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Vaccine hesitancy is one of the major roadblocks to ending the COVID-19 pandemic. However, little is known about individuals’ motivators and barriers to vaccination in Russia. We aimed to determine which factors were associated with COVID-19 vaccine acceptance in Russia. We conducted a cross-sectional, online survey across Russia at the end of 2021, during a time with increasing new cases of COVID-19. We used the Health Belief Model to test which of the constructs were associated with vaccine acceptance and controlled for demographic variables in the multivariate logistic regression analysis among our analytic sample of 550 respondents. About one-fifth (18.5%) of our study respondents reported vaccine hesitancy. Our multivariate analysis showed that perceived susceptibility, perceived benefits, perceived barriers, self-efficacy, and friends and family supporting vaccination (cue to action) all contributed to the likelihood of whether or not an individual was accepting of the COVID-19 vaccine. Analysis of our open-ended questions showed that individuals also considered the following factors in making decisions to vaccinate for COVID-19: policy restrictions, less worry, social responsibility, lack of trust, conspiracy theories, concerns about side effects and contraindications. Our results demonstrated the Health Belief Model to be a useful framework for understanding COVID-19 vaccine acceptance in Russia. Our study results highlight the need to focus on health beliefs in order to develop interventions to improve vaccine acceptance.
Introduction
Vaccine hesitancy is a major roadblock to ending the COVID-19 pandemic. A systematic review and meta-analysis of large nationally representative samples published in early 2021 showed that only 60% of people intended to vaccinate against COVID-19 (1). Research has shown that there is more vaccine acceptance in low- and middle-income countries compared with places such as the United States and Russia (2). A meta-analysis of data published in August 2022 concluded that the estimated global proportion of COVID-19 hesitancy was 25% (3).
The Russian Federation has one of the largest COVID-19 epidemics in the world. By the end of 2022 there were 21.49 million cumulative cases confirmed in the country (4). Officially, there were 385,789 deaths by the end of 2022 (4). However, by the end of the previous year (2021) excess deaths were already closer to one million in Russia (5). As anticipated there was a surge in early 2022 that peaked in mid-February, and the country did not report any such high subsequent peaks (4).
Russia was the first country to announce that it had developed a vaccine against COVID, in August 2020 (6). However, Russia has one of the lowest COVID-19 vaccination rates in the Eastern Europe/Central Asia region (7). Russia rolled out its public vaccination campaign in December 2020 with Sputnik V, an adenovirus vaccine, being offered free of charge to citizens. The vaccination program prioritized populations such as medical care workers, teachers, and those over 60 years of age. By January 2021, the general adult population was eligible for vaccination and soon after there were three domestic vaccines with emergency approval used in Russia, including Sputnik V, EpiVacCorona, and CoviVac. Only Russian-made vaccines received approval for use in Russia. By the end of 2021, 51% of the adult population had received at least one dose of a COVID-19 vaccine in Russia (4). More information is needed to understand what factors influenced vaccine acceptance in Russia.
The Health Belief Model (HBM) is a useful theoretical framework for analyzing individuals’ decisions about the uptake of preventative health services, such as vaccinations (8). The constructs of the HBM include perceived severity of the illness, perceived susceptibility to the illness, perceived benefits of vaccination, perceived barriers to vaccination, cues to action, and self-efficacy. The HBM has been used to determine what factors determine the likelihood of getting the COVID-19 vaccine in other settings such as Bangladesh (9), Hong Kong (10), Iraq (11), Israel (12), and the United States (13). However, to our knowledge, the HBM has not been applied to the study of COVID-19 vaccine acceptance in the Russian setting.
The end of 2021 was an ideal time to survey Russians’ motivators and barriers to COVID-19 vaccine acceptance. It was a time when Russia was experiencing another rapid increase in COVID-19 case counts, and an impending surge was likely, given Europe’s experience with the delta and omicron variants. By this point the government had rolled out large-scale vaccination campaigns, and Russian-made vaccines were offered free of charge to Russian citizens. While several cities announced forthcoming QR-code regulations that required proof of vaccination for some public activities, overall, decisions to vaccinate remained voluntary for the vast majority of the population. Our objective was to determine which health beliefs were associated with COVID-19 vaccination uptake in Russia.
Materials and methods
We conducted a cross-sectional, online survey across Russia. We used the Google Form platform to conduct the survey between 4 November and 17 December 2021. Our study design was exploratory and used a convenience sampling approach. For recruitment purposes, we utilized social and professional networks, social media and messenger services with targeted messaging beyond Moscow and St. Petersburg to help diversify the sample and reach of our survey to other regions across Russia. Inclusion criteria included: being 18 years or over, residing in Russia, ability to complete the survey in the Russian language, and ability to provide informed consent.
Vaccination status
We asked respondents whether they received the COVID-19 vaccine and provided three response options: ‘yes’, ‘no, but planning to’, ‘no, not planning to’. For our analysis, we created a dichotomous variable of ‘vaccine acceptance’ (those vaccinated or planning to get vaccinated) versus ‘vaccine hesitancy’ (those not vaccinated and not planning to).
HBM constructs
We included the HBM’s six constructs, using scales to measure three of the constructs and single items for the other three. Four-point Likert scales were used to measure the extent to which participants agreed with statements in each of the scales. Chronbach’s alpha was used to determine the scale reliability and Kaiser–Meyer–Olkin (KMO) was used to determine suitability of our data for factor analysis.
Perceived severity was measured using five items that asked about hypothetical COVID-19 infection resulting in serious health problems, long-lasting health problems, death, threat to family and friends, and threat to financial well-being. The scale showed both reliability (α = 0.8165) and appropriateness (KMO = 0.8128). Perceived barriers were assessed using six items related to beliefs that Russian vaccines had not been properly tested, that Russian vaccines are not effective, that vaccination is costly, that the vaccine will cause severe side effects, worry to receive a fake vaccine, and being generally against vaccinations. The scale showed strong reliability (α = 0.8437) and a KMO of 0.8439. Perceived benefits were assessed using six items related to beliefs about the vaccine offering protection from disease, vaccination mitigating fear of getting the disease, vaccination protecting family and close friends, vaccination protecting society from the spread of the virus, that potential side effects were less dangerous that the consequences of infection, and vaccination resulting in milder symptoms in the case of COVID-19 infection. The scale demonstrated a Chronbach’s alpha of 0.9311 and KMO of 0.8959.
Perceived susceptibility was measured using a single item to assess how respondents evaluate their risk of contracting COVID-19. We transformed the data into a dichotomous variable of ‘moderate or high risk’ versus ‘unlikely or low risk’. Self-efficacy was measured with a question about how easy it would be for the respondent to get vaccinated if they decide to do so. We created a dichotomous variable of ‘very easy or easy’ versus ‘difficult or very difficult’. We included four cues to action, for which respondents indicated yes or no to questions about healthcare providers talking to them about the need to vaccinate against COVID-19, personally knowing someone who was severely ill or died from COVID-19, personally knowing someone who was vaccinated against COVID-19, and that most family and friends support COVID-19 vaccination.
Control variables
We included the following control variables in our analyses: age, gender, marital status, living alone or with others, presence of chronic illness determined to be risk factors for severe illness due to COVID-19 infection, residence (living in Moscow or St. Petersburg versus elsewhere in Russia), monthly income, and education level.
Statistical data analysis
We included in our analysis only those respondents who completed all items on the survey of interest to our study. Of the 598 respondents who completed the survey, 48 respondents were excluded due to missing information on our variables of interest after testing to ensure that any missing values were random. Therefore, our analytic sample was 550 individuals. We first conducted a descriptive analysis for the entire analytic sample and by vaccine acceptance status. We used bivariate pairwise correlations to test and conclude that there was not any collinearity between the constructs. Then, we fitted logistic regression models to estimate the impact of the HBM constructs on the likelihood of COVID-19 vaccine acceptance. Given that we aimed to test the theoretical framework, we added all of the HBM constructs into the final model, regardless of their statistical significance in the bivariate analyses, with the set of control variables. We examined both the adjusted odds ratios (with 95% confidence intervals) and the marginal effect sizes in order to determine HBM constructs’ association with vaccine acceptance. All analysis was conducted using STATA.
Open-ended response questions
We asked participants three open-ended questions about their primary barriers and motivators to getting the COVID-19 vaccine with the goal to provide additional context and nuance to our statistical findings. We read through the answers and organized them into several categories that resulted in the themes around additional motivators and barriers to COVID-19 vaccination.
Results
The majority of our 550 analytical sample were female (73.8%), unmarried (58%), living in a household with others (79.4%), had no chronic diseases (82.2%) and had a university degree (73.5%). The average age was 35 years. Among our sample, 81.5% were accepting of the vaccine, while the remaining 18.5% of respondents expressed vaccine hesitancy. Table 1 shows the descriptive characteristics of our sample and the sociodemographic breakdown of our sample according to vaccine acceptance status. We did not find any statistically significant differences in sociodemographic characteristics by vaccination status, indicating that our sample was homogenous and allowed for precise observation of the association between HMB factors and vaccination status. Almost everyone (97.3%) reported that they knew someone who had been vaccinated. The vast majority (77.5%) responded that they knew someone who had either been very ill with or died from COVID-19. The majority also indicated that their family and friends support vaccination (63.6%). However, less than half (39.3%) said that a healthcare worker had recommended the COVID-19 vaccine.
Table 1.
Sociodemographic characteristics of the study participants.
| Total sample | Vaccine acceptance | Chi-square test and p-value | ||
|---|---|---|---|---|
| Yes | No | |||
| Gender | ||||
| Male | 144 (26.2%) | 115 (25.7%) | 29 (28.4%) | |
| Female | 406 (73.8%) | 333 (74.3%) | 73 (71.6%) | 2.78 (p = 0.25) |
| Marital status | ||||
| Married | 231 (42.0%) | 190 (42.4%) | 41 (40.2%) | |
| Unmarried | 319 (58.0%) | 258 (57.6%) | 61 (59.8%) | 0.17 (p = 0.68) |
| Household, lives alone | 113 (20.6%) | 89 (19.9%) | 24 (23.5%) | |
| Lives with others | 437 (79.4%) | 359 (80.1%) | 78 (76.5%) | 0.68 (p = 0.41) |
| Chronic diseases | ||||
| Yes, at least one | 98 (17.8%) | 80 (17.9%) | 18 (17.6%) | |
| No | 452 (82.2%) | 368 (82.1%) | 84 (82.4%) | 0.01 (p = 0.96) |
| Residence | ||||
| Moscow or St. Petersburg | 243 (44.2%) | 203 (45.3%) | 40 (39.2%) | |
| Elsewhere in Russia | 307 (55.8%) | 245 (54.7%) | 62 (60.8%) | 1.25 (p = 0.26) |
| Monthly income | ||||
| <20K roubles | 69 (12.5%) | 51 (11.4%) | 18 (17.7%) | |
| 20–40K roubles | 181 (32.9%) | 151 (33.7%) | 30 (29.4%) | |
| 40–60K roubles | 119 (21.6%) | 100 (22.3%) | 19 (18.6%) | |
| >60K roubles | 181 (32.6%) | 146 (32.6%) | 35 (34.3%) | 3.66 (p = 0.30) |
| Education | ||||
| University degree or above | 404 (73.5%) | 325 (72.5%) | 79 (77.5%) | |
| No higher education | 146 (26.5%) | 123 (27.5%) | 23 (22.5%) | 1.03 (p = 0.31) |
| Age | Mean=35 years | 34.8 (SD = 14.5) | 35.9 (SD = 12.6) | t = 0.71 (p = 0.48) |
Our multivariate logistic regression analysis indicates that there were significant associations between several of the HBM constructs and vaccine acceptance (Table 2). The marginal effect sizes show which of the HBM constructs contributed the most to the probability of getting vaccinated. Self-efficacy had the largest effect, meaning that if a respondent believes that it is easy to get vaccinated then they were 15% more likely to be vaccine accepting (p < 0.01). If a respondent believed that they are susceptible to COVID-19 infection, then they were about 9% more likely to get vaccinated (p < 0.05). Perceived benefits also had a significant effect on vaccination likelihood. An increase in perceived benefits was associated with an 11% increase in the probability of getting vaccinated (p < 0.01). An increase in perceived barriers was associated with a 6% decrease in the probability of vaccination (p < 0.01). Only one of our cues to action was statistically significant in the multivariate analysis. If family and friends supported COVID-19 vaccination, there was an approximately 9% increase in the probability of that respondent getting vaccinated (p < 0.01). While it remained in the anticipated direction of influence, perceived severity did not show a significant effect on vaccination acceptance in the multivariate analysis. The only two control variables to show effect were that having a university degree decreased the likelihood of vaccine acceptance by 8% (p < 0.01) and that being male decreased the likelihood of vaccine acceptance by 6% (p < .05). Table 2 shows both the marginal effects of each of the HBM constructs as well as the unadjusted and adjusted odds ratios for each variable with the 95% confidence intervals.
Table 2.
Bivariate and multivariate logistic regression analysis of Health Belief Model constructs and vaccine acceptance (N = 550).
| Dependent variable: vaccine acceptance | Bivariate logistic regression | Multivariate logistic regression | |
|---|---|---|---|
| OR (95% CI) | aOR (95% CI) | Marginal effect size (SE) | |
| Perceived severity | 1.55*** | 1.11 | 0.018 |
| Perceived susceptibility | 1.94 | 3.526* | 0.089** |
| Perceived benefits | 8.06**** | 4.64**** | 0.114** |
| Perceived barriers | 0.15**** | 0.46** | −0.058** |
| Self-efficacy for getting vaccinated | 10.63**** | 7.68**** | 0.152** |
| Friends and family support vaccination (cue to action) | 12.59**** | 3.31** | 0.089** |
| Healthcare providers said to get the vaccine (cue to action) | 1.67* | 1.38 | 0.024 |
| Knowing people who died from COVID-19 (cue to action) | 1.63* | 1.60 | 0.035 |
| Knowing people who are vaccinated for COVID-19 (cue to action) | 3.99** | 0.76 | −0.021 |
| Age | 0.993 | 0.99 | −0.001 |
| Male (ref.: female) | 0.93 | 0.43* | −0.063* |
| Married (ref.: unmarried) | 1.02 | 1.52 | 0.031 |
| Lives alone (ref.: lives with others) | 0.88 | 0.69 | −0.028 |
| Has chronic diseases (ref.: no chronic diseases) | 0.99 | 0.73 | −0.024 |
| Residence in Moscow or St. Petersburg (ref.: resides elsewhere in Russia) | 0.63 | 0.98 | −0.002 |
| Monthly income (20–40K roubles) (ref.: <20K) | 1.53 | 1.06 | 0.004 |
| Monthly income (40–60K roubles) (ref.: <20K) | 1.75 | 1.49 | 0.029 |
| Monthly income (>60K roubles) (ref.: <20K) | 1.34 | 1.14 | 0.010 |
| University degree or above (ref.: no higher education) | 0.81 | 0.35** | −0.079** |
*
p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Pseudo R2 = 0.501, Wald chi-square = 133.9, p-value < 0.0001.
OR: odds ratio; aOR: adjusted OR; CI: confidence interval; SE: standard error; ref.: reference
Our thematic analysis of the open-ended questions provides further insights into decisions to vaccinate. The most salient motivating factors centered on policy-level factors. The most commonly reported motivators among the range of sociodemographic characteristics in our sample included ‘QR-code and freedom of movement’, for which a person needs to register their vaccination into the government system. The next most common motivator listed was the ability to travel. This included both ‘to move freely around the city and country’ and also ‘to go abroad’, which was especially salient among the younger participants. Another salient motivator was that the person needed to be vaccinated for either work or study: ‘I won’t be let go from work without pay’ or ‘It [vaccination] will allow me to work and feed my family’. For some respondents, particularly those middle-aged or elderly, the main benefits included feeling calm, free, safe, and confident. Example statements included: ‘I could stop being paranoid’, ‘calmness, freedom, and comfort’ and ‘a decrease in the level of anxiety, feeling some kind of control over the situation, understanding that my chances to get sick go down’. Another important category of responses focused on social responsibility. Some examples included: ‘fulfilling their social duty’, ‘it is my social responsibility, feeling of fulfilling my duty’, ‘Understand that I can participate in the fight against this pandemic’, ‘95% sure that I will not add work for medical facilities and I won’t take up someone’s hospital bed’, ‘the satisfaction of personally participating in minimizing the consequences of COVID-19 on the economic situation in our country’ and ‘the pleasant internal feeling that I do not belong to the group of marginalized anti-vaxxers, the ideas and actions of which scare me’. While we did not identify patterns based on gender or residence, most of the participants mentioning social responsibility were in their 20s or 30s. The benefits of getting vaccinated among our respondents showed that policies and social norms were important to consider in addition to personal health beliefs.
Lack of trust was the most salient barrier among the range of sociodemographic characteristics in our sample. This included skepticism of a COVID-19 vaccine in general: ‘I don’t believe in the scientific possibility of a vaccine against SARS at this stage and I don’t believe that they could make one so quickly’. And some respondents expressed conspiracies such as ‘a raw and incomprehensible vaccine that was developed for unknown purposes’ or ‘It’s a business and not a health concern’. Part of the mistrust stemmed from concerns that the vaccines were developed too quickly.
‘It has not gone through all the clinical trial stages. This is the only thing stopping me from getting vaccinated. Despite being pro-vaccine in general and I understand that vaccination is the only way to stop the spread of coronavirus. But the vaccine needs to be scientifically proven to be effective!’
Other respondents expressed mistrust specifically in regard to the Russian-manufactured vaccines. Exemplary responses included: ‘there are a lot of lies about the Russian vaccines’ and ‘the lack of honest statistics in Russia about the side effects after vaccination’. A few respondents noted that it is not just the vaccines that they do not trust, but the lack of trust in the overall response to the COVID-19 pandemic. ‘If it weren’t for the media chasing after a sensation and all the hysterics of the pampered hypochondriacs, then no one would have even noticed this virus’. Some respondents’ lack of trust in the vaccine was a reflection of their attitudes towards the broader public health response in the country or ‘mistrust of the Russian public health system.’ Or, as one respondent wrote:
‘It is outrageous that is required to be vaccinated in such unsanitary conditions and during the surge of infections. Based on this, as well as the authorities actions against the health of citizens (optimization of medicine, raising the retirement age, cutting government spending in social policy), I consider this experiment of using an ineffective and potentially dangerous product of genetic engineering as another blow to the people’s health’.
Many respondents who were vaccine hesitant either elaborated on or reiterated their concerns about the side effects from vaccines. The worry about side effects is connected to the lack of trustworthy information as well. Some cited knowing people who had the vaccine and got very sick. Others noted that they were worried about conditions such as allergies, cancer or pregnancy that made them reluctant to get vaccinated. Some respondents mentioned that they already had COVID-19 so did not see the reason to get vaccinated, with statements such as ‘I already have the antibodies’. Several mentioned that they believed themselves to be healthy and not in need of any vaccinations. There were only a few respondents that stated they were generally against any types of vaccination. The more common concerns were about the specific COVID-19 vaccines, side effects (known and unknown), the lack of information about these vaccines, and the larger issues of distrust. Our analysis did not reveal distinct patterns based on the sociodemographic characteristics in our sample.
Discussion
Our study findings reflect that the HBM is a useful framework for identifying the motivators and barriers to COVID-19 vaccine acceptance in Russia. The vast majority (81.5%) of our respondents had either already been vaccinated for COVID-19 or were planning to get vaccinated. In the bivariate analysis, we found that each of the HBM constructs were significantly associated with vaccine acceptance and in the anticipated direction of association. In the multivariate logistic regression analysis, self-efficacy had the largest effect on vaccine acceptance, followed by perceived benefits and perceived susceptibility. Among the cues to action, only the idea that family and friends support COVID-19 vaccination had a statistically significant contribution to vaccine acceptance in our sample. Perceived barriers contributed to a decrease in probability that a respondent would get vaccinated. Overall, we were able to observe that health beliefs contribute to vaccine acceptance among our respondents in Russia.
Data from our open-ended questions provided further insight into how individuals perceive the benefits and barriers to vaccination against COVID-19 in the Russian social context. The motivators mentioned can be categorized around policy restrictions (for example, travel documents or QR codes), less worry and feeling calm, and social responsibility to get vaccinated and help stop the pandemic. The barriers centered on lack of trust in government and health officials, lack of trust of the Russian-made vaccines, conspiracy theories, and concerns about either the side effects or having other health issues for vaccinations could be problematic. The vast majority of our respondents did not express that they are part of the broader group of anti-vaxxers, but mentioned suspicion of vaccines developed specifically against COVID-19. These open-ended responses provide information about the Russian context as a starting point for further exploration about attitudes and perceptions towards COVID-19 vaccination beyond the constructs of the HBM.
Our results from Russia build on the growing global health literature that examines the HBM and COVID-19 vaccine acceptance. For example, a study from Israel demonstrated that the HBM, namely perceived benefits, cues to action, and perceived severity predicted intentions to get the COVID-19 vaccine (12). One of their significant cues to action was that friends and family advise to get vaccinated, which is similar to our study’s findings. A study using the HBM to explain COVID-19 vaccine acceptance in China also showed that having friends or family recommend the vaccine was an important predictor (14). Another study from China on HBM and intention to vaccinate demonstrated that perceived benefits and perceived barriers were strong predictors of vaccine acceptance (15). This China-based study showed that respondents had very high confidence in the domestically-produced vaccines (15), which differs from the results of our study in Russia. A study of the HBM and COVID-19 vaccine acceptance in Iraq (11) showed similar results to our study. Their analysis of the perceived barriers showed that concerns about vaccine storage was an important reason for vaccine hesitancy, which we did not measure and was not raised by our respondents in the open-ended responses. The researchers did conclude that public health campaigns need to pay attention to addressing concerns about the vaccine’s effectiveness and potential adverse effects (11), which is similar to our conclusions. The HBM is a useful model for examining COVID-19 vaccine acceptance and could be applied in even more settings to assess its utility among various populations with cultural differences.
Our results indicate that the HBM provides a good foundation for tailoring effective public health campaigns to promote autonomous vaccination in Russia. Our study shows the need to increase the ease with which people can get vaccinated in order to improve self-efficacy. More effort is also needed to get people to understand the benefits of getting vaccinated. Researchers have argued that motivational interviewing done by healthcare professionals is a promising approach to encourage voluntary COVID-19 vaccination and address the issue of mistrust (16). In our study, very few respondents indicated that a healthcare provider had recommended COVID-19 vaccination to them. Healthcare workers may be an important yet untapped resource for promoting vaccine uptake in Russia. However, more research is need to better understand what individual, institutional, and policy-level barriers may inhibit healthcare workers in promoting COVID-19 vaccines to their patients. Based on our finding that the cue to action of knowing others who support getting vaccinated was significantly associated with vaccine acceptance, we recommend a public health campaign focused on social norms. The open-ended results also indicate that social responsibility is an important motivator for vaccination. A social norms campaign could be used to address concerns about side effects and issues of mistrust, which serve as barriers to vaccine acceptance in Russia. Transparency of information, including providing data on the clinical trials for Russian-made vaccines, will be an important aspect in improving COVID-19 vaccine uptake among Russians.
Our study findings should be considered within their limitations. First, the use of convenience sampling means our sample is not representative of the entire Russian population. Second, our data are self-reported, which could skew the data based on social desirability. The online and anonymous format may have mitigated some of the social desirability. Third, the use of cross-sectional data does not allow us to measure changing attitudes and beliefs about the COVID-19 vaccine in Russia. We also were not able to track intentions to vaccinate into actual vaccinations, which a longitudinal study would be able to account for. While our open-ended questions allowed us to provide more details on the perceived benefits and barriers in the Russian context, our analysis was limited in data saturation on how these findings may differ among various sociodemographic characteristics and other constructs in the HBM. We recommend conducting a more in-depth qualitative study to expand on the findings that our open-ended questions provided in this study. Such research would help to elucidate the decision making process that people engage in about vaccination and help to inform the programs and policies needed to promote higher uptake of the COVID-19 vaccine in Russia.
Conclusions
Our study provides much needed information about individuals’ motivations and barriers for COVID-19 vaccination in Russia, a country that despite having the first registered vaccines, remains one of the places with the highest rates of vaccine hesitancy in this pandemic. At the start of 2023, only 60% of the Russian population had received at least one dose of a COVID-19 vaccine (4). Our results indicate that the HBM is a useful framework for examining which factors contribute to the likelihood of vaccine acceptance among Russians. Moreover, the results from our open-ended questions provide further information about individuals’ beliefs towards the COVID-19 vaccines and also about the social context in which these decisions are made. Our study results should be taken into consideration in developing public health campaigns to promote the uptake of COVID-19 vaccines in Russia.
Consent
Participants provided informed consent prior to starting the self-administered online survey.
Declaration of conflicting interests
The authors have no conflicts of interest to declare.
Ethical approval
The research was granted ethical approval by the St. Petersburg Association of Sociologists and determined as non-regulated research by the University of Michigan Institutional Review Board.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Elizabeth J. King https://orcid.org/0000-0001-8465-4607
© The Author(s) 2024