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The present paper examines longitudinally how subjective perceptions about COVID-19, one’s community, and the government predict adherence to public health measures to reduce the spread of the virus. Using an international survey ( N = 3040), we test how infection risk perception, trust in the governmental response and communications about COVID-19, conspiracy beliefs, social norms on distancing, tightness of culture, and community punishment predict various containment-related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal hygiene behavior was predicted by personal infection risk perception. At social level, social distancing behaviors such as abstaining from face-to-face contact were predicted by perceived social norms. Support for behavioral mandates was predicted by confidence in the government and cultural tightness, whereas support for anti-lockdown protests was predicted by (lower) perceived clarity of communication about the virus. Results are discussed in light of policy implications and creating effective interventions.
Scientic Reports | (2022) 12:3824 |
Predictors of adherence
to public health behaviors
for ghting COVID‑19 derived
from longitudinal data
Birga M. Schumpe1,74*, Caspar J. Van Lissa2,74, Jocelyn J. Bélanger3, Kai Ruggeri4,
Jochen Mierau5, Claudia F. Nisa3, Erica Molinario6, Michele J. Gelfand7, Wolfgang Stroebe5,
Maximilian Agostini5, Ben Gützkow5, Bertus F. Jeronimus5, Jannis Kreienkamp5,
Maja Kutlaca8, Edward P. Lemay Jr9, Anne Margit Reitsema5, Michelle R. vanDellen10,
Georgios Abakoumkin11, Jamilah Hanum Abdul Khaiyom12, Vjollca Ahmedi13,
Handan Akkas14, Carlos A. Almenara15, Mohsin Atta16, Sabahat Cigdem Bagci17,
Sima Basel3, Edona Berisha Kida13, Allan B. I. Bernardo18, Nicholas R. Buttrick19,
Phatthanakit Chobthamkit20, Hoon‑Seok Choi21, Mioara Cristea22, Sara Csaba23,
Kaja Damnjanović24, Ivan Danyliuk25, Arobindu Dash26, Daniela Di Santo27,
Karen M. Douglas28, Violeta Enea29, Daiane Faller30, Gavan J. Fitzsimons31,
Alexandra Gheorghiu29, Ángel Gómez32, Ali Hamaidia33, Qing Han34, Mai Helmy35,
Joevarian Hudiyana36, Ding‑Yu Jiang37, Veljko Jovanović38, Zeljka Kamenov39,
Anna Kende23, Shian‑Ling Keng40, Tra Thi Thanh Kieu41, Yasin Koc5, Kamila Kovyazina42,
Inna Kozytska25, Joshua Krause43, Arie W. Kruglanski9, Anton Kurapov25,
Nóra Anna Lantos23, Cokorda Bagus J. Lesmana44, Winnifred R. Louis45, Adrian Lueders46,
Najma Iqbal Malik16, Anton P. Martinez47, Kira O. McCabe48, Jasmina Mehulić39,
Mirra Noor Milla36, Idris Mohammed49, Manuel Moyano50, Hayat Muhammad51,
Silvana Mula27, Hamdi Muluk36, Solomiia Myroniuk5, Reza Naja52, Boglárka Nyúl23,
Paul A. O’Keefe53, Jose Javier Olivas Osuna54, Evgeny N. Osin55, Joonha Park56,
Gennaro Pica57, Antonio Pierro27, Jonas H. Rees58, Elena Resta27, Marika Rullo59,
Michelle K. Ryan60, Adil Samekin61, Pekka Santtila62, Edyta Sasin3, Heyla A. Selim63,
Michael Vicente Stanton64, Samiah Sultana5, Robbie M. Sutton28, Eleftheria Tseliou11,
Akira Utsugi65, Jolien A. van Breen66, Kees Van Veen5, Alexandra Vázquez32, Robin Wollast67,
Victoria Wai‑Lan Yeung68, Somayeh Zand69, Iris Lav Žeželj24, Bang Zheng70, Andreas Zick71,
Claudia Zúñiga72 & N. Pontus Leander73
The present paper examines longitudinally how subjective perceptions about COVID‑19, one’s
community, and the government predict adherence to public health measures to reduce the spread
of the virus. Using an international survey (N = 3040), we test how infection risk perception, trust
in the governmental response and communications about COVID‑19, conspiracy beliefs, social
norms on distancing, tightness of culture, and community punishment predict various containment
related attitudes and behavior. Autoregressive analyses indicate that, at the personal level, personal
hygiene behavior was predicted by personal infection risk perception. At social level, social distancing
behaviors such as abstaining from face‑to‑face contact were predicted by perceived social norms.
Support for behavioral mandates was predicted by condence in the government and cultural
tightness, whereas support for anti‑lockdown protests was predicted by (lower) perceived clarity
of communication about the virus. Results are discussed in light of policy implications and creating
eective interventions.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Scientic Reports | (2022) 12:3824 |
On March 11th, 2020, the World Health Organization (WHO) declared COVID-19 a pandemic. Since then, the
novel coronavirus SARS-CoV-2, which leads to the illness referred to as COVID-19, has put society to the test.
From washing hands to getting vaccinated, individual health behavior is the most important defense to curb the
spread of the virus 1,2, behavioral sciences need to be leveraged to build trust that encourages individuals to fully
utilize public health recommendations 3.
Predicting adherence to public health measures
Based on existing evidence regarding health behaviors, we focus this work on personal risk perception (rst-order
eects), subjective social and community norms (second-order eects), and broader political and governmental
perceptions (third-order eects).
First‑order eects. Individuals’ perceived personal risk of infection is oen studied in the context of com-
municable diseases such as H1N1 (‘Swine Flu’) or the original SARS outbreaks in 2002–2003. A greater per-
ception of personal risk was strongly related to increased willingness to take precautionary measures against
infection 46. Protection Motivation eory 7 proposes that the severity of a threatening event and the perceived
probability of its occurrence determines whether people engage in healthy behaviors. Recent ndings indicate
that perceived economic risk is also positively related to mitigation behavior and policy support 8.
Given these insights, we hypothesize that personal risk perception regarding COVID-19 predicts willingness
to engage in protective health behaviors and support for pandemic response related policies. Importantly, this
hypothesis has not previously been tested longitudinally, hence, claims of causality are not possible.
Second‑order eects. Social psychology has a long history of observing that social norms—the subjec-
tive perception of what others are doing and approving of—are a reliable predictor for people’s behavior across
contexts 911. Likewise, social norms play an important role in predicting health behaviors 12,13. Specically, for
the COVID-19 pandemic, social distancing norms might predict compliance with health measures. We predict
that injunctive social norms, that is, norms of “ought” 14 would predict more compliance over time with public
health measures.
Injunctive norms may be manifested in what community perceive they and others ought to do as well as
through subjective perceptions that norm violators are punished. at is, people may perceive their community
would impose punishments (e.g., scorn or nes) for not following public health recommendations enforced by
government authorities. Examples of these behaviors include breaking quarantine or not wearing face masks.
But does the perception of a punitive community encourage compliance in individuals? On the one hand,
increasing the intensity or duration of punishments can lead to greater suppression of targeted (unwanted)
behaviors 15. However, individuals are more likely to resist when they perceive their freedom to engage in a
1University of Amsterdam, Amsterdam, The Netherlands. 2Utrecht University, Utrecht, Netherlands. 3New York
University Abu Dhabi, Abu Dhabi, United Arab Emirates. 4Columbia University, New York, USA. 5University of
Groningen, Groningen, Netherlands. 6Florida Gulf Coast University, Fort Myers, USA. 7Stanford University, Palo
Alto, USA. 8Durham University, Durham, UK. 9University of Maryland, College Park, USA. 10University of Georgia,
Athens, USA. 11University of Thessaly, Volos, Greece. 12International Islamic University Malaysia, Kuala Lumpur,
Malaysia. 13Pristine University, Pristine, Kosovo. 14Ankara Science University, Ankara, Turkey. 15Universidad
Peruana de Ciencias Aplicadas, Lima, Peru. 16University of Sargodha, Sargodha, Pakistan. 17Sabanci University,
Istanbul, Turkey. 18De La Salle University, Manila, Philippines. 19University of Virginia, Charlottesville,
USA. 20Thammasat University, Pathumthani, Thailand. 21Sungkyunkwan University, Seoul, Korea. 22Heriot
Watt University, Edinburgh, UK. 23ELTE Eötvös Loránd University, Budapest, Hungary. 24University of Belgrade,
Belgarde, Serbia. 25Taras Shevchenko National University of Kyiv, Kyiv, Ukraine. 26Leuphana University of Lüneburg,
Lüneburg, Germany. 27University “La Sapienza”, Rome, Italy. 28University of Kent, Canterbury, UK. 29Alexandru
Ioan Cuza University, Iași, Romania. 30National University of Singapore, Singapore, Singapore. 31Duke University,
Durham, USA. 32Universidad Nacional de Educación a Distancia, Madrid, Spain. 33Setif 2 University, Setif,
Algeria. 34University of Bristol, Bristol, UK. 35Sultan Qaboos University, Muscat, Oman. 36Universitas Indonesia,
Depok, Indonesia. 37National Chung-Cheng University, Minxiong, Taiwan. 38University of Novi Sad, Novi Sad,
Serbia. 39University of Zagreb, Zagreb, Croatia. 40Monash University Malaysia, Subang Jaya, Malaysia. 41HCMC
University of Education, Ho Chi Minh City, Vietnam. 42Nur-Sultan, Kazakhstan. 43University of Groningen,
Kazakhstan, Netherlands. 44Udayana University, Denpasar, Indonesia. 45University of Queensland, Brisbane,
Australia. 46University of Limerick, Limerick, Ireland. 47University of Sheeld, Sheeld, UK. 48Carleton University,
Ottawa, Canada. 49Usmanu Danfodiyo University Sokoto, Sokoto, Nigeria. 50University of Cordoba, Cordoba,
Spain. 51University of Peshawar, Peshawar, Pakistan. 52University of Padova, Padova, Italy. 53Yale-NUS College,
Singapore, Singapore. 54National Distance Education University (UNED), Madrid, Spain. 55HSE University, Moscow,
Russia. 56NUCB Business School, Nagoya, Japan. 57University of Camerino, Camerino, MC, Italy. 58University of
Bielefeld, Bielefeld, Germany. 59University of Siena, Siena, Italy. 60University of Exeter, Exeter, UK. 61M. Narikbayev
KAZGUU University, Nur-Sultan, Kazakhstan. 62New York University Shanghai, Shanghai, China. 63King Saud
University, Riyadh, Saudi Arabia. 64California State University, East Bay, USA. 65Nagoya University, Nagoya,
Japan. 66Leiden University, Leiden, Netherlands. 67Université Clermont-Auvergne, Clermont-Ferrand,
France. 68Lingnan University, Tuen Mun, Hong Kong. 69University of Milano-Bicocca, Milan, Italy. 70Imperial College
London, London, UK. 71Bielefeld University, Bielefeld, Germany. 72Universidad de Chile, Santiago, Chile. 73Wayne
State University, Detroit, USA. 74These authors contributed equally: Birga M. Schumpe and Caspar J. van
Lissa. *email:
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Scientic Reports | (2022) 12:3824 |
specic behavior is threatened 16, especially if they perceive such resistance as the norm among their ingroup 17.
us, we test the predictive power of perceived punishments for adherence to healthy behavior recommenda-
tions from public health ocials over time.
Third‑order eects. On a general level, trust in the government is a predictor for adherence to recom-
mended health behaviors 18,19. In contrast, distrust in the government is known to be related to lower compliance
with, for example, policies aimed at stemming the Ebola outbreak 20. In the COVID-19 context, cross-sectional
studies support this assertion: people in the US who fear the authorities comply less with mitigation measures
to ght COVID-19 21. We predict that, over time, trust in the government to eectively ght COVID-19 would
lead to greater compliance with public health recommendations and greater support for governmental policies
to mitigate the COVID-19 pandemic.
Further, how risks are communicated is a critical consideration 22. Subjective perceptions that one receives
clear and unambiguous messages are deemed central for promoting compliance with public health measures 23.
Taken together, we expect that individuals who perceive to receive clear and unambiguous messages would show
increased adherence to public health recommendations over time.
In contrast to earlier points, the pandemic has fueled numerous misperceptions about the virus and society,
including conspiracy theories. For example, some people believe that the coronavirus was created to be used as
a population-control scheme. False beliefs regarding vaccines (e.g., that they contain chips or lead to genetic
modication) can result in deaths if individuals fail to get protected and avoidable transmission occurs. Indeed,
belief in conspiracy theories predicts resistance to preventive behaviors 24.
People may also perceive that society ought to tighten or loosen its injunctive norms. An important societal
level factor in cross-cultural research is tightness-looseness 24. Tightness is dened by having strong norms, strict
rules, and low tolerance of deviating behavior, oen considered characteristic of places such as Singapore or
Japan. Loose cultures, such as Italy or the United States, are characterized by weaker social norms and rules and
being generally more permissive about deviation 25. Since people in tighter cultures tend to follow rules more
strictly and are more accepting toward authoritarian leadership, cultural tightness should predict compliance
with public health measures. Importantly, a stronger preference for tighter structures aer the onset of the pan-
demic would be indicative of greater perception that societal circumstances demand for more control and rule
enforcement during these times 26,27.
In sum, prior research oers some evidence that subjective perceptions at the personal-, social-, or societal
level may predict various pandemic-related attitudes and behaviors. Yet, the existing literature has several limi-
tations. Firstly, no study has comprehensively investigated predictors at all three levels of analysis (personal-,
social-, and societal level). However, to design eective interventions, it would be useful to know whether certain
subjective perceptions are generally predictive, or whether a given subjective perception is most predictive of
a relevant outcome when considered at the same level of construal—be it at the personal-level, the social, or a
more generalized societal level 28. Such an analysis can also oer preliminary indication of whether and how
future research and policy should tailor interventions on subjective perception to the specic outcome of interest.
Second, most studies have examined these factors in the context of infectious diseases other than COVID-
19 29. e COVID-19 pandemic required instantaneous behavioral change at a global level. is mass nature of
the COVID-19 pandemic requires an understanding that cannot necessarily be translated from other infectious
diseases. For instance, the novel coronavirus (2019-nCoV) has a higher transmissibility than SARS (SARS-CoV)
and more patients with mild symptoms that fail to be isolated 2. As this occurred in a period when global travel
was much more accessible than in 2002, it meant a potentially large number of individual carriers were potentially
unknowingly spreading the disease before public health ocials had a chance to react.
Lastly, the studies that did address COVID-19 have been primarily of cross-sectional nature only 6,18. is
means previous research has not provided any information on temporal precedence of the eects. For example,
we do not know whether those subjective perceptions are indeed antecedents even though social psychology
has long recognized that subjective perceptions can change to match their prior behavior (e.g., self-perception
theory) 30. e present research seeks to overcome these limitations by studying predictors at all three levels of
analysis over time in the context of COVID-19 to establish temporal precedence. In doing so, we aim to identify
factors that predict compliance with preventative interventions that would allow policy makers to cra eective
interventions to mitigate the COVID-19 pandemic through behaviors such as hand washing, quarantining, and
social distancing.
We tested whether the hypothesized predictors were reliably associated with changes in health behavior and
support for public health recommendations, while controlling for stability of the dependent variable and the
inuence of age, gender, employment status, education, religion, political view, date survey taken, time interval
between measurements, as well as subjective proximity to COVID-19 cases.
We standardized all predictors with respect to the sample mean and standard deviation. is was considered
optimal as standardized coecients are not currently available for multilevel models with random slopes esti-
mated in Mplus. Such grand-mean centering is typically used when within-cluster eects are not the primary
target of inference 31. e regression coecients of the standardized predictors can be interpreted as approximate
indicators of relative variable importance, albeit disregarding dierences across levels and random eects (i.e.,
dierences in variable importance across countries). For each hypothesized eect, we report whether the average
eect was signicant across countries and whether there was signicant between-country variance in the eect.
e multilevel design was hence employed to address the country-level hypotheses.
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Scientic Reports | (2022) 12:3824 |
Adherence to recommended health behaviors. Table1 shows the theoretically-derived variables that
reliably predicted adherence to health behaviors recommended by public health guidelines. With regards to per-
sonal hygiene, hand washing (LL = −3950.25, AIC = 7984.50, BIC = 8241.23) showed a signicant autoregressive
eect (B = 0.80; p < 0.001; CI [0.54, 1.06]), indicating stability across the waves. is stability varied signicantly
across countries (B = 0.19; p < 0.001; CI [0.07, 0.31]). Aer controlling for this autoregressive eect, changes in
hand washing were predicted by perceived risk to becoming infected (B = 0.05; p < 0.001; CI [0.02, 0.09]), belief
in conspiracy theories (B = 0.04; p < 0.001; CI [0.02, 0.07]), and perceptions that one receives clear and unam-
biguous messages about what to do about the coronavirus (B = 0.04; p > 0.001; CI [0.01, 0.08]). is suggests that,
over time, individuals who perceive greater risk to becoming infected, greater conspiracies, and greater message
clarity washed their hands more frequently. ese eects did not dier between countries, ps > 0.40.
Willingness to wear a face mask (LL = -1806.83, AIC = 3669.67, BIC = 3809.73) showed high stability across
waves (B = 2.02; p < 0.001; CI [1.38, 2.21]). is stability did not vary between countries, p > 0.79. When control-
ling for the autoregressive eect, changes in willingness to wear a face mask were predicted by subjective proxim-
ity to COVID-19 cases (B = 0.30; p = 0.05; CI [0.00, 0.59]). is eect did not dier between countries, p > 0.99.
Avoiding crowds (LL = −3719.47, AIC = 7522.94, BIC = 7779.68) showed a signicant autoregressive eect
(B = 0.59; p < 0.001; CI [0.44, 0.73]). is stability varied signicantly across countries, B = 0.17; p < 0.001; CI [0.09,
0.25]). Changes in avoiding crowds were signicantly predicted by social distancing norms (B = 0.16; p < 0.001;
CI [0.09, 0.23]), risk perception to becoming infected (B = 0.06; p < 0.001; CI [0.03, 0.10]), people’s preference
for tight cultural structures (B = 0.04; p < 0.001; CI [0.02, 0.06]), and their belief in conspiracy theories (B = 0.02;
p = 0.01; CI [0.01, 0.04]). Hence, stronger norms regarding social distancing, higher perceived risk to becoming
infected, and a stronger preference for tight cultural structures predicted over-time increase in the tendency
to avoid crowds. Only the eect for social norms varied across countries (B = 0.06; p = 0.01; CI [0.02, 0.10]).
Further, authoritarianism (B = 0.04; p < 0.001; CI [0.01, 0.06]) and participants’ age (B = 0.05; p = 0.01; CI [0.01,
0.08]) positively predicted change over time in people’s tendency to avoid crowds. is suggests that, over time,
older and more authoritarian participants avoided crowds more. ese eects did not vary signicantly across
countries, ps > 0.82. Gender (B = −0.05; p = 0.04; CI [0.10, −0.00] was a signicant predictor, with women showing
greater change in the tendency to avoid crowds. Date of survey participation was also predictive; participants
who enrolled later showed decreases in the tendency to avoid crowds over-time (B = −0.01; p = 0.01; CI [−0.02,
−0.01]). ese eects did not dier between countries, ps > 0.09.
Quarantining (LL = −5320.38, AIC = 10,724.75, BIC = 10,981.48) showed a signicant autoregressive eect
(B = 1.06; p < 0.001; CI [0.96, 1.17]), which varied across countries (B = 0.28; p < 0.001; CI [0.20, 0.35]). Changes
in quarantining were predicted by social distancing norms (B = 0.09; p = 0.05; CI [0.00, 0.18]), time between
measurements (B = −0.13; p < 0.001; CI [−0.20, −0.06]), employment status (B = −0.09; p = 0.01; CI [−0.16, −0.03]),
age (B = −0.07; p < 0.001; CI [−0.11, −0.04]), and the date the survey was taken (B = −0.02; p < 0.001; CI [−0.03,
−0.01]). None of these eects did vary signicantly across countries, ps > 0.05.
In-person (face-to-face) contact with friends and family (LL = −7422.56, AIC = 14,929.13, BIC = 15,190.06)
showed signicant stability over time (B = 1.04; p < 0.001; CI [0.92, 1.17]), which varied across countries (B = 0.75;
p < 0.001; CI [0.60, 0.90]). Changes in social face-to-face contact with friends and family were predicted by social
norms on distancing (B = −0.17; p < 0.001; CI [−0.24, −0.09]), participants’ age (B = −0.19; p < 0.001; CI [−0.28,
−0.09]) and gender (B = 0.16; p = 0.02; CI [0.03, 0.29]). Overall, social distancing norms were the best predictor
for changes in face-to-face contact. Older participants and men had less in-person contact with friends and
family. ese eects did not dier between countries, ps > 0.24.
Social face to face contact with other people in general (“others”, LL = −7272.44, AIC = 14,628.88,
BIC = 14,889.61) showed signicant stability over time (B = 1.12; p < 0.001; CI [1.04, 1.21]). is autoregressive
eect varied across countries (B = 0.45; p < 0.001; CI [0.34, 0.57]). Controlling for this, changes in social face
to face contact with others were predicted by social distancing norms (B = −0.14; p < 0.001; CI [−0.22, −0.07])
Table 1. Predictors of health behaviors.
Health behavior Predictor B p CI
Hand washing
Perceived risk to becoming infected 0.05 < 0.001 [0.02, 0.09]
Belief in conspiracy theories 0.04 < 0.001 [0.02, 0.07]
Getting clear and unambiguous messages about what
to do 0.04 < 0.001 [0.01, 0.08]
Avoiding crowds
Social norms 0.16 < 0.001 [0.09, 0.23]
Perceived risk to becoming infected 0.06 < 0.001 [0.03, 0.10]
Preference for cultural tightness 0.04 < 0.001 [0.02, 0.06]
Belief in conspiracy theories 0.02 0.01 [0.01, 0.04]
Self-isolation/quarantine Social norms 0.09 0.05 [0.00, 0.18]
Face-to-face contact with friends and family Social norms −0.17 < 0.001 [−.24, −0.09]
Face to face contact with other people Social norms −0.14 < 0.001 [−0.22, −0.07]
Preference for cultural tightness −0.06 0.04 [−0.11, −0.00]
Days per week people le their house Social norms −0.06 < 0.001 [−0.08, −0.04]
Cultural tightness −0.04 0.04 [−0.07, −0.00]
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Scientic Reports | (2022) 12:3824 |
and preference for tightness (B = −0.06; p = 0.04; CI [−0.11, −0.00]. Another predictor was employment status
(B = 0.48; p < 0.001; CI [0.29, 0.66]), which diered across countries (B = 0.85; p > 0.001; CI [0.39, 1.31]), all other
ps > 0.10.
e number of days in a week people le their house (LL = −3861.18, AIC = 7806.35, BIC = 8067.47) showed
signicant stability over time (B = 0.79; p < 0.001; CI [0.76, 0.82]). Changes in the number of days in a week people
leaving their house were predicted by social norms on distancing (B = −0.06; p < 0.001; CI [−0.08, −0.04]), cultural
tightness (B = −0.04; p = 0.04; CI [−0.07, −0.00]), being employed (B = 0.13; p < 0.001; CI [0.08, 0.18]), and being
politically on the right side of the spectrum (B = 0.02; p = 0.04; CI [0.00, 0.04]). ere were no between country
dierences in these eects, all ps > 0.26.
Attitudes toward behavioral mandates. Table2 shows the theoretically derived variables that pre-
dict attitudes toward behavioral mandates. People’s support for mandatory vaccination (LL = −5688.01,
AIC = 11,460.03, BIC = 11,719.13) showed signicant stability over time (B = 1.36; p < 0.001; CI [1.25, 1.47]). is
autoregressive eect varied across countries (B = 0.13; p = 0.02; CI [0.02, 0.24]). Controlling for it, changes in
support for mandatory vaccination were predicted by participants’ trust in the government to ght COVID-19
(B = 0.07; p = 0.01; CI [0.01, 0.13]). is shows that people support mandatory vaccination more when they have
greater trust in the government to ght COVID-19 eectively. Moreover, changes in support for mandatory vac-
cination were predicted by social distancing norms (B = 0.07; p = 0.01; CI [0.02, 0.13]), being politically on the
right side of the spectrum (B = −0.04; p = 0.03; CI [−0.08, −0.00]), and the date the survey was taken (B = −0.02;
p < 0.001; CI [−0.03, −0.01]; no between country dierences, all ps > 0.58).
Results further revealed that support for mandatory quarantine (LL = −4965.04, AIC = 10,014.08,
BIC = 10,273.18) showed signicant stability over time (B = 0.66; p < 0.001; CI [0.58, 0.74]). is autoregressive
eect varied signicantly across countries (B = 0.19; p > 0.001; CI [0.12, 0.26]). Controlling for it, individuals’
changes in support for mandatory quarantine were predicted by social distancing norms (B = 0.17; p < 0.001;
CI [0.09, 0.24]), trust in the government to ght COVID-19 (B = 0.08; p > 0.001; CI [0.02, 0.34]), preference for
cultural tightness (B = 0.08; p < 0.001; CI [0.02, 0.13]), being female (B = −0.08; p = 0.05; CI [−0.17, −0.00]), age
(B = 0.05; p = 0.01; CI [0.01, 0.10]), authoritarianism (B = 0.05; p = 0.02; CI [0.01, 0.09]), time interval between
initial and follow-up measurement (B = −0.04; p = 0.01; CI [−0.07, −0.01]), and the date the survey was taken
(B = −0.01; p < 0.001; CI [−0.02, −0.00]). ere were no between country dierences, all ps > 0.36.
People’s readiness to protest containment measures (LL = −1803.26, AIC = 3662.53, BIC = 3806.61) showed
a signicant autoregressive eect, indicating stability across the waves (B = 1.06; p > 0.001; CI [0.88, 1.25]). is
stability varied signicantly across countries (B = 0.33; p > 0.001; CI [0.19, 0.47]). Controlling for the autoregres-
sive eect, changes in the readiness to protest containment measures were predicted by participants’ perception
that they were getting clear and unambiguous messages about the coronavirus (B = −0.07; p = 0.04; CI [−0.13,
−0.00]). Lastly, changes in readiness to protest containment measures was predicted by whether participants were
religious or not (B = 0.15; p > 0.001; CI [0.07, 0.23]; no between country dierences, ps > 0.09).
For behavioral science to successfully inform public health policy towards mitigating the pandemic, there is
a need to conduct holistic, cross-cultural, longitudinal research that identies the unique eects of various
candidate predictors on relevant outcomes of interests. Simultaneously testing multiple candidate predictors
can help to pinpoint the most important predictors for a given outcome or a set of outcomes. e global scale
of a pandemic may call for a unied global response, which means that a candidate predictor should be tested
across cultures to determine its generalizability. A longitudinal approach helps to establish temporal precedence
between predictors and outcomes, which gives early insight into potential causal inferences that can be tested
with intervention studies.
Given these aims, the present research used a longitudinal design to identify the subjective perceptions that
predict individuals’ changes, over time, in adherence to behaviors recommended by public health guidance and
support of general public health policies in the context of COVID-19. We specically tested several predictors
that have been found potentially relevant by prior literature, that is, risk perception, social norms, punishments,
trust in the government, clear and unambiguous messages, cultural tightness, and belief in conspiracy theories.
Several attitudes and behaviors related to public health (e.g., quarantining, wearing a face mask, etc.) were
assessed as critical outcome variables at numerous points in time. is approach helps to determine whether
certain factors are generally predictive, across multiple outcomes of interest, or whether dierent subjective
Table 2. Predictors of attitudes toward behavioral mandates.
Attitudes toward mandates Predictor B p CI
Mandatory vaccination Trust in the government to ght COVID-19 0.07 0.01 [0.01, 0.13]
Social norms 0.07 0.01 [0.02, 0.13]
Mandatory quarantine
Social norms 0.17 < 0.001 [0.09, 0.24]
Trust in the government to ght COVID-19 0.08 < 0.001 [0.02, 0.34]
Preference for cultural tightness 0.08 < 0.001 [0.02, 0.13]
Protest containment measures Getting clear and unambiguous messages about what to do −0.07 0.04 [−0.13, −0.00]
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perceptions predict changes in specic virus prevention behaviors and attitudes over time. In this vein, the
most generalizable predictor was social and community-level norms, which reliably predicted outcomes across
conceptual levels, such as personal hygiene, public contact, and attitudes towards behavioral mandates. In prac-
tices, this means public health ocials should strongly consider existing and developing social norms not only
in what measures are needed to mitigate a pandemic, but also precisely how to communicate those messages.
However, social norms did not predict all outcomes and, ultimately, each outcome had its own idiosyncratic set
of predictors.
First-order perceptions of perceived individual risk of becoming infected predicted changes in adherence to
important health behaviors such as frequent hand washing. A possible implication of these ndings could be to
install nudges (small changes in choice architecture that encourage optimal decisions without force or genuine
changes in the circumstance 32) in public bathrooms that are related to concepts of risk infection. At a minimum,
our ndings suggest a potential utility for nudges that would increase the salience regarding the risk of becoming
infected (e.g., pictures of hands in which germs made visible through U.V. light could trigger disgust 33.
None of the theoretically-relevant variables predicted a change in willingness to engage in another health
behavior. However, particularly for wearing face masks, participants in proximity to infected individuals showed
an increased tendency to use face masks. is proximity and familiarity eect may have been even stronger
when people know someone in their social network who is infected. Indeed, people make judgments about the
frequency or likelihood of events based on how available information is to them 34. Given this proximity eect,
there is reason for policymakers to consider encouraging individuals to share positive diagnoses, or at least aim
to reduce stigma about positive results. If the eect is accurate, being made aware of proximal infections could
directly reduce negative attitudes toward healthy behaviors, if not directly increase likelihood of engaging in
those behaviors recommended by public health ocials. Such a nding can therefore directly inform public
health messaging.
Second-order social distancing norms predict changes in social distancing behavior. Public health messages
could aim at changing social norms or making existing ones more salient. For instance, common approaches
to modifying social norms are to change possible misperceptions of social norms 35 or to make certain group
memberships more salient 36.
Testing third-order eects showed clearly that trust in the government to eectively ght the COVID-19
pandemic predicts increased support for mandatory measures. In other words, as may seem obvious but is criti-
cal to state outright: where there is trust in the government to be eective, there is substantially greater support
for initiatives to combat a pandemic. Some considerations of this nding mean that, for instance, it is important
for governments to have frequent and informative briengs, in which leaders address the nation and give clear
instructions on what to do.To build trust from these, there would ideally be information that shows how past
guidelines have been eective, and what to expect out of the newest recommendations.
Also, preference for cultural tightness predicted over-time increases in the tendency to support behavioral
mandates. is backs the idea that countries with tighter cultures are better prepared to handle the outbreak 37.
Hence, policy makers should sensitize the public for a temporary need for tighter structures.
To conclude, the present ndings specify the subjective perceptions that policy makers should use as levers
of intervention for containment of the coronavirus pandemic. Despite the surge in behavioral research related
directly to the COVID-19 pandemic, longitudinal analyses are still lacking. Compared to cross-sectional studies,
autoregressive longitudinal research can make a stronger claim to causality, because it meets the requirements
of Granger causality 38.
Despite several strengths, this study also has some limitations. Like most social scientic research, our
approach assumed linearity of associations between variables. Unfortunately, this assumption is dicult to check
in the structural equation modeling context—but no strong theoretical evidence exists to presume a dierent
shape for the associations presented here. Furthermore, the predictors were chosen based on their theoretical
merit. is is not to say that there are no other potential determining factors for the behaviors investigated
here such as cultural ones. Although our data comprised multiple countries, we did not consider the potential
inuence of cultural moderators. It should be noted, however, that 18 countries is a very small sample size for
detecting between-country dierences. Moreover, almost all random slopes in the study were non-signicant,
indicating that there was little to no variance to be explained at the between-country level.
e current research can directly inform ocial public health messaging and intervention eorts. First, the
potential eectiveness for nudges to encourage hand washing is appropriate for eld-testing. Concurrently, poli-
cymakers may wish to focus eorts on raising awareness of proximal infections, which appears to increase the
likelihood of engaging in healthy behaviors. In doing so, public health messages should either aim at promoting
positive social norms or making existing ones more salient. Finally, transparency in the process for all of these
has clear importance and governments should be applied this to maximize eectiveness and build trust for the
well-being of the communities they serve.
Participants and design. Our international survey (https:// www. psyco rona. org) conducted in April 2020
served as the baseline. Participants could opt-in for weekly follow-ups lasting until June 2020. is longitudinal
study is the focus of the present analysis. Tables3 and 4 describe the participants per wave and country in our
nal analysis (N = 3040). Full methodological details, including exact dates of the measurements and description
of items/questionnaires in all languages, are provided in the survey codebook (https:// osf. io/ qhyue). Informed
consent was obtained and all methods were performed in accordance with the relevant guidelines and regula-
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Scientic Reports | (2022) 12:3824 |
Predictor variables. Personal risk perception (rst-order eects). Risk perception infection. We asked:
“How likely is it that the following will happen to you in the next few months? You will get infected with corona-
virus” (1 = Exceptionally unlikely; 8 = Already happened).
Risk perception economic. We also ask how likely they thought their personal situation will get worse due to
economic consequences of coronavirus (1 = Exceptionally unlikely; 8 = Already happened).
Subjective social and community norms (second-order eects). Social norms. Participants indicated their
agreement with: “Right now, people in my area should self-isolate and engage in social distancing” (-3 = Strongly
disagree, 3 = Strongly agree).
Community punishment. “To what extent is your community punishing people who deviate from the rules that
have been put in place in response to the coronavirus?” (1 = Not at all; 6 = very much).
Political and governmental perceptions (third-order eects). Perceived government’s ecacy to ght
COVID-19. We asked participants how much they trusted the government of their country to take the right
measures to deal with the coronavirus pandemic (1 = Not at all; 5 = A great deal).
Clarity of communication. We measured the extent to which participants believe they are “getting clear, unam-
biguous messages about what to do about the coronavirus?” (1 = messages are completely unclear/ambiguous,
6 = messages are very clear/unambiguous).
Generic conspiracy beliefs. We used three items to assess participants’ perceptions of societal-level conspira-
cies (e.g., “I think that government agencies closely monitor all citizens; I think that politicians usually do not
tell us the true motives for their decisions” (0 = Certainly not 0%, 10 = Certainly 100%).
Cultural tightness. We used country-level scores for a country’s position on the looseness-tightness spectrum.
Additionally, we asked people to indicate to what extent they thought that the country they currently live in
should have the following characteristics right now? (1 = Have exible social norms, 9 = Have rigid social norms;
1 = Be loose, 9 = Be tight; 1 = Treat people who don’t conform to norms kindly, 9 = Treat people who don’t conform
to norms harsh).
Table 3. Observations per country for health behaviors and attitudes toward behavioral mandates.
Health behaviors Attitudes toward behavioral mandates
Hand washing Avoiding
crowds Self-isolation/
quarantine Wearing face
friends and
Face to face
contact other
Days per
week house
le Mandatory
vaccination Mandatory
USA 94 94 94 34 109 109 109 94 94 41
UK 191 191 191 99 232 231 232 191 191 116
Ukraine 103 103 103 33 142 141 142 103 103 37
Tur key 103 103 103 9 114 113 116 103 103 12
Spain 191 191 191 82 227 227 229 190 190 94
South Korea 5 5 5 2 11 11 11 5 5 2
Saudi Arabia 34 34 34 6 49 48 49 34 34 11
Russia 159 159 159 24 163 164 164 159 159 26
Netherlands 132 132 132 134 194 192 194 132 132 147
Japan 63 63 63 32 69 70 70 63 63 35
Italy 331 331 331 128 355 352 355 331 331 143
Indonesia 69 69 69 11 85 85 85 69 69 14
Greece 101 101 101 60 171 171 171 101 101 68
Germany 189 189 189 114 223 224 225 189 189 127
Canada 166 166 166 52 183 183 184 166 166 63
Brazil 166 166 166 59 180 179 181 166 166 65
Australia 151 151 151 58 161 160 161 151 151 69
Argentina 159 159 159 43 189 188 190 159 159 47
Tot a l 2407 2407 2407 980 2857 2848 2868 2406 2406 1117
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NAge Gender Education Religious
USA 116
18–24 = 1%
25–34 = 4%
35–44 = 15%
45–54 = 22%
55–64 = 30%
65–75 = 24%
75–85 = 5%
85 + = 0%
Female = 71%
Male = 30%
Other = 0%
Primary education = 2%
General secondary education = 22%
Vocational education = 9%
Higher education = 33%
Bachelor’s degree = 21%
Master’s degree = 11%
PhD degree = 3%
No = 43%
Yes = 57%
UK 241
18–24 = 1%
25–34 = 5%
35–44 = 14%
45–54 = 21%
55–64 = 21%
65–75 = 33%
75–85 = 5%
85 + = 1%
Female = 51%
Male = 49%
Other = 0%
Primary education = 0%
General secondary education = 33%
Vocational education = 24%
Higher education = 14%
Bachelor’s degree = 22%
Master’s degree = 6%
PhD degree = 0%
No = 68%
Yes = 32%
Ukraine 154
18–24 = 4%
25–34 = 21%
35–44 = 11%
45–54 = 13%
55–64 = 38%
65–75 = 13%
75–85 = 0%
85 + = 0%
Female = 54%
Male = 46%
Other = 0%
Primary education = 0%
General secondary education = 6%
Vocational education = 15%
Higher education = 45%
Bachelor’s degree = 7%
Master’s degree = 24%
PhD degree = 3%
No = 38%
Yes = 62%
Tur key 121
18–24 = 3%
25–34 = 26%
35–44 = 27%
45–54 = 17%
55–64 = 22%
65–75 = 4%
75–85 = 0%
85 + = 0%
Female = 56%
Male = 44%
Other = 0%
Primary education = 1%
General secondary education = 1%
Vocational education = 19%
Higher education = 10%
Bachelor’s degree = 56%
Master’s degree = 10%
PhD degree = 3%
No = 34%
Yes = 66%
Spain 242
18–24 = 2%
25–34 = 6%
35–44 = 7%
45–54 = 19%
55–64 = 30%
65–75 = 32%
75–85 = 4%
85 + = 0%
Female = 47%
Male = 53%
Other = 0%
Primary education = 4%
General secondary education = 19%
Vocational education = 14%
Higher education = 21%
Bachelor’s degree = 30%
Master’s degree = 7%
PhD degree = 5%
No = 57%
Yes = 43%
South Korea 12
18–24 = 0%
25–34 = 25%
35–44 = 42%
45–54 = 17%
55–64 = 17%
65–75 = 0%
75–85 = 0%
85 + = 0%
Female = 50%
Male = 50%
Other = 0%
Primary education = 0%
General secondary education = 8%
Vocational education = 0%
Higher education = 25%
Bachelor’s degree = 50%
Master’s degree = 8%
PhD degree = 8%
No = 50%
Yes = 50%
Saudi Arabia 54
18–24 = 6%
25–34 = 37%
35–44 = 35%
45–54 = 13%
55–64 = 9%
65–75 = 0%
75–85 = 0%
85 + = 0%
Female = 52%
Male = 48%
Other = 0%
Primary education = 0%
General secondary education = 11%
Vocational education = 2%
Higher education = 4%
Bachelor’s degree = 74%
Master’s degree = 7%
PhD degree = 2%
No = 17%
Yes = 83%
Russia 166
18–24 = 1%
25–34 = 10%
35–44 = 16%
45–54 = 26%
55–64 = 27%
65–75 = 20%
75–85 = 1%
85 + = 0%
Female = 58%
Male = 42%
Other = 0%
Primary education = 0%
General secondary education = 5%
Vocational education = 26%
Higher education = 44%
Bachelor’s degree = 9%
Master’s degree = 13%
PhD degree = 2%
No = 36%
Yes = 64%
Netherlands 224
18–24 = 0%
25–34 = 0%
35–44 = 8%
45–54 = 19%
55–64 = 33%
65–75 = 30%
75–85 = 9%
85 + = 0%
Female = 55%
Male = 45%
Other = 0%
Primary education = 4%
General secondary education = 25%
Vocational education = 40%
Higher education = 22%
Bachelor’s degree = 4%
Master’s degree = 5%
PhD degree = 1%
No = 54%
Yes = 46%
Japan 74
18–24 = 11%
25–34 = 15%
35–44 = 7%
45–54 = 16%
55–64 = 23%
65–75 = 23%
75–85 = 4%
85 + = 1%
Female = 53%
Male = 47%
Other = 0%
Primary education = 0%
General secondary education = 12%
Vocational education = 3%
Higher education = 16%
Bachelor’s degree = 62%
Master’s degree = 5%
PhD degree = 1%
No = 80%
Yes = 20%
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Outcome variables. Health behavior. Respondents were asked how much they agreed with: “To mini-
mize my chances of getting coronavirus, I” “…wash my hands more oen, “…avoid crowded spaces”, and “…put
myself in quarantine” (-3 = Strongly disagree, 3 = Strongly agree). We assessed face mask use: "In the past week, I
have covered my face in public places." (1 = (Almost) never, 2 = ((Almost) always).1 In addition, we asked: “In the
past 7days, how many days did you have in-person (face-to-face) contact with other people in general [friends
or relatives]” (0 = 0days, 7 = 7days). We also inquired: “In the past week, how oen did you leave your home?
(1 = I did not leave my home, 4 = Four times or more).
Attitudes toward behavioral mandates. Participants indicated their agreement with: "I would sign a petition that
supports…mandatory vaccination once a vaccine has been developed for coronavirus…mandatory quarantine
NAge Gender Education Religious
Italy 370
18–24 = 4%
25–34 = 10%
35–44 = 20%
45–54 = 18%
55–64 = 16%
65–75 = 30%
75–85 = 2%
85 + = 1%
Female = 46%
Male = 54%
Other = 0%
Primary education = 1%
General secondary education = 9%
Vocational education = 8%
Higher education = 52%
Bachelor’s degree = 6%
Master’s degree = 21%
PhD degree = 4%
No = 35%
Yes = 65%
Indonesia 88
18–24 = 22%
25–34 = 41%
35–44 = 17%
45–54 = 12%
55–64 = 8%
65–75 = 0%
75–85 = 0%
85 + = 0%
Female = 42%
Male = 58%
Other = 0%
Primary education = 0%
General secondary education = 28%
Vocational education = 9%
Higher education = 6%
Bachelor’s degree = 53%
Master’s degree = 3%
PhD degree = 0%
No = 16%
Yes = 84%
Greece 188
18–24 = 4%
25–34 = 5%
35–44 = 9%
45–54 = 20%
55–64 = 41%
65–75 = 20%
75–85 = 1%
85 + = 0%
Female = 47%
Male = 53%
Other = 0%
Primary education = 1%
General secondary education = 3%
Vocational education = 6%
Higher education = 27%
Bachelor’s degree = 45%
Master’s degree = 15%
PhD degree = 4%
No = 28%
Yes = 72%
Germany 243
18–24 = 2%
25–34 = 3%
35–44 = 10%
45–54 = 19%
55–64 = 29%
65–75 = 34%
75–85 = 4%
85 + = 0%
Female = 52%
Male = 48%
Other = 0%
Primary education = 0%
General secondary education = 7%
Vocational education = 54%
Higher education = 14%
Bachelor’s degree = 9%
Master’s degree = 13%
PhD degree = 2%
No = 72%
Yes = 28%
Canada 189
18–24 = 2%
25–34 = 6%
35–44 = 14%
45–54 = 30%
55–64 = 18%
65–75 = 25%
75–85 = 4%
85 + = 0%
Female = 50%
Male = 50%
Other = 0%
Primary education = 1%
General secondary education = 22%
Vocational education = 20%
Higher education = 17%
Bachelor’s degree = 26%
Master’s degree = 12%
PhD degree = 2%
No = 65%
Yes = 35%
Brazil 190
18–24 = 6%
25–34 = 18%
35–44 = 17%
45–54 = 24%
55–64 = 19%
65–75 = 13%
75–85 = 2%
85 + = 0%
Female = 55%
Male = 45%
Other = 0%
Primary education = 1%
General secondary education = 21%
Vocational education = 14%
Higher education = 26%
Bachelor’s degree = 28%
Master’s degree = 7%
PhD degree = 2%
No = 26%
Yes = 74%
Australia 172
18–24 = 0%
25–34 = 6%
35–44 = 16%
45–54 = 17%
55–64 = 24%
65–75 = 30%
75–85 = 7%
85 + = 1%
Female = 55%
Male = 45%
Other = 0%
Primary education = 1%
General secondary education = 24%
Vocational education = 24%
Higher education = 17%
Bachelor’s degree = 26%
Master’s degree = 6%
PhD degree = 1%
No = 63%
Yes = 37%
Argentina 196
18–24 = 2%
25–34 = 15%
35–44 = 12%
45–54 = 24%
55–64 = 33%
65–75 = 12%
75–85 = 3%
85 + = 0%
Female = 62%
Male = 38%
Other = 0%
Primary education = 3%
General secondary education = 24%
Vocational education = 16%
Higher education = 23%
Bachelor’s degree = 26%
Master’s degree = 8%
PhD degree = 1%
No = 43%
Yes = 57%
Table 4. Demographics of longitudinal sample.
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Scientic Reports | (2022) 12:3824 |
for those that have coronavirus and those that have been exposed to the virus” (-3 = Strongly disagree, 3 = Strongly
agree), and “I would join a protest against social distancing measures” (-2 = Strongly disagree, 2 = Strongly agree).
Also, we measured age, gender (0 = female; 1 = male), employment status, education, political view, religion,
the date the survey was taken, as well as whether participants knew of any COVID-19 cases among friends and
Strategy of analyses. We conducted our analyses in R (https:// www.R- proje ct. org/), using the workow
for open reproducible code in science (WORCS) to make the analyses reproducible 39. All code is available on
GitHub. Mplus 8.0 was used to estimate the multilevel models with full information maximum likelihood esti-
mation, which is robust to non-normality of residuals and makes use of all available data without imputing miss-
ing values. We automated analyses and tabulated results using the MplusAutomation (https:// CRAN.R- proje ct.
org/ packa ge= Mplus Autom ation) and tidySEM R-packages (www. github. com/ cjvan lissa/ tidyS EM).
To examine over-time predictive eects, we restructured the data to long format, with one observation per
time point per participant. Table5 shows how many time points were available for each variable. For each time
point t, the dependent variable was taken at t + 1. An autoregressive eect was included for the dependent vari-
able, thus controlling for stability, and we included the time dierence Dt as a control variable. e results should
thus be interpreted in terms of change in the dependent variable. We present the most parsimonious model with
only the direct eects, as preliminary tests of interactions between all predictors and Dt indicated convergence
issues and few were reliable. Regarding predictor variables, we selected all available data that coincided with
the available waves of the dependent variable. We entered the predictors simultaneously to isolate their unique
eects, above and beyond any explanatory variance shared among predictors. When repeated measures were
available for a predictor variable, it was used as a within-participants factor. us, for predictors with repeated
measures, the predictor at each time point t was used to predict the dependent variable at t + 1. If only a single
assessment of a predictor variable was available, it was used as a between-participants factor.
Open practices statement. e study was not formally preregistered but all data will be made available
online upon publication. Full methodological details, including exact dates of the waves and questionnaires in
all languages, are provided in the survey codebook (https:// osf. io/ qhyue). We conducted our analyses in R, using
the workow for open reproducible code in science to make the analyses reproducible. All code is available on
GitHub (https:// github. com/ cjvan lissa/ schum pe).
Ethics statement. e study was approved by the Ethics Committees of the University of Groningen (PSY-
1920-S-0390)and New York University Abu Dhabi (HRPP-2020-42).
Received: 20 February 2021; Accepted: 13 December 2021
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Table 5. Observations per time point for health behaviors and attitudes toward behavioral mandates.
Wav e
Health behaviors Attitudes toward behavioral mandates
Hand washing Avoiding
crowds Self-isolation/
quarantine Wearing face
contact with
friends and
Face to face
contact with
other people
Days per week
people le
their house Mandatory
vaccination Mandatory
Baseline 2403 2404 2404 2391 2381 2404 2403 2403 –
1 –
2 –
3 –
4 933 933 932 1127 1127
5 – 1297 1288 1300
6 – 169 203
7 –
8 – 930 1066
9 –
10 –
11 –
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Scientic Reports | (2022) 12:3824 |
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Author contributions
B.M.S. developed the study concept and wrote the paper with help by N.P.L. Data analysis were performed by
C.J.V.L. All other authors contributed to the study design, provided critical revisions, or contributed to data
collection. All authors approved the nal version of the manuscript for submission.
is research received support from the New York University Abu Dhabi (VCDSF/75-71015), the University of
Groningen (Sustainable Society & Ubbo Emmius Fund), and the Instituto de Salud Carlos III (COV20/00086),
co-funded by the EuropeanRegional Development Fund (ERDF) “A way to make Europe.
Competing interest
e authors declare no competing interests.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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... 8 However, empiric research on the factors influencing pandemic fatigue have been limited in depth. 3,4,[11][12][13][14][15] Few have studied the question longitudinally 14,15 and none of have clearly quantified adherence, providing scant guidance on which of the 20 or more potential factors 8 would be most useful to act upon. Moreover, the factors most important to pandemic fatigue likely vary by setting, given differences in governments' pandemic response measures, social structure, and cultural norms. ...
... 8 However, empiric research on the factors influencing pandemic fatigue have been limited in depth. 3,4,[11][12][13][14][15] Few have studied the question longitudinally 14,15 and none of have clearly quantified adherence, providing scant guidance on which of the 20 or more potential factors 8 would be most useful to act upon. Moreover, the factors most important to pandemic fatigue likely vary by setting, given differences in governments' pandemic response measures, social structure, and cultural norms. ...
Background: Pandemic fatigue emerged early during the COVID-19 pandemic and remains a concern as new variants emerge and ongoing public health measures are needed to control them. A wide range of factors can affect pandemic fatigue, but empiric research indicating which may be most important to adherence in specific populations is lacking. Design & methods: We conducted a longitudinal study of changes in physical distancing in two cohorts: adults living with children <18 years and adults ≥50 years old. Six types of non-work, non-household contacts were ascertained at six times from April to October 2020. We used generalized estimating equations Poisson regression to estimate the one-week change in contact rate and how this differed based on sociodemographic characteristics. Results: The rate of all contact types increased during the middle of the study period and decreased toward the end. Changes in contact rates over time differed according to several sociodemographic characteristics, including age, gender, race/ethnicity, education, household composition, and access to transportation. Furthermore, the factors influencing the rate of change in contact rates differed by the type or setting of the contact, for example contacts as a result of visiting another person's home versus during a retail outing. Conclusions: These results provide evidence for potential mechanisms by which pandemic fatigue has resulted in lower physical distancing adherence.
... According to social-ecological studies of protective behavior and related outcomes [29][30][31], individuals' interpersonal and community contexts are strongly related to their risk perceptions and adherence to social-distancing and masking behaviors [12,[32][33][34]. Tunçgenç et al. [32], for example, find that perceived levels of social circle adherence are the strongest predictor of personal adherence to protective measures, controlling for health-related beliefs. ...
... Kleitman et al.'s [38] cluster analysis of behavioral adherence) or they focus on the patterns and predictors of time-varying behaviors, or behavioral change (e.g. Schumpe et al.'s [34] longitudinal study of public health behavior). ...
Full-text available
The success of personal non-pharmaceutical interventions as a public health strategy requires a high level of compliance from individuals in private social settings. Strategies to increase compliance in these hard-to-reach settings depend upon a comprehensive understanding of the patterns and predictors of protective social behavior. Social cognitive models of protective behavior emphasize the contribution of individual-level factors while social-ecological models emphasize the contribution of environmental factors. This study draws on 28 waves of survey data from the Understanding Coronavirus in America survey to measure patterns of adherence to two protective social behaviors-private social-distancing behavior and private masking behavior-during the COVID-19 pandemic and to assess the role individual and environmental factors play in predicting adherence. Results show that patterns of adherence fall into three categories marked by high, moderate, and low levels of adherence, with just under half of respondents exhibiting a high level of adherence. Health beliefs emerge as the single strongest predictor of adherence. All other environmental and individual-level predictors have relatively poor predictive power or primarily indirect effects.
... Some evidence suggests no negative reinforcement but positive punishment toward adherence. Schumpe et al. (2022) found that the perception of receiving "punishment" when people deviate from the rules ("negative reinforcement") did not predict adherence to any protective behavior. Dillard et al. (2021) found that receiving negative responses toward mask-wearing, like teasing, validation, or anger (positive punishment), decreased mask-wearing while receiving corrections or anger responses by not wearing a mask did not increase adherence ("negative reinforcement"). ...
... Although we do not take measures to encourage behaviors, these may be greater than correcting behaviors. Our results at the population level are consistent with Dillard et al. (2021) and Schumpe et al. (2022), who found that perceived levels of correcting behavior did not predict the general level of adherence. ...
Full-text available
During the COVID-19 pandemic, individuals adjusted their adherence to protective behaviors according to the social situation. Also, social consequences partially influenced, given that they were the most contiguous and likely to adherence behavior. The behavior analysis framework can help to understand the social contingencies that maintain adherence on a large scale in natural settings. This study assessed how social antecedents and consequences influenced adherence to protective behaviors. The antecedents were indoor and outdoor. The social consequences were receiving social approval, avoiding social disapproval, receiving a social rejection, and losing gratification in social relationships. The protective behaviors were mask-wearing, physical distancing, hand-washing, and correcting others. Colombian residents completed an online survey based on the indirect functional assessment in which they reported their adherence to the social contexts and the perceived social contingencies. People reported higher adherence to protective behaviors in the outdoor context than indoors. Social approval was the main reinforcer, and losing gratification in social relationships was the main punisher. Also, avoiding being judged worked as a reinforcer, and receiving social rejection worked as a punisher. Social context interacted with social consequences. The effect of consequences under adherence behavior was more intense in indoor than outdoor contexts. Mask-wearing and physical distancing were the more socially controlled protective behaviors. We demonstrated that the probability of certain social consequences partially explained adherence's subjective value. Our results about social approval were consistent with injunctive norm literature. Social distance with people involved in each social context can explain our findings.
... Similar research studies have identified challenges in online education. Schumpe et al. (2022) stated that students, particularly those from underprivileged backgrounds who live in remote, outlying areas, appear to suffer more from these contextual realities than other students. Increasing numbers and various online assessments appear to be weighing them down, their educational experiences with online instruction generally appeared to have little impact that equal amount of effort must be made to improve the health of both teachers and students to counterbalance the overwhelming focus paid to online pedagogical techniques around the world. ...
The introduction of online distance learning in the Philippines was like the emergence of an uninvited houseguest who refused to leave. This pandemic disrupted the status quo of education, and as a result, institutions resorted to using online distance learning methods. The purpose of this research was to explore the home-based learning experiences of college education students. Phenomenological interviews were used to collect information from 15 participants of the study. Overall, a text analysis was structured around their perspectives on learning during the COVID-19 pandemic and found significant themes related to online distance education like methods and means regarding how college students manage their learning activities; challenges unique to online distance education including issues with technology and internet access, problems creating a conducive learning atmosphere, and a lack of financial resources for acquiring necessary learning resources. Yet, online distance education has positives like lower costs as travel and uniforms are cut; students become adaptable to the circumstances and independent learners; and some can work while learning to support themselves. Students' technological capacity, personal beliefs, and viewpoints to support learning resolve the problems.
... Beyond these psychological determinants, much of the research highlights two psychological factors associated with both enhanced and reduced support for COVID-19 PHBs: perceived risk and COVID-19 conspiracy beliefs. First, perceiving that COVID-19 poses a serious risk to one's health and well-being and the likelihood that one can become infected with the disease was consistently shown to be positively associated with COVID-19 PHB support (Abdelrahman, 2022;Bruine de Bruin & Bennett, 2020;Hughes et al., 2022;Schumpe et al., 2022;Wise et al., 2020). In essence, if people think they are at risk of infection and the corresponding potentially dire symptoms of COVID-19, they are more likely to follow recommended procedures to reduce this risk. ...
Full-text available
When people are confronted with research that contradicts their own personal experiences, they tend to deny the science. Using a secondary multinational data set collected during the early stages of the coronavirus disease 2019 (COVID‐19) pandemic ( N = 46,490), we tested this “seeing is believing” effect as it relates to the link between direct and indirect personal experience with COVID‐19 and public health behaviors (PHBs) through COVID‐19 conspiracy beliefs and perceived risk of infection. Indirect experience with COVID‐19 was associated with lower endorsement of COVID‐19 conspiracy beliefs, which negatively predicted risk perception of infection, and, in turn, positively predicted PHBs. However, direct experience positively predicted COVID‐19 conspiracy beliefs, while it negatively predicted perceived risk. Moreover, while indirect experience positively predicted PHBs, direct experience largely negatively predicted PHBs. Implications of these findings as it relates to the “seeing is believing” effect are discussed.
... 2,3 Like all preventive measures, people's willingness and ability to engage in these behaviors varied based on factors ranging from personal beliefs to nationality. [4][5][6][7][8] For example, Latkin et al 7 found that COVID-19 skepticism reduced engagement in COVID-19 prevention behaviors, whereas Anaki and Sergay 6 (described below) found variation in prevention behaviors by country. ...
Full-text available
Determinants of mask-wearing during a pandemic range from the personal to the political. Using a repeated measures design, we examined psychosocial predictors of self-reported mask-wearing 3 times during the early stage of the COVID-19 pandemic. Participants completed surveys at baseline (summer 2020), 3 months (fall 2020), and 6 months (winter 2020-2021). The survey assessed the frequency of mask-wearing and psychosocial predictors from various theories, including fear of COVID-19, perceived severity, perceived susceptibility, attitude, health locus of control, and self-efficacy. Results indicated that the strongest predictors of mask-wearing varied as a function of the stage in the pandemic. In the earliest phase, fear of COVID-19 and perceived severity were the strongest predictors. Three months later, attitude was the strongest predictor. Finally, another 3 months later, self-efficacy became the strongest predictor. Overall, the results suggest that the primary determinants of a novel protective behavior shift over time and with increased familiarity.
... Other institutional factors can shape citizens' compliance with COVID-19 preventive measures, but ultimately, the risk that the guidelines from governments of different orders on what citizens should or must do are not aligned is likely the key mechanism to understanding their effect. This is consistent with the fact that the perceived clarity of communication about the virus is linked to citizens' compliance with COVID-19 preventive measures (Schumpe et al. 2022). Decentralization (or more precisely the presence of several elected governments) is thus likely the most important institutional feature to focus on. ...
Full-text available
The COVID-19 pandemic was, and continues to be, extraordinary in many ways, forcing governments around the world to implement equally extraordinary preventive measures, some of which were highly restrictive. However, such preventive measures were not sufficient per se to contain the spread of the virus through non-pharmaceutical (e.g., stay-at-home orders, recommendations about face-mask usage) or even pharmaceutical (i.e., a vaccine) interventions: to be effective, citizens had to comply with the guidelines implemented by the state. Social scientists, in particular behavioralists, have thus been playing a prominent role in the management of the pandemic. How have the governments around the world generated compliance with COVID-19 preventive measures? In this article, I first review who was more prompted to comply with preventive measures. I then move to presenting the commonly used explanations to make sense of levels of compliance. These explanations revolve around human predispositions, (political) attitudes, partisanship, ideology, cues, and institutional factors. I conclude by highlighting the role of social sciences in providing the best data and analyses on the relationship between citizens and the state in times of crisis, for the COVID-19 pandemic and beyond.
... Specifically, Gilliam et al. found cultural tightness to be positively associated with several self-reported protective health behaviors (e.g., mask wearing, handwashing) during COVID-19 among 544 residents in the U.S. [42]. Based on longitudinal data from an international survey, Schumpe et al. provided empirical evidence for the critical role of the tightness of culture in predicting adherence to recommended health behaviors against COVID-19 [43]. In light of these previous findings, this study proposes the following hypothesis: Hypothesis 1 (H1). ...
Full-text available
The ongoing COVID-19 pandemic has imposed greater challenges and more stringent requirements on higher education institutions (HEIs). However, limited empirical research has been devoted to identifying external and internal factors that may promote individual preventive behaviors during the COVID-19 pandemic within the higher education context. This study proposed and examined an extended norm activation model (NAM) concerning the relationships among cultural tightness, original NAM components, and COVID-19 preventive behaviors. An online survey was conducted with a sample of 3693 university students from 18 universities in Beijing, China. The results showed that cultural tightness was positively associated with respondents' COVID-19 preventive behaviors. Three original NAM variables, namely, awareness of consequences, the ascription of responsibility, and personal norms, played a chain mediating role in the relationship between cultural tightness and COVID-19 preventive behaviors. Theoretical and practical implications regarding the findings of this study and suggestions for future research are discussed.
Full-text available
During a global public health event, i.e., COVID-19, interventions to improve the health behavior of the population have become the focus of health management strategies. The effects of various interventions during this period vary, and the effects of different variables on the intervention effects are not yet clear. This article screened 58 papers (n = 47264) through searching electronic databases, and revealed the optimal intervention through PMA and NMA, as well as the changes in intervention effectiveness under different conditions. Accordingly, future research should focus on the in-depth exploration of specific interventions to establish and improve the effectiveness of interventions.
Objective: COVID-19 conspiracy theories have become widespread since the onset of the pandemic and compound the existing challenges of decisional capacity assessment. This paper aims to review the literature pertaining to decisional capacity assessment in the context of COVID-19 conspiracy beliefs and synthesize a practical approach with an emphasis on differential diagnosis and clinical pearls for the practicing physician. Methods: We reviewed papers on decisional capacity assessment and differential diagnosis in the context of COVID-19 conspiracy beliefs. A literature search was conducted using the US National Library of Medicine's resource and Google Scholar. Results: The resulting article content was utilized to synthesize a practical approach to decisional capacity assessment in the context of COVID-19 conspiracy beliefs. Specifically, aspects related to the history, taxonomy, evaluation, and management are reviewed. Conclusions: Appreciating the nuanced differences between delusions, overvalued ideas, and obsessions while with integrating the non-cognitive domains of capacity into the assessment are crucial to navigating the wide differential diagnosis of COVID-19 conspiracy beliefs. It is important to attempt to clarify and optimize patient decision-making abilities by addressing circumstances, attitudes, and cognitive styles specific to patients with seemingly irrational beliefs about COVID-19.
Full-text available
This paper examines whether compliance with COVID-19 mitigation measures is motivated by wanting to save lives or save the economy (or both), and which implications this carries to fight the pandemic. National representative samples were collected from 24 countries (N = 25,435). The main predictors were (1) perceived risk to contract coronavirus, (2) perceived risk to suffer economic losses due to coronavirus, and (3) their interaction effect. Individual and country-level variables were added as covariates in multilevel regression models. We examined compliance with various preventive health behaviors and support for strict containment policies. Results show that perceived economic risk consistently predicted mitigation behavior and policy support—and its effects were positive. Perceived health risk had mixed effects. Only two significant interactions between health and economic risk were identified—both positive.
Full-text available
Background The effective implementation of government policies and measures for controlling the coronavirus disease 2019 (COVID-19) pandemic requires compliance from the public. This study aimed to examine cross-sectional and longitudinal associations of trust in government regarding COVID-19 control with the adoption of recommended health behaviours and prosocial behaviours, and potential determinants of trust in government during the pandemic. Methods This study analysed data from the PsyCorona Survey, an international project on COVID-19 that included 23 733 participants from 23 countries (representative in age and gender distributions by country) at baseline survey and 7785 participants who also completed follow-up surveys. Specification curve analysis was used to examine concurrent associations between trust in government and self-reported behaviours. We further used structural equation model to explore potential determinants of trust in government. Multilevel linear regressions were used to examine associations between baseline trust and longitudinal behavioural changes. Results Higher trust in government regarding COVID-19 control was significantly associated with higher adoption of health behaviours (handwashing, avoiding crowded space, self-quarantine) and prosocial behaviours in specification curve analyses (median standardised β = 0.173 and 0.229, p < 0.001). Government perceived as well organised, disseminating clear messages and knowledge on COVID-19, and perceived fairness were positively associated with trust in government (standardised β = 0.358, 0.230, 0.056, and 0.249, p < 0.01). Higher trust at baseline survey was significantly associated with lower rate of decline in health behaviours over time ( p for interaction = 0.001). Conclusions These results highlighted the importance of trust in government in the control of COVID-19.
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Misinformation about COVID-19 is a major threat to public health. Using five national samples from the UK (n= 1050 and n= 1150), Ireland (n = 700), the USA (n = 700), Spain (n= 700) and Mexico (n= 700), we examine predictors of belief in the most common statements about the virus that contain misinformation. We also investigate the prevalence of belief in COVID-19 misinformation across different countries and the role of belief in such misinformation in predicting relevant health behaviours. We find that while public belief in misinformation about COVID-19 is not particularly common, a substantial proportion views this type of misinformation as highly reliable in each country surveyed. In addition, a small group of participants find common factual information about the virus highly unreliable. We also find that increased susceptibility to misinformation negatively affects people's self-reported compliance with public health guidance about COVID-19, as well as people's willingness to get vaccinated against the virus and to recommend the vaccine to vulnerable friends and family. Across all countries surveyed, we find that higher trust in scientists and having higher numeracy skills were associated with lower susceptibility to coronavirus-related misinformation. Taken together, these results demonstrate a clear link between susceptibility to misinformation and both vaccine hesitancy and a reduced likelihood to comply with health guidance measures, and suggest that interventions which aim to improve critical thinking and trust in science may be a promising avenue for future research.
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The World Health Organization has declared the rapid spread of COVID-19 around the world a global public health emergency. It is well-known that the spread of the disease is influenced by people's willingness to adopt preventative public health behaviors, which are often associated with public risk perception. In this study, we present the first assessment of public risk perception of COVID-19 around the world using national samples (total N = 6,991) in ten countries across Europe, America, and Asia. We find that although levels of concern are relatively high, they are highest in the UK compared to all other sampled countries. Pooled across countries, personal experience with the virus, indi-vidualistic and prosocial values, hearing about the virus from friends and family, trust in government, science, and medical professionals, personal knowledge of government strategy, and personal and collective efficacy were all significant predictors of risk perception. Although there was substantial variability across cultures, individualistic worldviews, personal experience, prosocial values, and social amplification through friends and family in particular were found to be significant determinants in more than half of the countries examined. Risk perception correlated significantly with reported adoption of preventative health behaviors in all ten countries. Implications for effective risk communication are discussed.
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The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.
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The COVID-19 mitigation measures require a fundamental shift in human behavior. The present study assesses what factors influence Americans to comply with the stay at home and social distancing measures. It analyzes data from an online survey, conducted on April 3, 2020, of 570 participants from 35 states that have adopted such measures. The results show that while perceptual deterrence was not associated with compliance, people actually comply less when they fear the authorities. Further, two broad processes promote compliance. First, compliance depended on people's capacity to obey the rules, opportunity to break the rules, and self-control. As such, compliance results from their own personal abilities and the context in which they live. Second, compliance depended on people's intrinsic motivations, including substantive moral support and social norms. This paper discusses the implications of these findings for ensuring compliance to effectively mitigate the virus.
The severe acute respiratory syndrome (SARS) outbreak in 2003 resulted in more than 8000 cases and 800 deaths. SARS was eventually contained by means of syndromic surveillance, prompt isolation of patients, strict enforcement of quarantine of all contacts, and in some areas top-down enforcement of community quarantine. By interrupting all human-to-human transmission, SARS was effectively eradicated. By contrast, by Feb 28, 2020, within a matter of 2 months since the beginning of the outbreak of coronavirus disease 2019 (COVID-19), more than 82 000 confirmed cases of COVID-19 have been reported with more than 2800 deaths. Although there are striking similarities between SARS and COVID-19, the differences in the virus characteristics will ultimately determine whether the same measures for SARS will also be successful for COVID-19. COVID-19 differs from SARS in terms of infectious period, transmissibility, clinical severity, and extent of community spread. Even if traditional public health measures are not able to fully contain the outbreak of COVID-19, they will still be effective in reducing peak incidence and global deaths. Exportations to other countries need not result in rapid large-scale outbreaks, if countries have the political will to rapidly implement countermeasures.
Public health measures were decisive in controlling the SARS epidemic in 2003. Isolation is the separation of ill persons from non-infected persons. Quarantine is movement restriction, often with fever surveillance, of contacts when it is not evident whether they have been infected but are not yet symptomatic or have not been infected. Community containment includes measures that range from increasing social distancing to community-wide quarantine. Whether these measures will be sufficient to control 2019-nCoV depends on addressing some unanswered questions.
Background: A test-and-not-treat (TaNT) strategy has been developed to prevent people with high concentrations of circulating Loa loa microfilariae (>20 000 microfilariae per mL) developing serious adverse events after ivermectin treatment during mass drug administration to eliminate onchocerciasis. An important question related to cost and programmatic issues is whether annual retesting is required for everyone. We therefore aimed to investigate changes in L loa microfilarial densities during TaNT campaigns run 18 months apart. Methods: In this observational cohort study, we assessed the participants of two TaNT campaigns for onchocerciasis. These campaigns, which were run by a research team, together with personnel from the Ministry of Health and community health workers, were done in six health areas (in 89 communities) in Okola health district (Cameroon); the first campaign was run between Aug 10, and Oct 29, 2015, and the second was run between March 7, and May 26, 2017. All individuals aged 5 years and older were invited to be screened for Loa loa microfilaraemia before being offered ivermectin (unless contraindicated). L loa microfilarial density was measured at the point of care using the LoaScope. All those with a L loa microfilarial density of 20 000 microfilariae per mL or less were offered treatment; in the first 2 weeks of the 2015 campaign, a higher exclusion threshold of 26 000 microfilariae per mL or less was used. At both rounds of the intervention, participants were registered with a paper form, in which personal information were collected. In 2017, we also recorded whether each individual reported participation in the 2015 campaign. The primary outcome, assessed in all participants, was whether L loa microfilarial density was above or below the exclusion threshold (ie, the criteria that guided the decision to treat). Findings: In the 2015 TaNT campaign, 26 415 people were censused versus 29 587 people in the 2017 TaNT campaign. All individuals aged 5 years and older without other contraindications to treatment (22 842 people in 2015 and 25 421 people in 2017) were invited to be screened for L loa microfilaraemia before being offered ivermectin. In 2015, 16 182 individuals were examined with the LoaScope, versus 18 697 individuals in the same communities in 2017. 344 (2·1%) individuals were excluded from ivermectin treatment because of a high L loa microfilarial density in 2015, versus 283 (1·5%) individuals in 2017 (p<0·0001). Records from 2017 could be matched to those from 2015 for 6983 individuals (43·2% of the 2015 participants). In this cohort, in 2017, 6981 (>99·9%) of 6983 individuals treated with ivermectin in 2015 had L loa microfilariae density below the level associated with neurological serious adverse events. Interpretation: Individuals treated with ivermectin do not need to be retested for L loa microfilaraemia before the next treatment, provided that they can be re-identified. This adjusted approach will enable substantial cost savings and facilitate reaching programmatic goals for elimination of onchocerciasis in areas that are co-endemic for loiasis. Funding: Bill & Melinda Gates Foundation, Division of Intramural Research (National Institute of Allergy and Infectious Diseases, US National Institutes of Health).