Associations between psychological dispositions, pandemic-related variables and protection motivation theory determinants: a cross-sectional study

Associations between psychological dispositions, pandemic-related variables and protection motivation theory determinants: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations between psychological dispositions, pandemic-related variables and protection motivation theory determinants: a cross-sectional study Katja Schulze, Peter Windsheimer-Kolla, Martin Voss This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7627607/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Improving the understanding of the factors influencing COVID-19 health intentions is important for pandemic preparedness and public health promotion, as such insights can inform effective communication and policy strategies. With respect to this goal, protection motivation theory (PMT) is a prominent framework for understanding health-protective behavior, but it largely overlooks intrapersonal variables that may be linked to cognitive appraisals. This exploratory study investigated how pandemic-related factors (risk group self-identification, prior infection) and psychological dispositions (perceived infectability, general self-efficacy, risk propensity, life satisfaction, and subjective health) may be associated with PMT determinants (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs) and protective health intentions during the COVID-19 pandemic. The aim was to identify patterns that could inform future confirmatory studies, thereby generating hypotheses that may ultimately guide the development of effective interventions and strategies to strengthen public health and pandemic preparedness. Methods A cross-sectional online survey was conducted in Germany in January 2024 (n = 1,050; quota sample by age, gender, region, education). Hierarchical linear regressions were performed for each PMT determinant, sequentially entering (1) sociodemographic variables, (2) pandemic-specific factors, and (3) psychological dispositions. Results Intrapersonal factors contributed substantially to model fit (R² = 6%–28%). Threat appraisal was most closely associated with pandemic-specific variables and perceived infectability, whereas an unexpected inverse relationship emerged with life satisfaction. Coping appraisals were most strongly associated with lower risk propensity and higher life satisfaction. General self-efficacy and subjective health were also linked to various PMT constructs. These findings are best viewed as exploratory in nature and may serve to generate hypotheses for future research. Conclusion Incorporating intrapersonal variables into PMT was associated with greater explanatory power and provided a more nuanced understanding of health-protective behavior. This extended framework may not only broaden explanatory power but also guide the design of more targeted health interventions, a proposition that warrants further longitudinal and experimental testing. Protection motivation theory life satisfaction risk propensity perceived infectability perceived general self-efficacy health status risk group COVID-19 experience Figures Figure 1 Figure 2 Figure 3 Background The COVID-19 pandemic has had profound and far-reaching consequences for global health, economies, and societies [ 1 , 2 ]. In addition to its immediate impacts, the threat of future pandemics remains a persistent concern for public health and global security, with annual economic losses projected at approximately $ 500 billion [ 3 ]. These challenges underscore the critical importance of understanding and promoting health-protective behaviors to enhance pandemic preparedness. Individual behavior is central to the control of infectious disease outbreaks. Compliance with protective measures substantially influences disease transmission, and identifying the factors that shape such behaviors is essential for designing effective public health interventions [ 4 ]. Identifying the psychological determinants of protective behavior is therefore crucial both for managing current crises and for strengthening preparedness for future pandemics. Protection motivation theory Several theoretical models have been proposed to explain and predict health-related behaviors [ 5 ]. Among these, protection motivation theory (PMT) [ 6 , 7 , 8 ] provides a particularly influential framework for understanding health-protective behavior. It is among the most important health behavior theories for changing behavior [ 9 , 10 ] and has been frequently applied during the COVID-19 pandemic [ 11 ]. PMT explains how individuals react to persuasive communication, assess potential threats and take health-promoting measures, which can take the form of different coping modes, such as single acts (e.g., receiving an available vaccination) and repeated acts (e.g., wearing a mask), in the case of a threatening event. As a social cognition theory, it identifies important social–cognitive determinants of health behavior. According to theory, adaptive or maladaptive responses following a health threat are dependent on protection motivation, which results from the combined evaluation of two cognitive processes: the threat appraisal process and the coping appraisal process (see Fig. 1 ). The threat appraisal process involves assessing (1) perceived severity, which reflects the anticipated seriousness of the threat’s consequences; (2) perceived vulnerability, or the likelihood of being personally affected; (3) intrinsic rewards, such as the physical or psychological benefits of risky behavior; and (4) extrinsic rewards, such as social approval or other interpersonal gains. The second component, coping appraisal, refers to the evaluation of available protective strategies and the individual's ability to implement them. It includes (1) response efficacy, or the belief that a specific behavior will effectively mitigate the threat; (2) self-efficacy, the individual’s confidence in their ability to perform the protective behavior; and (3) perceived response costs, encompassing the potential barriers to action, such as time, effort, financial burdens, or social inconvenience. In many applications of PMT, a streamlined version of the model has been employed, emphasizing five principal components while omitting the following reward factors: perceived severity, perceived vulnerability, response efficacy, self-efficacy, and response costs [ 12 , 13 ]. This more concise formulation has proven effective in a range of empirical studies examining protective behavior in the context of public health risks [ 11 ]. Overall, PMT highlights how threat and coping appraisals jointly influence protection motivation and succeeds in combining both components into a consistent theory, thereby serving as a valuable framework for predicting health-related behaviors. Application of PMT to predict health behavior PMT reliably predicts health-related behavior in various contexts [ 8 , 14 , 15 ], with coping appraisals tending to show greater power than threat appraisals do [ 16 ]. In particular, during the COVID-19 pandemic, PMT has frequently been applied to examine and predict engagement in nonpharmaceutical protective behaviors [ 17 , 18 , 19 , 20 ]. A comprehensive meta-analysis by Hedayati et al. (2023) [ 11 ], which synthesized findings from 65 studies, provided strong empirical support for the predictive validity of PMT in the context of COVID-19. The analysis revealed that individuals who perceived the pandemic as more threatening, either in terms of its severity or their own vulnerability, were more likely to adopt recommended protective measures, including vaccination intentions. However, the overall levels of perceived vulnerability were relatively low. Furthermore, Hedayati et al. (2023) [ 11 ] demonstrated that coping appraisal components, particularly self-efficacy and response efficacy, are strongly associated with both behavioral intentions and actual engagement in protective actions. Among these factors, self-efficacy emerged as the most influential factor. In contrast, response costs, including both financial and nonfinancial burdens, were found to be weakly and negatively associated with protection motivation. These conclusions are further supported by findings from studies conducted in Germany, which reported similarly high relevance of coping appraisals in predicting protective behavior during the COVID-19 pandemic [ 21 , 22 ]. Taken together, these findings underscore the robustness of PMT in explaining a wide range of health-protective behaviors, including those adopted during the COVID-19 pandemic. They support PMT’s predictive power, with coping appraisals, particularly self-efficacy, emerging as the strongest drivers of protective behavior. At the same time, this emphasis on predictive validity leaves important questions about how the underlying appraisals, which constitute the basis of PMT, are themselves shaped and how their expressions are influenced by personal characteristics. Addressing this gap requires moving beyond the traditional focus on PMT constructs as isolated determinants and instead examining the factors that influence their formation. Factors influencing PMT constructs While research has demonstrated that PMT is a robust framework for predicting health-protective behaviors, recent research has investigated primarily the relationship between social–cognitive PMT determinants and protective behavior. This focus tends to neglect the role of intrapersonal and contextual factors that shape these appraisals and treats them as if they were formed independently of broader personal and environmental influences. In PMT, threat and coping appraisals are shaped by environmental and intrapersonal sources of information [ 23 ] (see Fig. 1 ). Environmental sources refer to verbal persuasion and observational learning. Rogers (1983) [ 23 ] identified personality variables and prior experiences with similar threats as intrapersonal sources. Intrapersonal factors, however, can include a much wider variety of individual characteristics, such as psychological factors and general beliefs or values. Figure 1 . Simplified illustration of protection motivation theory (PMT), adapted from Rogers (1983, p. 168). Intrapersonal factors (e.g., traits, values, attitudes) have rarely been integrated into PMT-based models, although emerging evidence suggests that such factors may impact how individuals perceive and respond to threats [ 6 , 14 ]. Research by Schneider & Dryhurst (2021) [ 24 ] has even indicated that psychological factors are more predictive of risk perception than an objective measure of situational severity is. Despite the widespread application of PMT, the extent to which individual differences, particularly psychological dispositions, are related to the motivational processes that lead to health-protective behavior remains largely unclear. The assumption of uniform cognitive processing appears increasingly inadequate in light of empirical evidence suggesting that individuals perceive and respond to threats in systematically different ways. To address this research gap, the present study deliberately adopts an exploratory empirical approach. It seeks to identify potential patterns and associations between (1) pandemic-related factors (e.g., belonging to a risk group, prior COVID-19 experience) and (2) psychological dispositions (e.g., perceived general infectability to illness, general self-efficacy, risk propensity, life satisfaction, and subjective health status) and the key PMT constructs: perceived severity, perceived vulnerability, self-efficacy, response efficacy, response costs, and behavioral intention. This approach is particularly appropriate given the limited existing evidence on these variables. By identifying relevant patterns and associations, this study aims to contribute to a more psychologically nuanced and person-centered understanding of protection motivation. Understanding these individual differences is not only valuable for tailoring interventions to psychological profiles but also essential for advancing broader public health goals. Accounting for psychological dispositions will facilitate better tailoring of health communication and behavioral strategies to diverse population segments, thereby enhancing compliance and overall public health outcomes. The incorporation of such intrapersonal and environmental factors into PMT can therefore enhance our understanding of why individuals differ in their health-protective responses, which in turn can serve as a valuable contribution for enhancing predictions and tailoring countermeasures in future pandemics. Pandemic-related variables To structure the analysis, we formulate guiding hypotheses (H1.1, H2.1, etc.). These should not be interpreted as strict tests of predefined causal mechanisms but as tentative assumptions of statistical associations within a broader exploratory framework on the basis of previous empirical work. Risk group self-identification Within the paradigm of pandemics, the term "high-risk group" refers to subsets of the population that are more likely to experience severe illness, complications, or death if they contract the disease. Therefore, it is reasonable to expect that high-risk groups perceive the threat as greater. Research supports the assumption of greater perceived severity, concern and fear among high-risk COVID-19 groups [ 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. For example, Kohler et al. (2021) [ 32 ] reported that individuals with multiple high-risk conditions rated their probability of hospitalization or death from COVID-19 as higher than their healthy counterparts did, directly motivating precautionary actions. Further research has revealed support for greater adherence intentions and protective behavior among individuals with preexisting conditions or among participants who perceived themselves to be in the high-risk group [ 18 , 33 , 34 , 35 ]. These findings are to be expected, as people who perceive greater severity are also more likely to protect themselves to avoid potential harm. To our knowledge, no research has investigated the correlations between high-risk group membership and other PMT constructs. Overall, perceiving oneself as belonging to a high-risk group appears to increase perceived severity and protective intentions, although its impact on other PMT constructs remains less clear. H1.1 : Individuals who perceive themselves as belonging to a high-risk group report greater perceived severity of COVID-19 than do individuals who do not perceive themselves as belonging to a high-risk group. H1.2 : Individuals who perceive themselves as belonging to a high-risk group report stronger intentions to engage in protective behaviors against COVID-19 than do those who do not perceive themselves as belonging to a high-risk group. RQ1.1 : How does the perception of belonging to a high-risk group relate to other PMT constructs, such as perceived vulnerability, self-efficacy, response efficacy, and response costs? COVID-19 experience The personal experience of individuals regarding infection by the SARS-CoV-2 virus may significantly affect their motivation to protect themselves and others, as well as their perception of the threat posed by the virus and their ability to cope with it. Indeed, individuals in various countries who have contracted the virus themselves tend to report heightened risk perceptions [ 36 ]. Schneider et al. (2021) [ 24 ] reported that this relationship remained stable over time. Research has indicated that prior infection impacts mainly perceived infectability, not severity [ 37 , 38 , 39 ]. Research suggests that infection experience indirectly affects protective behavior, which is mediated by risk perception [ 40 , 41 , 42 ]. However, some studies have demonstrated that previous COVID-19 infection has a negative effect on adherence to prevention measures [ 43 , 44 ]. This experience may have resulted in an increased sense of confidence, leading to perceived immunity to the illness or, alternatively, a perceived reduction in the severity of the infection owing to the milder symptoms experienced [ 43 , 45 ]. However, the extent to which prior experience with SARS-CoV-2 infection influences protective behavior remains to be thoroughly investigated. To our knowledge, no research has directly addressed whether previous COVID-19 infection alters self-efficacy, response efficacy or response costs. Prior COVID-19 infection may influence perceived vulnerability, but its effects on coping appraisals and protective behavior require further investigation. H2.1 : Individuals with a history of COVID-19 infection report greater perceived vulnerability (infectability) to the virus than those without prior infection. H2.2 : Previous COVID-19 infection is not significantly linked to the perceived severity of the virus. RQ2.1 : How does previous COVID-19 infection relate to other PMT constructs, such as self-efficacy, response efficacy, and response costs? RQ2.2 : How does previous COVID-19 infection relate to protective behavior? Psychological variables Perceived infectability Drawing on the theory of the behavioral immune system (BIS) [ 46 ], we investigate whether perceived infectability (PI) is associated with PMT determinants. PI, as part of the broader perceived vulnerability to disease (PVD) 1 [47] framework, refers to the extent to which individuals believe themselves to be particularly susceptible to infectious diseases in general. In contrast, perceived vulnerability in PMT captures the expected likelihood of being personally affected by a specific disease. Perceived infectability and perceived vulnerability differ in scope and temporal stability but share a common conceptual basis: both reflect an individual's perceived likelihood of contracting a disease. This overlap suggests that PI may serve as a dispositional antecedent to perceived vulnerability [ 46 , 48 ]. Nevertheless, the relationships between PI and perceived vulnerability or severity during specific pandemics have been the subject of only a few studies, which have indicated that high general perceived infectability is positively correlated with perceived personal risk and threat reactivity [ 33 , 49 ]. To our knowledge, the only study examining the link between perceived infectability (PI) and coping appraisal revealed that PI did not predict stronger beliefs in the effectiveness of public health measures [ 50 ]. The relationship between general perceived infectability and COVID-19 preventive behavior is ambivalent. For example, Stangier, Kananian et al. (2021) [ 51 ] reported that PI can predict an increase in self-reported preventive behavior. Similarly, Makhanova et al. (2020) [ 48 ] stated that PI was negatively associated with the frequency of store visits and outdoor physical activity but not with face‒to-face interactions. In a separate study, they reported that PI was positively associated with social distancing but not with proactive behaviors [ 48 ]. In contrast to these cross-sectional findings, a longitudinal study by Church et al. (2022) [ 52 ] revealed a correlation between PI and preventive behavior for the first investigation but not for the second. Furthermore, Hromatko et al. (2021) [ 33 ] reported no significant relationship between PI and behavior. Taken together, these findings indicate that PI may influence certain types of preventive behaviors more than others do, but the overall evidence is mixed and may depend on the behavioral context and study design. Perceived infectability may serve as a dispositional antecedent to perceived vulnerability and protection motivation, although its influence on other PMT constructs is still uncertain. H3.1 : Perceived infectability (PI) is positively associated with perceived vulnerability to COVID-19. H3.2 : Perceived infectability (PI) is positively associated with protection motivation. RQ3.1 : How does perceived infectability relate to other PMT constructs, such as perceived severity, self-efficacy, response efficacy, and response costs? Perceived general self-efficacy In contrast to domain-specific self-efficacy (SSE) as part of PMT, general self-efficacy (GSE) is defined as a relatively stable belief in one's global ability to handle a wide array of challenging situations [ 53 ]. Previous research has usually focused on either GSE or SSE as unique separate constructs. As part of PMT, SSE has been widely studied as a predictor of COVID-19-related protective behavior [ 11 ]. In contrast, the role of GSE remains underexplored. Initial findings suggest a positive relationship between GSE and COVID-19 compliance behaviors [ 54 ], but the specific mechanisms underlying this relationship are not yet well understood. From a theoretical perspective, GSE may serve as a foundational cognitive resource that facilitates the formation of SE. Research has shown that GSE can act as a predictor of domain-specific efficacy for a variety of tasks in various contexts [ 55 ], including health behavior and stress management [ 56 ]. These findings suggest that individuals who perceive themselves as generally competent and effective (high GSE) are also more likely to evaluate their capabilities positively in specific tasks and settings (SSE). To our knowledge, no prior research has systematically examined how GSE predicts SSE or other PMT constructs in shaping compliance with COVID-19 health-protective behaviors. General self-efficacy likely supports domain-specific self-efficacy, potentially enhancing the motivation to engage in COVID-19 protective behaviors. Its effect on further PMT determinants requires further investigation. H4.1 : General self-efficacy (GSE) is positively associated with domain-specific self-efficacy (SSE) in relation to COVID-19 protective behaviors. RQ4.1 : How does general self-efficacy (GSE) relate to other PMT constructs, such as perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation? Risk propensity Risk propensity refers to a stable individual disposition reflecting the tendency either to engage in risk-taking behavior or to avoid it [ 57 , 58 ]. Consistent with this definition, individuals with greater willingness to take risks tend to engage more in unhealthy behaviors [ 59 , 60 ]. In the context of COVID-19, research has demonstrated that higher levels of risk propensity are associated with lower compliance with containment measures [ 61 , 62 , 63 , 64 ]. Despite these findings, few studies have systematically investigated whether risk propensity is associated with the underlying cognitive mechanisms that PMT identifies as drivers of protective behavior. Prior work has yielded mixed results in this regard: Fu et al. (2024) [ 65 ] reported no significant effect of health risk aversion on threat appraisal, whereas Buelow et al. (2022) [ 66 ] demonstrated a significant negative association between general risk propensity and the perceived risks related to COVID-19. Furthermore, Thomas et al. (2022) [ 67 ] found a significant negative relationship between risk aversion and response efficacy for compliant behavior. These limited and partly conflicting findings suggest that the impact of risk propensity on PMT cognitive processes is not yet fully understood, underscoring the need for research that directly examines these relationships. Individuals with greater risk propensity may be less motivated to adopt protective behaviors, possibly reflecting their lower threat sensitivity and coping appraisal. H5.1 : Willingness to take risks is negatively associated with protection motivation. RQ5.1 : How does willingness to take risks relate to PMT constructs such as perceived severity, perceived vulnerability, self-efficacy, response efficacy, and response costs? Life satisfaction Although limited, existing evidence suggests a significant positive association between life satisfaction and COVID-19 health behavior [ 68 , 69 ]. Krekel et al. (2023) [ 68 ] theorized that individuals with higher life satisfaction may have more psychological resources, making it easier to comply with health guidelines. Although the positive impact of life satisfaction on mental health and adaptive coping mechanisms has been the focus of numerous investigations [ 70 , 71 , 72 ], few studies have directly tested its impact on PMT determinants. In contrast, numerous studies have investigated the impact of risk perception on life satisfaction during the COVID-19 pandemic, reflecting the dominance of unidirectional and mechanistic models and overlooking potential bidirectional or recursive effects within the broader psychological system. Regardless of the direction of the association, the majority of studies have reported a negative correlation with risk perception, often defined or operationalized as a combination of perceived severity and perceived vulnerability [ 71 , 73 , 74 , 75 , 76 ]. However, some studies have reported a positive association [ 77 ] or no significant association [ 78 ]. This conflicting evidence indicates that the relationship between life satisfaction and risk perception remains unresolved, particularly with respect to perceived severity and vulnerability. Clarifying these links is essential for understanding how life satisfaction influences protection motivation. Furthermore, higher life satisfaction has been shown to promote positive coping styles [ 70 , 79 ], which may lead individuals to perceive protective behavior as more effective and less burdensome. Nevertheless, the association between life satisfaction and coping appraisal remains underexplored. Taken together, the literature indicates that life satisfaction can promote health behavior. Although most findings point to a negative association with risk perception, findings are mixed, and connections to coping appraisal are scarcely studied. H6.1 : Life satisfaction is negatively associated with perceived vulnerability. H6.2 : Life satisfaction is negatively associated with perceived severity. H6.3 : Life satisfaction is positively associated with protection motivation. RQ6.1 : How does life satisfaction relate to other PMT constructs, such as self-efficacy, response efficacy, and response costs? Self-rated health status An individual’s perception of belonging to a risk group is associated with their self-rated health status, which is defined as an individual's subjective evaluation of their overall physical and mental well-being. Self-assessment of health is both broader and more holistic than the more specific belief concerning risk group membership. A limited number of studies have investigated the relationship between self-rated health status and protective behavior, indicating that individuals with poor self-rated health are more likely to engage in COVID-19 preventive behavior [ 80 , 81 , 82 ]. These findings suggest that individuals who perceive themselves to be in good health may underestimate the severity of a potential COVID-19 case. Empirical findings support this assumption, revealing a negative association between health status and the perceived risk posed by infection with SARS-CoV-2 [ 83 , 84 , 85 , 86 ]. Nevertheless, the link between self-rated health and PMT constructs has remained largely unexplored. Higher self-rated health appears to reduce perceived severity and protection motivation, suggesting that subjective health assessments shape engagement in protective actions. H7.1 : Self-rated health status is negatively associated with perceived severity. H7.2 : Self-rated health status is negatively associated with protection motivation. RQ7.1 : How does life satisfaction relate to other PMT constructs, such as perceived vulnerability, self-efficacy, response efficacy, and response costs? Methods This exploratory study aims to extend protection motivation theory (PMT) by examining how intrapersonal and pandemic-specific factors shape core PMT appraisals and, in turn, intentions to enact health-protective behaviors. Study design The study data were collected in January 2024 as part of a cross-sectional online survey conducted within the DRU (Disaster Research Unit) subproject of the SEMSAI (Self-Referential Multi-Scale Modelling and Simulation of Severe Infectious Disease) research initiative. The survey was administered in a single wave by a professional survey institute contracted for this purpose. A total of 1,050 adults from Germany participated in the survey. Power analysis indicated that this sample size provides sufficient statistical power (1 – β = .90, α = .05) to detect small incremental effects (ΔR² ≈ .03) in hierarchical regression analyses, with up to 16 predictors entered in four successive blocks. Quota sampling was applied to ensure that the sample reflected the German adult population in terms of age, sex, educational level, and federal state. A scenario-based approach was used, wherein respondents were asked to consider a hypothetical infection wave caused by a fictional new coronavirus variant. This variant was described as initiating a new wave of infections with characteristics similar to those of the original COVID-19 outbreak in 2019/2020. The scenario included behavioral strategies. The scenario was followed by a series of questions assessing pandemic-specific variables, psychological dispositions, PMT determinants, and behavioral responses. Measurements Pandemic-specific variables Risk group self-identification was assessed via a single item (“Are you a member of a COVID-19 risk group?”) with response options of 1 = yes and 0 = no. This item was adopted from the COSMO survey [ 87 ]. This variable was treated as categorical in the analyses. Personal experience with COVID-19 infection was measured via a single item: “Have you ever received a confirmed diagnosis of a coronavirus (SARS-CoV-2) infection?” The response options were 1 = yes and 0 = no. The item was suggested by the Rat für Sozial- und Wirtschaftsdaten (2023) [ 88 ]. For the purposes of statistical analysis, the variable was treated categorically. Psychological variables Perceived infectability (PI) was assessed via a four-item scale adapted from Ullah, Lin et al. (2021) [ 89 ]. The participants rated their agreement with statements such as “I am generally very susceptible to diseases” and “My immune system usually protects me from illnesses that others get” on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The internal consistency was acceptable (Cronbach’s α = .766). General self-efficacy (GSE) was determined via the abbreviated ASKU scale [ 90 ], which encompasses three items, including the affirmations “In difficult situations, I can rely on my abilities” and “I can usually solve demanding and complex tasks well.” Responses were provided on a 5-point Likert scale (1 = not at all true, 5 = completely true). The internal consistency was very good (Cronbach’s α = .837). Dohmen et al. (2011) [ 91 ] introduced a widely used single-item measure of risk propensity. In line with this approach, participants were asked “ How willing are you to take risks, in general?” and responded on an 11-point scale (0 = not at all willing to take risks, 10 = very willing to take risks). The participants’ self-rated health status (SRH) was measured via a single-item self-assessment adopted from the SOEP-CoV 2020 study [ 92 ]. The respondents were asked “How would you describe your current state of health?” Answers were provided on a five-point Likert scale (1 = very good, 5 = poor). This item was recoded for the analyses (1 = poor, 5 = very good). Life satisfaction (LS) was assessed with an item adapted from the GESIS studies [ 93 ]: “All things considered, how satisfied are you with your life at present?” Responses were provided on a 7-point scale (1 = not at all satisfied, 7 = completely satisfied). All the items included two fallback response options: -1 = “do not know” and − 2 = “not specified,” allowing participants to opt out of specific items. This was important because it allowed participants’ autonomy to decline or express uncertainty, thereby reducing the risk of measurement error. For analysis purposes, these responses were recoded as missing values. The intercorrelations among all the independent variables are presented in Table 1 . A medium correlation was found between life satisfaction and health status ( r = .439, p < .001). The positive correlation suggests a moderate and statistically significant association, indicating that individuals with better health status tend to report greater life satisfaction. Table 1 Intercorrelations of pandemic-specific and psychological variables. 1. 2. 3. 4. 5. 6. 7. 1. Risk group 1 2. Prior infection − .135*** 1 3. Perceived infectability .224*** .107*** 1 4. General self-efficacy − .069* − .024 − .243*** 1 5. Risk propensity − .184*** .129*** − .105*** .215*** 1 6. Life satisfaction − .111*** .064* − .198*** .267*** .134*** 1 7. Health status − .294*** .1** − .359*** .258*** .187*** .439*** 1 Note. Pearson correlation coefficients (r) are presented. *p < .05 **p < .01. ***p < .001. PMT determinants Perceived vulnerability was measured via three items assessing the subjective likelihood of becoming infected, developing illness in the event of an infection, and transmitting the virus to others (adapted from [ 87 , 94 , 95 , 96 ]). Responses were recorded on a 7-point Likert scale (1 = very unlikely, 7 = very likely), with the scale demonstrating good internal consistency (Cronbach’s α = .858). Perceived severity was measured via three items assessing participants’ subjective evaluation of the general threat, potential health consequences, and expected course of illness [ 87 , 97 ]. Responses were recorded on a 7-point Likert scale (1 = completely harmless, 7 = very dangerous). The scale demonstrated excellent internal consistency (Cronbach’s α = .939). The corresponding questionnaire included two items to assess self-efficacy regarding protection against the described new COVID-19 variant [ 17 , 98 , 99 ]. The participants rated their agreement with the statements on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The internal consistency was acceptable (Cronbach's α = .700). Three items (e.g., “The described measures can protect me from the new coronavirus variant”) were used to measure the perceived effectiveness of the recommended measures (response efficacy) [ 17 , 19 , 100 ]. Responses were given on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree; Cronbach’s α = .886). To measure response costs, two statements addressing perceived burden and financial constraints regarding the recommended measures were used (adapted from [ 101 , 102 ]). The participants rated their agreement on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). The two-item scale demonstrated a Cronbach’s α of .626, which is acceptable given the limited number of items, with a moderate interitem correlation of r = .456. Protection motivation was evaluated via a series of six items pertaining to recommended protective behaviors in public settings (e.g., maintaining a distance of at least 1.5 meters, wearing a mask, and avoiding crowded places) adapted from previous studies conducted in Germany [ 103 , 104 , 105 ]. The participants were invited to indicate the frequency with which they engaged in each behavior on a four-point scale (1 = never, 4 = almost always). The reliability of this scale was determined to be good (Cronbach's α = .857). Covariates In addition to the main variables of interest, the analyses controlled for age, gender, level of education, and cohabitation with children, as these sociodemographic factors have been shown to be associated with both threat appraisal and health-related behaviors in the context of COVID-19 [ 33 , 106 , 107 , 108 , 109 ]. Age was measured in years as a continuous variable. The participants’ gender identity was assessed via a single-item self-reported question: “which gender do you identify with?” with three response options: female, male, and diverse. For the purpose of analysis, gender was dummy coded (1 = female, 0 = male). One participant who selected “diverse” was excluded from analyses involving gender because the sample size was insufficient for statistical comparison. Educational attainment was assessed via participants’ highest completed level of education and categorized into three groups, namely, low, medium, and high, on the basis of standard classifications in the national education system. In the analysis, a low education level was used as the baseline value. Living with a child in the household was measured with a binary indicator (1 = at least one child younger than 18 living in the household, 0 = no child present). Data analyses Given the limited evidence on the role of intrapersonal variables in PMT, this study was designed to be hypothesis-generating and exploratory in nature. The aim was to identify patterns of associations between pandemic-related experiences and psychological dispositions and the key PMT determinants and protective intentions. These exploratory findings are intended to generate hypotheses for future confirmatory studies and to inform the development of targeted public health communication and intervention strategies to strengthen pandemic preparedness. Therefore, the hypotheses and research questions formulated (H1.1, RQ1.1, etc.) should be understood as guiding assumptions within an exploratory framework, not as confirmatory tests. To examine the associations between intrapersonal variables and protection motivation theory (PMT) determinants, hierarchical linear regression analyses were conducted. Separate regression models were estimated for each PMT outcome variable (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs). Variables were entered in successive blocks on the basis of their theoretical proximity to the outcome variable. In step 1, sociodemographic variables (age, gender, education, and living with children) were included to control for their influence. In the second block, pandemic-specific contextual variables (perceived belonging to a COVID-19 risk group and prior COVID-19 infection) were added. These variables represent situational factors that may be connected to individuals’ health perceptions and responses within the specific context of the pandemic. Finally, in step 3, psychological variables (general self-efficacy, risk propensity, subjective health status, life satisfaction, and general infectability) were entered. To investigate the predictors of protective behavior, further hierarchical linear regression analyses were performed by adding a fourth block. In step 4, PMT components were incorporated to examine their incremental contributions beyond the previous predictors. At each step, the incremental explanatory power of each block was assessed to test the unique contribution of psychological variables to demographic and pandemic-related associations. Standardized regression coefficients (β) were reported to compare effect sizes across variables. Multicollinearity among variables was assessed by calculating the variance inflation factor (VIF), with values under 5 indicating acceptable levels. Results To investigate the factors influencing COVID-19 health-protective intentions, we first examined how pandemic-specific experiences (risk group membership, prior infection) and psychological dispositions (perceived infectability, general self-efficacy, risk propensity, life satisfaction, and subjective health) were associated with PMT constructs (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs). Hierarchical linear regressions were conducted for each PMT determinant, sequentially assessing the contributions of sociodemographic, pandemic specific, and intrapersonal variables. To further explore the intrapersonal predictors of the PMT constructs, we conducted a hierarchical linear regression analysis across the different determinants of PMT. For each PMT construct, we estimated three models, incrementally incorporating sociodemographic characteristics (Table 2 ), pandemic-specific risk factors (Table 3 ), and psychological dispositions (Table 4 ). A heatmap of the standardized regression coefficients for associations between pandemic-related experiences, psychological dispositions, and PMT determinants visualizes the key findings (Fig. 2 ). To examine protective behavior, a fourth model (Table 5 ) was constructed that included all the PMT determinants. Table 3 Outputs of linear regression models examining associations between sociodemographic variables and PMT constructs. Vulnerability Severity Self-efficacy Response Efficacy Response Costs Protective Motivation Predictors std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p (Intercept) -0.29 (-0.42 – -0.17) < 0.001 -0.11 (-0.24–0.01) 0.083 -0.03 (-0.16–0.09) 0,624 -0.13 (-0.26 – -0.01) 0.035 0.28 (0.16–0.41) < 0.001 -0.19 (-0.31 – -0.06) 0.004 Age -0.04 (-0.11–0.03) 0.244 0.17 (0.10–0.24) < 0.001 0.10 (0.03–0.17) 0,005 0.18 (0.11–0.25) < 0.001 -0.24 (-0.30 – -0.17) < 0.001 0.24 (0.17–0.31) < 0.001 Gender (female) 0.07 (-0.05–0.19) 0.277 0.17 (0.04–0.29) 0.008 0.05 (-0.07–0.18) 0,392 0.05 (-0.07–0.17) 0.423 -0.09 (-0.21–0.04) 0.172 0.19 (0.06–0.31) 0.003 Education (middle) 0.14 (-0.02–0.30) 0.085 -0.05 (-0.22–0.11) 0.512 0.06 (-0.10–0.23) 0,449 0.03 (-0.13–0.19) 0.727 -0.24 (-0.40 – -0.08) 0.003 0.07 (-0.09–0.24) 0.391 Education (high) 0.47 (0.32–0.62) < 0.001 0106 (-0.06–0.25) 0.225 0.03 (-0.12–0.19) 0,701 0.31 (0.15–0.46) < 0.001 -0.51 (-0.67 – -0.36) < 0.001 0.24 (0.08–0.39) 0.003 Children (yes) 0.23 (0.07–0.38) 0.004 0.03 (-0.13–0.18) 0.727 -0.09 (-0.25–0.06) 0,237 -0.06 (-0.22–0.10) 0.448 0.05 (-0.10–0.21) 0.487 -0.05 (-0.21–0.11) 0.551 Observations 969 996 1009 992 987 949 R 2 /R 2 adjusted 0.072/0.067 0.029/0.024 0.014/0.009 0.032/0.027 0.066/0.061 0.055/0.050 The table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p < .05) are highlighted in bold. Table 4 Outputs of linear regression models examining associations between pandemic-related variables and PMT constructs. Vulnerability Severity Self-efficacy Response Efficacy Response Costs Protective Motivation Predictors std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p (Intercept) -0.73 (-0.87 – -0.58) < 0.001 -0.45 (-0.60 – -0.30) < 0.001 0.16 (0.00–0.31) 0.032 -0.16 (-0.31–0.00) 0.068 0.34 (0.18–0.49) < 0.001 -0.32 (-0.48 – -0.17) < 0.001 Age -0.07 (-0.14–0.01) 0.081 0.04 (-0.03–0.11) 0.267 0.11 (0.03–0.19) 0.007 0.14 (0.06–0.22) 0.001 -0.23 (-0.31 – -0.16) < 0.001 0.17 (0.09–0.25) < 0.001 Gender (female) 0.05 (-0.07–0.17) 0.405 0.12 (-0.00–0.24) 0.058 0.06 (-0.07–0.19) 0.366 0.04 (-0.09–0.17) 0.520 -0.06 (-0.19 – -0.07) 0.343 0.18 (0.05–0.31) 0.006 Education (middle) 0.11 (-0.04–0.27) 0.151 -0.06 (-0.22–0.10) 0.458 0.04 (-0.12–0.21) 0.607 -0.00 (-0.17–0.16) 0.954 -0.22 (-0.38 – -0.05) 0.010 0.09 (-0.08–0.25) 0.304 Education (high) 0.43 (0.28–0.58) < 0.001 0.12 (-0.03–0.27) 0.110 0.06 (-0.12–0.22) 0.455 0.29 (0.13–0.45) < 0.001 -0.46 (-0.62 – -0.31) < 0.001 0.25 (0.10–0.41) 0.002 Children (yes) 0.21 (0.06–0.35) 0.006 0.07 (-0.08–0.21) 0.366 -0.09 (-0.24–0.07) 0.286 -0.07 (-0.23–0.09) 0.388 -0.09 (-0.06–0.25) 0.247 -0.04 (-0.20–0.12) 0.611 Risk Group (yes) 0.45 (0.32–0.59) < 0.001 0.82 (0.68–0.96) < 0.001 -0.17 (-0.32 – -0.03) 0.021 0.16 (-0.01–0.31) 0.034 -0.05 (-0.19–0.10) 0.530 0.38 (0.23–0.52) < 0.001 Infection (yes) 0.52 (0.40–0.65) < 0.001 0.12 (-0.01–0.25) 0.066 -0.24 (-0.37 – -0.10) 0.001 -0.02 (-0.16–0.12) 0.775 -0.14 (-0.27 – -0.00) 0.047 -0.0 (-0.14–0.13) 0.985 Observations 926 949 961 946 940 903 R 2 /R 2 adjusted 0.172/0.166 0.155/0.149 0.031/0.024 0.033/0.026 0.065/0.057 0.081/0.074 The table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p < .05) are highlighted in bold. Table 5 Outputs of linear regression models examining the associations between psychological variables and PMT constructs. Vulnerability Severity Self-efficacy Response Efficacy Response Costs Protective Motivation Predictors std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p std. Beta (std. CI) p (Intercept) -0.60 (-0.74 – -0.45) < 0.001 -0.34 (-0.48 – -0.19) < 0.001 0.07 (-0.08–0.23) 0.422 -0.13 (-0.29–0.03) 0.110 0.26 (0.10–0.42) 0.003 -0.24 (-0.40 – -0.08) 0.003 Age -0.02 (-0.09–0.05) 0.591 0.06 (-0.01–0.14) 0.097 0.02 (-0.06–0.10) 0.642 0.09 (0.01–0.17) 0.032 -0.18 (-0.26 – -0.10) < 0.001 0.14 (0.06–0.22) < 0.001 Gender (female) -0.03 (-0.14–0.09) 0.636 0.05 (-0.07–0.17) 0.417 0.08 (-0.05–0.20) 0.210 -0.01 (-0.14–0.13) 0.918 -0.00 (-0.13–0.12) 0.946 0.09 (-0.04–0.22) 0.179 Education (middle) 0.14 (-0.02–0.29) 0.080 -0.03 (-0.19–0.12) 0.684 0.08 (-0.08–0.24) 0.337 0.00 (-0.17–0.17) 0.993 -0.16 (-0.33–0.00) 0.056 0.08 (-0.09–0.24) 0.373 Education (high) 0.48 (0.34–0.63) < 0.001 0.19 (0.04–0.34) 0.014 -0.00 (-0.16–0.16) 0.981 0.26 (0.10–0.43) 0.002 -0.36 (-0.52 – -0.20) < 0.001 0.29 (0.13–0.45) < 0.001 Children (yes) 0.16 (0.02–0.30) 0.024 0.05 (-0.10–0.19) 0.532 -0.11 (-0.26–0.04) 0.167 -0.09 (-0.25–0.06) 0.244 0.14 (-0.02–0.29) 0.078 -0.08 (-0.23–0.08) 0.334 Risk Group (yes) 0.23 (0.09–0.37) 0.002 0.60 (0.46–0.74) < 0.001 -0.00 (-0.15–0.15) 0.978 0.18 (-0.03–0.34) 0.022 -0.09 (-0.24–0.07) 0.267 0.28 (0.12–0.43) < 0.001 Infection (yes) 0.46 (0.33–0.58) < 0.001 0.06 (-0.07–0.18) 0.363 -0.17 (-0.31 – -0.04) 0.010 -0.01 (-0.15–0.13) 0.848 -0.14 (-0.28 – -0.01) 0.040 -0.02 (-0.15–0.12) 0.816 General infectibility 0.33 (0.26–0.39) < 0.001 0.26 (0.19–0.32) < 0.001 -0.26 (-0.33 – -0.19) < 0.001 -0.02 (-0.09–0.05) 0.537 -0.02 (-0.09–0.05) 0.625 0.10 (0.03–0.17) 0.007 General self-efficacy -0.04 (-0.10–0.02) 0.208 -0.07 (-0.13 – -0.01) 0.027 0.19 (0.12–0.25) < 0.001 0.02 (-0.05–0.09) 0.509 -0.02 (-0.09–0.04) 0.524 0.03 (-0.04–0.10) 0.404 Risk propensity -0.02 (-0.08–0.04) 0.468 -0.03 (-0.10–0.03) 0.287 -0.04 (-0.11–0.02) 0.194 -0.12 (-0.19 – -0.05) 0.001 0.15 (0.08–0.22) 0.001 -0.19 (-0.26 – -0.12) < 0.001 Life satisfaction 0.07 (0.00–0.13) 0.041 0.10 (0.03–0.17) 0.003 0.05 (-0.02–0.12) 0.147 0.14 (0.07–0.22) < 0.001 -0.20 (-0.27 – -0.13) < 0.001 0.14 (0.07–0.22) < 0.001 Self-rated health status -0.02 (-0.10–0.05) 0.493 -0.10 (-0.17 – -0.02) 0.009 -0.03 (-0.10–0.05) 0.450 -0.00 (-0.08–0.08) 0.967 -0.07 (-0.15–0.00) 0.058 -0.09 (-0.17 – -0.01) 0.029 Observation 887 908 917 904 897 869 R 2 /R 2 adjusted 0.277/0.267 0.239/0.229 0.148/0.137 0.064/0.051 0.123/0.112 0.140/0.128 The table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p < .05) are highlighted in bold. Figure 2 . Heatmap of standardized regression coefficients (β) of the final linear regression models. Legend. Only statistically significant coefficients (p < .05) for associations between pandemic-related experiences, psychological dispositions, and PMT determinants are shown in color; nonsignificant cells are displayed in gray. Positive coefficients are indicated by blue tones, and negative coefficients are indicated by red tones. Sociodemographic variables The study included a total of 1,050 participants. The participants in the study ranged in age from 18 to 67 years (mean = 48.7, SD = 16.176). Among the total sample, 527 participants (50.2%) identified as female, 522 (49.7%) as male, and one (0.1%) as diverse. With respect to educational attainment, the largest group reported a low educational level (40.3%, n = 423), followed by high educational attainment (36.7%, n = 385). A smaller proportion of respondents reported a middle level of education (23.0%, n = 242). The majority of respondents (79.1%, n = 831) indicated that they did not live with children, whereas 20.9% (n = 219) reported living with children in the household (see Table 2 ). Table 2 Sociodemographic characteristics of the study participants. Category Frequency Percentage (%) Mean SD Age Total 1050 100.0 48.7 16.176 Gender Female 527 50.2 Male 522 49.7 Divers 1 0.1 Total 1050 100.0 Education Low 423 40.3 Middle 242 23.0 High 385 36.7 Total 1050 100.0 Living with Children Yes 219 20.9 No 831 79.1 Total 1050 100.0 The results of the hierarchical linear regression analysis revealed that the sociodemographic variables accounted for a small to modest proportion of the variance in the PMT constructs (adjusted R² = .024–067) (Table 3 ). Among the investigated variables, age was positively associated with perceived severity ( β = .17, p < .001), self-efficacy ( β = .10, p = .005), response efficacy ( β = .18, p < .001), and protection motivation ( β = .17, p < .001). Furthermore, an inverse relationship was identified between age and response costs ( β = − .24, p < .001). As indicated by the findings, females exhibited higher levels of perceived severity ( β = .17, p = .008) and protection motivation ( β = .06, p = .003) than males did. Higher education was significantly associated with increased vulnerability ( β = .47, p < .001), response efficacy ( β = .31, p < .001), and protection motivation ( β = .24, p = .003). Education was also identified as a significant factor related to response costs: individuals with a medium ( β = − .24, p = .003) or high ( β = − .51, p < .001) education level reported lower response costs than those with lower education did. Living with children was associated with an increased perception of vulnerability ( β = .23, p = .004). Table 3 . Outputs of linear regression models examining associations between sociodemographic variables and PMT constructs. Overall, the findings suggest that sociodemographic factors, particularly age, gender, and education, are modest but significant predictors of PMT constructs, influencing individuals’ perceptions of risk, efficacy beliefs, and protection motivation. Pandemic-related variables The incorporation of pandemic-specific variables was associated with a substantial increase in model fit, particularly with respect to perceived vulnerability (adjusted R² = .166) and severity (adjusted R² = .149) (Table 4 ). The effects of the sociodemographic variables remained largely consistent. Notably, the previously significant effects of age and gender on perceived severity became nonsignificant ( p > .05) after pandemic-related variables were included, suggesting partial mediation through risk group classification. Table 4 . Outputs of linear regression models examining associations between pandemic-related variables and PMT constructs. The strongest correlation was demonstrated for risk group self-identification and perceived severity ( β = .82, p < .001). This finding indicates that individuals who identify as high risk perceive COVID-19 as particularly severe, thereby substantiating Hypothesis H1.1. Furthermore, positive associations were identified between self-identification as a risk group member and perceived vulnerability ( β = .45, p < .001), response efficacy ( β = .16, p = .034), and protection motivation ( β = .38, p < .001). These findings suggest that individuals who consider themselves vulnerable not only perceive greater threat but also report stronger motivation to adopt protective behaviors, thereby substantiating H1.2. Moreover, belonging to a risk group for COVID-19 was significantly associated with lower levels of self-efficacy ( β = –.17, p = .021), indicating that risk groups feel less confident in their ability to manage the threat than nonrisk groups are. With respect to infection history, respondents who reported prior COVID-19 infection demonstrated substantially higher levels of perceived vulnerability ( β = .52, p < .001), thus providing support for H2.1. As hypothesized (H2.2), a self-reported history of infection demonstrated a nonsignificant association with perceived severity. However, experiencing an infection was significantly associated with lower levels of self-efficacy ( β = –.24, p = .001) and lower perceived response costs ( β = − .14, p = .047). This pattern suggests that having been infected may undermine individuals’ confidence in their coping ability while simultaneously reducing the perceived burden of protective behaviors. Taken together, the results highlight that pandemic-specific factors are associated with perceived vulnerability and severity while also shaping efficacy beliefs and protection motivation. Psychological variables The incorporation of psychological variables resulted in a substantial increase in explanatory power in almost all the models (adjusted R² ranging from R² = .051 to R² = .267 ) (Table 5 ). The majority of sociodemographic and pandemic-specific variables (risk group membership and infection experience) maintained their statistical significance. Notably, the effects of age and risk group affiliation on self-efficacy became nonsignificant ( p > .05), indicating that these associations may be attributable to psychological factors. Table 5 . Outputs of linear regression models examining the associations between psychological variables and PMT constructs. In accordance with our hypothesis (H3.1), participants with higher general perceived infectability reported notably higher perceived vulnerability ( β = .33, p < .001). Similarly, greater perceived infectability was associated with greater perceived severity ( β = .26, p < .001) and increased protection motivation ( β = .10, p = .007), indicating that individuals who perceive themselves as more susceptible anticipate more severe outcomes and are somewhat more motivated to take protective actions. The latter finding lends support to Hypothesis H3.2. Conversely, a heightened perception of infectability was related to diminished situational self-efficacy ( β = –.26, p < .001), suggesting that feeling more vulnerable may reduce confidence in oneself to effectively implement protective measures. General self-efficacy demonstrated the strongest connection to situational self-efficacy ( β = .19, p < .001), highlighting that individuals with greater overall confidence are more confident in engaging in specific protective behaviors. This finding supports H4.1. Interestingly, general self-efficacy had a small but significant negative effect on severity ( β = –.07, p = .027), implying that more confident individuals may slightly downplay the severity of the threat. The evidence from the present study supported Hypothesis H5.1, which suggested that risk propensity behavior is negatively associated with protection motivation ( β = –.19, p < .001). This finding indicates that individuals who take more risks are less motivated to adopt protective behaviors. A higher risk propensity was also found to be related to a modest decrease in response efficacy ( β = − .12, p = .001) and a small increase in response costs ( β = .15, p < .001), suggesting that risk-takers perceive protective measures as less effective and more burdensome. Contrary to the initial suppositions (H6.1 and H6.2), the study revealed a positive correlation between life satisfaction and perceived vulnerability ( β = .07, p = .041) and severity ( β = .10, p = .003), suggesting that more satisfied individuals may recognize threats more acutely. Furthermore, the findings demonstrated positive correlations of life satisfaction with response efficacy ( β = .14, p < .001) and protection motivation ( β = .14, p < .001), thereby supporting Hypothesis H6.3. These findings indicate that those with greater life satisfaction are more confident in their protective actions and more motivated to engage in them. In addition, life satisfaction was negatively tied to response costs (β = − .20, p < .001), reflecting that individuals with greater well-being perceive fewer barriers to implementing protective behaviors. Finally, as hypothesized (H7.1 and H7.2), a higher self-rated health status was associated with diminished perceived severity ( β = − .10, p = .009) and diminished protection motivation ( β = − .09, p = .029), suggesting that individuals who perceive themselves as healthier feel less threatened and are marginally less motivated to engage in protective behaviors. In summary, the results indicate that the tested psychological factors, particularly perceived infectability, general self-efficacy, risk propensity, life satisfaction, and self-rated health, substantially shape protection motivation and related perceptions, often outweighing sociodemographic and pandemic-specific influences. PMT determinants To investigate the extent to which the PMT determinants (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs) impact protection motivation, a final hierarchical linear regression model was constructed (Table 6 ). This model was associated with 51% of the variance in protection motivation (adjusted R² = .497), indicating a substantial increase in explanatory power. Table 6 Output of linear regression models examining associations between PMT determinants and protective motivation. Protective Motivation Predictors std. Beta (std. CI) p (Intercept) -0.01 (-0.14–0.12) 0.844 Age 0.07 (0.01–0.14) 0.034 Gender (female) 0.06 (-0.04–0.16) 0.223 Education (middle) 0.07 (-0.06–0.20) 0.302 Education (high) 0.06 (-0.08–0.19) 0.411 Children (yes) -0.02 (-0.14–0.10) 0.764 Risk Group (yes) 0.02 (-0.11–0.14) 0.808 Infection (yes) -0.10 (-0.21–0.01) 0.075 General infectibility 0.01 (-0.05–0.07) 0.718 General self-efficacy 0.05 (-0.00–0.10) 0.063 Risk propensity -0.09 (-0.15 – -0.04) 0.001 Life satisfaction 0.01 (-0.05–0.07) 0.697 Self-rated health status -0.08 (-0.14 – -0.01) 0.015 Perceived vulnerability 0.04 (-0.02–0.11) 0.189 Perceived severity 0.25 (0.18–0.32) < 0.001 Self-efficacy -0.04 (-0.10–0.02) 0.165 Response efficacy 0.44 (0.38–0.50) < 0.001 Response costs -0.17 (-0.23 – -0.12) < 0.001 Observations 803 R 2 /R 2 adjusted 0.508/0.497 The table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p < .05) are highlighted in bold. Table 6 . Output of linear regression models examining associations between PMT determinants and protection motivation. The strongest predictor was response efficacy ( β = .44, p < .001), suggesting that individuals’ beliefs in the effectiveness of protective behaviors play a primary role in motivating protective actions. Perceived severity also significantly predicted protection motivation ( β = .25, p < .001), indicating that perceiving health threats as serious contributes meaningfully to motivation. Response costs were negatively associated with protection motivation ( β = –.17, p < .001), highlighting that higher perceived barriers or burdens reduce individuals’ willingness to engage in protective behaviors. The other PMT variables, including perceived vulnerability and self-efficacy, were not significant, suggesting that once efficacy beliefs and threat severity are accounted for, these factors may be less influential. Once these constructs were included, most intrapersonal predictors lost significance, except for age, risk group membership, and subjective health status. This finding indicates that certain demographic and health characteristics continue to influence protection motivation independently of the PMT construct. Nevertheless, it seems that for most demographic variables and health characteristics, their association with protection motivation is mediated by PMT constructs. Overall, these findings indicate that protection motivation is driven primarily by individuals’ beliefs in the effectiveness of protective behaviors and the perceived severity of the health threat, whereas perceived costs can deter motivation, highlighting the central role of PMT constructs over other intrapersonal factors. Discussion In the present study, we asked whether extending protection motivation theory (PMT) with intrapersonal dispositions and pandemic-specific experiences can increase our understanding of the protective effects of COVID-19. This study aimed to explore how intrapersonal sources of information, such as pandemic-related experiences and stable psychological dispositions, are related to the key cognitive construct in protection motivation theory (PMT). Using a German quota sample (January 2024), we found that adding psychological dispositions and experiences substantially improved model fit for all PMT determinants and, ultimately, protection motivation. Threat appraisals were most strongly tied to pandemic-specific factors and perceived infectability: self-identified risk group status and prior infection were associated with higher perceived severity and vulnerability, respectively, whereas perceived infectability robustly predicted both vulnerability and severity but was linked to lower situational self-efficacy. Coping appraisals were related to lower risk propensity and higher life satisfaction. Risk-prone individuals reported lower response efficacy and higher response costs, whereas greater life satisfaction was linked to higher response efficacy and lower costs. General self-efficacy mapped onto domain-specific self-efficacy, and better self-rated health status was related to lower perceived severity and slightly lower protection motivation. In the final model predicting protective intentions, PMT constructs dominated. Response efficacy (strongest), perceived severity (positive), and response costs (negative) explained ~ 50% of the variance, with perceived vulnerability and PMT self-efficacy playing smaller roles once other factors were included. Nevertheless, even after including the PMT constructs age, risk group membership and subjective health status retained a significant association with protection motivation. Taken together, these exploratory findings indicate that incorporating stable intrapersonal characteristics alongside pandemic-specific experiences yields a more nuanced and more powerful account of PMT processes and intentions than does PMT alone, supporting our rationale for a dispositional extension of PMT and motivating confirmatory longitudinal and experimental work to test and apply this framework in preparedness and targeted public health communication. Sociodemographic variables Consistent with prior research, sociodemographic variables accounted for only a small proportion of the variance. Older adults and highly educated individuals reported stronger beliefs in protective actions and lower response costs, likely reflecting differences in health literacy, access to information, or trust [ 110 , 111 ]. The observed gender differences regarding perceived severity may be partially explained by gender stereotypes, roles and norms [ 112 , 113 , 114 ]. People living with children demonstrated a heightened sense of vulnerability, possibly due to increased exposure through children’s social networks. Pandemic-related variables The incorporation of indicators specific to pandemics resulted in a substantial increase in explanatory power, primarily for threat appraisal models. Risk group self-identification The present findings lend further support to previous research [ 31 , 32 ] and provide evidence for the hypothesis that perceived severity is associated with risk group self-identification. In addition, the present study demonstrated that individuals who self-identify as part of a risk group perceive heightened vulnerability. This phenomenon may be attributed to a realistic evaluation of an increased probability of infection [ 115 ] or, alternatively, to the repeated emphasis of public health communications during the pandemic on the heightened risks associated with specific demographic groups. Consequently, individuals who have adopted this “at-risk” identity may be able to evaluate their probability of contracting the virus and experiencing severe outcomes more accurately than those who have not. Beyond threat appraisal, affiliation with risk groups was associated with increased response efficacy and decreased self-efficacy. However, the initially significant effect on self-efficacy did not remain significant after we accounted for psychological dispositions. These findings suggest that membership in risk groups may enhance attention to public health messaging tailored to their specific needs and heighten receptivity to intervention rationales, although this interpretation requires further investigation. Finally, the findings provide further support for our hypothesis that being part of a risk group is related to higher levels of protection motivation. However, the significance of this relationship disappears once the PMT determinants are introduced into the equation, thereby indicating that the effect of being part of a risk group on protection motivation is mediated through the PMT determinants. COVID-19 experience The association between prior COVID-19 infection and threat appraisal aligns with our expectations and supports the hypothesis that previous infection is strongly linked to perceived vulnerability but not to perceived severity [ 37 , 38 ]. Additionally, individuals with a history of infection demonstrated lower levels of self-efficacy. These findings indicate that a previous infection has the potential to compromise an individual's sense of self-efficacy in determining their health outcomes. Notably, the subjects also perceived a reduction in response costs, which may be attributable to an increased level of familiarity with the illness or a diminished perception of novelty and threat. These findings suggest that individuals' perceptions of protective behaviors may be linked to their personal experiences, potentially leading to a reduced perception of burden or disruption. Overall, previous infection had a complex relationship with the PMT components, heightening vulnerability while reducing self-efficacy but also response costs. These countervailing effects may provide a rationale for the absence of a significant link between infection history and protection motivation. Furthermore, previous research has demonstrated that the severity of symptoms, rather than the infection itself, is a more significant factor in promoting protective behavior [ 43 , 116 ]. Psychological variables Most notably, the results of this study demonstrated that psychological dispositions substantially enhanced the explanatory power of all the PMT determinants and significantly contributed to the perception of coping and threat appraisals. Perceived infectability In accordance with our hypothesis, general perceived infectability was strongly connected to perceived vulnerability. Furthermore, the study demonstrated a consistent effect of baseline infectability on severity, even after demographic and pandemic-specific variables were controlled. These positive associations support the notion that individuals with heightened sensitivity to pathogen threats, which is consistent with the behavioral immune system (BIS) [ 46 ], are more likely to consider infections to be personally relevant and serious. The results of the study suggest that general perceived infectability beliefs activate preexisting illness vulnerability and severity scripts, which, in turn, are linked to the estimation of outcomes for cases of the disease. Interestingly, a heightened perception of infectability was found to be associated with a decline in self-efficacy. It is plausible that individuals with high PI may perceive the environment as being saturated with unavoidable risks, thereby diminishing their belief in their own ability to prevent infection. As an alternative hypothesis, this negative association may be an indication of learned helplessness among individuals who consistently perceive themselves as biologically vulnerable and have experienced frequent or severe illness in the past. This perception may be attributed to a perceived lack of agency in disease prevention, particularly if vulnerability is ascribed to biological or uncontrollable factors. These findings underscore a potential trade-off within the behavioral immune system, wherein heightened threat sensitivity may be accompanied by a concomitant reduction in perceived control. In this study, general perceived infectability was found to be associated with greater protection motivation, although the association became nonsignificant after the incorporation of PMT determinants. Overall, these patterns suggest that while individuals who perceive themselves as more susceptible to infection may experience heightened initial motivation to adopt protective behaviors, this motivation may be mediated by specific cognitive appraisals, such as perceived severity, vulnerability, and self-efficacy. These findings align with previous reports of an ambivalent relationship between general perceived infectability and COVID-19 preventive behavior [ 48 , 51 , 52 ]. Perceived general self-efficacy As hypothesized, general self-efficacy (GSE) was strongly tied to COVID-19 self-efficacy, thereby reinforcing the notion that a disposition of confidence in one's capabilities generalizes to specific health-related challenges (social‒cognitive framework, [ 117 ]; see also [ 118 ]). Furthermore, the study demonstrated a negative correlation between general self-efficacy and perceived severity, although the effect was modest. These findings are corroborated by Zhou et al. (2021) [ 119 ]. The inverse association between self-efficacy and perceived severity may reflect a psychological distancing mechanism whereby high-efficacy individuals regulate threat appraisals to preserve agency. While this interpretation aligns with social-cognitive theory, it remains speculative and warrants further empirical investigation. Risk propensity In accordance with our hypotheses and prior research [ 61 , 62 ], this study revealed that risk propensity was negatively associated with protection motivation. Building on these findings, we revealed that risk-prone individuals presented lower levels of coping appraisal, reporting reduced response efficacy and higher perceived response costs. These findings are consistent with those of previous studies suggesting that individuals high in risk tolerance tend to underestimate the benefits of protective actions while overestimating their disadvantages [ 120 , 121 ]. This pattern may reflect cognitive dissonance [ 122 , 123 ] or a motivational preference for autonomy and sensation seeking, which diminishes the perceived value of health-protective behaviors. Interestingly, no significant correlation was found between risk propensity and threat perception. These findings contrast with earlier findings indicating that risk-tolerant individuals often display optimism bias and downplay personal vulnerability [ 124 , 125 ]. One interpretation is that risk propensity may be related to motivation more strongly than to cognition, affecting coping appraisal while leaving threat appraisal intact. Alternatively, intrapersonal variables (e.g., sensation seeking and psychological reactance) or the cultural framing of risk may modulate this relationship. Thus, individuals high in risk propensity may recognize the severity of a threat but remain unmotivated to act because of low levels of coping appraisal. Life satisfaction This study lends support to our hypothesis that individuals who report greater life satisfaction are more inclined to engage in health-protective behaviors, although this effect disappears once PMT constructs are introduced. Our findings suggest that this phenomenon functions principally through coping appraisal processes, as elevated levels of life satisfaction are associated with increased response efficacy and decreased response costs. Although these outcomes have received limited attention in PMT research, they align with the findings of positive psychology, indicating that elevated subjective well-being is associated with enhanced optimism, more effective positive coping strategies, and elevated resilience [ 126 , 127 , 128 , 129 ]. When transferred to life satisfaction, this association may explain the greater engagement in protective behaviors, driven by a stronger sense of control and lower perceived behavioral cost. The same line of reasoning can be used to argue that heightened life satisfaction serves to mitigate perceived risk. This assumption is supported by numerous previous studies examining the effect of risk perception on life satisfaction [ 73 , 74 ]. Consequently, a negative correlation between life satisfaction and perceived severity and vulnerability was hypothesized. However, the results of the present study revealed the opposite pattern. This difference highlights the importance of investigating potential bidirectional or recursive effects. These findings suggest that individuals who report higher levels of life satisfaction or better perceived health may place greater value on maintaining their situation and thus respond more strongly to perceived threats. Self-rated health status In accordance with the hypothesis and extant research [ 80 , 83 , 85 ], self-rated health status was negatively associated with perceived severity and protection motivation. These findings suggest that individuals who perceive themselves to be in good health may underestimate the potential severity of the disease and are less motivated to adopt protective behaviors. The findings of this study indicate that self-rated health status is not associated with perceived vulnerability, self-efficacy, response efficacy or response costs. PMT determinants The final analysis, which incorporated the core cognitive constructs of PMT to predict protection motivation, demonstrated that the PMT variables were the most powerful predictors of protection motivation, explaining more than 50% of the variance. These findings align with the extensive empirical evidence supporting PMT [ 16 , 130 ], thereby underscoring the significance of how individuals cognitively assess threats and available coping mechanisms. Among these variables, response efficacy had the strongest effect, suggesting that belief in the effectiveness of protective actions is a critical determinant of motivation, which is in line with prior research conducted in Germany [ 131 , 132 ]. In addition, the findings of the present study align with the postulation of PMT, which suggests that perceived response costs are negatively correlated with protection motivation. Notably, PMT-specific self-efficacy did not significantly predict motivation in the final model, despite its conceptual centrality to the theory. This phenomenon may be attributable to shared variance with general self-efficacy, which was incorporated earlier in the modeling and initially demonstrated a significant positive association with protection motivation. However, a body of research conducted in Germany has yielded equivocal results, identifying either a strong [ 22 ] or no significant [ 132 ] impact. Consistent with PMT, perceived severity has been demonstrated to be a significant predictor of behavioral intentions. The hypothesis that perceived vulnerability significantly predicts motivation was not supported by the data, which contradicts the assumption of PMT. Nevertheless, in the context of pandemic responses in Germany, perceived vulnerability appears to be weakly linked to protective behavior [ 22 , 131 , 132 ]. In addition to the PMT determinants, the analysis revealed that self-rated health status, risk propensity, and age were the only direct significant predictors of protection motivation. This pattern implies that individuals in younger age groups, those with better health status, and those with higher risk propensity are less likely to adhere to behavioral guidelines. After the PMT scores were added to the model, further pandemic-related and psychological determinants lost significance. These findings suggest that these factors indirectly affect protection motivation, presumably through their effect on PMT components. Implications The findings of the study highlight the limited explanatory power of sociodemographic variables and suggest that intrapersonal psychological factors may play a role in shaping responses to health threats. The aforementioned associations operated primarily indirectly through the cognitive appraisals specified in PMT. The study revealed that pandemic-related variables were particularly associated with threat appraisal, whereas psychological traits were strongly linked to both threat and coping appraisals. This pattern challenges the assumption of uniform cognitive processing in PMT and points to systematic interindividual differences in how people cognitively engage with health risks. Theoretical implications Consequently, this study contributes to a more differentiated understanding of the motivational dynamics underlying health-protective behavior. These findings corroborate the calls in the literature to move beyond situation-specific predictors and incorporate dispositional traits into models of health behavior [ 133 , 134 , 135 ]. On the basis of the observed associations, we outline a heuristic “dispositional extended PMT (dPMT)” (Fig. 3 ) that integrates pandemic-related experiences and psychological dispositions as upstream factors potentially associated with PMT processes. The framework is derived from the current exploratory findings and is intended to serve as a conceptual guide for future longitudinal and experimental research rather than as a tested theoretical model. The dPMT emphasizes that individual differences (e.g., risk group self-identification, perceived infectability, and general self-efficacy) could help explain the variability in threat and coping appraisal beyond traditional PMT determinants. This perspective may support the design of more person-centered public health interventions. Notably, additional factors may be considered. For example, research has demonstrated associations between PMT determinants and personality traits [ 136 , 137 , 138 ], trust in government [ 36 , 139 , 140 ], and values [ 141 ] Figure 3 . Dispositional extended PMT (dPMT). Legend. Intrapersonal sources of information, particularly pandemic-related factors (risk group self-identification, prior infection) and psychological factors (perceived infectability, general self-efficacy, risk propensity, life satisfaction, health status), influence cognitive evaluations of threats (vulnerability, severity) and coping capacities (self-efficacy, response efficacy, response costs). The interaction of these appraisals generates protection motivation, which in turn drives protective behavior. Practical implications Understanding the relationship between intrapersonal and PMT determinants has significant implications for the design of effective public health interventions and communication strategies during health crises such as the COVID-19 pandemic. The findings underscore the necessity of refined health interventions during pandemics. For instance, interventions targeting protective behavior could emphasize response efficacy clearly and repeatedly and address perceived response costs. This approach is particularly relevant for individuals with high risk propensity and low life satisfaction. In addition, the results support a person-centered approach to health behavior interventions. Rather than treating the PMT determinants as situationally induced appraisals, future interventions might profit from screening or segmenting populations on the basis of psychological predispositions. This approach aligns with recent advances in precision public health and behavioral science, which advocate for targeted, psychologically informed messaging. On the basis of our findings, the following initial ideas illustrate how tailored health communication could address distinct psychological dispositions. Individuals with high perceived infectability may benefit from messages that explicitly enhance self-efficacy and convey concrete, manageable actions to reduce perceived uncontrollability. Communication focused on people with prior infections could emphasize rebuilding a sense of personal competence and control. People with high life satisfaction may profit from positively framed, autonomy-enhancing messages that emphasize the preservation of valued life conditions. Messages for individuals with low life satisfaction could enhance personal agency, highlighting attainable behavioral gains, fostering response efficacy, and suggesting easy-to-implement behavioral strategies, as traditional threat-based messaging can be ineffective or even counterproductive. Individuals with high levels of self-rated health may underestimate threat severity. Communication could stress the relevance of protective actions even for those who perceive themselves as healthy. Individuals with high risk propensity may respond better to messages emphasizing personal benefits and low response costs rather than fear-based appeals. Resilience The findings of the study further underscore the value of integrating psychological resilience into public health strategies, particularly in the context of proactive preparedness for global health threats such as pandemics. By considering dispositional factors such as life satisfaction, subjective health status, and general self-efficacy, this study advances beyond traditional models focused solely on medical risk. This broader lens allows for a more precise identification of vulnerable groups, enabling interventions that align with individuals’ perceived vulnerabilities. Notably, the results suggest that general self-efficacy may serve as a foundational resource that facilitates the development of specific self-efficacy, ultimately enhancing compliance with protective behaviors. Interventions aimed at increasing general self-efficacy, particularly among people with limited health literacy or structural disadvantage, may therefore strengthen individuals' belief in their general capacity to cope with challenges, which, in turn, reinforces more context-specific protective actions. This finding highlights the importance of coupling targeted health communication with empowerment-based strategies that enhance psychological resources. Additionally, fostering life satisfaction may indirectly support behavior change by reducing the perceived burden of health behaviors and reinforcing adaptive coping appraisals. Public health messaging should thus adopt a dual approach: tailoring messages to individual risk profiles while simultaneously investing in psychological resilience through interventions that build agency, confidence, and a sense of personal control. Such psychologically grounded strategies are critical not only for improving immediate behavioral compliance but also for preparing populations to respond adaptively during future crises. Limitations and future research While the present findings offer important insights, it is necessary to mention some limitations and consider how they interact with PMT processes. Methodological constraints and causal inference The cross-sectional design represents a key limitation, as it precludes causal interpretations. Relationships between psychological dispositions (e.g., life satisfaction, perceived infectability) and PMT determinants may be bidirectional or influenced by unmeasured third variables. Longitudinal studies are therefore needed to disentangle these relationships over time. Experimental and quasiexperimental designs may also help clarify causal pathways and improve model robustness. While key covariates were included, the possibility of residual confounding remained. Future research should consider strategies such as sensitivity analyses, instrumental variable approaches, or negative control outcomes to evaluate potential biases arising from unobserved variables. In addition, mediation effects are suggested by the data, although these effects were not formally tested. Further research could employ mediation analyses to assess whether PMT variables mediate the relationship between general psychological dispositions and protection motivation. Moderation effects such as the role of trust, anxiety, or optimism in shaping threat and coping appraisals should also be examined. For instance, institutional trust may increase perceived response efficacy, whereas distrust could undermine it. Contextual and cultural limitations The geographical focus on Germany restricts the generalizability of the findings. Cultural factors, including public health messaging styles, healthcare system characteristics, and culturally shaped attitudes toward risk, can strongly influence intrapersonal variables and PMT determinants. Cross-cultural research is therefore needed to assess whether the observed patterns hold in other contexts and to identify culturally specific influences. Qualitative approaches could further enrich the understanding of such contextual dynamics and provide insight into unexpected associations such as the observed positive relationship between life satisfaction and threat appraisal. These methods could also help interpret complex or counterintuitive results that are difficult to explain through quantitative models alone. Conclusions This exploratory study aimed to contribute to a more psychologically nuanced and person-centered understanding of protection motivation by investigating the relationship between intrapersonal sources of information, such as pandemic-related experiences and stable psychological dispositions, and the key cognitive construct in PMT. The study highlights the idea that individual differences such as perceived infectability, risk propensity, and life satisfaction are systematically associated with PMT processes. While traditional determinants such as response efficacy and perceived severity remain central, the proposed heuristic dPMT framework expands the model by integrating pandemic-related experiences and psychological dispositions as upstream factors. Future longitudinal and experimental studies should test whether this approach can improve the explanatory scope of PMT and guide more effective, targeted interventions in diverse cultural contexts. Declarations Funding This paper is based on independent research conducted as part of the SEMSAI (031L0295C) project, which was commissioned and funded by the German Federal Ministry of Research, Technology and Space (BMFTR, formerly Federal Ministry of Education and Research, BMBF). Ethics declaration The study was conducted in accordance with relevant guidelines and regulations and approved by the ethics commission at the Freie Universität Berlin. All participants provided informed and written consent prior to participating. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author Contribution Project administration, K.S., M.V.; Conceptualization, K.S., P.W.; Data curation, P.W.; Data analysis, K.S., P.W.; Writing original draft, K.S.; Review and editing, P.W., M.V., K.S.; All the authors have read and approved the final manuscript. 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Lüdecke D, Knesebeck O. von dem. Protective Behavior in Course of the COVID-19 Outbreak-Survey Results From Germany. Front Public Health. 2020;8:572561. Dixon D, den Daas C, Hubbard G, Johnston M. Using behavioural theory to understand adherence to behaviours that reduce transmission of COVID-19; evidence from the CHARIS representative national study. Br J Health Psychol. 2022;27(1):116–35. Peters E, Baker DP, Dieckmann NF, Leon J, Collins J. Explaining the effect of education on health: a field study in Ghana. Psychol Sci. 2010;21(10):1369–76. Zajacova A, Lawrence EM. The Relationship Between Education and Health: Reducing Disparities Through a Contextual Approach. Annu Rev Public Health. 2018;39:273–89. Capraro V, Barcelo H. The effect of messaging and gender on intentions to wear a face covering to slow down COVID-19 transmission. arXiv preprint arXiv:2005.05467 2020. Galasso V, Pons V, Profeta P, Becher M, Brouard S, Foucault M. Gender differences in COVID-19 attitudes and behavior: Panel evidence from eight countries. Proc Natl Acad Sci U S A. 2020;117(44):27285–91. DeSalvo N, Lacasse K, Jackson TE. Gender norms shape perceived threat to self and others and mask wearing behavior in response to COVID-19. Translational Issues Psychol Sci. 2022;8(3):311–22. Jiang X, Sparks J, Wallace Z, Deng X, Li H, Lu N et al. Risk of COVID-19 among unvaccinated and vaccinated patients with systemic lupus erythematosus: a general population study. RMD Open 2023; 9(1). Lam CN, Kumar N, Herzig SE, Unger JB, Sood N. Associations between COVID-19 infection, symptom severity, perceived susceptibility, and long-term adherence to protective behaviors: The Los Angeles pandemic surveillance cohort study. PLoS ONE. 2025;20(6):e0326097. Bandura A. New York, NY, US: W H Freeman/Times Books. Self-efficacy: The exercise of control. Henry Holt & Co; 1997. (Self-efficacy: The exercise of control). Luszczynska A, Gutiérrez-Doña B, Schwarzer R. General self‐efficacy in various domains of human functioning: Evidence from five countries. Int J Psychol. 2005;40(2):80–9. Zhou C, Yue XD, Zhang X, Shangguan F, Zhang XY. Self-efficacy and mental health problems during COVID-19 pandemic: A multiple mediation model based on the Health Belief Model. Pers Indiv Differ. 2021;179:110893. Weinstein ND. Effects of personal experience on self-protective behavior. Psychol Bull. 1989;105(1):31. Slovic P. The perception of risk. London, England: Earthscan; 2000. (Risk, society, and policy series). Festinger L. A theory of cognitive dissonance (T. 2): Stanford university press.(Cf. p. 45, 128); 1957. Harmon-Jones E, Mills J. An introduction to cognitive dissonance theory and an overview of current perspectives on the theory. Cognitive dissonance: Reexamining a pivotal theory in psychology. 2nd ed. Washington, DC, US: American Psychological Association; 2019. pp. 3–24. Zhang K, Ye G, Xiang Q, Chang Y. Influencing mechanism of optimism bias on construction worker’s unsafe behavior: the role of risk perception and risk propensity. ECAM; 2025. Dohmen T, Quercia S, Willrodt J. Willingness to take risk: The role of risk conception and optimism; SOEPpapers on Multidisciplinary Panel Data Research. Berlin; 2019 1026. Denovan A, Macaskill A. Stress and Subjective Well-Being Among First Year UK Undergraduate Students. J Happiness Stud. 2017;18(2):505–25. Diener E, Oishi S, Tay L. Advances in subjective well-being research. Nat Hum Behav. 2018;2(4):253–60. Lyubomirsky S, King L, Diener E. The benefits of frequent positive affect: Does happiness lead to success? Psychol Bull. 2005;131(6):803. Scheier MF, Carver CS. Optimism, coping, and health: assessment and implications of generalized outcome expectancies. Health Psychol. 1985;4(3):219–47. Floyd DL, Prentice-Dunn S, Rogers RW. A Meta-Analysis of Research on Protection Motivation Theory. J Appl Social Pyschol. 2000;30(2):407–29. Nudelman G, Kamble SV, Otto K. Using Protection Motivation Theory to Predict Adherence to COVID-19 Behavioral Guidelines. Behav Med 2022:1–10. Kojan L, Burbach L, Ziefle M, Calero Valdez A. Perceptions of behaviour efficacy, not perceptions of threat, are drivers of COVID-19 protective behaviour in Germany. Humanit Soc Sci Commun 2022; 9(1). Steyer R, Mayer A, Geiser C, Cole DA. A theory of states and traits—revised. Annu Rev Clin Psychol. 2015;11:71–98. Bubeck P, Osberghaus D, Thieken AH. Explaining Changes in Threat Appraisal, Coping Appraisal, and Flood Risk-Reducing Behavior Using Panel Data From a Nation-Wide Survey in Germany. Environ Behav. 2023;55(4):211–35. Schneider IK, Dorrough AR, Frank C. Ambivalence and Adherence to Recommendations to Reduce the Spread of COVID-19; 2021. Shook NJ, Sevi B, Lee J, Oosterhoff B, Fitzgerald HN. Disease avoidance in the time of COVID-19: The behavioral immune system is associated with concern and preventative health behaviors. PLoS ONE 2020; 15(8). Tagini S, Brugnera A, Ferrucci R, Mazzocco Kea. Attachment, Personality and Locus of Control: Psychological Determinants of Risk Perception and Preventive Behaviors for COVID-19. Front Psychol 2021; 12. Qian K, Yahara T. Mentality and behavior in COVID-19 emergency status in Japan: Influence of personality, morality and ideology. PLoS ONE 2020. Hrbková L, Kudrnáč A. Fear, trust, and compliance with covid-19 measures: A study of the mediating effect of trust in government on the relationship between fear and compliance; 2024. Li S, Chen M, Ma X, Sun Z. Applying an Extended Protection Motivation Theory Model to Predict Resident Hospitality During the COVID-19 Crisis. J Travel Res 2023:1–8. Dimitrova T, Ilieva I. Consumption Behaviour towards Branded Functional Beverages among Gen Z in Post-COVID-19 Times: Exploring Antecedents and Mediators. Behav Sci (Basel) 2023; 13(8). Footnotes In addition to perceived infectability, the PVD construct also includes germ aversion. While germ aversion has been more extensively studied in relation to behavior, empirical research on the impact of general perceived infectability remains limited. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviewers invited by journal 17 Oct, 2025 Editor invited by journal 19 Sep, 2025 Editor assigned by journal 17 Sep, 2025 Submission checks completed at journal 17 Sep, 2025 First submitted to journal 16 Sep, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7627607","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544973734,"identity":"7e748769-80df-425e-9efb-b4e1f47eb668","order_by":0,"name":"Katja Schulze","email":"data:image/png;base64,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","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":true,"prefix":"","firstName":"Katja","middleName":"","lastName":"Schulze","suffix":""},{"id":544973735,"identity":"18571c06-b9d7-4572-b29b-adb75a7cc334","order_by":1,"name":"Peter Windsheimer-Kolla","email":"","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Windsheimer-Kolla","suffix":""},{"id":544973736,"identity":"ed0008a0-fc1e-4cb9-af92-95b27eb9fdff","order_by":2,"name":"Martin Voss","email":"","orcid":"","institution":"Freie Universität Berlin","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Voss","suffix":""}],"badges":[],"createdAt":"2025-09-16 08:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7627607/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7627607/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104385049,"identity":"949a6747-0d30-4020-b8e1-dcfdff6d6879","added_by":"auto","created_at":"2026-03-11 08:37:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":289918,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSimplified illustration of protection motivation theory (PMT), adapted from Rogers (1983, p. 168).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627607/v1/6269dc8511b12d39e6bb50e1.jpeg"},{"id":104385048,"identity":"39045744-e873-48c2-9f5d-75898f131dea","added_by":"auto","created_at":"2026-03-11 08:37:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":44856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHeatmap of standardized regression coefficients (β) of the final linear regression models.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOnly statistically significant coefficients (p \u0026lt; .05) for associations between pandemic-related experiences, psychological dispositions, and PMT determinants are shown in color; nonsignificant cells are displayed in gray. Positive coefficients are indicated by blue tones, and negative coefficients are indicated by red tones.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7627607/v1/dccea18033996e083eb50131.png"},{"id":104779913,"identity":"744e5f59-49ea-49b3-b1aa-052721b192de","added_by":"auto","created_at":"2026-03-17 07:47:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":216459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDispositional extended PMT (dPMT).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIntrapersonal sources of information, particularly pandemic-related factors (risk group self-identification, prior infection) and psychological factors (perceived infectability, general self-efficacy, risk propensity, life satisfaction, health status), influence cognitive evaluations of threats (vulnerability, severity) and coping capacities (self-efficacy, response efficacy, response costs). The interaction of these appraisals generates protection motivation, which in turn drives protective behavior.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7627607/v1/2be1e712d62ebf46562b9b91.png"},{"id":104784115,"identity":"fe569525-5aeb-4046-b943-56b62ca9ad90","added_by":"auto","created_at":"2026-03-17 08:05:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2155362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7627607/v1/3a82718f-f05c-4cd0-83d9-6caf06b76022.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations between psychological dispositions, pandemic-related variables and protection motivation theory determinants: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe COVID-19 pandemic has had profound and far-reaching consequences for global health, economies, and societies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to its immediate impacts, the threat of future pandemics remains a persistent concern for public health and global security, with annual economic losses projected at approximately \u003cspan\u003e$\u003c/span\u003e500\u0026nbsp;billion [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These challenges underscore the critical importance of understanding and promoting health-protective behaviors to enhance pandemic preparedness.\u003c/p\u003e\u003cp\u003eIndividual behavior is central to the control of infectious disease outbreaks. Compliance with protective measures substantially influences disease transmission, and identifying the factors that shape such behaviors is essential for designing effective public health interventions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Identifying the psychological determinants of protective behavior is therefore crucial both for managing current crises and for strengthening preparedness for future pandemics.\u003c/p\u003e\u003cp\u003eProtection motivation theory\u003c/p\u003e\u003cp\u003eSeveral theoretical models have been proposed to explain and predict health-related behaviors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Among these, protection motivation theory (PMT) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] provides a particularly influential framework for understanding health-protective behavior. It is among the most important health behavior theories for changing behavior [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and has been frequently applied during the COVID-19 pandemic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePMT explains how individuals react to persuasive communication, assess potential threats and take health-promoting measures, which can take the form of different coping modes, such as single acts (e.g., receiving an available vaccination) and repeated acts (e.g., wearing a mask), in the case of a threatening event. As a social cognition theory, it identifies important social\u0026ndash;cognitive determinants of health behavior. According to theory, adaptive or maladaptive responses following a health threat are dependent on protection motivation, which results from the combined evaluation of two cognitive processes: the threat appraisal process and the coping appraisal process (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe threat appraisal process involves assessing (1) perceived severity, which reflects the anticipated seriousness of the threat\u0026rsquo;s consequences; (2) perceived vulnerability, or the likelihood of being personally affected; (3) intrinsic rewards, such as the physical or psychological benefits of risky behavior; and (4) extrinsic rewards, such as social approval or other interpersonal gains.\u003c/p\u003e\u003cp\u003eThe second component, coping appraisal, refers to the evaluation of available protective strategies and the individual's ability to implement them. It includes (1) response efficacy, or the belief that a specific behavior will effectively mitigate the threat; (2) self-efficacy, the individual\u0026rsquo;s confidence in their ability to perform the protective behavior; and (3) perceived response costs, encompassing the potential barriers to action, such as time, effort, financial burdens, or social inconvenience.\u003c/p\u003e\u003cp\u003eIn many applications of PMT, a streamlined version of the model has been employed, emphasizing five principal components while omitting the following reward factors: perceived severity, perceived vulnerability, response efficacy, self-efficacy, and response costs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This more concise formulation has proven effective in a range of empirical studies examining protective behavior in the context of public health risks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, PMT highlights how threat and coping appraisals jointly influence protection motivation and succeeds in combining both components into a consistent theory, thereby serving as a valuable framework for predicting health-related behaviors.\u003c/p\u003e\u003cp\u003eApplication of PMT to predict health behavior\u003c/p\u003e\u003cp\u003ePMT reliably predicts health-related behavior in various contexts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], with coping appraisals tending to show greater power than threat appraisals do [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In particular, during the COVID-19 pandemic, PMT has frequently been applied to examine and predict engagement in nonpharmaceutical protective behaviors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A comprehensive meta-analysis by Hedayati et al. (2023) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which synthesized findings from 65 studies, provided strong empirical support for the predictive validity of PMT in the context of COVID-19. The analysis revealed that individuals who perceived the pandemic as more threatening, either in terms of its severity or their own vulnerability, were more likely to adopt recommended protective measures, including vaccination intentions. However, the overall levels of perceived vulnerability were relatively low.\u003c/p\u003e\u003cp\u003eFurthermore, Hedayati et al. (2023) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] demonstrated that coping appraisal components, particularly self-efficacy and response efficacy, are strongly associated with both behavioral intentions and actual engagement in protective actions. Among these factors, self-efficacy emerged as the most influential factor. In contrast, response costs, including both financial and nonfinancial burdens, were found to be weakly and negatively associated with protection motivation. These conclusions are further supported by findings from studies conducted in Germany, which reported similarly high relevance of coping appraisals in predicting protective behavior during the COVID-19 pandemic [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTaken together, these findings underscore the robustness of PMT in explaining a wide range of health-protective behaviors, including those adopted during the COVID-19 pandemic. They support PMT\u0026rsquo;s predictive power, with coping appraisals, particularly self-efficacy, emerging as the strongest drivers of protective behavior. At the same time, this emphasis on predictive validity leaves important questions about how the underlying appraisals, which constitute the basis of PMT, are themselves shaped and how their expressions are influenced by personal characteristics. Addressing this gap requires moving beyond the traditional focus on PMT constructs as isolated determinants and instead examining the factors that influence their formation.\u003c/p\u003e\u003cp\u003eFactors influencing PMT constructs\u003c/p\u003e\u003cp\u003eWhile research has demonstrated that PMT is a robust framework for predicting health-protective behaviors, recent research has investigated primarily the relationship between social\u0026ndash;cognitive PMT determinants and protective behavior. This focus tends to neglect the role of intrapersonal and contextual factors that shape these appraisals and treats them as if they were formed independently of broader personal and environmental influences.\u003c/p\u003e\u003cp\u003eIn PMT, threat and coping appraisals are shaped by environmental and intrapersonal sources of information [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Environmental sources refer to verbal persuasion and observational learning. Rogers (1983) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] identified personality variables and prior experiences with similar threats as intrapersonal sources. Intrapersonal factors, however, can include a much wider variety of individual characteristics, such as psychological factors and general beliefs or values.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eSimplified illustration of protection motivation theory (PMT), adapted from Rogers (1983, p. 168).\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIntrapersonal factors (e.g., traits, values, attitudes) have rarely been integrated into PMT-based models, although emerging evidence suggests that such factors may impact how individuals perceive and respond to threats [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Research by Schneider \u0026amp; Dryhurst (2021) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] has even indicated that psychological factors are more predictive of risk perception than an objective measure of situational severity is.\u003c/p\u003e\u003cp\u003eDespite the widespread application of PMT, the extent to which individual differences, particularly psychological dispositions, are related to the motivational processes that lead to health-protective behavior remains largely unclear. The assumption of uniform cognitive processing appears increasingly inadequate in light of empirical evidence suggesting that individuals perceive and respond to threats in systematically different ways. To address this research gap, the present study deliberately adopts an exploratory empirical approach. It seeks to identify potential patterns and associations between (1) pandemic-related factors (e.g., belonging to a risk group, prior COVID-19 experience) and (2) psychological dispositions (e.g., perceived general infectability to illness, general self-efficacy, risk propensity, life satisfaction, and subjective health status) and the key PMT constructs: perceived severity, perceived vulnerability, self-efficacy, response efficacy, response costs, and behavioral intention. This approach is particularly appropriate given the limited existing evidence on these variables.\u003c/p\u003e\u003cp\u003eBy identifying relevant patterns and associations, this study aims to contribute to a more psychologically nuanced and person-centered understanding of protection motivation. Understanding these individual differences is not only valuable for tailoring interventions to psychological profiles but also essential for advancing broader public health goals. Accounting for psychological dispositions will facilitate better tailoring of health communication and behavioral strategies to diverse population segments, thereby enhancing compliance and overall public health outcomes.\u003c/p\u003e\u003cp\u003eThe incorporation of such intrapersonal and environmental factors into PMT can therefore enhance our understanding of why individuals differ in their health-protective responses, which in turn can serve as a valuable contribution for enhancing predictions and tailoring countermeasures in future pandemics.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePandemic-related variables\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTo structure the analysis, we formulate guiding hypotheses (H1.1, H2.1, etc.). These should not be interpreted as strict tests of predefined causal mechanisms but as tentative assumptions of statistical associations within a broader exploratory framework on the basis of previous empirical work.\u003c/p\u003e\u003cp\u003eRisk group self-identification\u003c/p\u003e\u003cp\u003eWithin the paradigm of pandemics, the term \"high-risk group\" refers to subsets of the population that are more likely to experience severe illness, complications, or death if they contract the disease. Therefore, it is reasonable to expect that high-risk groups perceive the threat as greater. Research supports the assumption of greater perceived severity, concern and fear among high-risk COVID-19 groups [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For example, Kohler et al. (2021) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] reported that individuals with multiple high-risk conditions rated their probability of hospitalization or death from COVID-19 as higher than their healthy counterparts did, directly motivating precautionary actions. Further research has revealed support for greater adherence intentions and protective behavior among individuals with preexisting conditions or among participants who perceived themselves to be in the high-risk group [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These findings are to be expected, as people who perceive greater severity are also more likely to protect themselves to avoid potential harm. To our knowledge, no research has investigated the correlations between high-risk group membership and other PMT constructs.\u003c/p\u003e\u003cp\u003eOverall, perceiving oneself as belonging to a high-risk group appears to increase perceived severity and protective intentions, although its impact on other PMT constructs remains less clear.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1.1\u003c/b\u003e: Individuals who perceive themselves as belonging to a high-risk group report greater perceived severity of COVID-19 than do individuals who do not perceive themselves as belonging to a high-risk group.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH1.2\u003c/b\u003e: Individuals who perceive themselves as belonging to a high-risk group report stronger intentions to engage in protective behaviors against COVID-19 than do those who do not perceive themselves as belonging to a high-risk group.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ1.1\u003c/b\u003e: How does the perception of belonging to a high-risk group relate to other PMT constructs, such as perceived vulnerability, self-efficacy, response efficacy, and response costs?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCOVID-19 experience\u003c/p\u003e\u003cp\u003eThe personal experience of individuals regarding infection by the SARS-CoV-2 virus may significantly affect their motivation to protect themselves and others, as well as their perception of the threat posed by the virus and their ability to cope with it. Indeed, individuals in various countries who have contracted the virus themselves tend to report heightened risk perceptions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Schneider et al. (2021) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported that this relationship remained stable over time. Research has indicated that prior infection impacts mainly perceived infectability, not severity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Research suggests that infection experience indirectly affects protective behavior, which is mediated by risk perception [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, some studies have demonstrated that previous COVID-19 infection has a negative effect on adherence to prevention measures [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This experience may have resulted in an increased sense of confidence, leading to perceived immunity to the illness or, alternatively, a perceived reduction in the severity of the infection owing to the milder symptoms experienced [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, the extent to which prior experience with SARS-CoV-2 infection influences protective behavior remains to be thoroughly investigated. To our knowledge, no research has directly addressed whether previous COVID-19 infection alters self-efficacy, response efficacy or response costs.\u003c/p\u003e\u003cp\u003ePrior COVID-19 infection may influence perceived vulnerability, but its effects on coping appraisals and protective behavior require further investigation.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2.1\u003c/b\u003e: Individuals with a history of COVID-19 infection report greater perceived vulnerability (infectability) to the virus than those without prior infection.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH2.2\u003c/b\u003e: Previous COVID-19 infection is not significantly linked to the perceived severity of the virus.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ2.1\u003c/b\u003e: How does previous COVID-19 infection relate to other PMT constructs, such as self-efficacy, response efficacy, and response costs?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ2.2\u003c/b\u003e: How does previous COVID-19 infection relate to protective behavior?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePsychological variables\u003c/p\u003e\u003cp\u003ePerceived infectability\u003c/p\u003e\u003cp\u003eDrawing on the theory of the behavioral immune system (BIS) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], we investigate whether perceived infectability (PI) is associated with PMT determinants. PI, as part of the broader perceived vulnerability to disease (PVD)\u003csup\u003e1\u003c/sup\u003e [47] framework, refers to the extent to which individuals believe themselves to be particularly susceptible to infectious diseases in general. In contrast, perceived vulnerability in PMT captures the expected likelihood of being personally affected by a specific disease. Perceived infectability and perceived vulnerability differ in scope and temporal stability but share a common conceptual basis: both reflect an individual's perceived likelihood of contracting a disease. This overlap suggests that PI may serve as a dispositional antecedent to perceived vulnerability [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Nevertheless, the relationships between PI and perceived vulnerability or severity during specific pandemics have been the subject of only a few studies, which have indicated that high general perceived infectability is positively correlated with perceived personal risk and threat reactivity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. To our knowledge, the only study examining the link between perceived infectability (PI) and coping appraisal revealed that PI did not predict stronger beliefs in the effectiveness of public health measures [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe relationship between general perceived infectability and COVID-19 preventive behavior is ambivalent. For example, Stangier, Kananian et al. (2021) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] reported that PI can predict an increase in self-reported preventive behavior. Similarly, Makhanova et al. (2020) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] stated that PI was negatively associated with the frequency of store visits and outdoor physical activity but not with face‒to-face interactions. In a separate study, they reported that PI was positively associated with social distancing but not with proactive behaviors [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In contrast to these cross-sectional findings, a longitudinal study by Church et al. (2022) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] revealed a correlation between PI and preventive behavior for the first investigation but not for the second. Furthermore, Hromatko et al. (2021) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] reported no significant relationship between PI and behavior. Taken together, these findings indicate that PI may influence certain types of preventive behaviors more than others do, but the overall evidence is mixed and may depend on the behavioral context and study design.\u003c/p\u003e\u003cp\u003ePerceived infectability may serve as a dispositional antecedent to perceived vulnerability and protection motivation, although its influence on other PMT constructs is still uncertain.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3.1\u003c/b\u003e: Perceived infectability (PI) is positively associated with perceived vulnerability to COVID-19.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH3.2\u003c/b\u003e: Perceived infectability (PI) is positively associated with protection motivation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ3.1\u003c/b\u003e: How does perceived infectability relate to other PMT constructs, such as perceived severity, self-efficacy, response efficacy, and response costs?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003ePerceived general self-efficacy\u003c/p\u003e\u003cp\u003eIn contrast to domain-specific self-efficacy (SSE) as part of PMT, general self-efficacy (GSE) is defined as a relatively stable belief in one's global ability to handle a wide array of challenging situations [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Previous research has usually focused on either GSE or SSE as unique separate constructs. As part of PMT, SSE has been widely studied as a predictor of COVID-19-related protective behavior [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In contrast, the role of GSE remains underexplored. Initial findings suggest a positive relationship between GSE and COVID-19 compliance behaviors [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], but the specific mechanisms underlying this relationship are not yet well understood. From a theoretical perspective, GSE may serve as a foundational cognitive resource that facilitates the formation of SE. Research has shown that GSE can act as a predictor of domain-specific efficacy for a variety of tasks in various contexts [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], including health behavior and stress management [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. These findings suggest that individuals who perceive themselves as generally competent and effective (high GSE) are also more likely to evaluate their capabilities positively in specific tasks and settings (SSE). To our knowledge, no prior research has systematically examined how GSE predicts SSE or other PMT constructs in shaping compliance with COVID-19 health-protective behaviors.\u003c/p\u003e\u003cp\u003eGeneral self-efficacy likely supports domain-specific self-efficacy, potentially enhancing the motivation to engage in COVID-19 protective behaviors. Its effect on further PMT determinants requires further investigation.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH4.1\u003c/b\u003e: General self-efficacy (GSE) is positively associated with domain-specific self-efficacy (SSE) in relation to COVID-19 protective behaviors.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ4.1\u003c/b\u003e: How does general self-efficacy (GSE) relate to other PMT constructs, such as perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eRisk propensity\u003c/p\u003e\u003cp\u003eRisk propensity refers to a stable individual disposition reflecting the tendency either to engage in risk-taking behavior or to avoid it [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Consistent with this definition, individuals with greater willingness to take risks tend to engage more in unhealthy behaviors [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. In the context of COVID-19, research has demonstrated that higher levels of risk propensity are associated with lower compliance with containment measures [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these findings, few studies have systematically investigated whether risk propensity is associated with the underlying cognitive mechanisms that PMT identifies as drivers of protective behavior. Prior work has yielded mixed results in this regard: Fu et al. (2024) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] reported no significant effect of health risk aversion on threat appraisal, whereas Buelow et al. (2022) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] demonstrated a significant negative association between general risk propensity and the perceived risks related to COVID-19. Furthermore, Thomas et al. (2022) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] found a significant negative relationship between risk aversion and response efficacy for compliant behavior. These limited and partly conflicting findings suggest that the impact of risk propensity on PMT cognitive processes is not yet fully understood, underscoring the need for research that directly examines these relationships.\u003c/p\u003e\u003cp\u003eIndividuals with greater risk propensity may be less motivated to adopt protective behaviors, possibly reflecting their lower threat sensitivity and coping appraisal.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH5.1\u003c/b\u003e: Willingness to take risks is negatively associated with protection motivation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ5.1\u003c/b\u003e: How does willingness to take risks relate to PMT constructs such as perceived severity, perceived vulnerability, self-efficacy, response efficacy, and response costs?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eLife satisfaction\u003c/p\u003e\u003cp\u003eAlthough limited, existing evidence suggests a significant positive association between life satisfaction and COVID-19 health behavior [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Krekel et al. (2023) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] theorized that individuals with higher life satisfaction may have more psychological resources, making it easier to comply with health guidelines. Although the positive impact of life satisfaction on mental health and adaptive coping mechanisms has been the focus of numerous investigations [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], few studies have directly tested its impact on PMT determinants. In contrast, numerous studies have investigated the impact of risk perception on life satisfaction during the COVID-19 pandemic, reflecting the dominance of unidirectional and mechanistic models and overlooking potential bidirectional or recursive effects within the broader psychological system. Regardless of the direction of the association, the majority of studies have reported a negative correlation with risk perception, often defined or operationalized as a combination of perceived severity and perceived vulnerability [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. However, some studies have reported a positive association [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e] or no significant association [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This conflicting evidence indicates that the relationship between life satisfaction and risk perception remains unresolved, particularly with respect to perceived severity and vulnerability. Clarifying these links is essential for understanding how life satisfaction influences protection motivation. Furthermore, higher life satisfaction has been shown to promote positive coping styles [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], which may lead individuals to perceive protective behavior as more effective and less burdensome. Nevertheless, the association between life satisfaction and coping appraisal remains underexplored.\u003c/p\u003e\u003cp\u003eTaken together, the literature indicates that life satisfaction can promote health behavior. Although most findings point to a negative association with risk perception, findings are mixed, and connections to coping appraisal are scarcely studied.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH6.1\u003c/b\u003e: Life satisfaction is negatively associated with perceived vulnerability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH6.2\u003c/b\u003e: Life satisfaction is negatively associated with perceived severity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH6.3\u003c/b\u003e: Life satisfaction is positively associated with protection motivation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ6.1\u003c/b\u003e: How does life satisfaction relate to other PMT constructs, such as self-efficacy, response efficacy, and response costs?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003cp\u003eAn individual\u0026rsquo;s perception of belonging to a risk group is associated with their self-rated health status, which is defined as an individual's subjective evaluation of their overall physical and mental well-being. Self-assessment of health is both broader and more holistic than the more specific belief concerning risk group membership. A limited number of studies have investigated the relationship between self-rated health status and protective behavior, indicating that individuals with poor self-rated health are more likely to engage in COVID-19 preventive behavior [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. These findings suggest that individuals who perceive themselves to be in good health may underestimate the severity of a potential COVID-19 case. Empirical findings support this assumption, revealing a negative association between health status and the perceived risk posed by infection with SARS-CoV-2 [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Nevertheless, the link between self-rated health and PMT constructs has remained largely unexplored.\u003c/p\u003e\u003cp\u003eHigher self-rated health appears to reduce perceived severity and protection motivation, suggesting that subjective health assessments shape engagement in protective actions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH7.1\u003c/b\u003e: Self-rated health status is negatively associated with perceived severity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eH7.2\u003c/b\u003e: Self-rated health status is negatively associated with protection motivation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRQ7.1\u003c/b\u003e: How does life satisfaction relate to other PMT constructs, such as perceived vulnerability, self-efficacy, response efficacy, and response costs?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis exploratory study aims to extend protection motivation theory (PMT) by examining how intrapersonal and pandemic-specific factors shape core PMT appraisals and, in turn, intentions to enact health-protective behaviors.\u003c/p\u003e\u003cp\u003eStudy design\u003c/p\u003e\u003cp\u003eThe study data were collected in January 2024 as part of a cross-sectional online survey conducted within the DRU (Disaster Research Unit) subproject of the SEMSAI (Self-Referential Multi-Scale Modelling and Simulation of Severe Infectious Disease) research initiative. The survey was administered in a single wave by a professional survey institute contracted for this purpose. A total of 1,050 adults from Germany participated in the survey. Power analysis indicated that this sample size provides sufficient statistical power (1 \u0026ndash; β\u0026thinsp;=\u0026thinsp;.90, α\u0026thinsp;=\u0026thinsp;.05) to detect small incremental effects (ΔR\u0026sup2; \u0026asymp; .03) in hierarchical regression analyses, with up to 16 predictors entered in four successive blocks. Quota sampling was applied to ensure that the sample reflected the German adult population in terms of age, sex, educational level, and federal state.\u003c/p\u003e\u003cp\u003eA scenario-based approach was used, wherein respondents were asked to consider a hypothetical infection wave caused by a fictional new coronavirus variant. This variant was described as initiating a new wave of infections with characteristics similar to those of the original COVID-19 outbreak in 2019/2020. The scenario included behavioral strategies. The scenario was followed by a series of questions assessing pandemic-specific variables, psychological dispositions, PMT determinants, and behavioral responses.\u003c/p\u003e\u003cp\u003eMeasurements\u003c/p\u003e\u003cp\u003ePandemic-specific variables\u003c/p\u003e\u003cp\u003eRisk group self-identification was assessed via a single item (\u0026ldquo;Are you a member of a COVID-19 risk group?\u0026rdquo;) with response options of 1\u0026thinsp;=\u0026thinsp;yes and 0\u0026thinsp;=\u0026thinsp;no. This item was adopted from the COSMO survey [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. This variable was treated as categorical in the analyses.\u003c/p\u003e\u003cp\u003ePersonal experience with COVID-19 infection was measured via a single item: \u0026ldquo;Have you ever received a confirmed diagnosis of a coronavirus (SARS-CoV-2) infection?\u0026rdquo; The response options were 1\u0026thinsp;=\u0026thinsp;yes and 0\u0026thinsp;=\u0026thinsp;no. The item was suggested by the Rat f\u0026uuml;r Sozial- und Wirtschaftsdaten (2023) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. For the purposes of statistical analysis, the variable was treated categorically.\u003c/p\u003e\u003cp\u003ePsychological variables\u003c/p\u003e\u003cp\u003ePerceived infectability (PI) was assessed via a four-item scale adapted from Ullah, Lin et al. (2021) [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The participants rated their agreement with statements such as \u0026ldquo;I am generally very susceptible to diseases\u0026rdquo; and \u0026ldquo;My immune system usually protects me from illnesses that others get\u0026rdquo; on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree). The internal consistency was acceptable (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.766).\u003c/p\u003e\u003cp\u003eGeneral self-efficacy (GSE) was determined via the abbreviated ASKU scale [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], which encompasses three items, including the affirmations \u0026ldquo;In difficult situations, I can rely on my abilities\u0026rdquo; and \u0026ldquo;I can usually solve demanding and complex tasks well.\u0026rdquo; Responses were provided on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;not at all true, 5\u0026thinsp;=\u0026thinsp;completely true). The internal consistency was very good (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.837).\u003c/p\u003e\u003cp\u003eDohmen et al. (2011) [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] introduced a widely used single-item measure of risk propensity. In line with this approach, participants were asked \u003cem\u003e\u0026ldquo;\u003c/em\u003eHow willing are you to take risks, in general?\u0026rdquo; and responded on an 11-point scale (0\u0026thinsp;=\u0026thinsp;not at all willing to take risks, 10\u0026thinsp;=\u0026thinsp;very willing to take risks).\u003c/p\u003e\u003cp\u003eThe participants\u0026rsquo; self-rated health status (SRH) was measured via a single-item self-assessment adopted from the SOEP-CoV 2020 study [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. The respondents were asked \u0026ldquo;How would you describe your current state of health?\u0026rdquo; Answers were provided on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;very good, 5\u0026thinsp;=\u0026thinsp;poor). This item was recoded for the analyses (1\u0026thinsp;=\u0026thinsp;poor, 5\u0026thinsp;=\u0026thinsp;very good).\u003c/p\u003e\u003cp\u003eLife satisfaction (LS) was assessed with an item adapted from the GESIS studies [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]: \u0026ldquo;All things considered, how satisfied are you with your life at present?\u0026rdquo; Responses were provided on a 7-point scale (1\u0026thinsp;=\u0026thinsp;not at all satisfied, 7\u0026thinsp;=\u0026thinsp;completely satisfied).\u003c/p\u003e\u003cp\u003eAll the items included two fallback response options: -1 = \u0026ldquo;do not know\u0026rdquo; and \u0026minus;\u0026thinsp;2 = \u0026ldquo;not specified,\u0026rdquo; allowing participants to opt out of specific items. This was important because it allowed participants\u0026rsquo; autonomy to decline or express uncertainty, thereby reducing the risk of measurement error. For analysis purposes, these responses were recoded as missing values.\u003c/p\u003e\u003cp\u003eThe intercorrelations among all the independent variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A medium correlation was found between life satisfaction and health status (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.439, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The positive correlation suggests a moderate and statistically significant association, indicating that individuals with better health status tend to report greater life satisfaction.\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\u003eIntercorrelations of pandemic-specific and psychological variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1. Risk group\u003c/b\u003e\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2. Prior infection\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.135***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\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\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3. Perceived infectability\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.224***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.107***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4. General self-efficacy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.069*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.243***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5. Risk propensity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.184***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.129***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.105***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.215***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6. Life satisfaction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.111***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.064*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.198***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.267***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.134***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7. Health status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.294***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.1**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.359***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.258***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.187***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.439***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNote. Pearson correlation coefficients (r) are presented. *p\u0026thinsp;\u0026lt;\u0026thinsp;.05 **p\u0026thinsp;\u0026lt;\u0026thinsp;.01. ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePMT determinants\u003c/p\u003e\u003cp\u003ePerceived vulnerability was measured via three items assessing the subjective likelihood of becoming infected, developing illness in the event of an infection, and transmitting the virus to others (adapted from [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]). Responses were recorded on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;very unlikely, 7\u0026thinsp;=\u0026thinsp;very likely), with the scale demonstrating good internal consistency (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.858).\u003c/p\u003e\u003cp\u003ePerceived severity was measured via three items assessing participants\u0026rsquo; subjective evaluation of the general threat, potential health consequences, and expected course of illness [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Responses were recorded on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;completely harmless, 7\u0026thinsp;=\u0026thinsp;very dangerous). The scale demonstrated excellent internal consistency (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.939).\u003c/p\u003e\u003cp\u003eThe corresponding questionnaire included two items to assess self-efficacy regarding protection against the described new COVID-19 variant [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. The participants rated their agreement with the statements on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree). The internal consistency was acceptable (Cronbach's \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.700).\u003c/p\u003e\u003cp\u003eThree items (e.g., \u0026ldquo;The described measures can protect me from the new coronavirus variant\u0026rdquo;) were used to measure the perceived effectiveness of the recommended measures (response efficacy) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Responses were given on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree; Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.886).\u003c/p\u003e\u003cp\u003eTo measure response costs, two statements addressing perceived burden and financial constraints regarding the recommended measures were used (adapted from [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]). The participants rated their agreement on a 7-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 7\u0026thinsp;=\u0026thinsp;strongly agree). The two-item scale demonstrated a Cronbach\u0026rsquo;s α of .626, which is acceptable given the limited number of items, with a moderate interitem correlation of \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.456.\u003c/p\u003e\u003cp\u003eProtection motivation was evaluated via a series of six items pertaining to recommended protective behaviors in public settings (e.g., maintaining a distance of at least 1.5 meters, wearing a mask, and avoiding crowded places) adapted from previous studies conducted in Germany [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. The participants were invited to indicate the frequency with which they engaged in each behavior on a four-point scale (1\u0026thinsp;=\u0026thinsp;never, 4\u0026thinsp;=\u0026thinsp;almost always). The reliability of this scale was determined to be good (Cronbach's \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.857).\u003c/p\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003cp\u003eIn addition to the main variables of interest, the analyses controlled for age, gender, level of education, and cohabitation with children, as these sociodemographic factors have been shown to be associated with both threat appraisal and health-related behaviors in the context of COVID-19 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Age was measured in years as a continuous variable. The participants\u0026rsquo; gender identity was assessed via a single-item self-reported question: \u0026ldquo;which gender do you identify with?\u0026rdquo; with three response options: female, male, and diverse. For the purpose of analysis, gender was dummy coded (1\u0026thinsp;=\u0026thinsp;female, 0\u0026thinsp;=\u0026thinsp;male). One participant who selected \u0026ldquo;diverse\u0026rdquo; was excluded from analyses involving gender because the sample size was insufficient for statistical comparison. Educational attainment was assessed via participants\u0026rsquo; highest completed level of education and categorized into three groups, namely, low, medium, and high, on the basis of standard classifications in the national education system. In the analysis, a low education level was used as the baseline value. Living with a child in the household was measured with a binary indicator (1\u0026thinsp;=\u0026thinsp;at least one child younger than 18 living in the household, 0\u0026thinsp;=\u0026thinsp;no child present).\u003c/p\u003e\u003cp\u003eData analyses\u003c/p\u003e\u003cp\u003eGiven the limited evidence on the role of intrapersonal variables in PMT, this study was designed to be hypothesis-generating and exploratory in nature. The aim was to identify patterns of associations between pandemic-related experiences and psychological dispositions and the key PMT determinants and protective intentions. These exploratory findings are intended to generate hypotheses for future confirmatory studies and to inform the development of targeted public health communication and intervention strategies to strengthen pandemic preparedness. Therefore, the hypotheses and research questions formulated (H1.1, RQ1.1, etc.) should be understood as guiding assumptions within an exploratory framework, not as confirmatory tests.\u003c/p\u003e\u003cp\u003eTo examine the associations between intrapersonal variables and protection motivation theory (PMT) determinants, hierarchical linear regression analyses were conducted. Separate regression models were estimated for each PMT outcome variable (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs). Variables were entered in successive blocks on the basis of their theoretical proximity to the outcome variable. In step 1, sociodemographic variables (age, gender, education, and living with children) were included to control for their influence. In the second block, pandemic-specific contextual variables (perceived belonging to a COVID-19 risk group and prior COVID-19 infection) were added. These variables represent situational factors that may be connected to individuals\u0026rsquo; health perceptions and responses within the specific context of the pandemic. Finally, in step 3, psychological variables (general self-efficacy, risk propensity, subjective health status, life satisfaction, and general infectability) were entered. To investigate the predictors of protective behavior, further hierarchical linear regression analyses were performed by adding a fourth block. In step 4, PMT components were incorporated to examine their incremental contributions beyond the previous predictors.\u003c/p\u003e\u003cp\u003eAt each step, the incremental explanatory power of each block was assessed to test the unique contribution of psychological variables to demographic and pandemic-related associations. Standardized regression coefficients (β) were reported to compare effect sizes across variables. Multicollinearity among variables was assessed by calculating the variance inflation factor (VIF), with values under 5 indicating acceptable levels.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo investigate the factors influencing COVID-19 health-protective intentions, we first examined how pandemic-specific experiences (risk group membership, prior infection) and psychological dispositions (perceived infectability, general self-efficacy, risk propensity, life satisfaction, and subjective health) were associated with PMT constructs (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs). Hierarchical linear regressions were conducted for each PMT determinant, sequentially assessing the contributions of sociodemographic, pandemic specific, and intrapersonal variables.\u003c/p\u003e\u003cp\u003eTo further explore the intrapersonal predictors of the PMT constructs, we conducted a hierarchical linear regression analysis across the different determinants of PMT. For each PMT construct, we estimated three models, incrementally incorporating sociodemographic characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e), pandemic-specific risk factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e), and psychological dispositions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A heatmap of the standardized regression coefficients for associations between pandemic-related experiences, psychological dispositions, and PMT determinants visualizes the key findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To examine protective behavior, a fourth model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e) was constructed that included all the PMT determinants.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOutputs of linear regression models examining associations between sociodemographic variables and PMT constructs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eVulnerability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSeverity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eResponse Efficacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eResponse Costs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eProtective Motivation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.29\u003c/p\u003e\u003cp\u003e(-0.42 \u0026ndash; -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003cp\u003e(-0.24\u0026ndash;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e(-0.16\u0026ndash;0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003cp\u003e(-0.26 \u0026ndash; -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e(0.16\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003cp\u003e(-0.31 \u0026ndash; -0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003cp\u003e(-0.11\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e(0.10\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003cp\u003e(0.03\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0,005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003cp\u003e(0.11\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003cp\u003e(-0.30 \u0026ndash; -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003cp\u003e(0.17\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(-0.05\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e(0.04\u0026ndash;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.07\u0026ndash;0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.07\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.21\u0026ndash;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003cp\u003e(0.06\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (middle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(-0.02\u0026ndash;0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003cp\u003e(-0.22\u0026ndash;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003cp\u003e(-0.13\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003cp\u003e(-0.40 \u0026ndash; -0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (high)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003cp\u003e(0.32\u0026ndash;0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0106\u003c/p\u003e\u003cp\u003e(-0.06\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003cp\u003e(-0.12\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003cp\u003e(0.15\u0026ndash;0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003cp\u003e(-0.67 \u0026ndash; -0.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003cp\u003e(0.08\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003cp\u003e(0.07\u0026ndash;0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003cp\u003e(-0.13\u0026ndash;0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.25\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0,237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003cp\u003e(-0.22\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003cp\u003e(-0.21\u0026ndash;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e969\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/R\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.072/0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.029/0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.014/0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.032/0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.066/0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.055/0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eThe table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) are highlighted in bold.\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\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOutputs of linear regression models examining associations between pandemic-related variables and PMT constructs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eVulnerability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSeverity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eResponse Efficacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eResponse Costs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eProtective Motivation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.73\u003c/p\u003e\u003cp\u003e(-0.87 \u0026ndash; -0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.45\u003c/p\u003e\u003cp\u003e(-0.60 \u0026ndash; -0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003cp\u003e(0.00\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003cp\u003e(-0.31\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003cp\u003e(0.18\u0026ndash;0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.32\u003c/p\u003e\u003cp\u003e(-0.48 \u0026ndash; -0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003cp\u003e(-0.14\u0026ndash;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003cp\u003e(-0.03\u0026ndash;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003cp\u003e(0.03\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(0.06\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003cp\u003e(-0.31 \u0026ndash; -0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003cp\u003e(0.09\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.07\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003cp\u003e(-0.00\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.07\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003cp\u003e(-0.19 \u0026ndash; -0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003cp\u003e(0.05\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (middle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003cp\u003e(-0.04\u0026ndash;0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003cp\u003e(-0.22\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003cp\u003e(-0.12\u0026ndash;0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003cp\u003e(-0.17\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.22\u003c/p\u003e\u003cp\u003e(-0.38 \u0026ndash; -0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (high)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003cp\u003e(0.28\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003cp\u003e(-0.03\u0026ndash;0.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.12\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003cp\u003e(0.13\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.46\u003c/p\u003e\u003cp\u003e(-0.62 \u0026ndash; -0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e(0.10\u0026ndash;0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003cp\u003e(0.06\u0026ndash;0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.24\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003cp\u003e(-0.23\u0026ndash;0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.06\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003cp\u003e(-0.20\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Group (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003cp\u003e(0.32\u0026ndash;0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003cp\u003e(0.68\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003cp\u003e(-0.32 \u0026ndash; -0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003cp\u003e(-0.01\u0026ndash;0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003cp\u003e(-0.19\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003cp\u003e(0.23\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003cp\u003e(0.40\u0026ndash;0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003cp\u003e(-0.01\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003cp\u003e(-0.37 \u0026ndash; -0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.16\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003cp\u003e(-0.27 \u0026ndash; -0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.0\u003c/p\u003e\u003cp\u003e(-0.14\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/R\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.172/0.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.155/0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.031/0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033/0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.065/0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.081/0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eThe table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) are highlighted in bold.\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\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOutputs of linear regression models examining the associations between psychological variables and PMT constructs.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eVulnerability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSeverity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eResponse Efficacy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eResponse Costs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eProtective Motivation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.60\u003c/p\u003e\u003cp\u003e(-0.74 \u0026ndash; -0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.34\u003c/p\u003e\u003cp\u003e(-0.48 \u0026ndash; -0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003cp\u003e(-0.29\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003cp\u003e(0.10\u0026ndash;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003cp\u003e(-0.40 \u0026ndash; -0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.01\u0026ndash;0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003cp\u003e(-0.06\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e(0.01\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.18\u003c/p\u003e\u003cp\u003e(-0.26 \u0026ndash; -0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(0.06\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e(-0.14\u0026ndash;0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.07\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003cp\u003e(-0.05\u0026ndash;0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003cp\u003e(-0.14\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003cp\u003e(-0.13\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e(-0.04\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (middle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(-0.02\u0026ndash;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e(-0.19\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003cp\u003e(-0.17\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003cp\u003e(-0.33\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (high)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003cp\u003e(0.34\u0026ndash;0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003cp\u003e(0.04\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003cp\u003e(-0.16\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003cp\u003e(0.10\u0026ndash;0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003cp\u003e(-0.52 \u0026ndash; -0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003cp\u003e(0.13\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003cp\u003e(0.02\u0026ndash;0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003cp\u003e(-0.26\u0026ndash;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.25\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(-0.02\u0026ndash;0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003cp\u003e(-0.23\u0026ndash;0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Group (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003cp\u003e(0.09\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003cp\u003e(0.46\u0026ndash;0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003cp\u003e(-0.15\u0026ndash;0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003cp\u003e(-0.03\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.24\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003cp\u003e(0.12\u0026ndash;0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003cp\u003e(0.33\u0026ndash;0.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.07\u0026ndash;0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003cp\u003e(-0.31 \u0026ndash; -0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003cp\u003e(-0.15\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003cp\u003e(-0.28 \u0026ndash; -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.15\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral infectibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003cp\u003e(0.26\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003cp\u003e(0.19\u0026ndash;0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003cp\u003e(-0.33 \u0026ndash; -0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003cp\u003e(0.03\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral self-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003cp\u003e(-0.13 \u0026ndash; -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003cp\u003e(0.12\u0026ndash;0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003cp\u003e(-0.05\u0026ndash;0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.09\u0026ndash;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003cp\u003e(-0.04\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk propensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.468\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003cp\u003e(-0.11\u0026ndash;0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003cp\u003e(-0.19 \u0026ndash; -0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003cp\u003e(0.08\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003cp\u003e(-0.26 \u0026ndash; -0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(0.00\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003cp\u003e(0.03\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.02\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(0.07\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003cp\u003e(-0.27 \u0026ndash; -0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003cp\u003e(0.07\u0026ndash;0.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003cp\u003e(-0.17 \u0026ndash; -0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003cp\u003e(-0.15\u0026ndash;0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.17 \u0026ndash; -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/R\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.277/0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.239/0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.148/0.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.064/0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.123/0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.140/0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003eThe table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) are highlighted in bold.\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\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. \u003cem\u003eHeatmap of standardized regression coefficients (β) of the final linear regression models.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eLegend. Only statistically significant coefficients (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) for associations between pandemic-related experiences, psychological dispositions, and PMT determinants are shown in color; nonsignificant cells are displayed in gray. Positive coefficients are indicated by blue tones, and negative coefficients are indicated by red tones.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eSociodemographic variables\u003c/p\u003e\u003cp\u003eThe study included a total of 1,050 participants. The participants in the study ranged in age from 18 to 67 years (mean\u0026thinsp;=\u0026thinsp;48.7, SD\u0026thinsp;=\u0026thinsp;16.176). Among the total sample, 527 participants (50.2%) identified as female, 522 (49.7%) as male, and one (0.1%) as diverse. With respect to educational attainment, the largest group reported a low educational level (40.3%, n\u0026thinsp;=\u0026thinsp;423), followed by high educational attainment (36.7%, n\u0026thinsp;=\u0026thinsp;385). A smaller proportion of respondents reported a middle level of education (23.0%, n\u0026thinsp;=\u0026thinsp;242). The majority of respondents (79.1%, n\u0026thinsp;=\u0026thinsp;831) indicated that they did not live with children, whereas 20.9% (n\u0026thinsp;=\u0026thinsp;219) reported living with children in the household (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics of the study participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDivers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\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\u003cp\u003e423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiving with Children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the hierarchical linear regression analysis revealed that the sociodemographic variables accounted for a small to modest proportion of the variance in the PMT constructs (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .024\u0026ndash;067) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among the investigated variables, age was positively associated with perceived severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), self-efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005), response efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, an inverse relationship was identified between age and response costs (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). As indicated by the findings, females exhibited higher levels of perceived severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008) and protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) than males did. Higher education was significantly associated with increased vulnerability (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), response efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003). Education was also identified as a significant factor related to response costs: individuals with a medium (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003) or high (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) education level reported lower response costs than those with lower education did. Living with children was associated with an increased perception of vulnerability (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.004).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eOutputs of linear regression models examining associations between sociodemographic variables and PMT constructs.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOverall, the findings suggest that sociodemographic factors, particularly age, gender, and education, are modest but significant predictors of PMT constructs, influencing individuals\u0026rsquo; perceptions of risk, efficacy beliefs, and protection motivation.\u003c/p\u003e\u003cp\u003ePandemic-related variables\u003c/p\u003e\u003cp\u003eThe incorporation of pandemic-specific variables was associated with a substantial increase in model fit, particularly with respect to perceived vulnerability (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .166) and severity (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .149) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The effects of the sociodemographic variables remained largely consistent. Notably, the previously significant effects of age and gender on perceived severity became nonsignificant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05) after pandemic-related variables were included, suggesting partial mediation through risk group classification.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. \u003cem\u003eOutputs of linear regression models examining associations between pandemic-related variables and PMT constructs.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe strongest correlation was demonstrated for risk group self-identification and perceived severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This finding indicates that individuals who identify as high risk perceive COVID-19 as particularly severe, thereby substantiating Hypothesis H1.1. Furthermore, positive associations were identified between self-identification as a risk group member and perceived vulnerability (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), response efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034), and protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). These findings suggest that individuals who consider themselves vulnerable not only perceive greater threat but also report stronger motivation to adopt protective behaviors, thereby substantiating H1.2. Moreover, belonging to a risk group for COVID-19 was significantly associated with lower levels of self-efficacy (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.021), indicating that risk groups feel less confident in their ability to manage the threat than nonrisk groups are.\u003c/p\u003e\u003cp\u003eWith respect to infection history, respondents who reported prior COVID-19 infection demonstrated substantially higher levels of perceived vulnerability (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), thus providing support for H2.1. As hypothesized (H2.2), a self-reported history of infection demonstrated a nonsignificant association with perceived severity. However, experiencing an infection was significantly associated with lower levels of self-efficacy (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) and lower perceived response costs (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047). This pattern suggests that having been infected may undermine individuals\u0026rsquo; confidence in their coping ability while simultaneously reducing the perceived burden of protective behaviors.\u003c/p\u003e\u003cp\u003eTaken together, the results highlight that pandemic-specific factors are associated with perceived vulnerability and severity while also shaping efficacy beliefs and protection motivation.\u003c/p\u003e\u003cp\u003ePsychological variables\u003c/p\u003e\u003cp\u003eThe incorporation of psychological variables resulted in a substantial increase in explanatory power in almost all the models (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e ranging from \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .051 to \u003cem\u003eR\u0026sup2; = .267\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The majority of sociodemographic and pandemic-specific variables (risk group membership and infection experience) maintained their statistical significance. Notably, the effects of age and risk group affiliation on self-efficacy became nonsignificant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05), indicating that these associations may be attributable to psychological factors.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e. \u003cem\u003eOutputs of linear regression models examining the associations between psychological variables and PMT constructs.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn accordance with our hypothesis (H3.1), participants with higher general perceived infectability reported notably higher perceived vulnerability (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Similarly, greater perceived infectability was associated with greater perceived severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and increased protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007), indicating that individuals who perceive themselves as more susceptible anticipate more severe outcomes and are somewhat more motivated to take protective actions. The latter finding lends support to Hypothesis H3.2. Conversely, a heightened perception of infectability was related to diminished situational self-efficacy (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that feeling more vulnerable may reduce confidence in oneself to effectively implement protective measures.\u003c/p\u003e\u003cp\u003eGeneral self-efficacy demonstrated the strongest connection to situational self-efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), highlighting that individuals with greater overall confidence are more confident in engaging in specific protective behaviors. This finding supports H4.1. Interestingly, general self-efficacy had a small but significant negative effect on severity (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.07, p\u0026thinsp;=\u0026thinsp;.027), implying that more confident individuals may slightly downplay the severity of the threat.\u003c/p\u003e\u003cp\u003eThe evidence from the present study supported Hypothesis H5.1, which suggested that risk propensity behavior is negatively associated with protection motivation (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). This finding indicates that individuals who take more risks are less motivated to adopt protective behaviors. A higher risk propensity was also found to be related to a modest decrease in response efficacy (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001) and a small increase in response costs (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that risk-takers perceive protective measures as less effective and more burdensome.\u003c/p\u003e\u003cp\u003eContrary to the initial suppositions (H6.1 and H6.2), the study revealed a positive correlation between life satisfaction and perceived vulnerability (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.041) and severity (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.10, p\u0026thinsp;=\u0026thinsp;.003), suggesting that more satisfied individuals may recognize threats more acutely. Furthermore, the findings demonstrated positive correlations of life satisfaction with response efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), thereby supporting Hypothesis H6.3. These findings indicate that those with greater life satisfaction are more confident in their protective actions and more motivated to engage in them. In addition, life satisfaction was negatively tied to response costs (β = \u0026minus;\u0026thinsp;.20, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), reflecting that individuals with greater well-being perceive fewer barriers to implementing protective behaviors.\u003c/p\u003e\u003cp\u003eFinally, as hypothesized (H7.1 and H7.2), a higher self-rated health status was associated with diminished perceived severity (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.10, p\u0026thinsp;=\u0026thinsp;.009) and diminished protection motivation (\u003cem\u003eβ\u003c/em\u003e = \u0026minus;\u0026thinsp;.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.029), suggesting that individuals who perceive themselves as healthier feel less threatened and are marginally less motivated to engage in protective behaviors.\u003c/p\u003e\u003cp\u003eIn summary, the results indicate that the tested psychological factors, particularly perceived infectability, general self-efficacy, risk propensity, life satisfaction, and self-rated health, substantially shape protection motivation and related perceptions, often outweighing sociodemographic and pandemic-specific influences.\u003c/p\u003e\u003cp\u003ePMT determinants\u003c/p\u003e\u003cp\u003eTo investigate the extent to which the PMT determinants (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs) impact protection motivation, a final hierarchical linear regression model was constructed (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This model was associated with 51% of the variance in protection motivation (adjusted \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .497), indicating a substantial increase in explanatory power.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOutput of linear regression models examining associations between PMT determinants and protective motivation.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eProtective Motivation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePredictors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003estd. Beta\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(std. CI)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003cp\u003e(-0.14\u0026ndash;0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.844\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(0.01\u0026ndash;0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.04\u0026ndash;0.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (middle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003cp\u003e(-0.06\u0026ndash;0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation (high)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003cp\u003e(-0.08\u0026ndash;0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChildren (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.14\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Group (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003cp\u003e(-0.11\u0026ndash;0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfection (yes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003cp\u003e(-0.21\u0026ndash;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral infectibility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003cp\u003e(-0.05\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral self-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003cp\u003e(-0.00\u0026ndash;0.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.063\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk propensity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.15 \u0026ndash; -0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003cp\u003e(-0.05\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-rated health status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003cp\u003e(-0.14 \u0026ndash; -0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived vulnerability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003cp\u003e(-0.02\u0026ndash;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003cp\u003e(0.18\u0026ndash;0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003cp\u003e(-0.10\u0026ndash;0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003cp\u003e(0.38\u0026ndash;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse costs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003cp\u003e(-0.23 \u0026ndash; -0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e/R\u003csup\u003e2\u003c/sup\u003e adjusted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.508/0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eThe table presents standardized beta coefficients (Std. β), 95% confidence intervals (standardized CI), and p values for each variable. Significant effects (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) are highlighted in bold.\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. \u003cem\u003eOutput of linear regression models examining associations between PMT determinants and protection motivation.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe strongest predictor was response efficacy (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that individuals\u0026rsquo; beliefs in the effectiveness of protective behaviors play a primary role in motivating protective actions. Perceived severity also significantly predicted protection motivation (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.25, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that perceiving health threats as serious contributes meaningfully to motivation. Response costs were negatively associated with protection motivation (\u003cem\u003eβ\u003c/em\u003e = \u0026ndash;.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), highlighting that higher perceived barriers or burdens reduce individuals\u0026rsquo; willingness to engage in protective behaviors. The other PMT variables, including perceived vulnerability and self-efficacy, were not significant, suggesting that once efficacy beliefs and threat severity are accounted for, these factors may be less influential. Once these constructs were included, most intrapersonal predictors lost significance, except for age, risk group membership, and subjective health status. This finding indicates that certain demographic and health characteristics continue to influence protection motivation independently of the PMT construct. Nevertheless, it seems that for most demographic variables and health characteristics, their association with protection motivation is mediated by PMT constructs.\u003c/p\u003e\u003cp\u003eOverall, these findings indicate that protection motivation is driven primarily by individuals\u0026rsquo; beliefs in the effectiveness of protective behaviors and the perceived severity of the health threat, whereas perceived costs can deter motivation, highlighting the central role of PMT constructs over other intrapersonal factors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we asked whether extending protection motivation theory (PMT) with intrapersonal dispositions and pandemic-specific experiences can increase our understanding of the protective effects of COVID-19. This study aimed to explore how intrapersonal sources of information, such as pandemic-related experiences and stable psychological dispositions, are related to the key cognitive construct in protection motivation theory (PMT). Using a German quota sample (January 2024), we found that adding psychological dispositions and experiences substantially improved model fit for all PMT determinants and, ultimately, protection motivation.\u003c/p\u003e\u003cp\u003eThreat appraisals were most strongly tied to pandemic-specific factors and perceived infectability: self-identified risk group status and prior infection were associated with higher perceived severity and vulnerability, respectively, whereas perceived infectability robustly predicted both vulnerability and severity but was linked to lower situational self-efficacy.\u003c/p\u003e\u003cp\u003eCoping appraisals were related to lower risk propensity and higher life satisfaction. Risk-prone individuals reported lower response efficacy and higher response costs, whereas greater life satisfaction was linked to higher response efficacy and lower costs. General self-efficacy mapped onto domain-specific self-efficacy, and better self-rated health status was related to lower perceived severity and slightly lower protection motivation.\u003c/p\u003e\u003cp\u003eIn the final model predicting protective intentions, PMT constructs dominated. Response efficacy (strongest), perceived severity (positive), and response costs (negative) explained\u0026thinsp;~\u0026thinsp;50% of the variance, with perceived vulnerability and PMT self-efficacy playing smaller roles once other factors were included. Nevertheless, even after including the PMT constructs age, risk group membership and subjective health status retained a significant association with protection motivation.\u003c/p\u003e\u003cp\u003eTaken together, these exploratory findings indicate that incorporating stable intrapersonal characteristics alongside pandemic-specific experiences yields a more nuanced and more powerful account of PMT processes and intentions than does PMT alone, supporting our rationale for a dispositional extension of PMT and motivating confirmatory longitudinal and experimental work to test and apply this framework in preparedness and targeted public health communication.\u003c/p\u003e\u003cp\u003eSociodemographic variables\u003c/p\u003e\u003cp\u003eConsistent with prior research, sociodemographic variables accounted for only a small proportion of the variance. Older adults and highly educated individuals reported stronger beliefs in protective actions and lower response costs, likely reflecting differences in health literacy, access to information, or trust [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. The observed gender differences regarding perceived severity may be partially explained by gender stereotypes, roles and norms [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. People living with children demonstrated a heightened sense of vulnerability, possibly due to increased exposure through children\u0026rsquo;s social networks.\u003c/p\u003e\u003cp\u003ePandemic-related variables\u003c/p\u003e\u003cp\u003eThe incorporation of indicators specific to pandemics resulted in a substantial increase in explanatory power, primarily for threat appraisal models.\u003c/p\u003e\n\u003ch3\u003eRisk group self-identification\u003c/h3\u003e\n\u003cp\u003eThe present findings lend further support to previous research [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and provide evidence for the hypothesis that perceived severity is associated with risk group self-identification. In addition, the present study demonstrated that individuals who self-identify as part of a risk group perceive heightened vulnerability. This phenomenon may be attributed to a realistic evaluation of an increased probability of infection [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e] or, alternatively, to the repeated emphasis of public health communications during the pandemic on the heightened risks associated with specific demographic groups. Consequently, individuals who have adopted this \u0026ldquo;at-risk\u0026rdquo; identity may be able to evaluate their probability of contracting the virus and experiencing severe outcomes more accurately than those who have not.\u003c/p\u003e\u003cp\u003eBeyond threat appraisal, affiliation with risk groups was associated with increased response efficacy and decreased self-efficacy. However, the initially significant effect on self-efficacy did not remain significant after we accounted for psychological dispositions. These findings suggest that membership in risk groups may enhance attention to public health messaging tailored to their specific needs and heighten receptivity to intervention rationales, although this interpretation requires further investigation. Finally, the findings provide further support for our hypothesis that being part of a risk group is related to higher levels of protection motivation. However, the significance of this relationship disappears once the PMT determinants are introduced into the equation, thereby indicating that the effect of being part of a risk group on protection motivation is mediated through the PMT determinants.\u003c/p\u003e\n\u003ch3\u003eCOVID-19 experience\u003c/h3\u003e\n\u003cp\u003eThe association between prior COVID-19 infection and threat appraisal aligns with our expectations and supports the hypothesis that previous infection is strongly linked to perceived vulnerability but not to perceived severity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additionally, individuals with a history of infection demonstrated lower levels of self-efficacy. These findings indicate that a previous infection has the potential to compromise an individual's sense of self-efficacy in determining their health outcomes. Notably, the subjects also perceived a reduction in response costs, which may be attributable to an increased level of familiarity with the illness or a diminished perception of novelty and threat. These findings suggest that individuals' perceptions of protective behaviors may be linked to their personal experiences, potentially leading to a reduced perception of burden or disruption.\u003c/p\u003e\u003cp\u003eOverall, previous infection had a complex relationship with the PMT components, heightening vulnerability while reducing self-efficacy but also response costs. These countervailing effects may provide a rationale for the absence of a significant link between infection history and protection motivation. Furthermore, previous research has demonstrated that the severity of symptoms, rather than the infection itself, is a more significant factor in promoting protective behavior [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePsychological variables\u003c/p\u003e\u003cp\u003eMost notably, the results of this study demonstrated that psychological dispositions substantially enhanced the explanatory power of all the PMT determinants and significantly contributed to the perception of coping and threat appraisals.\u003c/p\u003e\n\u003ch3\u003ePerceived infectability\u003c/h3\u003e\n\u003cp\u003eIn accordance with our hypothesis, general perceived infectability was strongly connected to perceived vulnerability. Furthermore, the study demonstrated a consistent effect of baseline infectability on severity, even after demographic and pandemic-specific variables were controlled. These positive associations support the notion that individuals with heightened sensitivity to pathogen threats, which is consistent with the behavioral immune system (BIS) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], are more likely to consider infections to be personally relevant and serious. The results of the study suggest that general perceived infectability beliefs activate preexisting illness vulnerability and severity scripts, which, in turn, are linked to the estimation of outcomes for cases of the disease.\u003c/p\u003e\u003cp\u003eInterestingly, a heightened perception of infectability was found to be associated with a decline in self-efficacy. It is plausible that individuals with high PI may perceive the environment as being saturated with unavoidable risks, thereby diminishing their belief in their own ability to prevent infection. As an alternative hypothesis, this negative association may be an indication of learned helplessness among individuals who consistently perceive themselves as biologically vulnerable and have experienced frequent or severe illness in the past. This perception may be attributed to a perceived lack of agency in disease prevention, particularly if vulnerability is ascribed to biological or uncontrollable factors. These findings underscore a potential trade-off within the behavioral immune system, wherein heightened threat sensitivity may be accompanied by a concomitant reduction in perceived control. In this study, general perceived infectability was found to be associated with greater protection motivation, although the association became nonsignificant after the incorporation of PMT determinants. Overall, these patterns suggest that while individuals who perceive themselves as more susceptible to infection may experience heightened initial motivation to adopt protective behaviors, this motivation may be mediated by specific cognitive appraisals, such as perceived severity, vulnerability, and self-efficacy. These findings align with previous reports of an ambivalent relationship between general perceived infectability and COVID-19 preventive behavior [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePerceived general self-efficacy\u003c/h3\u003e\n\u003cp\u003eAs hypothesized, general self-efficacy (GSE) was strongly tied to COVID-19 self-efficacy, thereby reinforcing the notion that a disposition of confidence in one's capabilities generalizes to specific health-related challenges (social‒cognitive framework, [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]; see also [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]). Furthermore, the study demonstrated a negative correlation between general self-efficacy and perceived severity, although the effect was modest. These findings are corroborated by Zhou et al. (2021) [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe inverse association between self-efficacy and perceived severity may reflect a psychological distancing mechanism whereby high-efficacy individuals regulate threat appraisals to preserve agency. While this interpretation aligns with social-cognitive theory, it remains speculative and warrants further empirical investigation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRisk propensity\u003c/h2\u003e\u003cp\u003eIn accordance with our hypotheses and prior research [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], this study revealed that risk propensity was negatively associated with protection motivation. Building on these findings, we revealed that risk-prone individuals presented lower levels of coping appraisal, reporting reduced response efficacy and higher perceived response costs. These findings are consistent with those of previous studies suggesting that individuals high in risk tolerance tend to underestimate the benefits of protective actions while overestimating their disadvantages [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e, \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. This pattern may reflect cognitive dissonance [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e] or a motivational preference for autonomy and sensation seeking, which diminishes the perceived value of health-protective behaviors.\u003c/p\u003e\u003cp\u003eInterestingly, no significant correlation was found between risk propensity and threat perception. These findings contrast with earlier findings indicating that risk-tolerant individuals often display optimism bias and downplay personal vulnerability [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. One interpretation is that risk propensity may be related to motivation more strongly than to cognition, affecting coping appraisal while leaving threat appraisal intact. Alternatively, intrapersonal variables (e.g., sensation seeking and psychological reactance) or the cultural framing of risk may modulate this relationship. Thus, individuals high in risk propensity may recognize the severity of a threat but remain unmotivated to act because of low levels of coping appraisal.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLife satisfaction\u003c/h3\u003e\n\u003cp\u003eThis study lends support to our hypothesis that individuals who report greater life satisfaction are more inclined to engage in health-protective behaviors, although this effect disappears once PMT constructs are introduced. Our findings suggest that this phenomenon functions principally through coping appraisal processes, as elevated levels of life satisfaction are associated with increased response efficacy and decreased response costs. Although these outcomes have received limited attention in PMT research, they align with the findings of positive psychology, indicating that elevated subjective well-being is associated with enhanced optimism, more effective positive coping strategies, and elevated resilience [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. When transferred to life satisfaction, this association may explain the greater engagement in protective behaviors, driven by a stronger sense of control and lower perceived behavioral cost.\u003c/p\u003e\u003cp\u003eThe same line of reasoning can be used to argue that heightened life satisfaction serves to mitigate perceived risk. This assumption is supported by numerous previous studies examining the effect of risk perception on life satisfaction [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Consequently, a negative correlation between life satisfaction and perceived severity and vulnerability was hypothesized. However, the results of the present study revealed the opposite pattern. This difference highlights the importance of investigating potential bidirectional or recursive effects. These findings suggest that individuals who report higher levels of life satisfaction or better perceived health may place greater value on maintaining their situation and thus respond more strongly to perceived threats.\u003c/p\u003e\n\u003ch3\u003eSelf-rated health status\u003c/h3\u003e\n\u003cp\u003eIn accordance with the hypothesis and extant research [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], self-rated health status was negatively associated with perceived severity and protection motivation. These findings suggest that individuals who perceive themselves to be in good health may underestimate the potential severity of the disease and are less motivated to adopt protective behaviors. The findings of this study indicate that self-rated health status is not associated with perceived vulnerability, self-efficacy, response efficacy or response costs.\u003c/p\u003e\u003cp\u003ePMT determinants\u003c/p\u003e\u003cp\u003eThe final analysis, which incorporated the core cognitive constructs of PMT to predict protection motivation, demonstrated that the PMT variables were the most powerful predictors of protection motivation, explaining more than 50% of the variance. These findings align with the extensive empirical evidence supporting PMT [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], thereby underscoring the significance of how individuals cognitively assess threats and available coping mechanisms.\u003c/p\u003e\u003cp\u003eAmong these variables, response efficacy had the strongest effect, suggesting that belief in the effectiveness of protective actions is a critical determinant of motivation, which is in line with prior research conducted in Germany [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. In addition, the findings of the present study align with the postulation of PMT, which suggests that perceived response costs are negatively correlated with protection motivation. Notably, PMT-specific self-efficacy did not significantly predict motivation in the final model, despite its conceptual centrality to the theory. This phenomenon may be attributable to shared variance with general self-efficacy, which was incorporated earlier in the modeling and initially demonstrated a significant positive association with protection motivation. However, a body of research conducted in Germany has yielded equivocal results, identifying either a strong [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] or no significant [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e] impact.\u003c/p\u003e\u003cp\u003eConsistent with PMT, perceived severity has been demonstrated to be a significant predictor of behavioral intentions. The hypothesis that perceived vulnerability significantly predicts motivation was not supported by the data, which contradicts the assumption of PMT. Nevertheless, in the context of pandemic responses in Germany, perceived vulnerability appears to be weakly linked to protective behavior [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to the PMT determinants, the analysis revealed that self-rated health status, risk propensity, and age were the only direct significant predictors of protection motivation. This pattern implies that individuals in younger age groups, those with better health status, and those with higher risk propensity are less likely to adhere to behavioral guidelines. After the PMT scores were added to the model, further pandemic-related and psychological determinants lost significance. These findings suggest that these factors indirectly affect protection motivation, presumably through their effect on PMT components.\u003c/p\u003e\u003cp\u003eImplications\u003c/p\u003e\u003cp\u003eThe findings of the study highlight the limited explanatory power of sociodemographic variables and suggest that intrapersonal psychological factors may play a role in shaping responses to health threats. The aforementioned associations operated primarily indirectly through the cognitive appraisals specified in PMT. The study revealed that pandemic-related variables were particularly associated with threat appraisal, whereas psychological traits were strongly linked to both threat and coping appraisals. This pattern challenges the assumption of uniform cognitive processing in PMT and points to systematic interindividual differences in how people cognitively engage with health risks.\u003c/p\u003e\u003cp\u003eTheoretical implications\u003c/p\u003e\u003cp\u003eConsequently, this study contributes to a more differentiated understanding of the motivational dynamics underlying health-protective behavior. These findings corroborate the calls in the literature to move beyond situation-specific predictors and incorporate dispositional traits into models of health behavior [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e]. On the basis of the observed associations, we outline a heuristic \u0026ldquo;dispositional extended PMT (dPMT)\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) that integrates pandemic-related experiences and psychological dispositions as upstream factors potentially associated with PMT processes. The framework is derived from the current exploratory findings and is intended to serve as a conceptual guide for future longitudinal and experimental research rather than as a tested theoretical model. The dPMT emphasizes that individual differences (e.g., risk group self-identification, perceived infectability, and general self-efficacy) could help explain the variability in threat and coping appraisal beyond traditional PMT determinants. This perspective may support the design of more person-centered public health interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNotably, additional factors may be considered. For example, research has demonstrated associations between PMT determinants and personality traits [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e, \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e], trust in government [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e, \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e], and values [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eDispositional extended PMT (dPMT).\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eLegend. Intrapersonal sources of information, particularly pandemic-related factors (risk group self-identification, prior infection) and psychological factors (perceived infectability, general self-efficacy, risk propensity, life satisfaction, health status), influence cognitive evaluations of threats (vulnerability, severity) and coping capacities (self-efficacy, response efficacy, response costs). The interaction of these appraisals generates protection motivation, which in turn drives protective behavior.\u003c/em\u003e\u003c/p\u003e\u003cp\u003ePractical implications\u003c/p\u003e\u003cp\u003eUnderstanding the relationship between intrapersonal and PMT determinants has significant implications for the design of effective public health interventions and communication strategies during health crises such as the COVID-19 pandemic. The findings underscore the necessity of refined health interventions during pandemics. For instance, interventions targeting protective behavior could emphasize response efficacy clearly and repeatedly and address perceived response costs. This approach is particularly relevant for individuals with high risk propensity and low life satisfaction.\u003c/p\u003e\u003cp\u003eIn addition, the results support a person-centered approach to health behavior interventions. Rather than treating the PMT determinants as situationally induced appraisals, future interventions might profit from screening or segmenting populations on the basis of psychological predispositions. This approach aligns with recent advances in precision public health and behavioral science, which advocate for targeted, psychologically informed messaging.\u003c/p\u003e\u003cp\u003eOn the basis of our findings, the following initial ideas illustrate how tailored health communication could address distinct psychological dispositions.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIndividuals with high perceived infectability may benefit from messages that explicitly enhance self-efficacy and convey concrete, manageable actions to reduce perceived uncontrollability.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCommunication focused on people with prior infections could emphasize rebuilding a sense of personal competence and control.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePeople with high life satisfaction may profit from positively framed, autonomy-enhancing messages that emphasize the preservation of valued life conditions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMessages for individuals with low life satisfaction could enhance personal agency, highlighting attainable behavioral gains, fostering response efficacy, and suggesting easy-to-implement behavioral strategies, as traditional threat-based messaging can be ineffective or even counterproductive.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIndividuals with high levels of self-rated health may underestimate threat severity. Communication could stress the relevance of protective actions even for those who perceive themselves as healthy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIndividuals with high risk propensity may respond better to messages emphasizing personal benefits and low response costs rather than fear-based appeals.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eResilience\u003c/p\u003e\u003cp\u003eThe findings of the study further underscore the value of integrating psychological resilience into public health strategies, particularly in the context of proactive preparedness for global health threats such as pandemics. By considering dispositional factors such as life satisfaction, subjective health status, and general self-efficacy, this study advances beyond traditional models focused solely on medical risk. This broader lens allows for a more precise identification of vulnerable groups, enabling interventions that align with individuals\u0026rsquo; perceived vulnerabilities.\u003c/p\u003e\u003cp\u003eNotably, the results suggest that general self-efficacy may serve as a foundational resource that facilitates the development of specific self-efficacy, ultimately enhancing compliance with protective behaviors. Interventions aimed at increasing general self-efficacy, particularly among people with limited health literacy or structural disadvantage, may therefore strengthen individuals' belief in their general capacity to cope with challenges, which, in turn, reinforces more context-specific protective actions. This finding highlights the importance of coupling targeted health communication with empowerment-based strategies that enhance psychological resources.\u003c/p\u003e\u003cp\u003eAdditionally, fostering life satisfaction may indirectly support behavior change by reducing the perceived burden of health behaviors and reinforcing adaptive coping appraisals. Public health messaging should thus adopt a dual approach: tailoring messages to individual risk profiles while simultaneously investing in psychological resilience through interventions that build agency, confidence, and a sense of personal control. Such psychologically grounded strategies are critical not only for improving immediate behavioral compliance but also for preparing populations to respond adaptively during future crises.\u003c/p\u003e\u003cp\u003eLimitations and future research\u003c/p\u003e\u003cp\u003eWhile the present findings offer important insights, it is necessary to mention some limitations and consider how they interact with PMT processes.\u003c/p\u003e\u003cp\u003eMethodological constraints and causal inference\u003c/p\u003e\u003cp\u003eThe cross-sectional design represents a key limitation, as it precludes causal interpretations. Relationships between psychological dispositions (e.g., life satisfaction, perceived infectability) and PMT determinants may be bidirectional or influenced by unmeasured third variables. Longitudinal studies are therefore needed to disentangle these relationships over time. Experimental and quasiexperimental designs may also help clarify causal pathways and improve model robustness.\u003c/p\u003e\u003cp\u003eWhile key covariates were included, the possibility of residual confounding remained. Future research should consider strategies such as sensitivity analyses, instrumental variable approaches, or negative control outcomes to evaluate potential biases arising from unobserved variables.\u003c/p\u003e\u003cp\u003eIn addition, mediation effects are suggested by the data, although these effects were not formally tested. Further research could employ mediation analyses to assess whether PMT variables mediate the relationship between general psychological dispositions and protection motivation. Moderation effects such as the role of trust, anxiety, or optimism in shaping threat and coping appraisals should also be examined. For instance, institutional trust may increase perceived response efficacy, whereas distrust could undermine it.\u003c/p\u003e\u003cp\u003eContextual and cultural limitations\u003c/p\u003e\u003cp\u003eThe geographical focus on Germany restricts the generalizability of the findings. Cultural factors, including public health messaging styles, healthcare system characteristics, and culturally shaped attitudes toward risk, can strongly influence intrapersonal variables and PMT determinants. Cross-cultural research is therefore needed to assess whether the observed patterns hold in other contexts and to identify culturally specific influences.\u003c/p\u003e\u003cp\u003eQualitative approaches could further enrich the understanding of such contextual dynamics and provide insight into unexpected associations such as the observed positive relationship between life satisfaction and threat appraisal. These methods could also help interpret complex or counterintuitive results that are difficult to explain through quantitative models alone.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis exploratory study aimed to contribute to a more psychologically nuanced and person-centered understanding of protection motivation by investigating the relationship between intrapersonal sources of information, such as pandemic-related experiences and stable psychological dispositions, and the key cognitive construct in PMT. The study highlights the idea that individual differences such as perceived infectability, risk propensity, and life satisfaction are systematically associated with PMT processes. While traditional determinants such as response efficacy and perceived severity remain central, the proposed heuristic dPMT framework expands the model by integrating pandemic-related experiences and psychological dispositions as upstream factors. Future longitudinal and experimental studies should test whether this approach can improve the explanatory scope of PMT and guide more effective, targeted interventions in diverse cultural contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis paper is based on independent research conducted as part of the SEMSAI (031L0295C) project, which was commissioned and funded by the German Federal Ministry of Research, Technology and Space (BMFTR, formerly Federal Ministry of Education and Research, BMBF).\u003c/p\u003e\u003cp\u003eEthics declaration\u003c/p\u003e\u003cp\u003e The study was conducted in accordance with relevant guidelines and regulations and approved by the ethics commission at the Freie Universit\u0026auml;t Berlin. All participants provided informed and written consent prior to participating.\u003c/p\u003e\u003cp\u003e Consent for publication\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003eCompeting interests\u003c/p\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eProject administration, K.S., M.V.; Conceptualization, K.S., P.W.; Data curation, P.W.; Data analysis, K.S., P.W.; Writing original draft, K.S.; Review and editing, P.W., M.V., K.S.; All the authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe want to thank all the study participants for their participation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKim SY, Yeniova A\u0026Ouml;. 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Behav Med 2022:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKojan L, Burbach L, Ziefle M, Calero Valdez A. Perceptions of behaviour efficacy, not perceptions of threat, are drivers of COVID-19 protective behaviour in Germany. Humanit Soc Sci Commun 2022; 9(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteyer R, Mayer A, Geiser C, Cole DA. A theory of states and traits\u0026mdash;revised. Annu Rev Clin Psychol. 2015;11:71\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBubeck P, Osberghaus D, Thieken AH. Explaining Changes in Threat Appraisal, Coping Appraisal, and Flood Risk-Reducing Behavior Using Panel Data From a Nation-Wide Survey in Germany. Environ Behav. 2023;55(4):211\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchneider IK, Dorrough AR, Frank C. 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Fear, trust, and compliance with covid-19 measures: A study of the mediating effect of trust in government on the relationship between fear and compliance; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S, Chen M, Ma X, Sun Z. Applying an Extended Protection Motivation Theory Model to Predict Resident Hospitality During the COVID-19 Crisis. J Travel Res 2023:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDimitrova T, Ilieva I. Consumption Behaviour towards Branded Functional Beverages among Gen Z in Post-COVID-19 Times: Exploring Antecedents and Mediators. Behav Sci (Basel) 2023; 13(8).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e In addition to perceived infectability, the PVD construct also includes germ aversion. While germ aversion has been more extensively studied in relation to behavior, empirical research on the impact of general perceived infectability remains limited.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Protection motivation theory, life satisfaction, risk propensity, perceived infectability, perceived general self-efficacy, health status, risk group, COVID-19 experience","lastPublishedDoi":"10.21203/rs.3.rs-7627607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7627607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eImproving the understanding of the factors influencing COVID-19 health intentions is important for pandemic preparedness and public health promotion, as such insights can inform effective communication and policy strategies. With respect to this goal, protection motivation theory (PMT) is a prominent framework for understanding health-protective behavior, but it largely overlooks intrapersonal variables that may be linked to cognitive appraisals. This exploratory study investigated how pandemic-related factors (risk group self-identification, prior infection) and psychological dispositions (perceived infectability, general self-efficacy, risk propensity, life satisfaction, and subjective health) may be associated with PMT determinants (perceived vulnerability, perceived severity, self-efficacy, response efficacy, and response costs) and protective health intentions during the COVID-19 pandemic. The aim was to identify patterns that could inform future confirmatory studies, thereby generating hypotheses that may ultimately guide the development of effective interventions and strategies to strengthen public health and pandemic preparedness.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional online survey was conducted in Germany in January 2024 (n\u0026thinsp;=\u0026thinsp;1,050; quota sample by age, gender, region, education). Hierarchical linear regressions were performed for each PMT determinant, sequentially entering (1) sociodemographic variables, (2) pandemic-specific factors, and (3) psychological dispositions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIntrapersonal factors contributed substantially to model fit (R\u0026sup2; = 6%\u0026ndash;28%). Threat appraisal was most closely associated with pandemic-specific variables and perceived infectability, whereas an unexpected inverse relationship emerged with life satisfaction. Coping appraisals were most strongly associated with lower risk propensity and higher life satisfaction. General self-efficacy and subjective health were also linked to various PMT constructs. These findings are best viewed as exploratory in nature and may serve to generate hypotheses for future research.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIncorporating intrapersonal variables into PMT was associated with greater explanatory power and provided a more nuanced understanding of health-protective behavior. This extended framework may not only broaden explanatory power but also guide the design of more targeted health interventions, a proposition that warrants further longitudinal and experimental testing.\u003c/p\u003e","manuscriptTitle":"Associations between psychological dispositions, pandemic-related variables and protection motivation theory determinants: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 08:37:53","doi":"10.21203/rs.3.rs-7627607/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-29T14:11:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165875629722779440403005431691952509725","date":"2025-10-19T02:41:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-17T18:57:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-19T15:17:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T14:13:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-17T14:11:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-16T07:59:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff28e99a-20fb-436f-bfa9-91e8f55ad6ac","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T08:37:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 08:37:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7627607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7627607","identity":"rs-7627607","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}