Acceptance, Hesitancy, and Refusal in anti-COVID-19 vaccination. A cluster analysis aiming at the typology behind these three concepts

Acceptance, Hesitancy, and Refusal in anti-COVID-19 vaccination. 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A cluster analysis aiming at the typology behind these three concepts Darie Cristea, Dragoș-Georgian Ilie, Claudia Constantinescu, Valeriu Fîrțală This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1702368/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Most sociological studies on the intention to vaccinate against COVID-19 in the last year have divided the public into people who want to be vaccinated, people who are against vaccination and hesitant. Opinion polls also described how the public relates to specific motivations for agreement, refusal or hesitation about vaccination. Our study proposes a typology of attitude towards vaccination against covid constructed by cluster analysis, based on a battery of questions that indicate the attitude of the subjects towards this vaccination on several dimensions. The questions were deliberately placed by the authors of the study in a nationally representative opinion poll for Romania, in order to collect these data and to study the resulting typology. Introduction Vaccination was a major topic at the intersection of medical and social sciences even before 2020, but after the advent of the COVID 19 pandemic, it became clear that the issue was much more complicated than we imagined. In the past two years, countless articles have been published on the acceptance and refusal of vaccination, especially in the context of the pandemic. One thing is clear – so much has been published that we are far from benefiting from all the information contained in these studies. Because it's so much so that it is virtually impossible to assimilate. A clarification and systematization of the typologies and causalities identified would be necessary. We see that all this, however, moves in a space bounded by several concepts, well-founded in specialized studies, measurable and empirically measured over and over again: vaccine acceptance, vaccine confidence, vaccine hesitancy, vaccine refusal[i][ii][iii][iv][v]. One problem is whether we are dealing with a continuum or with separate phenomena. It's a scaling problem, common in the social sciences. In general, the attitude or intention of vaccination is measured in opinion polls with an ordinal question or with a nominal one of acceptance / indecision / refusal type, in which sometimes the indecision is detailed in 2-3 other variants of answer regarding motivations. But what if the acceptance and refusal of vaccination are not steps on a scale, but qualitatively different phenomena, with motivations from different spectra and attributable to clearly differentiated human typologies? Also, the pandemic has shown us that the triangle of acceptance / hesitation / refusal also has a special dynamic, depending on the availability of the vaccine, what rumors / fake-news appear about covid and vaccine, trust in the authorities, fear, perception of the social norm, imposition of restrictions for the unvaccinated, etc. In rural areas, the dynamics of this triangle can also be influenced by the population's access to health services, and the development of Multifunctional Integrated Community Centers could be useful in this regard [vi] . The pandemic context and the imperative of regulating social relations through special norms specific to the state of emergency or the state of alert have determined in many situations the association of vaccination with the need to avoid the restrictions imposed by the authorities on the unvaccinated. Thus, appeared a non-medical motivation for vaccine intake: to avoid restrictions). Or, on the contrary, vaccine refusal appeared as a form of social resistance to the high normative pressure too high of the authorities. Such a paradoxical social polarization of this type raises serious questions about the most effective regulatory method implemented by the authorities in any future crisis situation that they would try to manage through increased pressure (increasing the motivation of compliance with the regulation by those who want to avoid restrictions being in counterweight to the accentuation of non-compliance with the regulation by those who want to emphasize the resistance to restrictions aimed at aspects sensitive on human rights). This paradox of social conduct is all the more relevant because in crisis situations social discipline and compliance with norms are much more important than in other social and historical situations. Studies show that there are multiple explanations regarding the acceptance/refusal of vaccination. In short, the acceptance, hesitation or refusal of vaccination (in the case of covid 19 and beyond) have been explained by the following types of causal models: 1. Models that refer to distrust in the vaccine (in the content of the serum, or the production method), to distrust in medicine, and to scientific illiteracy (although, the relationship between scientific literacy and the intention to vaccinate is much more complicated than it seems); [vii] [viii] 2. Models that refer to the distrust of the authorities, conspiracy theories, the assumption of hidden intentions of the organization of the vaccination process by the authorities; [ix] [x] [xi] [xii] [xiii] 3. Models that refer to differences in perception of vaccine and vaccination between different socio-demographic categories (age, education, gender, national membership, religion, etc.); [xiv] [xv] [xvi] 4. Models that relate primarily to the channel, the source of information on the virus, on the pandemic, on the vaccine and the vaccination process. [xvii] [xviii] 4. Psychosociological models, which take into account purely psychosociological variables that are associated with acceptance or hesitation in the face of vaccination - for example, in a previous article we argued that there is a very strong correlation between positive attitudes to vaccination and the belief that most others want to get vaccinated. [xix] The question is whether this typology based on the triangle acceptance/hesitation/refusal is too manifest and too simplistic. If we describe these three attitudes through more pragmatic indicators we could obtain a much more operational typology, which would also show us what are the resorts of this division of the public according to the attitude towards vaccination, a division that in the last year has already acquired sociopolitical valences. So our research question is the following: we could get a more explicit typology of the attitude towards vaccination in Romania if instead of the direct question on the intention to vaccinate we use a set of questions that would break down on several dimensions the triplet acceptance / hesitation / refusal in the case of vaccination for covid 19. Such a typology could show us which are the resorts that underlie this public opinion/attitude vis-à-vis the covid vaccination. We intend to build this typology through cluster analysis (k-means clustering). Details about the data used we have in the Materials and Methods section. But first, a brief review of three studies of this type published lately on the anti-covid vaccination. One is based on data collected in the US, two other refer to the case of Europe and Romania in particular, our study being based on data from this last country. A survey conducted in 2021 on vaccine acceptance and hesitancy among 2491 Healthcare Workers in Southern California aims to prove there is a heterogeneous group (and sub-groups) with varying attitude toward vaccination, not only „anti” and „pro” vaccine. The clustering analysis conducted by the authors of this study starts from the idea of a continuum between total acceptance and complete refusal and describe groups and sub-groups of Healthcare Workers holding varying degrees of indecision about vaccination. The respondents to the study were grouped in four clusters: (1) misinformed, (2) uninformed, (3) undecided and (4) unconcerned. Thus, there is a diversity in vaccine hesitancy and their conclusions is that „messaging should be tailored to specific sub-groups to increase the understanding of the science behind vaccine”[xx]. Vulpe and Rughinis, using the Eurobarometer 91.2 survey conducted between 15th-29th March 2019, identified three belief configurations as regards vaccine effectiveness, safety and usefulness: hesitant, confident and trade-off. The authors’ conclusion upon conducting a cluster analysis includes the reference to the substantial variation at the country level, but they cannot find „strong sociodemographic differences among the three belief clusters”. One of the findings of this study is the „needed to address the socially amplified risk of probable vaccine damage and to consider the trade-off patterns of concomitant trust and mistrust in assessing vaccines.”[xxi] The study „Social worlds of attitude towards anti-Covid-19 vaccination: Romania in the European context” published in 2021 is based on the Flash Eurobarometer State of the European Union. The further clustering analysis starts from the idea that the attitudes towards vaccine are not only quantitative between the two polls: pro and anti-vaccine. The cluster analysis combines three indicators and generates six types of vaccine attitudes. Romania is positioned in the European context from the point of view of specific profile of attitude along with Poland, Cech Republic and Lithuania. An important finding of the author is that there are not only pro-vaccine, anti-vaccine or hesitants, but interposing categories qualitativelely structured. It appears to the researcher that both the attitudes tawords vaccination and the intentions to get vaccinated are depend to the socio-demographic elements and the previous life experiences. The conclusion of this study is that we deal with “social worlds of the Covid -19 vaccine attitude in the senes of language communities on the topic” and these social worlds are structured differently in groups of countries. A more accurate understanding can be achieved by further studies including variables like stage of infection with SarsCoV2, migration experiences, the sense of belonging to national / regional spaces with different cultural models.[xxii] Materials And Methods The data on the basis of which this analysis was carried out come from a larger public opinion survey, conducted on a nationally representative sample for Romania. Two of the co-authors of this article were decisively involved in conducting the survey (see note , explaining the full context of the survey and giving all the data necessary for its identification, including the research report). The data was collected between October 1–10, 2021 by LARICS, a public opinion analysis laboratory known in Romania, under the auspices of the Institute of Political Science and International Relations within the Romanian Academy. The analysis was therefore carried out on a database of 1002 respondents on the territory of Romania; the sample was statistically representative at the level of the entire non-institutionalized population in Romania, aged between 18 and + 65 years, it was a multilayer probabilistic sample, and the margin of error was one of 3.1%. The questionnaire was applied by phone using CATI as the method of coordinating the interviews and the sample was validated based on the data of the National Institute of Statistics in Romania. In order to identify how the studied population structures itself when it comes to attitudes towards vaccination, we decided to use an algorithm for classifying cases. The cluster is a grouping of subjects or objects that have common traits and are grouped together based on this similarity (Jain, 1988). The components of the cluster are similar to each other, but are different from the elements that make up another cluster (Everitt, 1974). To study the distribution of cases in clusters we decided to use the "k-means" clustering algorithm method. This procedure consists of measuring the proximity of cases to the average of a cluster. "K" is the number of clusters in an analysis that is chosen by the researcher. The "k-means" algorithm proposed by J. MacQueen assigns a random average for each cluster, for example for 3 clusters we will have 3 averages, then measures the closeness of the observed cases to this initial average, after which it calculates the final average of the cases in the cluster and groups them within these clusters (MacQueen, 1967). The belonging of an observed case to the cluster is determined by the proximity of the observed value to the mean value "k" of the cluster. Using the IBM SPSS 20 statistical analysis program, we conducted a series of tests on our database, including, most importantly, "k-means" cluster analysis. Our analysis resulted in a series of tables, for models with 2, 3, 4 and 5 clusters. Table 1 K-means cluster analysis; distribution of the sample by clusters (model with 3 clusters) Final Cluster Centers Cluster 1 2 3 A) Vaccination against covid 19 is safe 2 5 2 B) Vaccination against COVID 19 is a measure abusively promoted by the authorities 5 3 3 C) Vaccination against COVID 19 is the only measure that can stop the pandemic 2 4 3 D) This pandemic is more of a lie or an exaggeration 4 2 3 E) Vaccination against COVID 19 is not effective 4 2 3 F) Vaccination against COVID 19 should be mandatory 1 3 3 Results Tests in SPSS were done with distribution in 2, 3, 4 and 5 clusters. Analyzing the distribution of cases in these cases we found that a distribution of cases in 3 clusters (see Table 1 ) represents an unfragmented classification of the population, in other words, the population studied tends to group around these average values of the clusters and we can observe the significant differences between them. Clustering was made around 6 statements that measured scalarly, on a 5-step scale, the anchor to: confidence in the safety of vaccination against Covid-19, the confidence that vaccination against Covid-19 is abusively promoted by the state authorities, the belief that vaccination against Covid-19 is the only way the pandemic can be stopped, the fact that the pandemic does not exist or is an exaggeration, the fact that vaccination against Covid-19 is not effective, the fact that vaccination against Covid-19 should be mandatory. The lower the value in the table, the lower the confidence level. Clusters have the following composition in terms of number of subjects: Cluster 1: 378 subjects Cluster 2: 356 subjects Cluster 3: 260 subjects. The result is a number of 994 subjects who answered this set of questions, out of a total of 1002 participants, 8 subjects refusing to answer these questions. In order to identify what are the significant differences that occur between the members of the 3 clusters we decided to apply Fischer's "z-test", the results can be seen in Table 2 . Table 2 Results of the test z (Fischer) Comparisons of Column Proportions b Cluster Number of Case 1 2 3 (A) (B) (C) Age Yound B C Adult A B Old A C A Residence Urban C C Rural A B Income Low A B Medium C C High C C Education level: Low A A Medium B A B High C C I get the information about the pandemic and vaccination first from: TV A A B Friends Facebook & Internet B C Family C Official gov. websites C C Your family doctor: Advised you to get vaccinated against COVID 19 A C Advised you not to get vaccinated against COVID 19 B A B Advised you to wait A B You have not discussed the vaccine with your family doctor B C Results are based on two-sided tests with significance level ,05. For each significant pair, the key of the category with the smaller column proportion appears under the category with the larger column proportion. The subjects were comparatively analyzed according to the following variables: age, sex, residence environment, income, level of studies, from where they tend to get informed about the pandemic situation and how the family doctor advised them whether to get vaccinated or not. In the table above we analyze the statistically significant differences between the values on the columns, so we can say that the members of the first cluster are rather young, tend to reside in urban areas, have a medium or high level of income, have a medium or high level of income, have a medium or higher education, tend to be rather male, to inform themselves about vaccination and the pandemic from official sources, social media or the internet and tend either not to have talked to the family doctor about the vaccination, or to declare that the family doctor advised them not to get vaccinated against Covid-19. The subjects in cluster 2 tend to be rather old, as opposed to those in cluster 3 tend to be rather from urban areas, to have a medium or high income, to have either primary education or higher education, to be rather women, to inform themselves from TV, from the family or from official websites regarding the pandemic and it is the group that seems to have been advised to is vaccinated against Covid-19 by the family doctor. The subjects in cluster 3 tend to be rather adults or old people, tend to come from rural areas, tend to have low incomes, primary or secondary education, tend to get information from TV, and the family doctor has advised them either not to get vaccinated against Covid-19 or to wait. Discussion We recall that in Table 1 above the result of the cluster analysis is found. We also recall that the values describing the clusters move between 1 and 5, 1 indicating total disagreement with the respective statement, and 5 a total agreement. The middle value is 3. As we have already mentioned, we have chosen a typology with three categories. Cluster 1 is composed of antivaccination subjects : they don't think covid vaccination is safe, nor is it the only method that can stop the pandemic; they think vaccination is being promoted abusively by the authorities and it's not effective. They consider the pandemic a lie/exaggeration and strongly oppose a possible mandatory vaccination. Cluster 2 is that of the followers of vaccination - we notice from the start that this cluster is much more nuanced and less firm in its positions than that of antivaccinists. The subjects in cluster 2 are very convinced that vaccination is safe and believe (not as strongly) that vaccination is the only measure that will stop the pandemic. Obviously, they're not conspirativists (they don’t think the pandemic is a lie) and they don't think covid vaccination isn't effective. On the other hand, they do not have a firm opinion on compulsory vaccination and they do not agree or disagree with the idea that vaccination is a measure abusively promoted by the authorities, although they would rather have been expected to disagree with this idea, being vaccinists. Cluster 3 has almost all the coefficients at the middle of the range (value: 3), which indicates to us not so much a cluster of hesitants, but rather of people who have not formed an opinion – the opinionless (they are rather undecided or, perhaps, uninterested). The only item at which the coefficient 3 is not obtained is the first (the idea that the vaccine is safe) and there the coefficient 2 indicates a slight disagreement, so a slight fear regarding the safety of the vaccine. In this cluster there are significantly fewer cases than in the other two. In general, when opinion polls measure vaccination attitudes or vaccination intent, they operate with categories such as: acceptancy/hesitancy/refusal or strong acceptancy/mild acceptancy/mild refusal/strong refusal. Below we show how our clusters may change the optics on this typology. Firstly we focus the discussion not on a simple linear scale attitude towards vaccination against COVID-19, but our subjects attitude towards the COVID-19 Pandemic, on the way the government managed this crisis and on the way the subject viewed the pandemic. Our findings show that young people from Romania tend to form the ‘antivaccination subjects’ (cluster 1), adults being the ones that tended to group in the ‘followers of vaccination’ (cluster 2) and older subjects tended to group in ‘the opinionless’ cluster (cluster 3). Contrary to what it may be expected, the members of cluster 1 had a tendency to be more educated than the members of cluster 3, they tended to reside in urban areas, be male and have higher income than members of the 3rd cluster. The ‘Followers of vaccination’ cluster tends to be composed more by older women with higher education living in urban areas. This may reveal that education, medium of residency and income are not predictors of vaccine acceptance or vaccine hesitancy in Romania. As it can be seen from Table 2 , from the structure of the clusters and as we argued above, sociodemographic traits are not a relevant predictor of vaccination attitude in Romania, this matter tends to be centered more on the subjective view of the subject. We believe that the failure of the Romanian vaccination campaign is based on the rather big size of clusters 1 and 3 and their high incidence in the Romanian population, a little more than 63% of our sample could be classified as being part of one of these two clusters, which is interesting given the fact that Romania has an official vaccination rate a little over 40%. In order to create efficient public health policies for the future pandemics, governments need to understand that cluster 1 and 3 form around mistrust in the state, possibly misinformation and the population’s lack of understanding of a health crisis in general. The proper way in which we can prevent future deaths due to pathogens, future closings of our medical systems, is to find ways to reduce the sizes of clusters 1 and 3 and increase cluster 2. In order to observe the differences between the clusters regarding attitude toward we decided to compare the cluster membership with the responses from the following question from our survey “What do you think about the vaccine against covid-19?”. As we will show below, there are some significative differences between the three clusters when it comes to what members think about vaccinating against COVID-19. Table 3 Cluster cases responses to the question measuring attitude towards vaccination What do you think about the vaccine against covid-19? Cluster Number of Case 1. antivaccination subjects 2. followers of vaccination 3. opinionless Row N % Column N % Row N % Column N % Row N % Column N % I generally don't believe in vaccination 63,0% 21,2% 3,9% 1,4% 33,1% 16,2% I don't have a problem with vaccination in general, but I don't trust vaccination against covid-19 61,1% 34,9% 5,6% 3,4% 33,3% 27,7% It didn't convince me, nor am I against it yet 49,1% 30,2% 9,9% 6,5% 40,9% 36,5% I think it's good to get vaccinated against covid-19 14,6% 12,4% 76,0% 68,5% 9,3% 11,5% I believe that vaccination against covid 19 should be mandatory for adults who do not have medical contraindications 5,3% 1,3% 73,7% 19,7% 21,1% 7,7% N/A 0,0% 0,0% 66,7% 0,6% 33,3% 0,4% In Table 3 we created a matrix that shows the affirmative answer to a set of affirmations regarding vaccination in general (other than the anti-COVID shot), vaccination against COVID-19 inside our 3 clusters. As it can be observed from Table 3 antivaccination subjects tend not to have a problem with vaccination in general, but with the anti COVID-19 one. Members of this cluster tend to think it’s not good to get the anti COVID-19 vaccine, therefore oppose a mandatory vaccine mandate from the government. The problem seems to be not with vaccination in general, but with vaccinating against COVID-19. Another interesting fact is that 49,1% of the respondents that stated they weren’t convinced about vaccinating against COVID-19 were in this cluster. This shows that the cluster is composed from a mix of respondents that are either anti vaccination against COVID-19 or are unconvinced about getting vaccinated. The second cluster, followers of vaccination , has a very positive attitude towards getting vaccinated against COVID-19. Members in this cluster seem to believe that vaccinating is good and all people should get vaccinated. The third cluster, the opinionless , as per their name, don’t tend to have a strong opinion regarding this subject, they only score high on the ‘I was not convinced’ question, as we could expect. Table 4 Results of the second z-test for the differences between the clusters on the “What do you think about the vaccine against covid-19?” question Comparisons of Column Proportions b What do you think about the vaccine against covid-19? Cluster Number of Case 1. antivaccination subjects 2. followers of vaccination 3. opinionless (A) (B) (C) I generally don't believe in vaccination B B I don't have a problem with vaccination in general, but I don't trust vaccination against covid-19 B B It didn't convince me, nor am I against it yet B B I think it's good to get vaccinated against covid-19 A C I believe that vaccination against covid 19 should be mandatory for adults who do not have medical contraindications A C A Results are based on two-sided tests with significance level ,05. For each significant pair, the key of the category with the smaller column proportion appears under the category with the larger column proportion. As it can be observed from Table 4 , there are significant differences between the attitudes of the members that comprise the three clusters. Cluster 1 and cluster 3 tend to differ from cluster 2 in the matters of believing in vaccination, in not being convinced to get vaccinated against COVID-19. Cluster 2 differs from the other two in thinking that the vaccine is good for people and that it should be mandatory to get vaccinated against COVID-19. However, even in cluster 2, only 1/5 of the subjects agree with compulsory vaccination. Conclusions In order to understand the attitude towards vaccination in times of crisis such as the COVID-19 pandemic (and not only), it is necessary to go beyond the simple categories: agreement, refusal, hesitation. They play a crucial role in the statistical description and even in the prediction of the public's attitudes towards vaccination or towards a particular vaccine. But a cluster analysis on relevant indicators can break down this typology into its essential attributes. Of course, depending on the questions available and included in the analysis, clusters may be more or less relevant or may acquire new valences and may highlight specific dimensions. We believe that the indicators on which we built the clusters, combined with the time of the survey (autumn 2021) and the fact that we had data from a nationally representative sociological survey (for Romania) allowed us to achieve a concise typology, but which highlighted the dominants behind the acceptance / rejection / hesitation scheme. In short, studies prior to the pandemic showed that, in general, Romanians were not a significantly opposite population to vaccination . To the extent that they did not agree with the vaccination, they were rather hesitant. This hesitation has many sources: postponement, disinterest in the problem, waiting for what others are doing, waiting for clarifications or impositions from the authorities, etc. Managing hesitation and indecision is actually the secret to success and failure of vaccination campaigns, perhaps more than managing rejection. At the end of 2021, strictly on vaccination against Romania, Romania was one of the most unvaccinated countries in the EU. The studies cited in the introductory section of the article show us that, from country to country, it is more behind the segmentation of the public according to the attitude towards vaccination against vaccines than just acceptance, hesitation, refusal (notes 20–22). Some of these attitudes may be dominated by information, others by disinterest, some by fear, others by (dis) confidence in the authorities, or by the belief that the problem will be resolved until it is your turn to vaccinate. Agreement is not simply agreement, just as refusal is not simply refusal. And the hesitant segment is a world in itself. As a result, the main conclusions of our study are: · If we refer to the indicators taken into analysis, we simply do not have a typology agreement / hesitation / refusal on covid vaccination in Romania. We have a favorable cluster, one unfavorable to vaccination and one more disinterested in the problem than hesitant. · The vaccine cluster is the most coherent: they do not believe in vaccination or pandemic, they are also bothered by the promotion of vaccination by the authorities. · The provaccination cluster is not a fanatical one: it believes that vaccination is safe and will solve the problem, but it does not necessarily trust the way the authorities promote it. · The provaccination cluster accepts mandatory vaccination compared to the other two, but is not very happy with the idea, if we look at the score in the analysis (Table 1 ). In short, our photo looks like this: we have a cluster of anti-vaccines (over a third of the sample) that brings together traditional anti-vaccine (those who oppose any vaccination - quite a few in Romania so far) and covid-era anti-vaccine (those who have manifested this attitude in the context of the current pandemic). This cluster is strongly oriented against the vaccine, against the public policy to promote vaccination and to a certain extent even denies the real size of the pandemic. The pro-vaccination cluster (about a third of the sample) believe that the vaccine is both effective and safe but do not consider that vaccination should be imposed and are reluctant about how the state manages / communicates the pandemic. They are rather reasonable in relation to the sensibilities of others and are not fanatical or intrusive, which largely explains the apparent minority of pro-vaccination discourse in the Romanian public space during 2021. The third cluster, just under a third of the public, they are rather disinterested or without a formed opinion on vaccination and the pandemic. Most likely, the hesitant ones, depending on the motivation or the type of hesitation, fall into all these three categories. Declarations Ethics Approval and Consent to Participate : Our methodology was reviewed and approved by the Ethics Committee of the Faculty of Sociology and Social Work (University of Bucharest, Romania). Informed consent was obtained from all subjects involved in the study. The study was conducted in accordance with the guidelines of Helsinki Declaration. Consent for Publication Not applicable. Availability of data and materials : The results of the survey can be publicly viewed at this link: https://larics.ro/wp-content/uploads/2021/10/Barometru-Securitate_octombrie-2021-complet.pdf (accessed on 20 May 2022). The datasets or analysed during the current study are not publicly available due to contractual restrictions not to make the entire database public, imposed by LARICS, but are available from the corresponding author on reasonable request. Competing interests The authors declare no conflict of interest. Funding This research received no external funding. The data from the survey we analyzed were provided to us by LARICS, the entity that conducted it, due to the fact that two of the co-authors of this article (D.C. and D.-G.I.) contributed decisively to the survey. Acknowledgment We would like to thank Dan Dungaciu, the founder of LARICS, for our collaboration with the LARICS Center for Sociological Research in the field of public opinion polling. Author's Contribution Conceptualization: D.C. and D.-G.I.; methodology: D.C. and D.-G.I.; formal analysis: D.C., D.-G.I., C.C. and V.F.; investigation: D.C., D.-G.I., C.C. and V.F.; resources: D.C., D.-G.I., C.C. and V.F.; data curation: D.C., D.-G.I., C.C. and V.F.; writing—original draft preparation: D.C., D.-G.I., C.C. and V.F.; writing—review and editing: D.C., D.-G.I., C.C. and V.F.; supervision: D.C. and D.-G.I. All authors have read and agreed to the published version of the manuscript. Author's information (optional) Not applicable. References Machingaidze S, Wiysonge CS. 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Vaccine Refusal in the Czech Republic Is Associated with Being Spiritual but Not Religiously Affiliated. Vaccines 2021 , 9 , 1157. https://doi.org/10.3390/vaccines9101157 Sallam M, Al-Sanafi M, Sallam M. A Global Map of COVID-19 Vaccine Acceptance Rates per Country: An Updated Concise Narrative Review. J Multidiscip Healthc . 2022;15:21-45 https://doi.org/10.2147/JMDH.S347669 Garett, Renee, and Sean D Young. “Online misinformation and vaccine hesitancy.” Translational behavioral medicine Vol. 11,12 (2021): 2194-2199. doi:10.1093/tbm/ibab128 Baraybar-Fernández, A.; Arrufat-Martín, S.; Rubira-García, R. Public Information, Traditional Media and Social Networks during the COVID-19 Crisis in Spain. Sustainability 2021 , 13 , 6534. https://doi.org/10.3390/su13126534 Cristea, D.; Ilie, D.-G.; Constantinescu, C.; Fîrțală, V. Vaccinating against COVID-19: The Correlation between Pro-Vaccination Attitudes and the Belief That Our Peers Want to Get Vaccinated. Vaccines 2021 , 9 , 1366. https://doi.org/10.3390/vaccines9111366 Dubov, A.; Distelberg, B.J.; Abdul-Mutakabbir, J.C.; Beeson, W.L.; Loo, L.K.; Montgomery, S.B.; Oyoyo, U.E.; Patel, P.; Peteet, B.; Shoptaw, S.; Tavakoli, S.; Chrissian, A.A. Predictors of COVID-19 Vaccine Acceptance and Hesitancy among Healthcare Workers in Southern California: Not Just “Anti” vs. “Pro” Vaccine. Vaccines 2021, 9, 1428. https://doi.org/10.3390/vaccines9121428 Vulpe, Simona & Rughinis, Cosima. (2021). Social amplification of risk and ‘‘probable vaccine damage”: A typology of vaccination beliefs in 28 European countries. Vaccine. 39. 1508-1515. 10.1016/j.vaccine.2021.01.063. Sandu, D. (2021). Lumi sociale ale atitudinii față de vaccinarea anti-COVID-19: românii în context european (Social worlds of attitude towards anti-COVID-19 vaccination: Romanians in the European context), 2021. 10.13140/RG.2.2.14389.19687. Dungaciu, D. (coordinator), Cristea, D. (coordinator), Ilie, D.-G., Petrescu, D.A., Barometrul de securitate a României , LARICS: Bucharest, Romania, October 2021 - National representative sociological survey ; available at Barometru Securitate_octombrie 2021 (larics.ro) (accessed on 19 May 2022) Cristea, D.; Jderu, G. (coordinators), Atitudinea Populației Față de Vaccinuri și Vaccinare—Sondaj Național ; INSCOP Research: Bucharest, Romania, 2019; Your Presentation Name (inscop.ro); Available online: https://www.inscop.ro/wp-content/uploads/2019/03/Sondaj-INSCOP-selectie.pdf (accessed on 29 July 2021) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1702368","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":111958723,"identity":"44147930-b275-481a-8527-801ad15cafef","order_by":0,"name":"Darie Cristea","email":"","orcid":"","institution":"University of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Darie","middleName":"","lastName":"Cristea","suffix":""},{"id":111958725,"identity":"6eaf7777-6ffc-43ec-801f-472427c4a3ad","order_by":1,"name":"Dragoș-Georgian Ilie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDCCAxCKh4+ZgfEBA4NEAvFa2JgZmA1I0sLABkQSQJqwFr7bhx+/+PCHQYaNnflYNe8OizyD4wfYPnzAo0XyXJqZ5cw2kMPY0m7znpEoNjiTwDxzBh4tBmcYzIx5G0BaeMxu87ZJJG64wcDMzINXC/s3Y54/IC3834qJ1MJj/JiHDWwLGzNRWiTP8JQxzmyTAPnFWHJum0Sx5JnEZkZ8fuE7w775w4c/Nvb8/IcffnjbVpfHd/zwYQZ8IcYAiQ4JZAHGBvwaGBiYCRg5CkbBKBgFIx4AANE+QTZ3ge3cAAAAAElFTkSuQmCC","orcid":"","institution":"University of Bucharest","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Dragoș-Georgian","middleName":"","lastName":"Ilie","suffix":""},{"id":111958726,"identity":"984683d3-573b-4d8d-a0b0-fd79185ff4ec","order_by":2,"name":"Claudia Constantinescu","email":"","orcid":"","institution":"University of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Constantinescu","suffix":""},{"id":111958729,"identity":"a5952bd7-e040-4140-87f2-fed7899c81ef","order_by":3,"name":"Valeriu Fîrțală","email":"","orcid":"","institution":"University of Bucharest","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Valeriu","middleName":"","lastName":"Fîrțală","suffix":""}],"badges":[],"createdAt":"2022-05-28 09:59:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1702368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1702368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22872212,"identity":"f0a11f9b-a3b1-4994-be8c-e3f77b0f5da7","added_by":"auto","created_at":"2022-06-21 07:59:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":551217,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1702368/v1/596c6af7-8017-40d9-a484-b2610afa3eff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Acceptance, Hesitancy, and Refusal in anti-COVID-19 vaccination. A cluster analysis aiming at the typology behind these three concepts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVaccination was a major topic at the intersection of medical and social sciences even before 2020, but after the advent of the COVID 19 pandemic, it became clear that the issue was much more complicated than we imagined.\u003c/p\u003e\n\u003cp\u003eIn the past two years, countless articles have been published on the acceptance and refusal of vaccination, especially in the context of the pandemic. One thing is clear \u0026ndash; so much has been published that we are far from benefiting from all the information contained in these studies. Because it\u0026apos;s so much so that it is virtually impossible to assimilate. A clarification and systematization of the typologies and causalities identified would be necessary.\u003c/p\u003e\n\u003cp\u003eWe see that all this, however, moves in a space bounded by several concepts, well-founded in specialized studies, measurable and empirically measured over and over again: vaccine acceptance, vaccine confidence, vaccine hesitancy, vaccine refusal[i][ii][iii][iv][v].\u003c/p\u003e\n\u003cp\u003eOne problem is whether we are dealing with a continuum or with separate phenomena. It\u0026apos;s a scaling problem, common in the social sciences. In general, the attitude or intention of vaccination is measured in opinion polls with an ordinal question or with a nominal one of acceptance / indecision / refusal type, in which sometimes the indecision is detailed in 2-3 other variants of answer regarding motivations. But what if the acceptance and refusal of vaccination are not steps on a scale, but qualitatively different phenomena, with motivations from different spectra and attributable to clearly differentiated human typologies?\u003c/p\u003e\n\u003cp\u003eAlso, the pandemic has shown us that the triangle of acceptance / hesitation / refusal also has a special dynamic, depending on the availability of the vaccine, what rumors / fake-news appear about covid and vaccine, trust in the authorities, fear, perception of the social norm, imposition of restrictions for the unvaccinated, etc. In rural areas, the dynamics of this triangle can also be influenced by the population\u0026apos;s access to health services, and the development of Multifunctional Integrated Community Centers could be useful in this regard\u003ca href=\"#_edn6\" name=\"_ednref6\" title=\"\"\u003e[vi]\u003c/a\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe pandemic context and the imperative of regulating social relations through special norms specific to the state of emergency or the state of alert have determined in many situations the association of vaccination with the need to avoid the restrictions imposed by the authorities on the unvaccinated. Thus, appeared a non-medical motivation for vaccine intake: to avoid restrictions). Or, on the contrary, vaccine refusal appeared as a form of social resistance to the high normative pressure too high of the authorities. Such a paradoxical social polarization of this type raises serious questions about the most effective regulatory method implemented by the authorities in any future crisis situation that they would try to manage through increased pressure (increasing the motivation of compliance with the regulation by those who want to avoid restrictions being in counterweight to the accentuation of non-compliance with the regulation by those who want to emphasize the resistance to restrictions aimed at aspects sensitive on human rights). This paradox of social conduct is all the more relevant because in crisis situations social discipline and compliance with norms are much more important than in other social and historical situations.\u003c/p\u003e\n\u003cp\u003eStudies show that there are multiple explanations regarding the acceptance/refusal of vaccination. In short, the acceptance, hesitation or refusal of vaccination (in the case of covid 19 and beyond) have been explained by the following types of causal models:\u003c/p\u003e\n\u003cp\u003e1. Models that refer to distrust in the vaccine (in the content of the serum, or the production method), to distrust in medicine, and to scientific illiteracy (although, the relationship between scientific literacy and the intention to vaccinate is much more complicated than it seems);\u003ca href=\"#_edn7\" name=\"_ednref7\" title=\"\"\u003e[vii]\u003c/a\u003e\u003ca href=\"#_edn8\" name=\"_ednref8\" title=\"\"\u003e[viii]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e2. Models that refer to the distrust of the authorities, conspiracy theories, the assumption of hidden intentions of the organization of the vaccination process by the authorities;\u003ca href=\"#_edn9\" name=\"_ednref9\" title=\"\"\u003e[ix]\u003c/a\u003e\u003ca href=\"#_edn10\" name=\"_ednref10\" title=\"\"\u003e[x]\u003c/a\u003e\u003ca href=\"#_edn11\" name=\"_ednref11\" title=\"\"\u003e[xi]\u003c/a\u003e\u003ca href=\"#_edn12\" name=\"_ednref12\" title=\"\"\u003e[xii]\u003c/a\u003e\u003ca href=\"#_edn13\" name=\"_ednref13\" title=\"\"\u003e[xiii]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e3. Models that refer to differences in perception of vaccine and vaccination between different socio-demographic categories (age, education, gender, national membership, religion, etc.);\u003ca href=\"#_edn14\" name=\"_ednref14\" title=\"\"\u003e[xiv]\u003c/a\u003e\u003ca href=\"#_edn15\" name=\"_ednref15\" title=\"\"\u003e[xv]\u003c/a\u003e\u003ca href=\"#_edn16\" name=\"_ednref16\" title=\"\"\u003e[xvi]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e4. Models that relate primarily to the channel, the source of information on the virus, on the pandemic, on the vaccine and the vaccination process.\u003ca href=\"#_edn17\" name=\"_ednref17\" title=\"\"\u003e[xvii]\u003c/a\u003e\u003ca href=\"#_edn18\" name=\"_ednref18\" title=\"\"\u003e[xviii]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e4. Psychosociological models, which take into account purely psychosociological variables that are associated with acceptance or hesitation in the face of vaccination - for example, in a previous article we argued that there is a very strong correlation between positive attitudes to vaccination and the belief that most others want to get vaccinated.\u003ca href=\"#_edn19\" name=\"_ednref19\" title=\"\"\u003e[xix]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eThe question is whether this typology based on the triangle acceptance/hesitation/refusal is too manifest and too simplistic. If we describe these three attitudes through more pragmatic indicators we could obtain a much more operational typology, which would also show us what are the resorts of this division of the public according to the attitude towards vaccination, a division that in the last year has already acquired sociopolitical valences.\u003c/p\u003e\n\u003cp\u003eSo our research question is the following: we could get a more explicit typology of the attitude towards vaccination in Romania if instead of the direct question on the intention to vaccinate we use a set of questions that would break down on several dimensions the triplet acceptance / hesitation / refusal in the case of vaccination for covid 19. Such a typology could show us which are the resorts that underlie this public opinion/attitude vis-\u0026agrave;-vis the covid vaccination.\u003c/p\u003e\n\u003cp\u003eWe intend to build this typology through cluster analysis (k-means clustering). Details about the data used we have in the Materials and Methods section. But first, a brief review of three studies of this type published lately on the anti-covid vaccination. One is based on data collected in the US, two other refer to the case of Europe and Romania in particular, our study being based on data from this last country.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA survey conducted in 2021 on vaccine acceptance and hesitancy among 2491 Healthcare Workers in Southern California aims to prove there is a heterogeneous group (and sub-groups) with varying attitude toward vaccination, not only \u0026bdquo;anti\u0026rdquo; and \u0026bdquo;pro\u0026rdquo; vaccine. The clustering analysis conducted by the authors of this study starts from the idea of a continuum between total acceptance and complete refusal and describe groups and sub-groups of Healthcare Workers holding varying degrees of indecision about vaccination.\u003c/p\u003e\n\u003cp\u003eThe respondents to the study were grouped in four clusters: (1) misinformed, (2) uninformed, (3) undecided and (4) unconcerned. Thus, there is a diversity in vaccine hesitancy and their conclusions is that \u0026bdquo;messaging should be tailored to specific sub-groups to increase the understanding of the science behind vaccine\u0026rdquo;[xx].\u003c/p\u003e\n\u003cp\u003eVulpe and Rughinis, using the Eurobarometer 91.2 survey conducted between 15th-29th March 2019, identified three belief configurations as regards vaccine effectiveness, safety and usefulness: hesitant, confident and trade-off.\u003c/p\u003e\n\u003cp\u003eThe authors\u0026rsquo; conclusion upon conducting a cluster analysis includes the reference to the substantial variation at the country level, but they cannot find \u0026bdquo;strong sociodemographic differences among the three belief clusters\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eOne of the findings of this study is the \u0026bdquo;needed to address the socially amplified risk of probable vaccine damage and to consider the trade-off patterns of concomitant trust and mistrust in assessing vaccines.\u0026rdquo;[xxi]\u003c/p\u003e\n\u003cp\u003eThe study \u0026bdquo;Social worlds of attitude towards anti-Covid-19 vaccination: Romania in the European context\u0026rdquo; published in 2021 is based on the Flash Eurobarometer State of the European Union. The further clustering analysis starts from the idea that the attitudes towards vaccine are not only quantitative between the two polls: pro and anti-vaccine. The cluster analysis combines three indicators and generates six types of vaccine attitudes. Romania is positioned in the European context from the point of view of specific profile of attitude along with Poland, Cech Republic and Lithuania.\u003c/p\u003e\n\u003cp\u003eAn important finding of the author is that there are not only pro-vaccine, anti-vaccine or hesitants, but interposing categories qualitativelely structured. It appears to the researcher that both the attitudes tawords vaccination and the intentions to get vaccinated are depend to the socio-demographic elements and the previous life experiences. The conclusion of this study is that we deal with \u0026ldquo;social worlds of the Covid -19 vaccine attitude in the senes of language communities on the topic\u0026rdquo; and these social worlds are structured differently in groups of countries. A more accurate understanding can be achieved by further studies including variables like stage of infection with SarsCoV2, migration experiences, the sense of belonging to national / regional spaces with different cultural models.[xxii]\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eThe data on the basis of which this analysis was carried out come from a larger public opinion survey, conducted on a nationally representative sample for Romania. Two of the co-authors of this article were decisively involved in conducting the survey (see note\u003ca class=\"FNLink\" href=\"#Fn23\" id=\"#FNLinkFn23\"\u003e\u003c/a\u003e, explaining the full context of the survey and giving all the data necessary for its identification, including the research report). The data was collected between October 1\u0026ndash;10, 2021 by LARICS, a public opinion analysis laboratory known in Romania, under the auspices of the Institute of Political Science and International Relations within the Romanian Academy.\u003c/p\u003e \u003cp\u003eThe analysis was therefore carried out on a database of 1002 respondents on the territory of Romania; the sample was statistically representative at the level of the entire non-institutionalized population in Romania, aged between 18 and +\u0026thinsp;65 years, it was a multilayer probabilistic sample, and the margin of error was one of 3.1%. The questionnaire was applied by phone using CATI as the method of coordinating the interviews and the sample was validated based on the data of the National Institute of Statistics in Romania.\u003c/p\u003e \u003cp\u003eIn order to identify how the studied population structures itself when it comes to attitudes towards vaccination, we decided to use an algorithm for classifying cases. The cluster is a grouping of subjects or objects that have common traits and are grouped together based on this similarity (Jain, 1988). The components of the cluster are similar to each other, but are different from the elements that make up another cluster (Everitt, 1974). To study the distribution of cases in clusters we decided to use the \"k-means\" clustering algorithm method. This procedure consists of measuring the proximity of cases to the average of a cluster. \"K\" is the number of clusters in an analysis that is chosen by the researcher. The \"k-means\" algorithm proposed by J. MacQueen assigns a random average for each cluster, for example for 3 clusters we will have 3 averages, then measures the closeness of the observed cases to this initial average, after which it calculates the final average of the cases in the cluster and groups them within these clusters (MacQueen, 1967). The belonging of an observed case to the cluster is determined by the proximity of the observed value to the mean value \"k\" of the cluster.\u003c/p\u003e \u003cp\u003eUsing the IBM SPSS 20 statistical analysis program, we conducted a series of tests on our database, including, most importantly, \"k-means\" cluster analysis. Our analysis resulted in a series of tables, for models with 2, 3, 4 and 5 clusters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eK-means cluster analysis; distribution of the sample by clusters (model with 3 clusters)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFinal Cluster Centers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA) Vaccination against covid 19 is safe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB) Vaccination against COVID 19 is a measure abusively promoted by the authorities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC) Vaccination against COVID 19 is the only measure that can stop the pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD) This pandemic is more of a lie or an exaggeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE) Vaccination against COVID 19 is not effective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF) Vaccination against COVID 19 should be mandatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTests in SPSS were done with distribution in 2, 3, 4 and 5 clusters. Analyzing the distribution of cases in these cases we found that a distribution of cases in 3 clusters (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) represents an unfragmented classification of the population, in other words, the population studied tends to group around these average values of the clusters and we can observe the significant differences between them. Clustering was made around 6 statements that measured scalarly, on a 5-step scale, the anchor to: confidence in the safety of vaccination against Covid-19, the confidence that vaccination against Covid-19 is abusively promoted by the state authorities, the belief that vaccination against Covid-19 is the only way the pandemic can be stopped, the fact that the pandemic does not exist or is an exaggeration, the fact that vaccination against Covid-19 is not effective, the fact that vaccination against Covid-19 should be mandatory. The lower the value in the table, the lower the confidence level.\u003c/p\u003e \u003cp\u003eClusters have the following composition in terms of number of subjects:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCluster 1: 378 subjects\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCluster 2: 356 subjects\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCluster 3: 260 subjects.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe result is a number of 994 subjects who answered this set of questions, out of a total of 1002 participants, 8 subjects refusing to answer these questions.\u003c/p\u003e \u003cp\u003eIn order to identify what are the significant differences that occur between the members of the 3 clusters we decided to apply Fischer's \"z-test\", the results can be seen in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the test z (Fischer)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eComparisons of Column Proportions\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCluster Number of Case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(C)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation level:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eI get the information about the pandemic and vaccination first from:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFriends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacebook \u0026amp; Internet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOfficial gov. websites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eYour family doctor:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvised you to get vaccinated against COVID 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvised you not to get vaccinated against COVID 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvised you to wait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYou have not discussed the vaccine with your family doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eResults are based on two-sided tests with significance level ,05. For each significant pair, the key of the category with the smaller column proportion appears under the category with the larger column proportion.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe subjects were comparatively analyzed according to the following variables: age, sex, residence environment, income, level of studies, from where they tend to get informed about the pandemic situation and how the family doctor advised them whether to get vaccinated or not.\u003c/p\u003e \u003cp\u003eIn the table above we analyze the statistically significant differences between the values on the columns, so we can say that the members of the first cluster are rather young, tend to reside in urban areas, have a medium or high level of income, have a medium or high level of income, have a medium or higher education, tend to be rather male, to inform themselves about vaccination and the pandemic from official sources, social media or the internet and tend either not to have talked to the family doctor about the vaccination, or to declare that the family doctor advised them not to get vaccinated against Covid-19.\u003c/p\u003e \u003cp\u003eThe subjects in cluster 2 tend to be rather old, as opposed to those in cluster 3 tend to be rather from urban areas, to have a medium or high income, to have either primary education or higher education, to be rather women, to inform themselves from TV, from the family or from official websites regarding the pandemic and it is the group that seems to have been advised to is vaccinated against Covid-19 by the family doctor.\u003c/p\u003e \u003cp\u003eThe subjects in cluster 3 tend to be rather adults or old people, tend to come from rural areas, tend to have low incomes, primary or secondary education, tend to get information from TV, and the family doctor has advised them either not to get vaccinated against Covid-19 or to wait.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe recall that in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e above the result of the cluster analysis is found. We also recall that the values describing the clusters move between 1 and 5, 1 indicating total disagreement with the respective statement, and 5 a total agreement. The middle value is 3. As we have already mentioned, we have chosen a typology with three categories.\u003c/p\u003e\n\u003cp\u003eCluster 1 is composed of \u003cstrong\u003eantivaccination subjects\u003c/strong\u003e: they don\u0026apos;t think covid vaccination is safe, nor is it the only method that can stop the pandemic; they think vaccination is being promoted abusively by the authorities and it\u0026apos;s not effective. They consider the pandemic a lie/exaggeration and strongly oppose a possible mandatory vaccination.\u003c/p\u003e\n\u003cp\u003eCluster 2 is that of \u003cstrong\u003ethe followers of vaccination\u003c/strong\u003e - we notice from the start that this cluster is much more nuanced and less firm in its positions than that of antivaccinists. The subjects in cluster 2 are very convinced that vaccination is safe and believe (not as strongly) that vaccination is the only measure that will stop the pandemic. Obviously, they\u0026apos;re not conspirativists (they don\u0026rsquo;t think the pandemic is a lie) and they don\u0026apos;t think covid vaccination isn\u0026apos;t effective. On the other hand, they do not have a firm opinion on compulsory vaccination and they do not agree or disagree with the idea that vaccination is a measure abusively promoted by the authorities, although they would rather have been expected to disagree with this idea, being vaccinists.\u003c/p\u003e\n\u003cp\u003eCluster 3 has almost all the coefficients at the middle of the range (value: 3), which indicates to us not so much a cluster of hesitants, but \u003cstrong\u003erather of people who have not formed an opinion \u0026ndash; the opinionless\u003c/strong\u003e (they are rather undecided or, perhaps, uninterested). The only item at which the coefficient 3 is not obtained is the first (the idea that the vaccine is safe) and there the coefficient 2 indicates a slight disagreement, so a slight fear regarding the safety of the vaccine. In this cluster there are significantly fewer cases than in the other two.\u003c/p\u003e\n\u003cp\u003eIn general, when opinion polls measure vaccination attitudes or vaccination intent, they operate with categories such as: acceptancy/hesitancy/refusal or strong acceptancy/mild acceptancy/mild refusal/strong refusal. Below we show how our clusters may change the optics on this typology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFirstly we focus the discussion not on a simple linear scale attitude towards vaccination against COVID-19, but our subjects attitude towards the COVID-19 Pandemic, on the way the government managed this crisis and on the way the subject viewed the pandemic. Our findings show that young people from Romania tend to form the \u0026lsquo;antivaccination subjects\u0026rsquo; (cluster 1), adults being the ones that tended to group in the \u0026lsquo;followers of vaccination\u0026rsquo; (cluster 2) and older subjects tended to group in \u0026lsquo;the opinionless\u0026rsquo; cluster (cluster 3).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContrary to what it may be expected, the members of cluster 1 had a tendency to be more educated than the members of cluster 3, they tended to reside in urban areas, be male and have higher income than members of the 3rd cluster. The \u0026lsquo;Followers of vaccination\u0026rsquo; cluster tends to be composed more by older women with higher education living in urban areas. This may reveal that education, medium of residency and income are not predictors of vaccine acceptance or vaccine hesitancy in Romania.\u003c/p\u003e\n\u003cp\u003eAs it can be seen from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, from the structure of the clusters and as we argued above, sociodemographic traits are not a relevant predictor of vaccination attitude in Romania, this matter tends to be centered more on the subjective view of the subject. \u003cstrong\u003eWe believe that the failure of the Romanian vaccination campaign is based on the rather big size of clusters 1 and 3 and their high incidence in the Romanian population, a little more than 63% of our sample could be classified as being part of one of these two clusters, which is interesting given the fact that Romania has an official vaccination rate a little over 40%.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to create efficient public health policies for the future pandemics, governments need to understand that cluster 1 and 3 form around mistrust in the state, possibly misinformation and the population\u0026rsquo;s lack of understanding of a health crisis in general. The proper way in which we can prevent future deaths due to pathogens, future closings of our medical systems, is to find ways to reduce the sizes of clusters 1 and 3 and increase cluster 2.\u003c/p\u003e\n\u003cp\u003eIn order to observe the differences between the clusters regarding attitude toward we decided to compare the cluster membership with the responses from the following question from our survey \u0026ldquo;What do you think about the vaccine against covid-19?\u0026rdquo;. As we will show below, there are some significative differences between the three clusters when it comes to what members think about vaccinating against COVID-19.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCluster cases responses to the question measuring attitude towards vaccination\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eWhat do you think about the vaccine against covid-19?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCluster Number of Case\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1. \u003cstrong\u003eantivaccination subjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2. \u003cstrong\u003efollowers of vaccination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3. \u003cstrong\u003eopinionless\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRow N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColumn N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRow N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColumn N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRow N %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eColumn N %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI generally don\u0026apos;t believe in vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI don\u0026apos;t have a problem with vaccination in general, but I don\u0026apos;t trust vaccination against covid-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27,7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt didn\u0026apos;t convince me, nor am I against it yet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30,2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36,5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI think it\u0026apos;s good to get vaccinated against covid-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68,5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11,5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI believe that vaccination against covid 19 should be mandatory for adults who do not have medical contraindications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73,7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19,7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66,7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33,3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0,4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e we created a matrix that shows the affirmative answer to a set of affirmations regarding vaccination in general (other than the anti-COVID shot), vaccination against COVID-19 inside our 3 clusters.\u003c/p\u003e\n\u003cp\u003eAs it can be observed from Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cstrong\u003eantivaccination subjects\u003c/strong\u003e tend not to have a problem with vaccination in general, but with the anti COVID-19 one. Members of this cluster tend to think it\u0026rsquo;s not good to get the anti COVID-19 vaccine, therefore oppose a mandatory vaccine mandate from the government. The problem seems to be not with vaccination in general, but with vaccinating against COVID-19. Another interesting fact is that 49,1% of the respondents that stated they weren\u0026rsquo;t convinced about vaccinating against COVID-19 were in this cluster. This shows that the cluster is composed from a mix of respondents that are either anti vaccination against COVID-19 or are unconvinced about getting vaccinated.\u003c/p\u003e\n\u003cp\u003eThe second cluster, \u003cstrong\u003efollowers of vaccination\u003c/strong\u003e, has a very positive attitude towards getting vaccinated against COVID-19. Members in this cluster seem to believe that vaccinating is good and all people should get vaccinated.\u003c/p\u003e\n\u003cp\u003eThe third cluster, the\u0026nbsp;\u003cstrong\u003eopinionless\u003c/strong\u003e, as per their name, don\u0026rsquo;t tend to have a strong opinion regarding this subject, they only score high on the \u0026lsquo;I was not convinced\u0026rsquo; question, as we could expect.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of the second z-test for the differences between the clusters on the \u0026ldquo;What do you think about the vaccine against covid-19?\u0026rdquo; question\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eComparisons of Column Proportions\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eWhat do you think about the vaccine against covid-19?\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCluster Number of Case\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. \u003cstrong\u003eantivaccination subjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. \u003cstrong\u003efollowers of vaccination\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. \u003cstrong\u003eopinionless\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(C)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI generally don\u0026apos;t believe in vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI don\u0026apos;t have a problem with vaccination in general, but I don\u0026apos;t trust vaccination against covid-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIt didn\u0026apos;t convince me, nor am I against it yet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI think it\u0026apos;s good to get vaccinated against covid-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI believe that vaccination against covid 19 should be mandatory for adults who do not have medical contraindications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eResults are based on two-sided tests with significance level ,05. For each significant pair, the key of the category with the smaller column proportion appears under the category with the larger column proportion.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAs it can be observed from Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, there are significant differences between the attitudes of the members that comprise the three clusters. Cluster 1 and cluster 3 tend to differ from cluster 2 in the matters of believing in vaccination, in not being convinced to get vaccinated against COVID-19. Cluster 2 differs from the other two in thinking that the vaccine is good for people and that it should be mandatory to get vaccinated against COVID-19. However, even in cluster 2, only 1/5 of the subjects agree with compulsory vaccination.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn order to understand the attitude towards vaccination in times of crisis such as the COVID-19 pandemic (and not only), it is necessary to go beyond the simple categories: agreement, refusal, hesitation. They play a crucial role in the statistical description and even in the prediction of the public's attitudes towards vaccination or towards a particular vaccine. But a cluster analysis on relevant indicators can break down this typology into its essential attributes. Of course, depending on the questions available and included in the analysis, clusters may be more or less relevant or may acquire new valences and may highlight specific dimensions.\u003c/p\u003e \u003cp\u003eWe believe that the indicators on which we built the clusters, combined with the time of the survey (autumn 2021) and the fact that we had data from a nationally representative sociological survey (for Romania) allowed us to achieve a concise typology, but which highlighted the dominants behind the acceptance / rejection / hesitation scheme.\u003c/p\u003e \u003cp\u003eIn short, studies prior to the pandemic showed that, in general, Romanians were not a significantly opposite population to vaccination\u003ca class=\"FNLink\" href=\"#Fn24\" id=\"#FNLinkFn24\"\u003e\u003c/a\u003e. To the extent that they did not agree with the vaccination, they were rather hesitant. This hesitation has many sources: postponement, disinterest in the problem, waiting for what others are doing, waiting for clarifications or impositions from the authorities, etc. Managing hesitation and indecision is actually the secret to success and failure of vaccination campaigns, perhaps more than managing rejection. At the end of 2021, strictly on vaccination against Romania, Romania was one of the most unvaccinated countries in the EU.\u003c/p\u003e \u003cp\u003eThe studies cited in the introductory section of the article show us that, from country to country, it is more behind the segmentation of the public according to the attitude towards vaccination against vaccines than just acceptance, hesitation, refusal (notes 20\u0026ndash;22). Some of these attitudes may be dominated by information, others by disinterest, some by fear, others by (dis) confidence in the authorities, or by the belief that the problem will be resolved until it is your turn to vaccinate. Agreement is not simply agreement, just as refusal is not simply refusal. And the hesitant segment is a world in itself.\u003c/p\u003e \u003cp\u003eAs a result, the main conclusions of our study are:\u003c/p\u003e \u003cp\u003e\u0026middot; If we refer to the indicators taken into analysis, we simply do not have a typology agreement / hesitation / refusal on covid vaccination in Romania. We have a favorable cluster, one unfavorable to vaccination and one more disinterested in the problem than hesitant.\u003c/p\u003e \u003cp\u003e\u0026middot; The vaccine cluster is the most coherent: they do not believe in vaccination or pandemic, they are also bothered by the promotion of vaccination by the authorities.\u003c/p\u003e \u003cp\u003e\u0026middot; The provaccination cluster is not a fanatical one: it believes that vaccination is safe and will solve the problem, but it does not necessarily trust the way the authorities promote it.\u003c/p\u003e \u003cp\u003e\u0026middot; The provaccination cluster accepts mandatory vaccination compared to the other two, but is not very happy with the idea, if we look at the score in the analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn short, our photo looks like this: we have a cluster of anti-vaccines (over a third of the sample) that brings together traditional anti-vaccine (those who oppose any vaccination - quite a few in Romania so far) and covid-era anti-vaccine (those who have manifested this attitude in the context of the current pandemic). This cluster is strongly oriented against the vaccine, against the public policy to promote vaccination and to a certain extent even denies the real size of the pandemic. The pro-vaccination cluster (about a third of the sample) believe that the vaccine is both effective and safe but do not consider that vaccination should be imposed and are reluctant about how the state manages / communicates the pandemic. They are rather reasonable in relation to the sensibilities of others and are not fanatical or intrusive, which largely explains the apparent minority of pro-vaccination discourse in the Romanian public space during 2021. The third cluster, just under a third of the public, they are rather disinterested or without a formed opinion on vaccination and the pandemic. Most likely, the hesitant ones, depending on the motivation or the type of hesitation, fall into all these three categories.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate :\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur methodology was reviewed and approved by the Ethics Committee of the Faculty of Sociology and Social Work (University of Bucharest, Romania).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the guidelines of Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials :\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the survey can be publicly viewed at this link: https://larics.ro/wp-content/uploads/2021/10/Barometru-Securitate_octombrie-2021-complet.pdf (accessed on 20 May 2022).\u003c/p\u003e\n\u003cp\u003eThe datasets or analysed during the current study are not publicly available due \u0026nbsp;to contractual restrictions not to make the entire database public, \u0026nbsp;imposed by LARICS, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding. The data from the survey we analyzed were provided to us by LARICS, the entity that conducted it, due to the fact that two of the co-authors of this article (D.C. and D.-G.I.) contributed decisively to the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Dan Dungaciu, the founder of LARICS, for our collaboration with the LARICS Center for Sociological Research in the field of public opinion polling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor's Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: D.C. and D.-G.I.; methodology: D.C. and D.-G.I.; formal analysis: D.C., D.-G.I., C.C. and V.F.; investigation: D.C., D.-G.I., C.C. and V.F.; resources: D.C., D.-G.I., C.C. and V.F.; data curation: D.C., D.-G.I., C.C. and V.F.; writing—original draft preparation: D.C., D.-G.I., C.C. and V.F.; writing—review and editing: D.C., D.-G.I., C.C. and V.F.; supervision: D.C. and D.-G.I. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor's information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMachingaidze S, Wiysonge CS. Understanding COVID-19 vaccine hesitancy. Nat Med. 2021 Aug;27(8):1338-1339. doi: 10.1038/s41591-021-01459-7. PMID: 34272500.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization \u0026ndash; Regional Office for Europe, \u003cem\u003eOperational guidance: acceptance and uptake and covid-19 vaccine\u003c/em\u003e, January 2021, available at WHO-EURO-2021-1867-41618-56856-eng.pdf (Accessed: March 20th 2022)\u003c/li\u003e\n \u003cli\u003ePollard, A.J., Bijker, E.M. 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Public Information, Traditional Media and Social Networks during the COVID-19 Crisis in Spain. \u003cem\u003eSustainability\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e, 6534. https://doi.org/10.3390/su13126534\u003c/li\u003e\n \u003cli\u003eCristea, D.; Ilie, D.-G.; Constantinescu, C.; F\u0026icirc;rțală, V. Vaccinating against COVID-19: The Correlation between Pro-Vaccination Attitudes and the Belief That Our Peers Want to Get Vaccinated. \u003cem\u003eVaccines\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e, 1366. https://doi.org/10.3390/vaccines9111366\u003c/li\u003e\n \u003cli\u003e\u003cu\u003eDubov, A.; Distelberg, B.J.; Abdul-Mutakabbir, J.C.; Beeson, W.L.; Loo, L.K.; Montgomery, S.B.; Oyoyo, U.E.; Patel, P.; Peteet, B.; Shoptaw, S.; Tavakoli, S.; Chrissian, A.A. 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(coordinator), Ilie, D.-G., Petrescu, D.A., \u003cem\u003eBarometrul de securitate a Rom\u0026acirc;niei\u003c/em\u003e, LARICS: Bucharest, Romania, October 2021 - \u003cem\u003eNational representative sociological survey\u003c/em\u003e; available at Barometru Securitate_octombrie 2021 (larics.ro) (accessed on 19 May 2022)\u003c/li\u003e\n \u003cli\u003eCristea, D.; Jderu, G. (coordinators), \u003cem\u003eAtitudinea Populației Față de Vaccinuri și Vaccinare\u0026mdash;Sondaj Național\u003c/em\u003e; INSCOP Research: Bucharest, Romania, 2019; Your Presentation Name (inscop.ro); Available online: \u003cstrong\u003ehttps://www.inscop.ro/wp-content/uploads/2019/03/Sondaj-INSCOP-selectie.pdf\u003c/strong\u003e (accessed on 29 July 2021)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1702368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1702368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMost sociological studies on the intention to vaccinate against COVID-19 in the last year have divided the public into people who want to be vaccinated, people who are against vaccination and hesitant. Opinion polls also described how the public relates to specific motivations for agreement, refusal or hesitation about vaccination.\u003c/p\u003e\u003cp\u003eOur study proposes a typology of attitude towards vaccination against covid constructed by cluster analysis, based on a battery of questions that indicate the attitude of the subjects towards this vaccination on several dimensions.\u003c/p\u003e\u003cp\u003eThe questions were deliberately placed by the authors of the study in a nationally representative opinion poll for Romania, in order to collect these data and to study the resulting typology.\u003c/p\u003e","manuscriptTitle":"Acceptance, Hesitancy, and Refusal in anti-COVID-19 vaccination. A cluster analysis aiming at the typology behind these three concepts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-10 16:12:38","doi":"10.21203/rs.3.rs-1702368/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bfad31d-d268-4737-a50c-1a242b73cdf0","owner":[],"postedDate":"June 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-21T07:59:28+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-10 16:12:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1702368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1702368","identity":"rs-1702368","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}