Vaccine: X 14 (2023) 100301 Contents lists available at ScienceDirect Vaccine: X journal homepage: www.elsevier.com/locate/jvacx Predictive factors of hesitancy to vaccination against SARS-CoV-2 virus in young adults in Spain: Results from the PSY-COVID study Corel Mateo-Canedo a,1,11, Juan Pablo Sanabria-Mazo a,b,1,3,11, Laura Comendador a,1,4,5,9, Juan Sebastián Rojas a,1, Meritxell Carmona a,1, Neus Crespo-Puig a,1, Fiorella Anyosa a,1,6, Clara Selva d,7, Albert Feliu-Soler a,2,3, Narcís Cardoner a,c,8,9, Juan Deus a,2, Juan V. Luciano a,b,2,3, Jorge Luis Méndez-Ülrich e,10, Antoni Sanz a,1,⇑ a Universitat Autònoma de Barcelona, Spain b Parc Sanitari Sant Joan de Déu, Spain c Hospital de la Santa Creu i Sant Pau, Spain d Universitat Oberta de Catalunya, Spain e Universitat de Barcelona, Spain a r t i c l e i n f o a b s t r a c t Article history: Widespread population vaccination against the SARS-CoV-2 virus is a matter of great interest to public Received 29 November 2022 health as it is the main pharmacological measure to contain the COVID-19 pandemic. Hesitancy/reluc- Received in revised form 5 April 2023 tance to vaccination has become a main barrier to containing the pandemic. Young adults are the age Accepted 7 April 2023 group with the greatest resistance to vaccination, even in countries with the highest vaccination rates Available online 18 April 2023 during this pandemic. The objective of this study was to identify the main predictive factors of vaccina- tion intention and profile people with hesitancy/reluctance to vaccinate against SARS-CoV-2 virus in Keywords: young adults living in Spain during the pandemic. A cross-sectional study was conducted based on the COVID-19 Hesitancy administration of an online survey (PSY-COVID-2) that evaluated the intention of vaccination together Reluctance with a wide range of sociodemographic, social, cognitive, behavioral and affective variables in a sample Vaccination of 2210 young adults. 14% of the sample showed hesitancy/reluctance to vaccination at the beginning of Attitudes their vaccination campaign. A total of 35 factors were associated (small to medium effect sizes) with the Beliefs intention to get vaccinated. A reduced set of 4 attitudinal and social variables explained 41% of the vari- ability in vaccination intention: attitude to the vaccination, trust in health staff/scientists, conspiracy beliefs about SARS-CoV-2 and time spent being informed about COVID-19. These variables showed good sensitivity/specificity for classifying people as reluctant/not reluctant to vaccination, properly classifying ⇑ Corresponding author. E-mail addresses: corel.mateo@uab.cat (C. Mateo-Canedo), juanpablo.sanabria@uab.cat (J.P. Sanabria-Mazo), laura.comendador@uab.cat (L. Comendador), Juansebastian. rojas@autonoma.cat (J.S. Rojas), meritxell.carmona@uab.cat (M. Carmona), neus.crespo@uab.cat (N. Crespo-Puig), fiorella.anyosa@uab.cat (F. Anyosa), cselvao@uoc.edu (C. Selva), albert.feliu@uab.cat (A. Feliu-Soler), narcis.cardoner@uab.cat (N. Cardoner), joan.deus@uab.cat (J. Deus), juanvicente.luciano@sjd.es (J.V. Luciano), antonio.sanz@uab. cat (A. Sanz). 1 Departament de Psicologia Bàsica, Evolutiva i de l’Educació, Carrer de la Fortuna, s/n. Campus de Bellaterra, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. 2 Departament de Psicologia Clínica i de la Salut, Carrer de la Fortuna, s/n. Campus de Bellaterra, Universitat Autònoma de Barcelona. 08193 Cerdanyola del Vallès, Spain. 3 Teaching, Research & Innovation Unit, Parc Sanitari Sant Joan de Déu, St. Boi de Llobregat, Spain. 4 Departament de Psiquiatria i de Medicina Legal. Avinguda de Can Domènech, 737. Campus de Bellaterra, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. 5 Servei de Salut Mental. Hospital Universitari Parc Taulí. Unitat Mixta de Neurociència Traslacional I3PT-UAB. 08208 Sabadell, Barcelona, Spain. 6 Departament de Psicologia Social. Carrer de la Fortuna, s/n. Campus de Bellaterra, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. 7 Estudis de Psicologia i Ciències de l’Educació, Rambla del Poblenou, 156, 08018 Barcelona, Spain. 8 Sant Pau Mental Health Group, Institut d’Investigació Biomèdica Sant Pau (IBB-Sant Pau), Hospital de la Sant Creu i Sant Pau, Institut d’Innovació i investigació Parc Taulí (I3PT), Centro de Investigación Biomédica en Red de Salut Mental (CIBERSAM), Universitat Autònoma de Barcelona (UAB), Barcelona, Catalonia, Spain. 9 Departament de Psiquitaria i Medicina Legal, Avinguda de Can Domènech, 737. Campus de Bellaterra, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain. 10 Departament de Mètodes d’Investigació i Diagnòstic en Educació, Passeig de la Vall d’Hebrón 171, Campus Mundet, Universitat de Barcelona, 08035 Barcelona, Spain. 11 This author should be considered as co-first author. https://doi.org/10.1016/j.jvacx.2023.100301 2590-1362/Ó 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). C. Mateo-Canedo, J.P. Sanabria-Mazo, L. Comendador et al. Vaccine: X 14 (2023) 100301 86% of people. Psychosocial processes related to attitudes, trust and information are the main predictors of vaccination intention in a highly reluctant group such as the young adult population. Ó 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). 1. Introduction sample of young adults in Spain. Unlike previous studies, focused on a specific and reduced set of predictive variables (sociodemo- Vaccination has historically greatly reduced the effect of infec- graphic characteristics, specific personality traits, media, etc.), the tious diseases and is generally safer and more effective than cura- assessment tool used in the present study was addressed to collect tive drugs [1,2]. The benefits of vaccination transcend the a broad spectrum of potential predictors which could be related to prevention of infection, morbidity and mortality of people, because deciding whether to get vaccinated against SARS-CoV-2. it also contributes to the reduction of the costs for the public health system and promotes the disappearance of new resistant strains 2. Method [3]. In turn, vaccination provides social benefits, such as indirect protection to people that cannot be vaccinated (due to age, chronic 2.1. Design and procedure diseases, etc.), while also combating the socioeconomic inequity of access to health compared to other types of pharmacological treat- The study presented here is part of the PSY-COVID project. This ments [3,4]. project is an international collaborative research initiative devel- The World Health Organization (WHO) [5], stated that one of the oped during 2020 to 2022 with the purpose of generating a large biggest risks to preventing the spread of disease is hesitation over database to study the psychosocial impact of the COVID-19 pan- vaccination. This phenomenon underlies a set of negative beliefs, demic. A total of 180 researchers from 55 research centers in 28 attitudes, and behaviors regarding vaccination. Previous studies countries participated, developing a standardized and cross- indicate that resistance to vaccination is sustained despite its pro- cultural assessment instrument for mental health, SARS-CoV-2 ven success against common and serious diseases [3]. prevention behaviors, lifestyle, and possible predictive factors The development and mass administration of vaccines is seen (physical, cognitive, social). Likewise, 3 waves of the study were as crucial to stop the spread of the SARS-CoV-2 virus, as well as carried out between 2020 and 2022, in which more than 92,000 to achieve herd immunity and curb the emergence of new variants people participated. This project received the approval of the Ani- of the virus. Given the characteristics of the virus and the vaccines mal and Human Experimentation Ethics Committee of the Autono- administered, it is necessary that between 80 and 90 % of the pop- mous University of Barcelona (CEEAH-5197). The study was ulation be vaccinated to achieve herd immunity [6,7]. The lack of carried out in accordance with The Code of Ethics of the World precedent for an international health emergency such as the Medical Association (Declaration of Helsinki) for experiments COVID-19 pandemic contributes to the fact that information involving humans. This study was carried out from a cross- strategies on the virus and the implementation of the protection sectional perspective. Participants answered the PSY-COVID-2 and prevention measures have not followed a pre-established online questionnaire anonymously from the Google Forms plat- action plan [8]. In turn, given its novelty and accelerated produc- form, with an approximate duration of 15 min. The design of this tion process, widespread initial uncertainty has emerged regarding survey was based on the literature both on mental health and pre- the efficacy and side effects of the vaccine. Although currently ventive behaviors. The list of variables to be measured and instru- available vaccines have been shown to be safe and effective, espe- ments to be included in the questionnaire was validated by a group cially for the prevention of serious cases, hospitalization, and of 30 health researchers and translated into Spanish, Catalan and death, there is a sizable section of the world’s population that is English. Informed consent was included, and participation was vol- reluctant to be vaccinated [5]. untary and anonymous. This questionnaire was developed as a At a time when new variants of SARS-CoV-2 are continuously variant of the original PSY-COVID questionnaire with the aim of emerging, the need to achieve a global level of immunity is high- carrying out a second wave of the PSY-COVID study during 2021 lighted, and it is critical for global public health to identify resistant and 2022. The survey was distributed through social networks population groups as well as the main barriers associated with (FacebookÒ, InstagramÒ, TwitterÒ, WhatsAppÒ). The form was also hesitance/reluctance to vaccination. Several studies have analyzed disseminated in 12 of the Spanish universities participating in the the predictors of resistance to vaccination. There is some consen- PSY-COVID project, including the Network of Healthy and Sustain- sus regarding the sociodemographic characteristics that predict able Universities, from June 1 to July 31, 2021, using the snowball vaccination resistance: being young [9,10], being male [10–12], a sampling method. The dissemination campaign of the study ques- low level of education and income [9,10], a conservative political tionnaire during the second wave was mainly addressed to young ideology [9,10,13], being black [13–15] and not having received adults in Spain, coinciding with the start of the first vaccination the flu vaccine [13,16]. Other studies have identified psychosocial campaign against SARS-CoV-2 for the general young adult popula- factors related to hesitance to vaccination, such as perceived low tion in this country (Fig. 1). vulnerability to illness [12,13,17] or low media confidence [18]. Although some studies [19–21] have indicated that the young 2.2. Participants population showed high levels of vaccination intention before access to vaccines available to them, the reports in Spain and in The eligibility criteria of the sample analyzed in this article the rest of the world [23] highlight that people aged 18 to 49 are were: (1) to be between 18 and 39 years old, (2) to be resident the adult population with the lowest vaccination rate. Therefore, in Spain and (3) to answer the PSY-COVID-2 questionnaire it is necessary to analyze in a global perspective the factors related between 15 and 30 June 2021. This last criterion was adopted to the intention to get vaccinated in this age range. because of the Spanish Public Health Commission approved on June This research presents an exploratory approach, seeking to 15, 2021, the vaccination of age groups between 12 and 39 years. identify the predictive power of affective, cognitive, behavioral, Thus, the data collection period analyzed in this study is limited social, and sociodemographic factors of vaccination intention in a to the weeks of the start of the implementation of the first dose 2 C. Mateo-Canedo, J.P. Sanabria-Mazo, L. Comendador et al. Vaccine: X 14 (2023) 100301 vaccination in the age range of interest. Given that an effect size (n = 1272) reported medium income, 10.8 % (n = 229) was health equal to 1 % of the explained variance was determined according staff, 11.4 % (n = 242) teaching staff, and 16.3 % (n = 346) reported to Funder & Ozer [22] as the minimum to be detected, it was esti- disability, mental health, or chronic illness. 11.2 % (n = 242) mated that the necessary sample to extract from the database was reported having passed COVID-19. n = 1046, for r = 0.1, with a type-I error of a = 0.05 and a type-II error of 1-b = 0.9, relative to two-tailed Pearson bivariate correla- 3.2. Profiling the vaccination hesitancy/reluctance tions. Out of 2723 participants in the second wave of the PSY- COVID study, 2120 met the eligibility criteria, thus the sample size The comparison of means for quantitative predictors and the was sufficient. contingency tests for dichotomous or polytomous variables identi- fied a total of 20 potential variables that discriminated between 2.3. Instruments people with and without vaccination intention (Table 2). Belonging to health staff (v2 = 3.09; p =.04), perceive a future threat to own This study evaluated a set of 67 variables as possible predictors health, F(1,2119) = 6.33; p =.01), tolerance of the restrictions of intention to get vaccinated. The PSY-COVID-2 questionnaire asks (F(1,2119) = 24.65; p <.001), having favorable attitude to mobility participants on a) sociodemographic characteristics (age, gender, restrictions (F(1,2119) = 24.65; p <.001), to preventive measures income, education), b) coronavirus impact (perceived risk, per- (F(1,2119) = 193.67; p <.001) and to vaccination ceived vulnerability, future threats, experience of contagion; c) (F(1,2119) = 1093.67; p <.001), a positive evaluation of the response adaption to restrictions, d) agreement with preventive measures, of the public system facing COVID-19 crisis (F(1,2119) = 10.19; e) psychological variables, f) coping strategies, g) areas of impact, p <.001), trust in authorities (F(1,2119) = 24.65; p <.001) and health and h) use of coping strategies. The psychometric properties of staff/scientists (F(1,2119) = 271.90; p <.001), extraversion instruments constituted by two or more items are indicated in (F(1,2119) = 6.00; p =.01), agreeableness (F(1,2119) = 12.80; p <.001), Table 1. The instruments of the study are detailed in the supple- time (F(1,2119) = 18.63; p <.001) and quality of information about mentary material (Table S1). COVID-19 (F(1,2119) = 69.10; p <.001), and search for social support (F(1,2119) = 9.0.85; p <.001) were conditions associated with vacci- nation intention. Contrarily, conspiracy beliefs about SARS-CoV-2 2.4. Data analysis (F(1,2119) = 215.56; p <.001), availability of public resources for mental health (F(1,2119) = 6.70; p =.01), good post-pandemic mood Statistical analysis was performed using IBM-SPSSÓ (Statistical (F(1,2119) = 3.89, p =.04), and coping style characterized by family Package for the Social Sciences) version 28 and was carried out on support-seeking (F(1,2119) = 10.23; p <.001) and substance use PSY-COVID-2 Dataset [24]. Descriptive statistics were extracted (F(1,2119) = 8.58; p <.001) were conditions associated with hesi- through measures of central tendency and dispersion (M and SD) tancy/reluctance to vaccination. in quantitative variables, and absolute and relative frequencies in qualitative variables. Once the scores of the different multiple- 3.3. Predictive modeling of vaccination intention item instruments were obtained, the internal consistency was estimated. In order to perform the linear multiple regression modeling of For the purposes of some inferential statistical analysis, the the vaccination intention, a Pearson’s product-moment correlation original 5-points Likert scale on vaccination intention was recoded matrix was carried out (see Supplemental Table S2). A total of 35 as a dichotomous variable: intention (strongly agree, agree) versus variables met criterion 1 (a significant correlation for a = 0.05), reluctance/hesitance of vaccination (neutral, disagree, strongly dis- and a total of 8 variables additionally met criterion 2 (coefficient agree). One-way analyses of variance were then carried out on the of determination R2 greater than 1 % adopted according to Funder quantitative predictor variables for the profiling of hesitant/reluc- & Ozer statement about the smaller effect (r = 0.10) that has poten- tant people in relation to their vaccination intention. With the tial to be consequential [22]). These variables ordered by effect size same purpose, contingency chi-square tests were carried out in were attitude to vaccination (r = 0.62; p <.001), trust in health relation to dichotomous or polytomous predictor variables. staff/scientists (r =. 37; p <.001), conspiracy beliefs about SARS- A matrix of Pearson’s product-moment bivariate correlations CoV-2 (r = 0.34; p <.001), attitude towards non-pharmacological between the vaccination intention and its possible regressors preventive measures (r = 0.31; p <.001), time spent to get informed was obtained, which allowed the identification of variables that about COVID-19 (r = 0.20; p <.001), attitude to mobility restrictions met a double criterion: (1) a significant correlation for a = 0.05 (r = 0.20; p <.001), trust in authorities (r = 0.15; p <.001) and adap- and (2) a coefficient of determination (R2) greater than 1 % on tation to mobility restrictions (r = 0.12; p <.001). Then, the multiple the intention to vaccination. Then, a multiple regression model linear regression model was performed with the stepwise proce- (stepwise method) was carried out including the variables that dure (Table 3), in which the vaccination intention was introduced met these eligibility criteria as regressors of the intention to get as a dependent variable and the eight variables that met the two vaccinated as the dependent variable. Finally, a discriminant anal- eligibility criteria indicated above were included as regressors. ysis was carried out with the predictive variables identified in the The results indicated that a model with 4 regressors showed a mul- multiple regression model to quantify their degree of sensitivity tiple correlation of r = 0.65 and thus a predictive capacity (R2adjusted) and specificity when classifying people as hesitant/reluctant about of 41 % (F(1,2111) = 500.95; p <.001): Attitude to vaccination, trust in vaccination. health staff/scientists and time of information about COVID-19 showed positive b scores, but conspiracy beliefs about SARS-CoV-2 3. Results showed a negative b score. 3.1. Sample description 3.4. Discriminant analysis of vaccination intention The age range of the sample (n = 2120) was between 18 and Once the set of variables with predictive capacity of the vacci- 39 years (M = 22.15; SD = 4.39), with a higher representation of nation intention was identified, a discriminant analysis was per- women (n = 1531; 72.2 %); 97.1 % (n = 2058) of the participants formed. To this end, a dichotomous classification derived from had university studies (started or completed). Sixty percent the vaccination intention scale was adopted, which involves the 3 C. Mateo-Canedo, J.P. Sanabria-Mazo, L. Comendador et al. Vaccine: X 14 (2023) 100301 Fig. 1. This figure shows the data collection for this study overlapped with the vaccination rate (%) of the sample of this study (first dose). The age groups are those reported by the Spanish Ministry of Health. The discontinuity seen in the graph is due to a change in the distribution criteria in age groups, which coincided with the start of general vaccination in the age range of the participants analyzed in this study. Data source: https://github.com/datadista/datasets/blob/master/COVID%2019/ccaa_vacunas_grupos_ etarios_1_dosis.csv#L4659. Table 1 Internal consistency of the instruments constituted by two items or more. Scale Subescale Cronbach’s Instrument a Vulnerability to 0.79 Ad-hoc coronavirus Severity 0.64 Ad-hoc Threats 0.75 Ad-hoc Trust Authorities 0.82 Ad-hoc Experts (health staff/scientists) 0.79 Post-traumatic growth Post-traumatic growth 0.79 Post-traumatic Growth Inventory (PTGI-SF; Cann et al., 2010) Depression Depression 0.82 Patient Health Questionnaire-4 (PHQ-2; Löwe et al., 2010) Anxiety symptoms Anxiety 0.82 Generalized Anxiety Disorder (GAD-2; Spitzer et al., 2006) Somatization symptoms Somatization 0.77 Derived from systematic review. Somatization Symptoms Scale (SSQ-5; Zijlema et al., 2013) Resilience Recover after illness or 0.74 Connor-Davidson Resilience Scale (CD-RISC-2; Vaishnavi et al., 2007) difficulties distinction between people who reject or doubt their intention to so far. At a time when it seems to be necessary to reach at least get vaccinated (13.9 %) from those with a vaccination intention 90 % of vaccinated to achieve group immunity [6] and considering (86.1 %). A robust discriminant function was obtained as a result that there is a high degree of the population that for various rea- (intention centroid: 0.31; non-intention centroid: 1.89; Wilk’s sons (age, chronic diseases, etc.) cannot be vaccinated, it is neces- k = 0.63, rcanonic = 0.64; p <.001), which allowed the correct classi- sary to ensure the maximization of the intention to get vaccinated fication of 86 % of cases (sensitivity = 89 %; specificity = 70 % for the by the rest of the population [4,26]. intention to get vaccinated against the SARS-CoV-2 virus) based on Secondly, the results identify a pool of heterogeneous condi- three predictors: conspiracy beliefs about SARS-CoV-2, attitude on tions from different domains related to the intention of getting population vaccination and time spent to be informed about vaccinated. Belonging to health staff, perceiving a threat to own COVID-19. health, high tolerance of confinement, favorable attitude to mobil- ity restrictions, to preventive measures and to the vaccine, trust in 4. Discussion the authorities and in health staff/scientists, confidence in the pub- lic system, personality (extraversion and agreeableness), time and The purpose of this study was to identify the main predictors of quality of information about COVID-19 and seeking emotional sup- vaccination intention in a population with high hesitation/reluc- port were conditions associated with the intention to get vacci- tance in a country with a high vaccination rate. Firstly, it was found nated. In contrast, conspiracy beliefs about SARS-CoV-2, that 86 % of the people in this study (Spanish young adults) availability of public resources for mental health, good post- expressed intention to get vaccinated against SARS-CoV-2. It is pandemic mood, and coping style characterized by seeking family known that Spain is one of the countries in Europe with the highest support and substance use were conditions associated with hesi- acceptance of vaccination [25], probably because it was one of the tancy/reluctance to vaccination. countries most affected by this virus in the first wave in early 2020, Within the broad pool of variables analyzed, the model that best with more than 100,000 deaths and 11.5 million of cases confirmed predicts intention to get vaccinated includes four variables 4 C. Mateo-Canedo, J.P. Sanabria-Mazo, L. Comendador et al. Vaccine: X 14 (2023) 100301 Table 2 Comparison between the characteristics of vaccination-intent and hesitance participants. Total (n = 2120) Hesitance (n = 292) Intention (n = 1828) F/v2 p Socio-demographics Age: M (SD) 22.14 (4.37) 21.91 (4.43) 22.17 (4.36) 0.92a 0.34 Gender (female): f (%) 1523 (71.8) 209 (71.6) 1314 (71.9) 1.99b 0.37 Income level (medium): f (%) 1272 (60) 180 (61.6) 1092 (59.7) 0.36b 0.55 Education level (university): f (%) 2059 (97.1) 282 (96.6) 1777 (97.2) 0.36b 0.55 Health staff: f (%) 230 (10.8) 23 (7.9) 207 (11.3) 3,09b 0.04 Teaching staff: f (%) 242 (11.4) 41 (14) 201 (11) 2.31b 0.08 Population with disability: f (%) 20 (0.9) 5 (1.7) 15 (0.8) 2.14b 0.13 Population with mental health disorder: f (%) 209 (9.9) 21 (7.2) 18.8 (10.3) 2.71b 0.06 Population with chronic illness: f (%) 116 (5.5) 14 (4.8) 102 (5.6) 0.30b 0.35 Having coronavirus: f (%) 237 (11.2) 35 (12.1) 202 (11.1) 6.43b 0.09 A = Fisher’s F test; b = Chi-square v2 test Total (n = 2120) Hesitance (n = 292) Intention (n = 1828) F p Coronavirus impact: M (SD) Perceived risk of contagion in the future (range: 0 to 4) 1.62 (0.96) 1.68 (0.96) 1.61 (0.96) 1.38 0.24 Perceived risk of contagion from others (range: 0 to 4) 1.74 (0.96) 1.84 (0.97) 1.73 (0.95) 3.64 0.06 Perceived vulnerability to coronavirus (range: 0 to 4) 1.27 (0.84) 1.28 (0.93) 1.26 (0.83) 0.15 0.70 Perceived vulnerability of others to coronavirus (range: 0 to 4) 2.03 (0.91) 2.02 (0.95) 2.04 (0.90) 0.12 0.73 Future threat: employment or studies (range: 0 to 3) 1.50 (0.97) 1.58 (0.98) 1.49 (0.96) 2.20 0.14 Future threat: income (range: 0 to 3) 1.53 (0.96) 1.57 (0.96) 1.52 (0.96) 0.46 0.50 Future threat: health (range: 0 to 3) 1.41 (0.83) 1.30 (0.82) 1.43 (0.83) 6.33 0.01 Future threat: personal relationships (range: 0 to 3) 1.48 (0.90) 1.49 (0.94) 1.48 (0.89) 0.02 0.88 Adaptation to restrictions: M (SD) Life changes: physical activity (range: 2 to + 2) 0.38 (1.24) 0.32 (1.30) 0.39 (1.23) 0.79 0.37 Life changes: sleep habits (range: 2 to + 2) 0.53 (1.00) 0.53 (1.05) 0.53 (0.99) 0.11 0.92 Life changes: diet (range: 2 to + 2) 0.15 (0.99) 0.13 (1.02) 0.15 (0.98) 0.13 0.72 Life changes: income level (range: 2 to + 2) 0.30 (0.95) 0.36 (0.93) 0.29 (0.95) 1.39 0.24 Life changes: work activity (range: 2 to + 2) 0.38 (0.98) 0.39 (0.93) 0.38 (0.99) 0.02 0.88 Life changes: relationships with friends/family (range: 2 to + 2) 0.22 (1.03) 0.19 (0.99) 0.22 (1.03) 0.31 0.58 Life changes: hobbies (range: 2 to + 2) 0.37 (1.06) 0.45 (1.04) 0.36 (1.06) 1.83 0.18 Adaptation to general changes (range: 0 to 4) 2.32 (1.00) 2.28 (0.99) 2.32 (1.00) 0.54 0.46 Time perception (range: 2 to + 2) 0.26 (1.37) 0.18 (1.39) 0.27 (1.37) 1.19 0.28 Tolerance of confinement (range: 0 to 4) 2.12 (1.25) 1.78 (1.41) 2.18 (1.22) 24.65 < 0.001 Leaving home during restrictions (range: 0 to 4) 2.80 (1.11) 2.85 (1.12) 2.79 (1.11) 0.61 0.43 Time spent on coronavirus information (range: 0 to 3) 0.82 (0.61) 0.61 (0.65) 0.85 (0.59) 3.11 0.08 Agreement with preventive measures: M (SD) Socioeconomics reasons virus (range: 2 to + 2) 0.89 (1.18) 0.01 (1.26) 1.03 (1.10) 215.56 < 0.001 Necessary mobility restrictions (range: 2 to + 2) 0.64 (1.12) 0.13 (1.24) 0.72 (1.07) 74.15 < 0.001 Necessary preventive measures (range: 2 to + 2) 1.51 (0.73) 0.98 (1.01) 1.60 (0.64) 193.67 < 0.001 Administration of vaccine (range: 2 to + 2) 1.62 (0.76) 0.51 (1.14) 1.80 (0.48) 1093.67 < 0.001 Adequate information on coronavirus (range: 2 to + 2) 0.37 (1.09) 0.86 (1.08) 0.29 (1.07) 69.10 < 0.001 Response of the education system (range: 2 to + 2) 0.46 (1.17) 0.66 (1.14) 0.43 (1.17) 10.19 < 0.001 Public resources for mental health (range: 2 to + 2) 1.26 (1.11) 1.10 (1.25) 1.28 (1.08) 6.70 0.01 Trust in government (range: 0 to 3) 1.52 (1.34) 1.02 (1.25) 1.60 (1.33) 48.95 < 0.001 Trust in health staff and scientists) (range: 0 to 3) 4.91 (1.27) 3.84 (1.56) 5.09 (1.13) 271.90 < 0.001 Psychological follow-up (range: 2 to + 2) 0.83 (1.36) 0.92 (1.35) 0.82 (1.36) 1.45 0.23 Change of mood post pandemic (range: 2 to + 2) 0.73 (1.03) 0.62 (1.10) 0.75 (1.02) 3.89 0.04 Psychological variables: M (SD) Personality: extraversion (range: 2 to + 2) 0.16 (1.17) 0.00 (1.20) 0.18 (1.16) 6.00 0.01 Personality: conscientiousness (range: 2 to + 2) 0.47 (1.13) 0.47 (1.13) 0.47 (1.13) 0.00 0.97 Personality: agreeableness (range: 2 to + 2) 0.02 (1.19) 0.21 (1.18) 0.06 (1.19) 12.80 < 0.001 Personality: neuroticism (range: 2 to + 2) 0.41 (1.13) 0.34 (1.10) 0.42 (1.13) 1.41 0.23 Personality: openness to experience (range: 2 to + 2) 0.71 (1.14) 0.65 (1.18) 0.72 (1.13) 0.89 0.34 Loneliness (range: 0 to 3) 1.20 (1.00) 1.18 (1.03) 1.21 (0.99) 0.16 0.69 Perceived competence (range: 2 to + 2) 0.91 (0.85) 0.92 (0.85) 0.91 (0.85) 0.04 0.83 Depression symptoms (PHQ-2) (range: 0 to 6) 3.03 (1.71) 3.13 (1.69) 3.01 (1.71) 1.13 0.29 Anxiety symptoms (GAD-2) (range: 0 to 6) 3.19 (1.81) 3.07 (1.80) 3.21 (1.81) 1.56 0.21 Somatization symptoms (SSQ-5) (range: 0 to 15) 4.56 (3.13) 4.57 (3.12) 4.55 (3.13) 0.01 0.92 Post-traumatic growth (PTGI) (range: 0 to 15) 6.40 (3.35) 6.51 (3.76) 6.38 (3.28) 0.37 0.54 Resilience (CD-RIS) (range: 0 to 6) 4.36 (1.08) 4.33 (1.11) 4.36 (1.80) 0.27 0.60 Coping strategies, M (SD) Hobbies (range: 2 to + 2) 1.11 (0.79) 1.03 (0.88) 1.13 (0.78) 3.54 0.06 Family (range: 2 to + 2) 0.92 (0.86) 0.77 (0.90) 0.95 (0.85) 10.23 0.001 Social activities (range: 12 to + 12) 1.95 (5.27) 1.82 (5.15) 1.97 (5.29) 0.19 0.66 Academic (range: 6 to + 6) 3.04 (2.52) 2.82 (2.74) 3.07 (2.48) 2.11 0.15 Work impact (range: 2 to + 2) 0.39 (0.85) 0.44 (0.88) 0.38 (0.84) 1.47 0.22 Hospitalization (range: 2 to + 2) 0.43 (0.84) 0.40 (0.84) 0.43 (0.84) 0.42 0.51 Health habits (range: 6 to + 6) 0.66 (2.33) 0.59 (2.34) 0.67 (2.33) 0.30 0.58 Information about coronavirus (range: 2 to + 2) 0.05 (0.89) 0.27 (0.87) 0.02 (0.89) 18.63 < 0.001 Technological activities (range: 2 to + 2) 0.36 (0.96) 0.35 (0.99) 0.36 (0.96) 0.03 0.96 Use of social media (range: 2 to + 2) 0.12 (1.12) 0.04 (1.10) 0.13 (1.12) 1.54 0.21 Meet with friends (range: 2 to + 2) 0.29 (1.44) 0.17 (1.43) 0.31 (1.44) 2,01 0.16 Use of coping strategies, M (DE) (continued on next page) 5 C. Mateo-Canedo, J.P. Sanabria-Mazo, L. Comendador et al. Vaccine: X 14 (2023) 100301 Table 2 (continued) Total (n = 2120) Hesitance (n = 292) Intention (n = 1828) F p Focus on coping with adversity (range: 0 to 3) 1.38 (0.80) 1.38 (0.79) 1.38 (0.81) 0.00 0.98 Substance use (range: 0 to 3) 0.43 (0.76) 0.55 (0.89) 0.41 (0.74) 8.58 < 0.01 Express feeling bad (range: 0 to 3) 1.55 (0.87) 1.50 (0.94) 1.56 (0.86) 1.28 0.26 Seek emotional support (range: 0 to 3) 1.58 (0.91) 1.43 (0.95) 1.61 (0.89) 9.85 < 0.01 Denial (range: 0 to 3) 0.33 (0.65) 0.37 (0.72) 0.32 (0.63) 1.16 0.28 Joke about circumstances (range: 0 to 3) 1.66 (1.02) 1.62 (1.00) 1.67 (1.02) 0.63 0.43 Seek help from God (range: 0 to 3) 0.21 (0.61) 0.27 (0.68) 0.21 (0.59) 2.49 0.11 Table 3 Multiple linear regression for the intention to get vaccinated. b t Change in adjusted R2 Adjusted F R2 Intention to vaccinate 0.41 365.35*** Attitude to vaccine 0.52 27.11*** 0.37 Trust in health staff and scientists 0.13 6.83*** 0.02 Conspiracy beliefs about SARS-CoV-2 -0.11 6.26*** 0.01 Time spent in coronavirus information 0.03 1.98* 0.01 * p <.05; ** p <.01; ***p <.001. accounting for 41 % of the interindividual variability: attitude false news are two of the main reasons for the growing doubt toward pharmacological measures for SARS-CoV-2, trust in experts about vaccination [41,42]. While fake news has always existed, (scientists and health professionals), conspiracy beliefs about its spread in the digital age is growing exponentially and making SARS-CoV-2 and time expend information. Moreover, the discrim- it difficult to control [43]. Dubè [44] argues that false news is inant analysis showed that by asking just three questions, it is pos- one of the main factors that explain the low level of immunity. sible to identify, with an accuracy of 86 %, young adults who are Within the dissemination of fake news, the effect of the dissemina- reluctant or hesitant about vaccination: 1) ‘‘Is it necessary to tion of conspiracy theories has had a clear negative effect on the administer the vaccine to the population?” (Attitude towards phar- intention to get vaccinated [45,46]. This effect had also been previ- macological measures against COVID-19); 2) ‘‘What degree of trust ously observed with respect to non-pharmacological prevention have scientists and health professionals deserved during the coro- measures against SARS-CoV-2. navirus crisis?” (Trust in health staff/scientists) and 3) ‘‘do you As a synthesis, it is worthy to be highlighted that these main think SARS-CoV-2 is a virus created for socioeconomic purposes”? predictive factors of reluctance or hesitation about vaccination (Conspiracy beliefs about SARS-CoV-2). (vaccine attitude, trust on key actors, conspiracy beliefs, and infor- Our findings are in line with previous literature that has high- mation on COVID-19) are (1) of a social nature and (2) modifiable lighted that to believe that the vaccine is needed in the population through the adoption of effective communication strategies, to eradicate the virus strongly predicts the intention to get vacci- related to trust in key groups, attitude towards vaccination, infor- nated. Various studies have shown that the attitude to population mation of quality about the pandemic and prevention measures vaccination to face COVID-19 disease is closely linked to the per- (pharmacological and non-pharmacological) and the control of sonal intention to get vaccinated [27,28]. Lugo-González [29] fake news. Therefore, they must be at the core of public health poli- found that positive beliefs and attitudes regarding vaccination cies in future pandemics. are one of the most relevant psychosocial factors in the process Not all the results obtained in this study are in line with the of immunizing the population against SARS-CoV-2. findings of the previous literature. Surprisingly and contrary to Like our study, lack of trust in authorities in general, and in our findings, most studies report that women show a lower inten- science in particular, has been found to be a major predictor of var- tion of vaccination against SARS-CoV-2 [47,48], contrary to what ious conspiracy theories [30,31] mediating the negative effect of has been observed in terms of non-pharmacological measures to conspiracy theories on the level of adherence to health indications prevent the virus [49,50]. It has been argued that this may be [32,33]. Cavojova [34] found that preventing the spread of conflict- due to a gender gap with respect to lower risk-taking by women, ing scientific knowledge facilitates greater understanding and as well as being more proactive about preventative behaviors com- belief in science, directly influencing people’s intention to get vac- pared to men [51,52]. This, together with the lack of evidence in cinated. The diversity of scientific discourses, showing different our study (contrary to that indicated by other previous studies) points of view on the same object of study, affects the level of trust regarding the relationship between various sociodemographic fac- they inspire in the population [34]. This phenomenon responds to tors and vaccination intention, may be related to the particularities the usual functioning of science, which does not necessarily follow of the study population, which is discussed in greater detail later. linear patterns [35,36]. However, the level of uncertainty and the need for immediate responses from both the health system and cit- 4.1. Strengths and limitations izens, has meant that all eyes are on their discourse [37]. Since the outbreak of SARS-CoV-2, confidence in health institutions and in The main strength of this study is that its results allow an explo- the opinions of health experts has been weakening [38,39], result- rative analysis of a wider variety of variables associated with reluc- ing in lower adherence to essential health recommendations and tance to vaccination (compared to studies that focus on a narrow contributing to the transmission of the virus [38,40]. spectrum of probable predictive variables) which leads to a com- In a context of great uncertainty and little precedent, the need prehensive model. The diversity of the pool of variables collected, to cling to as much information as possible is understood. How- which includes affective, cognitive, contextual and sociodemo- ever, the lack of validated information and the dissemination of graphic factors, has made it possible to understand the heterogene- 6 C. Mateo-Canedo, J.P. Sanabria-Mazo, L. Comendador et al. Vaccine: X 14 (2023) 100301 ity of the processes behind the decision to vaccinate or not against celona (CEEAH-5197), following the guidelines of the Declaration SARS-CoV-2. of Helsinki. Another important strength of this study is that the date of dis- semination of the questionnaire coincided exactly with the time of CRediT authorship contribution statement access to the vaccine for the age range of 18 to 39 years, which was the population of interest for this study because it was the most Corel Mateo-Canedo: Conceptualization, Validation, Investiga- resistant to vaccination. tion, Resources, Data curation, Writing – original draft, Writing – Among the limitations we highlight the level of homogeneity of review & editing, Visualization. Juan Pablo Sanabria Mazo: Con- the sample. The sample included in the study was highly specific ceptualization, Validation, Writing – original draft, Writing – and showed relevant biases (97 % had a university degree in progress review & editing. Laura Comendador: Conceptualization, Valida- or completed, and 72 % were female) that must be considered with tion, Investigation, Resources, Data curation, Writing – original regard to the generalizability of the findings of this study. Regarding draft, Writing – review & editing. Juan Sebastián Rojas: Validation, the gender bias of the representativeness of the sample, this limita- Investigation, Resources, Data curation, Writing – review & editing. tion would have been a major problem if the gender variable had Meritxell Carmona: Validation, Investigation, Resources, Data appeared as a variable with great explanatory power for the inten- curation, Writing – review & editing. Neus Crespo Puig: Validation, tion to vaccinate. In any case, this may explain why no significant Investigation, Resources, Writing – review & editing. Fiorella Any- correlations were found between sociodemographic variables and osa: Validation, Investigation, Resources, Writing – review & edit- vaccination intention, contrary to findings in previous literature. ing. Clara Selva: Validation, Investigation, Resources, Writing – Concretely, it has been observed that having a high level of education review & editing. Albert Feliu-Soler: Validation, Investigation, is a strong predictor of the intention to get vaccinated [15,53], asso- Resources. Narcís Cardoner: Validation, Investigation, Resources, ciated with greater access and processing of vaccination information Writing – review & editing. Juan Deus: Validation, Investigation, received regarding the vaccine and its effects. Resources, Writing – review & editing. Juan V. Luciano: Validation, However, a bias in the sample of this study (predominance of Resources, Funding acquisition. Jorge Luis Méndez-Ülrich: Valida- people with a university level with access to online media) endows tion, Investigation, Resources, Writing – review & editing. Antoni an important learned lesson: even in a population characterized by Sanz: Conceptualization, Methodology, Validation, Formal analysis, a high level of education and access to information, which presup- Investigation, Resources, Data curation, Writing – original draft, poses access to quality information and capacity for critical analy- Writing – review & editing, Visualization, Supervision, Project sis, the conspiracy ideas derived from fake news and the lack of administration, Funding acquisition. information can have a robust influence on the intention to get vaccinated. Declaration of Competing Interest 5. Conclusions The authors declare that they have no known competing finan- We conclude that this cross-sectional research makes it possi- cial interests or personal relationships that could have appeared ble to identify, within a wide and heterogeneous range of variables to influence the work reported in this paper. evaluated, the factors that best predict the intention of vaccination among young adults in Spain in the beginning of their vaccination Appendix A. Supplementary material campaign: Attitude to vaccination, trust in health staff/scientists, time of information and conspiracy beliefs about SARS-CoV-2. Also, Supplementary data to this article can be found online at this study has made it possible to delineate in detail the profile of https://doi.org/10.1016/j.jvacx.2023.100301. people with high doubts or rejection of vaccination. References Funding [1] Phadke VK, Bednarczyk RA, Salmon DA, Omer SB. 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