Predicting engagement in behaviors to reduce the spread of COVID-19 the roles of the health belief model and political party affiliation

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<p>Full Terms & Conditions of access and use can be found at</p><p>https://www.tandfonline.com/action/journalInformation?journalCode=cphm20</p><p>Psychology, Health & Medicine</p><p>ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/cphm20</p><p>Predicting engagement in behaviors to reduce the</p><p>spread of COVID-19: the roles of the health belief</p><p>model and political party affiliation</p><p>Carolyn Rabin & Sunny Dutra</p><p>To cite this article: Carolyn Rabin & Sunny Dutra (2022) Predicting engagement in behaviors</p><p>to reduce the spread of COVID-19: the roles of the health belief model and political party</p><p>affiliation, Psychology, Health & Medicine, 27:2, 379-388, DOI: 10.1080/13548506.2021.1921229</p><p>To link to this article: https://doi.org/10.1080/13548506.2021.1921229</p><p>Published online: 27 Apr 2021.</p><p>Submit your article to this journal</p><p>Article views: 858</p><p>View related articles</p><p>View Crossmark data</p><p>Citing articles: 17 View citing articles</p><p>https://www.tandfonline.com/action/journalInformation?journalCode=cphm20</p><p>https://www.tandfonline.com/journals/cphm20?src=pdf</p><p>https://www.tandfonline.com/action/showCitFormats?doi=10.1080/13548506.2021.1921229</p><p>https://doi.org/10.1080/13548506.2021.1921229</p><p>https://www.tandfonline.com/action/authorSubmission?journalCode=cphm20&show=instructions&src=pdf</p><p>https://www.tandfonline.com/action/authorSubmission?journalCode=cphm20&show=instructions&src=pdf</p><p>https://www.tandfonline.com/doi/mlt/10.1080/13548506.2021.1921229?src=pdf</p><p>https://www.tandfonline.com/doi/mlt/10.1080/13548506.2021.1921229?src=pdf</p><p>http://crossmark.crossref.org/dialog/?doi=10.1080/13548506.2021.1921229&domain=pdf&date_stamp=27 Apr 2021</p><p>http://crossmark.crossref.org/dialog/?doi=10.1080/13548506.2021.1921229&domain=pdf&date_stamp=27 Apr 2021</p><p>https://www.tandfonline.com/doi/citedby/10.1080/13548506.2021.1921229?src=pdf</p><p>https://www.tandfonline.com/doi/citedby/10.1080/13548506.2021.1921229?src=pdf</p><p>ARTICLE</p><p>Predicting engagement in behaviors to reduce the spread of</p><p>COVID-19: the roles of the health belief model and political</p><p>party affiliation</p><p>Carolyn Rabin and Sunny Dutra</p><p>Clinical Psychology Department, William James College, Newton, MA, USA</p><p>ABSTRACT</p><p>Efforts to control the spread of COVID-19 within the United States</p><p>have been compromised by varying levels of engagement in pre-</p><p>ventive behaviors, such as mask wearing, social distancing and</p><p>vaccine uptake. The purpose of this study was to evaluate potential</p><p>predictors of both (1) engagement in behaviors aimed at reducing</p><p>the spread of COVID-19 and (2) intention to get vaccinated against</p><p>COVID-19. It was hypothesized that Health Belief Model (HBM)</p><p>constructs would predict each outcome. Additionally, given the</p><p>politicization of the pandemic in the US, HBM constructs were</p><p>considered as possible mediators of a relationship between political</p><p>party affiliation and each outcome. A total of 205 participants</p><p>completed an online survey, and data from 186 were analyzed</p><p>using linear and ordinal regressions. Findings indicate that greater</p><p>perceived response efficacy predicted greater engagement in pre-</p><p>ventive behaviors and intention to get vaccinated. Other HBM</p><p>constructs were not significant predictors of either outcome.</p><p>Mediation analyses indicated that affiliation with the Republican</p><p>Party predicted reduced engagement in preventive health beha-</p><p>viors and vaccination intention, although effects were fully and</p><p>partially mediated by lower levels of response efficacy.</p><p>Understanding the predictors of adherence to recommended stra-</p><p>tegies is essential to developing effective public health campaigns</p><p>that address risk factors for non-adherence and target those least</p><p>likely to adhere. Public health interventions aimed at reducing the</p><p>spread of COVID-19 in the US should emphasize the efficacy of</p><p>preventive behaviors and encourage public trust in the safety and</p><p>efficacy of the COVID-19 vaccines, particularly among Republicans.</p><p>ARTICLE HISTORY</p><p>Received 12 November 2020</p><p>Accepted 16 April 2021</p><p>KEYWORDS</p><p>Coronavirus; health beliefs;</p><p>political party; health</p><p>behaviors; vaccination</p><p>The COVID-19 pandemic represents one of the few health events during which</p><p>Americans have been asked or required to adopt a set of health behaviors – such as</p><p>social distancing and mask wearing – to reduce the spread of an illness. Adoption of these</p><p>strategies has varied across the United States, however, compromising efforts to control</p><p>the virus. Likewise, even after the development of highly effective COVID-19 vaccines,</p><p>only 60% of adults in the US intend to get vaccinated (Funk & Tyson, 2020). Given this, it</p><p>is imperative to develop a better understanding of the factors predicting adherence to</p><p>CONTACT Carolyn Rabin Carolyn_Rabin@williamjames.edu Clinical Psychology DepartmentWilliam James</p><p>College, 1 1 Wells Avenue, Newton, MA 02459, USA</p><p>PSYCHOLOGY, HEALTH & MEDICINE</p><p>2022, VOL. 27, NO. 2, 379–388</p><p>https://doi.org/10.1080/13548506.2021.1921229</p><p>© 2021 Informa UK Limited, trading as Taylor & Francis Group</p><p>http://www.tandfonline.com</p><p>https://crossmark.crossref.org/dialog/?doi=10.1080/13548506.2021.1921229&domain=pdf&date_stamp=2022-03-04</p><p>preventive health behaviors and vaccination intention. Understanding the risk factors for</p><p>non-adherence is essential to developing effective public health campaigns that address</p><p>any modifiable factors and target those least likely to adhere.</p><p>Constructs from the Health Belief Model (HBM; Rosenstock, 1974), and COVID-</p><p>related anxiety, may help to explain differential adoption of COVID-related preventive</p><p>behaviors. According to the HBM, several constructs determine the likelihood of an</p><p>individual adopting a health-related behavior (e.g., social distancing) in response to</p><p>a salient health threat (e.g., infectious disease): perceived vulnerability to the threat,</p><p>perceived seriousness of the threat, perceived ability of the behavior to mitigate the</p><p>threat (i.e., response efficacy) and perceived barriers to implementing the behavior</p><p>(Rosenstock, 1974). Perceived ability to perform the preventive behavior (i.e., self-</p><p>efficacy) was subsequently added to the HBM (Rosenstock et al., 1988).</p><p>Research conducted during the H1N1 pandemic and (outside the US) during</p><p>COVID-19 suggests that HBM constructs and health anxiety may predict engagement</p><p>in preventive behaviors. Greater perceived seriousness of the H1N1 virus predicted</p><p>greater engagement in behaviors such as hand washing and avoiding crowds (Liao</p><p>et al., 2014; Loustalot et al., 2011). Likewise, greater perceived vulnerability to H1N1</p><p>predicted more hand washing and willingness to be vaccinated (Ibuka et al., 2010; Jones</p><p>et al., 2009). Consistent with this, research conducted in Italy and internationally during</p><p>COVID-19 found that perceived vulnerability predicted engagement in preventive beha-</p><p>viors (Dryhurst et al., 2020; Rubaltelli et al., 2020). The HBM constructs of self-efficacy</p><p>and response efficacy also appear to motivate behavior (Rosenstock, 1974; Rosenstock</p><p>et al., 1988). During H1N1, individuals were more likely to adopt a preventive behavior,</p><p>such as attention to hand hygiene, if they had greater self-efficacy for executing the</p><p>behavior, and were more likely to get vaccinated if they perceived vaccination to be more</p><p>efficacious (Liao et al., 2010; Nan & Kim, 2014). International research conducted during</p><p>COVID-19 also indicates that response efficacy predicts engagement in preventive</p><p>behaviors (Clark et al., 2020). Finally, there is evidence that those who experienced</p><p>greater H1N1 anxiety were more likely to report preventive behaviors such as social</p><p>distancing, attention to hand hygiene, and maintaining good indoor ventilation (Jones</p><p>et al., 2009; Liao et al., 2014, 2010). It remains unclear whether health beliefs or anxiety</p><p>are impacting adoption of preventive behaviors in the US during COVID-19, however, as</p><p>the limited research on predictors of COVID-preventive behavior in the US has focused</p><p>primarily on demographic predictors (e.g., Malik</p><p>et al., 2020; Pasion et al., 2020).</p><p>In addition to theory-based predictors, political party affiliation may help to explain</p><p>differential engagement in COVID-related health behaviors. The COVID-19 pandemic</p><p>has been politicized in the US, with some Republican politicians downplaying COVID-</p><p>19 risks and reluctant to promote public health strategies such as mask wearing.</p><p>A study conducted in April and May of 2020 found that Democrats perceived</p><p>a significantly greater risk to COVID-19 than Republicans and were more likely to</p><p>report mask wearing and avoiding crowds (De Bruin et al., 2020). This study focused</p><p>on a limited number of preventive behaviors and was conducted early in the pandemic</p><p>when only certain parts of the country had been significantly impacted by COVID-19.</p><p>Nonetheless, it suggests that political party affiliation may help to account for indivi-</p><p>dual differences in key HBM variables, which may in turn predict engagement in</p><p>COVID-related health behaviors.</p><p>380 C. RABIN AND S. DUTRA</p><p>The aims of this study, therefore, were to assess whether HBM constructs and political</p><p>affiliation impact engagement in COVID-related preventive behaviors and intention to</p><p>get vaccinated against COVID-19. Cross-sectional survey data were used to test the</p><p>following hypotheses. It was hypothesized that individuals would be more likely to</p><p>implement preventive behaviors if they viewed COVID-19 as more serious, felt more</p><p>vulnerable to infection, felt more confident they could protect themselves against</p><p>COVID-19, believed preventive behaviors (e.g., mask wearing) were effective and wor-</p><p>ried more about getting infected. The same factors were hypothesized to predict greater</p><p>intention to get vaccinated against COVID-19. Furthermore, we hypothesized that</p><p>political party affiliation would predict both engagement in preventive health behaviors</p><p>and vaccination intention and that any HBM construct(s) found to significantly predict</p><p>these outcomes in the first models would mediate these relationships.</p><p>Materials & method</p><p>Institutional review board approval was obtained prior to initiating this research. The</p><p>study was advertised as a compensated task on Amazon Mechanical Turk (MTurk). Data</p><p>were collected on July 27 and 28, 2020 which coincidentally overlapped with the summer</p><p>of 2020 peak in COVID-19 cases in the US. Recruiting participants through MTurk</p><p>ensured that they met two eligibility criteria: they were at least 18 years of age and had</p><p>sufficient English fluency to complete an online survey. Individuals were excluded from</p><p>participation if they had ever received a diagnosis (or been told they had a presumptive</p><p>case) of COVID-19 from a healthcare provider, as prior experience with COVID-19</p><p>might impact engagement in preventive behaviors. Those interested in participating in</p><p>the study were directed to the online consent form. Those who provided consent were</p><p>directed to the online survey. Survey measures are detailed below.</p><p>Measures</p><p>The survey included a measure of standard demographic information (e.g., age and</p><p>gender identity) and political party affiliation and assessed four HBM constructs.</p><p>Perceived seriousness was assessed using a single-item measure, based on one used by</p><p>Loustalot et al. (2011), that read, ‘How would you rate the seriousness of becoming</p><p>infected with COVID-19?’ Participants responded using a Likert scale from 1 (not very</p><p>serious at all) to 4 (very serious). Perceived vulnerability was assessed using a single-item</p><p>measure, based on a measure used by Ibuka et al. (2010): ‘In your opinion, what is the</p><p>likelihood that you will be infected with COVID-19?’ Participants responded using</p><p>a scale from 0% to 100% (with 10% increments). Self-efficacy to protect against</p><p>COVID-19 was assessed using a single-item measure, based on one by Liao et al.</p><p>(2010): ‘How confident are you that you can do what is needed to protect yourself against</p><p>COVID-19?’ Participants selected a response from 1 (not at all confident) to 4 (very</p><p>confident). Response efficacy (for a variety of preventive behaviors, not including vacci-</p><p>nation) was assessed by asking participants to rate the ability of 10 behaviors to prevent</p><p>COVID-19 infection using a scale from 1 (not at all) to 4 (very much). Behaviors included</p><p>hand hygiene; staying at least 6 ft. from those not in the same household; avoiding those</p><p>showing signs of illness; mask wearing; avoiding school/work (or attending remotely);</p><p>PSYCHOLOGY, HEALTH & MEDICINE 381</p><p>avoiding those in contact with infected people; using disinfectant at home; disinfecting</p><p>groceries; avoiding large gatherings; and avoiding public transportation. A composite</p><p>score was created by calculating the mean of ratings for the 10 behaviors. Internal</p><p>consistency was high (α = .89).</p><p>Anxiety about COVID-19 infection was assessed using a single-item measure based on</p><p>the one used by Liao et al. (2010): ‘Over the past week, how worried did you feel about</p><p>your chance of being infected with COVID-19?’ Participants responded using a 5-point</p><p>Likert scale from 1 (Never thought about it) to 5 (Extremely worried).</p><p>The outcome measure of engagement in preventive behaviors was based on two prior</p><p>measures (Jones et al., 2009; Liao et al., 2010). Participants rated how often they engaged in</p><p>the same 10 behaviors included in the response efficacy measure, using a scale from 1</p><p>(never) to 4 (always). A composite score was created by calculating the mean of responses</p><p>to the 10 items. Internal consistency was high for this measure (α = .86). The second</p><p>outcome was a single item assessing likelihood of vaccination once a COVID-19 vaccine</p><p>was available. Participants responded using a scale from 1 (not at all likely) to 4 (very likely).</p><p>Data analyses</p><p>To assess predictors of engagement in preventive health behaviors, a multiple linear</p><p>regression was conducted. Five predictors were included in the model: (1) response</p><p>efficacy of preventive behaviors; (2) perceived seriousness of COVID-19; (3) perceived</p><p>vulnerability to COVID-19; (4) self-efficacy to prevent infection; and (5) anxiety about</p><p>infection. To assess predictors of self-reported likelihood of getting vaccinated, an</p><p>ordinal logistic regression was conducted using the same predictors. Ordinal regression</p><p>was used as vaccination intention was measured ordinally (McCullagh, 1980), using</p><p>a single item with a Likert response scale from 0 (not at all likely to vaccinate) to 4</p><p>(very likely to vaccinate).</p><p>Nineteen participants were excluded from both regressions due to missing data from</p><p>predictor variables, leaving 186 participants. Six additional participants who were miss-</p><p>ing data from the measure of engagement in preventive behaviors were excluded from</p><p>that regression; consequently, 180 participants were included in the regression predicting</p><p>engagement in preventive behaviors. One participant did not complete the item assessing</p><p>the likelihood of vaccination and was excluded from the regression predicting that</p><p>variable, leaving 185 participants for that analysis.</p><p>Given differential messaging across political parties about the efficacy of preventive</p><p>behaviors, party affiliation was tested as a predictor of both outcomes (engagement in</p><p>preventive behaviors and vaccination intention), and any significant predictor(s) of these</p><p>outcomes found in the first set of analyses was tested as mediator(s) of these relationships</p><p>using the PROCESS macro in SPSS. Only participants reporting affiliation with the</p><p>Democratic (n= 102) or Republican (n = 37) Party were included.</p><p>Results</p><p>Demographic characteristics of the 186 participants included in the analyses are reported</p><p>in Table 1. Means, standard deviations, and a Pearson correlation matrix for continuous</p><p>variables are presented in Table 2.</p><p>382 C. RABIN AND S. DUTRA</p><p>Engagement in preventive behaviors</p><p>Prior to conducting the multiple regression, data were evaluated to ensure assumptions</p><p>were met. Linearity was present as assessed by a plot of studentized</p><p>residuals against the</p><p>predicted values and by partial regression plots. There was independence of residuals, as</p><p>assessed by a Durbin–Watson statistic of 1.39, and homoscedasticity, based on visual</p><p>Table 1. Sample demographic characteristics.</p><p>Demographic characteristics (N = 186)</p><p>M SD</p><p>Age 41.34 10.97</p><p>N %</p><p>Gender</p><p>Man 102 54.8</p><p>Woman 83 44.6</p><p>Nonbinary 1 0.5</p><p>Ethnicity</p><p>Hispanic/Latinx 6 3.2</p><p>Non-Hispanic/Latinx 179 96.2</p><p>Race</p><p>White 150 80.6</p><p>Black/African American 16 8.6</p><p>Asian 13 7.0</p><p>Native Hawaiian/Pacific Islander 1 0.5</p><p>Other 5 2.7</p><p>Education completed</p><p>Some high school 3 1.6</p><p>High school diploma 24 12.9</p><p>Some college 24 12.9</p><p>Vocational/trade school</p><p>associate’s degree</p><p>6</p><p>23</p><p>3.2</p><p>12.4</p><p>Bachelor’s degree 85 45.7</p><p>Graduate degree 21 11.3</p><p>Occupational status</p><p>Work full-time 139 74.7</p><p>Work part-time</p><p>Retired</p><p>Homemaker</p><p>19</p><p>11</p><p>4</p><p>10.2</p><p>5.9</p><p>2.2</p><p>Unemployed 13 7.0</p><p>Marital status</p><p>Single 87 46.8</p><p>Married 71 38.2</p><p>Living with partner 14 7.5</p><p>Divorced 9 4.8</p><p>Widowed 3 1.6</p><p>Separated 1 0.5</p><p>Table 2. Means, standard deviations, and Pearson correlation matrix for continuous variables</p><p>(N = 186).</p><p>M SD 1 2 3 4 5 6 7</p><p>1. Perceived vulnerability 4.06 2.04 –</p><p>2. Perceived seriousness 2.91 0.93 .35** –</p><p>3. Response efficacy 3.45 0.53 .18* .41** –</p><p>4. Self-efficacy to prevent 2.96 0.84 .37** −.15* .08 –</p><p>5. Anxiety 2.52 1.02 .40** .55** .36** .22** –</p><p>6. Behavioral engagement 3.34 0.57 .28** .41** .80** −.02 .36** –</p><p>7. Vaccination intention 3.04 1.11 .12 .23** .40** −.06 .16* .40** –</p><p>*p = < .05; **p < .01.</p><p>PSYCHOLOGY, HEALTH & MEDICINE 383</p><p>inspection of a plot of studentized residuals and unstandardized predicted values.</p><p>Predictors did not show multicollinearity, as evidenced by VIF values less than 1.7 for</p><p>all predictors. Residuals were normally distributed as assessed by a P-P plot. Three</p><p>outliers were identified (studentized residuals > ± 3 standard deviations) and excluded,</p><p>leaving 177 participants in the final analysis.</p><p>The multiple regression model significantly predicted engagement in preventive</p><p>health behaviors, F(5, 171) = 68.99, p< .001, adj. R2 = .66. Only response efficacy added</p><p>significantly to the model, p< .001, with higher levels of response efficacy predicting</p><p>greater engagement in health behaviors. Regression coefficients and standard errors can</p><p>be found in Table 3. These and all subsequent analyses were re-run with age, gender and</p><p>race included in the model; the pattern of results did not change.</p><p>Likelihood of vaccination</p><p>Prior to conducting the ordinal logistic regression, data were evaluated to ensure</p><p>assumptions were met. Predictors did not show multicollinearity, as evidenced by VIF</p><p>values less than 1.7 for all predictors. The assumption of proportional odds was met, as</p><p>assessed by a full likelihood ratio test comparing the fit of the proportional odds location</p><p>model to a model with varying location parameters, χ2(10) = 11.33, p= .33.</p><p>The final model significantly predicted the likelihood of vaccination over and above</p><p>the intercept-only model, χ2(5) = 32.09, p< .001. The deviance goodness-of-fit test</p><p>indicated that the model was a good fit to the observed data, χ2(508) = 410.236,</p><p>p = .999. An increase in perceived response efficacy of behavioral strategies was associated</p><p>with an increase in the odds of greater self-rated likelihood of vaccination, with an odds</p><p>ratio of 4.36, 95% CI [2.31, 8.20], Wald χ2(1) = 20.79, p< .001 (see Table 4). The</p><p>remaining predictors did not contribute significantly to the model (ps >.34).</p><p>Political affiliation, response efficacy, and engagement in preventive behaviors</p><p>A simple linear regression revealed that political party significantly predicted engage-</p><p>ment in preventive health behaviors (β = .24, t= 2.77, p= .006), with greater engagement</p><p>among Democrats (M= 3.50, SD = 0.40) versus Republicans (M= 3.23, SD = 0.65). Based</p><p>on prior results, we utilized the PROCESS macro version 3.5 in SPSS (Hayes, 2017) to</p><p>examine whether response efficacy mediated this relationship. Results indicated that</p><p>Table 3. Multiple regression results for engagement in preventive health behaviors.</p><p>95% CI for B</p><p>B LL UL SE B β R2</p><p>Model .67***</p><p>Perceived vulnerability .03 −.00 .06 .01 .10</p><p>Perceived seriousness .03 −.04 .10 .03 .05</p><p>Response efficacy .83 .72 .94 .05 .77***</p><p>Self-efficacy −.03 −.10 .04 .03 −.04</p><p>Anxiety .01 −.05 .07 .03 .01</p><p>B: unstandardized regression coefficient; CI: confidence interval; LL: lower limit; UL: upper limit; SE B: standard error of the</p><p>coefficient; β: standardized coefficient; R2: coefficient of determination.</p><p>*p = < .05; **p < .01; ***p < .001.</p><p>384 C. RABIN AND S. DUTRA</p><p>political party significantly predicted response efficacy (coefficient = 0.28, SE = 0.09,</p><p>t= 3.04, p= .003, 95% CI = .09, .46), and response efficacy significantly predicted</p><p>engagement in preventive behaviors (coefficient = 0.81, SE = 0.06, t= 13.50, p< .001,</p><p>95% CI = .69, .93). Political party was no longer a significant predictor of engagement in</p><p>preventive behaviors after controlling for the mediator, response efficacy (coeffi-</p><p>cient = 0.04, SE = 0.06, t= 0.65, p= .52, 95% CI = −.08, .17), consistent with full mediation.</p><p>The indirect effect was tested using a percentile bootstrap estimation approach with 5,000</p><p>samples and was significant (effect = 0.22, SE = 0.09, 95% CI = .04, .41). Approximately</p><p>62% of the variance in engagement in health behaviors was accounted for by the</p><p>predictors (R2 = 0.62).</p><p>Political affiliation, response efficacy, and vaccination intention</p><p>An ordinal regression with political party predicting vaccination intention revealed</p><p>a significant effect: affiliation with the Democratic party was associated with an increase</p><p>in the odds of greater self-rated likelihood of vaccination (odds ratio = 4.69, 95%</p><p>CI = 2.25, 9.77, Wald χ2(1) = 17.09, p< .001). Again, the PROCESS macro was utilized</p><p>to test response efficacy as a possible mediator of this relationship. Results indicated that</p><p>political party significantly predicted response efficacy (coefficient = 0.27, SE = 0.09,</p><p>t= 2.99, p= .003, 95% CI = .09, .45), and response efficacy significantly predicted</p><p>vaccination intention (coefficient = 0.70, SE = 0.19, t = 3.59, p < .001, CI = .31, 1.08).</p><p>Political party remained a significant predictor of vaccination intention after controlling</p><p>for the mediator (coefficient = 0.69, SE = 0.21, t= 3.36, p= .001, CI = .28, 1.10), consistent</p><p>with partial mediation. The indirect effect was tested using a percentile bootstrap</p><p>estimation approach with 5,000 samples and was significant (effect = 0.19, SE = 0.10,</p><p>95% CI = .02, .42). Approximately 20% of the variance in vaccination intention was</p><p>accounted for by the predictors (R2 = 0.20).</p><p>Discussion</p><p>The global impact of COVID-19 has been pervasive and devastating. Although beha-</p><p>vioral strategies can slow viral spread, implementation of these strategies has varied</p><p>across the US. One aim of this study was to determine whether constructs from the HBM</p><p>explain differential engagement in protective health behaviors and intention to get</p><p>vaccinated against COVID-19. Perceived efficacy of engaging in preventive behaviors –</p><p>i.e., response efficacy – emerged as a key predictor of both outcomes (even though</p><p>Table 4. Ordinal logistic regression results for vaccination intention.</p><p>Estimated value Standard error Wald χ2 OR</p><p>95% CI</p><p>LL UL P</p><p>Perceived vulnerability .01 .08 .01 1.01 −.16 .17 .94</p><p>Perceived seriousness .18 .19 .87 1.19 −.20 .55 .35</p><p>Response efficacy 1.47 .33 20.79 4.36 .84 2.10 <.001</p><p>Self-efficacy −.13 .18 .53 .88 −.50 .23 .47</p><p>Anxiety −.07 .18 .17 .93 −.42 .27 .68</p><p>OR: odds ratio; LL: lower limit; UL: upper limit.</p><p>PSYCHOLOGY, HEALTH & MEDICINE 385</p><p>vaccination was not one of the behaviors included in the response efficacy measure). This</p><p>is consistent with prior research conducted in the US in which the perceived efficacy of</p><p>the H1N1 vaccine predicted vaccination though the perceived severity of H1N1 and self-</p><p>efficacy of vaccination did not (Nan & Kim, 2014). The findings are inconsistent</p><p>with</p><p>research conducted outside the US, however, in which perceived risk and fear of COVID-</p><p>19 significantly predicted COVID-19 preventive behaviors (Yildirim et al., 2020). This</p><p>inconsistency suggests that COVID-19 fear may be a less potent motivator of health</p><p>behaviors in the US than abroad and that increasing Americans’ belief in the efficacy of</p><p>preventive behaviors may go further to increasing engagement in these behaviors.</p><p>This study also evaluated the impact of political party on outcomes. Findings indicate</p><p>that those affiliated with the Republican party are less inclined to engage in preventive</p><p>behaviors and get vaccinated against COVID-19. Further, relationships between party</p><p>affiliation and outcomes appear to be fully or partially mediated by the perceived efficacy</p><p>of COVID-preventive behaviors (with Republicans reporting lower perceived response</p><p>efficacy). These results are similar to, and potentially intertwined with, prior research</p><p>findings indicating that lower levels of trust in science mediate the relationship between</p><p>political conservatism and lower levels of compliance with COVID-19 guidelines (Plohl &</p><p>Musil, 2020). Thus, Republicans may be less inclined to engage in preventive behaviors or</p><p>pursue vaccination as they have less trust in the science supporting the efficacy of pre-</p><p>ventive strategies.</p><p>The findings from this study should be considered within the context of some metho-</p><p>dological limitations. Recruiting via MTurk yielded a sample that may not have been</p><p>representative of the US population (e.g., lower percentage of those identifying as Black</p><p>or African American), potentially biasing findings. Additionally, some of the measures used</p><p>introduced methodological limitations. Single-item measures (e.g., perceived seriousness)</p><p>may have failed to predict outcomes as they were less psychometrically robust than multi-</p><p>item measures. The measure of self-efficacy referred to participants’ confidence that they</p><p>could ‘do what is needed’ to protect themselves but may have been a stronger predictor if it</p><p>referenced confidence in executing preventive behaviors. Although most HBM constructs</p><p>were assessed, perceived barriers to implementing preventive behaviors were not. The</p><p>measure of response efficacy did not include an assessment of the perceived efficacy of</p><p>vaccination; thus, additional research is needed to confirm that perceived efficacy of</p><p>vaccination specifically predicts vaccination intention. Likewise, the vaccination outcome</p><p>assessed vaccination intention which does not always translate into behavior (Williams</p><p>et al., 2015). Finally, the measure of engagement in preventive health behaviors relied on</p><p>participant self-report, which may have been inaccurate. Direct, objective measures of</p><p>behavior would have better tested the associations between health beliefs/anxiety and</p><p>engagement in preventive behaviors. Future studies should sample individuals from the</p><p>general population and include objective measures of behaviors such as uptake of</p><p>vaccination.</p><p>Methodological limitations notwithstanding the outcomes from this study may be</p><p>useful when crafting public health campaigns to encourage engagement in health beha-</p><p>viors, including vaccination, that can reduce the spread of COVID-19. Findings suggest</p><p>that messaging may be most effective if it emphasizes the potency of recommended</p><p>strategies as opposed to individual vulnerability, feasibility of implementation or the</p><p>severity of COVID-19. The findings also suggest that campaigns to encourage the uptake</p><p>386 C. RABIN AND S. DUTRA</p><p>of preventive behaviors and COVID-19 vaccination should target those identifying as</p><p>Republican and aim to engender trust in science generally and the safety and efficacy of</p><p>these strategies specifically.</p><p>Acknowledgments</p><p>This research was supported by a Faculty Seed Grant from William James College.</p><p>Disclosure statement</p><p>No potential conflict of interest was reported by the author(s).</p><p>Funding</p><p>This work was supported by the William James College Seed Grant [N/A].</p><p>Project Registration and Data Availability</p><p>This project has been registered and data shared publicly through the Open Science Framework at</p><p>this DOI: 10.17605/OSF.IO/VRUX4.</p><p>References</p><p>Clark, C., Davila, A., Regis, M., & Kraus, S. (2020). Predictors of COVID-19 voluntary compliance</p><p>behaviors: An international investigation. Global Transitions, 2, 76–82. https://doi.org/10.1016/</p><p>j.glt.2020.06.003</p><p>De Bruin, W. B., Saw, H., & Goldman, D. P. (2020). Political polarization in US residents’ COVID-</p><p>19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and</p><p>Uncertainty. https://doi.org/10.1007/s11166-020-09336-3</p><p>Dryhurst, S., Schneider, C. R., Kerr, J., Freeman, A. L. J., Recchia, G., Van Der Bles, A. M.,</p><p>Spiegelhalter, D., & Van Der Linden, S. (2020). Risk perception of COVID-19 around the world.</p><p>Journal of Risk Research, 23(7–8), 994–1006. https://doi.org/10.1080/13669877.2020.1758193</p><p>Funk, C., & Tyson, A. (2020). Intent to get a COVID-19 vaccine rises to 60% as confidence in</p><p>research and development process increases (Pew Research Center). Pew Research Center.</p><p>https://www.pewresearch.org/science/2020/12/03/intent-to-get-a-covid-19-vaccine-rises-to</p><p>-60-as-confidence-in-research-and-development-process-increases</p><p>Hayes, A. F. (2017). Introduction to mediation, moderation, and conditional process analysis</p><p>(second edition ed.). Guilford Press.</p><p>Ibuka, Y., Chapman, G. B., Meyers, L. A., Li, M., & Galvani, A. P. (2010). The dynamics of risk</p><p>perceptions and precautionary behavior in response to 2009 (H1N1) pandemic influenza. BMC</p><p>Infectious Diseases, 10(1), 1–11. https://doi.org/10.1186/1471-2334-10-296</p><p>Jones, J. H., Salathe, M., & Von Elm, E. (2009). Early assessment of anxiety and behavioral</p><p>responses to novel swine-origin influenza A(H1N1). PLoS One, 4(12), 1–8. https://doi.org/10.</p><p>1371/journal.pone.0008032</p><p>Liao, Q., Cowling, B., Lam, W. T., Ng, M. W., & Fielding, R. (2014). Anxiety, worry and cognitive</p><p>risk estimate in relation to protective behaviors during the 2009 influenza A/H1N1 pandemic in</p><p>Hong Kong: Ten cross-sectional surveys. BMC Infectious Diseases, 14(1), 1–11. https://doi.org/</p><p>10.1186/1471-2334-14-169</p><p>PSYCHOLOGY, HEALTH & MEDICINE 387</p><p>https://doi.org/10.1016/j.glt.2020.06.003</p><p>https://doi.org/10.1016/j.glt.2020.06.003</p><p>https://doi.org/10.1007/s11166-020-09336-3</p><p>https://doi.org/10.1080/13669877.2020.1758193</p><p>https://www.pewresearch.org/science/2020/12/03/intent-to-get-a-covid-19-vaccine-rises-to-60-as-confidence-in-research-and-development-process-increases</p><p>https://www.pewresearch.org/science/2020/12/03/intent-to-get-a-covid-19-vaccine-rises-to-60-as-confidence-in-research-and-development-process-increases</p><p>https://doi.org/10.1186/1471-2334-10-296</p><p>https://doi.org/10.1371/journal.pone.0008032</p><p>https://doi.org/10.1371/journal.pone.0008032</p><p>https://doi.org/10.1186/1471-2334-14-169</p><p>https://doi.org/10.1186/1471-2334-14-169</p><p>Liao, Q., Cowling, B., Lam, W. T., Ng, M. W., Fielding, R., & Ng, L. F. P. (2010). Situations</p><p>awareness and health protective responses to pandemic influenza A (H1N1) in Hong Kong: A</p><p>cross-sectional study. PLoS One, 5(10), 1–10. https://doi.org/10.1371/journal.pone.0013350</p><p>Loustalot, F., Silk, B. J., Gaither, A., Shim, T., Lamias, M., Dawood, F., Morgan, O. W.,</p><p>Fishbein, D., Guerra, S., Verani, J. R., Carlson, S. A., Fonseca, V. P., & Olsen, S. J. (2011).</p><p>Household transmission of 2009 pandemic influenza A (H1N1) and nonpharmaeutical inter-</p><p>ventions among households of high school students in San Antonio, Texas. Clinical Infectious</p><p>Diseases, 52(suppl_1), S146– S153. https://doi.org/10.1093/cid/ciq057</p><p>Malik, A. A., McFadden, S. M., Elharake, J., & Omer, S. B. (2020). Determinants of COVID-19</p><p>vaccine acceptance in the US. EClinical Medicine, 26, 1–8. doi: 10.1016/j.eclinm.2020.100495</p><p>McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society.</p><p>Series B (Methodological), 42(2), 109–142.</p><p>https://doi.org/10.1111/j.2517-6161.1980.tb01109.x</p><p>Nan, X., & Kim, J. (2014). Predicting H1N1 vaccine uptake and H1N1-related health beliefs: The</p><p>role of individual difference in consideration of future consequences. Journal of Health</p><p>Communication, 19(3), 376–388. https://doi.org/10.1080/10810730.2013.821552</p><p>Pasion, R., Paiva, T. O., Fernandes, C., & Barbosa, F. (2020). The age effect on protective behaviors</p><p>during the COVID-19 outbreak: Sociodemographic perceptions and psychological accounts.</p><p>Frontiers in Psychology, 11,1–14. https://doi.org/10.3389/fpsyg.2020.561785</p><p>Plohl, N., & Musil, B. (2020). Modeling compliance with COVID-19 prevention guidelines: The</p><p>critical role of trust in science. Psychology, Health & Medicine, 23(1), 1–12. https://doi.org/10.</p><p>1080/13548506.2020.1772988</p><p>Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education</p><p>Monographs, 2(4), 328–335. https://doi.org/10.1177/109019817400200403</p><p>Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health</p><p>belief model. Health Education Quarterly, 15(2), 175–183. https://doi.org/10.1177/</p><p>109019818801500203</p><p>Rubaltelli, E., Tedaldi, E., Orabona, N., & Scrimin, S. (2020). Environmental and psychological</p><p>variables influencing reactions to the COVID-19 outbreak. British Journal of Health Psychology,</p><p>25(4), 1020–1038. https://doi.org/10.1111/bjhp.12473</p><p>Williams, L., Rasmussen, S., Kleczkowski, A., Maharaj, S., & Cairns, N. (2015). Protection</p><p>motivation theory and social distancing behavior in response to a simulated infectious disease</p><p>epidemic. Psychology, Health & Medicine, 20(7), 832–837. https://doi.org/10.1080/13548506.</p><p>2015.1028946</p><p>Yildirim, M., Geçer, E., & Akgül, Ö. (2020). The impacts of vulnerability, perceived risk, and fear</p><p>on preventive behaviours against COVID-19. Psychology, Health & Medicine, 26(1), 35–43.</p><p>DOI: 10.1080/13548506.2020.1776891</p><p>388 C. RABIN AND S. DUTRA</p><p>https://doi.org/10.1371/journal.pone.0013350</p><p>https://doi.org/10.1093/cid/ciq057</p><p>https://doi.org/10.1016/j.eclinm.2020.100495</p><p>https://doi.org/10.1111/j.2517-6161.1980.tb01109.x</p><p>https://doi.org/10.1080/10810730.2013.821552</p><p>https://doi.org/10.3389/fpsyg.2020.561785</p><p>https://doi.org/10.1080/13548506.2020.1772988</p><p>https://doi.org/10.1080/13548506.2020.1772988</p><p>https://doi.org/10.1177/109019817400200403</p><p>https://doi.org/10.1177/109019818801500203</p><p>https://doi.org/10.1177/109019818801500203</p><p>https://doi.org/10.1111/bjhp.12473</p><p>https://doi.org/10.1080/13548506.2015.1028946</p><p>https://doi.org/10.1080/13548506.2015.1028946</p><p>https://doi.org/10.1080/13548506.2020.1776891</p><p>Abstract</p><p>Materials & method</p><p>Measures</p><p>Data analyses</p><p>Results</p><p>Engagement in preventive behaviors</p><p>Likelihood of vaccination</p><p>Political affiliation, response efficacy, and engagement in preventive behaviors</p><p>Political affiliation, response efficacy, and vaccination intention</p><p>Discussion</p><p>Acknowledgments</p><p>Disclosure statement</p><p>Funding</p><p>Project Registration and Data Availability</p><p>References</p>