 and from all other causes. The independent variables were area (m2) per individual in the household, the age structure of the household, type of housing, confirmed cases of COVID-19 in the borough, and neighbourhood population density. All models were adjusted for individual age, sex, country of birth, income, and education. Findings: Of 279 961 individuals identified to be aged 70 years or older on March 12, 2020, and residing in Stockholm in December, 2019, 274 712 met the eligibility criteria and were included in the study population. Between March 12 and May 8, 2020, 3386 deaths occurred, of which 1301 were reported as COVID-19 deaths. In fully adjusted models, household and neighbourhood characteristics were independently associated with COVID-19 mortality among older adults. Compared with living in a household with individuals aged 66 years or older, living with someone of working age (<66 years) was associated with increased COVID-19 mortality (hazard ratio 1·6; 95% CI 1·3-2·0). Living in a care home was associated with an increased risk of COVID-19 mortality (4·1; 3·5-4·9) compared with living in independent housing. Living in neighbourhoods with the highest population density (≥5000 individuals per km2) was associated with higher COVID-19 mortality (1·7; 1·1-2·4) compared with living in the least densely populated neighbourhoods (0 to <150 individuals per km2). Interpretation: Close exposure to working-age household members and neighbours is associated with increased COVID-19 mortality among older adults. Similarly, living in a care home is associated with increased mortality, potentially through exposure to visitors and care workers, but also due to poor underlying health among care-home residents. These factors should be considered when developing strategies to protect this group. Funding: Swedish Research Council for Health, Working Life and Welfare (FORTE), Swedish Foundation for Humanities and Social Sciences.
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Efforts to change behaviour are critical in minimizing the spread of highly transmissible pandemics such as COVID-19. However, it is unclear whether individuals are aware of disease risk and alter their behaviour early in the pandemic. We investigated risk perception and self-reported engagement in protective behaviours in 1591 United States-based individuals cross-sectionally and longitudinally over the first week of the pandemic. Subjects demonstrated growing awareness of risk and reported engaging in protective behaviours with increasing frequency but underestimated their risk of infection relative to the average person in the country. Social distancing and hand washing were most strongly predicted by the perceived probability of personally being infected. However, a subgroup of individuals perceived low risk and did not engage in these behaviours. Our results highlight the importance of risk perception in early interventions during large-scale pandemics.
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Objective To characterise the clinical features of children and young people admitted to hospital with laboratory confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the UK and explore factors associated with admission to critical care, mortality, and development of multisystem inflammatory syndrome in children and adolescents temporarily related to coronavirus disease 2019 (covid-19) (MIS-C). Design Prospective observational cohort study with rapid data gathering and near real time analysis. Setting 260 hospitals in England, Wales, and Scotland between 17 January and 3 July 2020, with a minimum follow-up time of two weeks (to 17 July 2020). Participants 651 children and young people aged less than 19 years admitted to 138 hospitals and enrolled into the International Severe Acute Respiratory and emergency Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK study with laboratory confirmed SARS-CoV-2. Main outcome measures Admission to critical care (high dependency or intensive care), in-hospital mortality, or meeting the WHO preliminary case definition for MIS-C. Results Median age was 4.6 (interquartile range 0.3-13.7) years, 35% (225/651) were under 12 months old, and 56% (367/650) were male. 57% (330/576) were white, 12% (67/576) South Asian, and 10% (56/576) black. 42% (276/651) had at least one recorded comorbidity. A systemic mucocutaneous-enteric cluster of symptoms was identified, which encompassed the symptoms for the WHO MIS-C criteria. 18% (116/632) of children were admitted to critical care. On multivariable analysis, this was associated with age under 1 month (odds ratio 3.21, 95% confidence interval 1.36 to 7.66; P=0.008), age 10-14 years (3.23, 1.55 to 6.99; P=0.002), and black ethnicity (2.82, 1.41 to 5.57; P=0.003). Six (1%) of 627 patients died in hospital, all of whom had profound comorbidity. 11% (52/456) met the WHO MIS-C criteria, with the first patient developing symptoms in mid-March. Children meeting MIS-C criteria were older (median age 10.7 (8.3-14.1) v 1.6 (0.2-12.9) years; P<0.001) and more likely to be of non-white ethnicity (64% (29/45) v 42% (148/355); P=0.004). Children with MIS-C were five times more likely to be admitted to critical care (73% (38/52) v 15% (62/404); P<0.001). In addition to the WHO criteria, children with MIS-C were more likely to present with fatigue (51% (24/47) v 28% (86/302); P=0.004), headache (34% (16/47) v 10% (26/263); P<0.001), myalgia (34% (15/44) v 8% (21/270); P<0.001), sore throat (30% (14/47) v (12% (34/284); P=0.003), and lymphadenopathy (20% (9/46) v 3% (10/318); P<0.001) and to have a platelet count of less than 150 × 10 ⁹ /L (32% (16/50) v 11% (38/348); P<0.001) than children who did not have MIS-C. No deaths occurred in the MIS-C group. Conclusions Children and young people have less severe acute covid-19 than adults. A systemic mucocutaneous-enteric symptom cluster was also identified in acute cases that shares features with MIS-C. This study provides additional evidence for refining the WHO MIS-C preliminary case definition. Children meeting the MIS-C criteria have different demographic and clinical features depending on whether they have acute SARS-CoV-2 infection (polymerase chain reaction positive) or are post-acute (antibody positive). Study registration ISRCTN66726260.
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Significance We investigate what drives OECD countries to adopt COVID-19 restrictive policies such as lockdowns and school closures, and find that government policies are strongly driven by the policies initiated in other countries. The level of democracy also matters: While strong democracies are slower to initiate restrictive policies, they are more likely to follow the policies of nearby countries. Following the lead of others rather than making decisions based on the specific situation of the country may have led to countries locking down either too early or too late. Conversely, if countries follow each other when easing restrictive policies or reinitiate such policies, there may be a situation where countries adopt epidemiologically suboptimal policies.
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While severe social-distancing measures have proven effective in slowing the coronavirus disease 2019 (COVID-19) pandemic, second-wave scenarios are likely to emerge as restrictions are lifted. Here we integrate anonymized, geolocalized mobility data with census and demographic data to build a detailed agent-based model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in the Boston metropolitan area. We find that a period of strict social distancing followed by a robust level of testing, contact-tracing and household quarantine could keep the disease within the capacity of the healthcare system while enabling the reopening of economic activities. Our results show that a response system based on enhanced testing and contact tracing can have a major role in relaxing social-distancing interventions in the absence of herd immunity against SARS-CoV-2. An agent-based model of SARS-CoV-2 transmission shows that testing, contact tracing and household quarantine could keep new COVID-19 waves under control while allowing the reopening of the economy with minimal social-distancing interventions.
What is already known about this topic? Before August 2020, minimal data were available about outbreaks and disease transmission in institutes of higher education within the United States. What is added by this report? A North Carolina university experienced a rapid increase in COVID-19 cases and clusters within 2 weeks of opening the campus to students. Student gatherings and congregate living settings, both on and off campus, likely contributed to the rapid spread of COVID-19 in this setting. What are the implications for public health practice? Enhanced measures are needed to reduce transmission at institutes of higher education and could include reducing on-campus housing density, ensuring adherence to masking and other mitigation strategies, increasing testing for SARS-CoV-2, and discouraging student gatherings. © 2020 Department of Health and Human Services. All rights reserved.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 35 million people globally, with more than 1 million deaths recorded by WHO as of Oct 12, 2020. As a second wave of COVID-19 affects Europe, and with winter approaching, we need clear communication about the risks posed by COVID-19 and effective strategies to combat them. Here, we share our view of the current evidence-based consensus on COVID-19.