Predicting Acceptance of COVID-19 Vaccination: Application of A Modified Health Belief Model

Health Belief Model and its Modified Version

The health belief model (HBM) was proposed to explain and predict health-related behaviors by focusing on individuals’ beliefs and attitudes toward health conditions and preventive actions (Rosentock, 1966; Rosentock et al., 1988). The model posits that health-related behaviors are determined by four key factors: perceived susceptibility, perceived severity, perceived benefits, and perceived costs (or perceived barriers). Perceived susceptibility refers to “one’s subjective perception of the risk of contracting a condition” (Janz & Becker, 1984, p. 2). Perceived severity reflects beliefs about the seriousness of the consequences associated with the condition, including both medical and social consequences (Janz & Becker, 1984). Perceived benefits refer to desirable outcomes, such as reduction of the health threat, which motivate individuals to take health actions (Glanz et al., 2015). Perceived costs (often referred to as perceived barriers) include the psychological, financial, and other costs (e.g., side effects) of the action that may hinder individuals from adopting the recommended behavior (Rosenstock, 1966). In addition to those four variables, Rosenstock et al. (1988) later added self-efficacy, or confidence that one can successfully execute the behaviors required to produce the outcomes (Bandura, 1997), as a predictor for preventive behavior.

HBM has been tested by numerous studies in various health contexts (e.g., Farquharson et al., 2004; Kaufert et al., 1986; N. A. Smith et al., 1987). Even though many studies supported HBM, some studies found weak or null effects of health belief variables proposed in HBM (Carpenter, 2010; Cho et al., 2013; Harrison et al., 1992). In a meta-analysis to evaluate the effectiveness of the conventional HBM with 18 studies (total N = 2,702), Carpenter (2010) found that both perceived benefits (r = .27) and perceived costs (r = .30) had a moderate association with preventive actions. However, perceived severity had a relatively weak association with preventive actions (r = .15) and perceived susceptibility had an almost null relationship with preventive actions (r = .05). Such results showed that HBM has limitations in its explanation, especially in explaining the impact of susceptibility.

To enhance HBM’s predictability of preventive actions, Ronis (1992) made two major changes to the conventional HBM and proposed “the integrated model” (p. 127) of health behaviors. The first change relates to the conceptualization of perceived risk factors. The initial version of HBM did not consider individuals’ future action plans in conceptualizing perceived risks (i.e., perceived susceptibility and severity). In the modified HBM, Ronis highlighted the conditionality of risk perceptions, focusing specifically on how a person’s sense of vulnerability or threat severity depends on their behavioral choices. Specifically, the modified HBM includes two types of perceived susceptibility: inaction-conditional susceptibility (i.e., perceived susceptibility when preventive actions are not taken) and action-conditional susceptibility (i.e., perceived susceptibility when preventive actions are taken). If individuals believe that they are likely to get the disease if they do not take action (i.e., high inaction-conditional susceptibility), they are more inclined to take preventive action. By contrast, individuals are less inclined to take preventive action if they believe that they are likely to get the disease even if they take the action (i.e., high action-conditional susceptibility). Similarly, Ronis suggested that perceived severity should be distinguished based on the conditionality of future actions. High inaction-conditional severity would increase preventive action intentions whereas high inaction-conditional severity would decrease preventive action intentions.

A closer examination of the original HBM reveals that perceived susceptibility and severity refer to individuals’ subjective assessments of risk before taking any preventive action. However, early studies of HBM did not conceptually clarify the relationship between risk perceptions and preventive behavior. For instance, Janz and Becker (1984, p. 2) define risk perceptions simply as “one’s subjective perception of the risk of contracting a condition.” As a result of such a ambiguous conceptualization, perceived susceptibility has often been measured merely as the likelihood of developing a disease in the future—for example, by asking questions such as, “How likely do you think you are to get the COVID-19 virus within the next two months?” (Kowalski & Black, 2021, p. 17). In response to such items, individuals who are objectively at high risk but already intend to engage in preventive behaviors may report a low perceived susceptibility. This ambiguity in measurement may partly explain why Carpenter’s (2010) meta-analysis found that the average correlation between perceived susceptibility and preventive behavior was close to zero (Y. Kim & Chung, 2023). Ronis’ (1992) conceptual refinement—distinguishing between perceived risk perecptions assuming no preventive behavior and perceived risks assuming preventive behavior—helped eliminate measurement ambiguity at its source. This distinction has since been adopted by multiple researchers (Brewer et al., 2004; van der Velde et al., 1996).

The other change to the conventional HBM involves the mediating role of perceived benefits. As in the conventional HBM, Ronis (1992) defined perceived benefits of the preventive action as “desirable nonhealth outcomes (e.g., “fresh breath” as an outcome of tooth brushing) and reduction of the health threat (by reduced susceptibility, reduced severity, or both)” (p. 127). Ronis hypothesized that perceived benefits have a positive effect on intentions for preventive actions. However, unlike the conventional HBM, Ronis hypothesized that perceived benefits would mediate the effects of perceived susceptibility and perceived severity on the behavioral outcomes. Ronis’ hypothesis regarding the mediating effect of perceived benefits was based on the idea that perceived benefits are determined by the amount of the reduction in perceived susceptibility and perceived severity. The perceived benefits of a preventive action are determined by the difference between inaction-conditional susceptibility and action-conditional susceptibility, as well as by the difference between inaction-conditional severity and action-conditional severity. Thus, perceived benefits would be greater when the reduction of the perceived susceptibility increases and the reduction of the perceived severity increases.

Consequently, perceived benefits mediate the effects of perceived susceptibility and severity on the behavioral outcomes.

Ronis’ modified HBM was first validated in his own study, which applied the full model to the prevention of infectious diseases and oral health conditions. Subsequent studies did not employ the full model but focused primarily on the susceptibility component. In Ronis’ original research, experimental manipulations involved presenting participants with varying information about the likelihood of infection with or without vaccination in the case of a hypothetical infectious disease. The findings supported the full model. Brewer et al. (2004) applied the susceptibility component of the modified HBM to the context of the Lyme disease vaccine and found results consistent with the model. Similarly, van der Velde et al. (1996) applied the susceptibility component of the modified HBM in an AIDS-related context and also reported findings that aligned with the model. Notably, since Ronis’ original work, no studies to our knowledge have tested the full modified HBM. This represents a critical oversight, as Ronis’ modifications—designed to enhance both predictive accuracy and theoretical coherence—remain empirically underexamined. Focusing only on individual components has limited insight into how the model’s constructs interact, weakening the foundation for designing effective, theory-driven interventions. This study addresses that gap by applying the full modified HBM to the prediction of vaccination behavior, offering a more rigorous test of the model in a real-world health context. Although the original HBM has been widely applied to infectious disease prevention (e.g., Kowalski & Black, 2021; Ma & Ma, 2021), our study is the first to apply Ronis’ modified version to predict actual preventive behaviors in response to real-world infectious disease threats such as COVID-19.

Application of the Modified HBM to Predict Vaccination Intention against COVID-19

In the current study, we adjusted the modified HBM to fit the vaccination context to effectively predict vaccination acceptance. Perceived susceptibility should be measured both in the condition of not getting vaccinated (i.e., susceptibility without vaccination) and in the condition of getting vaccinated (i.e., susceptibility with vaccination). As susceptibility without vaccination increases, vaccination intention would be expected to increase, but as susceptibility with vaccination increases, vaccination intention would be expected to decrease. Similarly, perceived severity should be measured both in the condition of not getting vaccinated (i.e., severity without vaccination) and in the condition of getting vaccinated (i.e., severity with vaccination). It is expected that, as severity without vaccination increases, vaccination intention will increase, while as severity with vaccination increases, vaccination intention will decrease.

• H1. Vaccination intention is (a) positively associated with susceptibility without vaccination, but (b) negatively associated with susceptibility with vaccination.
• H2. Vaccination intention is (a) positively associated with severity without vaccination increases, but (b) negatively associated with severity with vaccination.

Perceived Benefits of Vaccination

As was the case in the conventional HBM, Ronis (1992) hypothesized that perceived benefits have a positive effect on intentions for preventive actions. Ronis also defined the perceived benefits of preventive action in the form of the reduction of health threats. In the vaccination context, one of the main benefits of getting vaccinated would be the reduction in perceived susceptibility between when no vaccination is taken and when the vaccination is taken. That is, the greater the decrease in perceived susceptibility when vaccinated compared to when no vaccine is taken, the greater the estimated benefit of getting vaccinated. As with perceived susceptibility, the reduction in perceived severity between when no vaccination is taken and when the vaccination is taken is another main benefit of getting vaccinated.

• H3. Vaccination intention is positively associated with vaccination benefits.
• H4. Vaccination benefits mediate the relationship between (a) susceptibility without vaccination and vaccination intention and (b) susceptibility with vaccination and vaccination intention
• H5. Vaccination benefits mediate the relationship between (a) severity without vaccination and vaccination intention and (b) severity with vaccination and vaccination intention

Perceived Costs of Vaccination

Following the conventional HBM, Ronis (1992) defined perceived costs as the consequences of undesirables or negatives and proposed that perceived costs have negative effects on health behavior intentions. In the vaccination context, one of the most salient perceived costs is the side effects of vaccination (e.g., fever, headache, and myocarditis). Indeed, fear of the side effects of vaccination was found to be a major factor of COVID-19 vaccine hesitancy (Sudharsanan et al., 2022). Many people hesitate to be vaccinated because of concerns regarding the safety of the vaccine or potential adverse effects (H. K. Kim et al., 2022; Padamsee et al., 2022). As the side effects of vaccination are also a type of health risk, perceived susceptibility and perceived severity can be used to measure individual’s perceived risks of side effects of vaccination. That is, the perceived chance of having side effects after getting vaccinated (i.e., susceptibility to vaccination side-effects) and the perceived severity of the side effects of vaccination (i.e., severity of vaccination side-effects) can negatively affect vaccination intention.

• H6. Vaccination intention is negatively associated with perceived susceptibility to vaccination side-effects.
• H7. Vaccination intention is negatively associated with perceived severity of vaccination side-effects.

Self-Efficacy for Vaccination

Self-efficacy refers to one’s confidence that they can successfully execute the behaviors required to produce the outcomes (Bandura, 1977), and it has been found to be an important predictor of health behavior intention (Sheeran et al., 2016). Several studies have tested the effect of self-efficacy on the intention of vaccination for infectious disease, with the results showing that self-efficacy had a positive effect on vaccination intention (Mehta et al., 2013; Petrovic et al., 2011; Wu et al., 2022). Even though Ronis (1992) did not test the effect of self-efficacy on health behavior intention in the modified HBM, we include self-efficacy as a health belief that predicts vaccination intention.

• H8. Vaccination intention is positively associated with self-efficacy for vaccination.

Figure 1 represents the proposed hypotheses as a causal model.

Figure 1.

A Modified Model of Health Belief Model to Predict Acceptance of VaccineNote. H4a: Susceptibility without Vaccination → Vaccination Benefits → Vaccination IntentionH4b: Susceptibility with Vaccination → Vaccination Benefits → Vaccination IntentionH5a: Severity without Vaccination → Vaccination Benefits → Vaccination IntentionH5b: Severity with Vaccination → Vaccination Benefits → Vaccination Intention

Survey Participants and Procedures

In total, 266 participants (133 females) aged between 20 and 69 years old (M = 44.87, SD = 14.01) were recruited through an online survey firm in Korea (Macromill Embrain) using quota sampling. IRB approval was obtained from the first author's instsitution. After providing informed consent, participants answered questions about their demographic information. Participants were then shown promotional material and instructions providing information about COVID-19 bivalent vaccine boosters, as published by the Korea Disease Control and Prevention Agency and the Ministry of Health and Welfare of Korea. To minimize potential message effects on vaccination acceptance, only basic and limited information was included in the message. Participants then answered questions about their various beliefs about COVID-19 and the bivalent vaccine.

Measures

For most measures, we used an 11-point Likert scale (0 = Not agree at all; 10 = Completely agree); the scale used for measurement is only reported when another scale has been used.

Vaccination Intention

An individual’s intention of getting the COVID-19 bivalent vaccine was measured utilizing a single item based on measures used in previous studies (Guvenc et al., 2016; Li et al., 2022): “Do you have any intention of getting a COVID-19 bivalent vaccine booster dose this winter (by March 2023)? Please indicate your level of agreement with the following single statement: I will get a COVID-19 bivalent vaccine” (M = 3.51, SD = 2.62).

Perceived Susceptibility

Conditional and unconditional perceived susceptibility to COVID-19 were measured using items that were adapted from previous studies (Chung et al., 2022; Patwary et al., 2021). Susceptibility without vaccinations was measured using two items: First, “If I do not get a COVID-19 bivalent vaccine booster dose, I will get infected with COVID-19”, and second, “If I do not get a COVID-19 bivalent vaccine booster dose, the possibility that I will get infected with COVID-19 is [ ]%.” To the second question, participants indicated the perceived likelihood of getting infected with COVID-19 on a 10% interval scale. The responses to these two items were averaged into a composite score, with higher scores indicating a higher perceived susceptibility when not getting vaccination (Cronbach’s α = .76, M = 4.08, SD = 1.92).

To measure susceptibility with vaccination, we used the following statement: “If I get a COVID-19 bivalent vaccine booster dose, I will get infected with COVID-19” and “If I get a COVID-19 bivalent vaccine booster dose, the possibility that I will get infected with COVID-19 is [ ]%” The responses to these two items were averaged into a composite score, with higher scores indicating a higher perceived susceptibility when getting vaccination (Cronbach’s α = .86, M = 4.12, SD = 1.84).

Perceived Severity

The perceived severity of COVID-19 was measured in two ways: severity without vaccination and severity with vaccination. Severity without vaccination was measured using two items: “The severity of symptoms of COVID-19 if you do not get a COVID-19 bivalent vaccine” (0 = Not severe at all; 10 = Completely severe), and “If I do not get a COVID-19 bivalent vaccine, being infected with COVID-19 will be very fatal to me” (0 = Not fatal at all; 10 = Completely fatal). The responses to these two items were averaged into a composite score, with higher scores indicating a higher perceived severity when not getting the vaccination (Cronbach’s α = .86, M = 4.40, SD = 1.87).

Severity with vaccination was measured using two items: “The severity of symptoms of COVID-19 if you get a COVID-19 bivalent vaccine” (0 = Not severe at all; 10 = Completely severe), and “If I get a COVID-19 bivalent vaccine, being infected with COVID-19 will be very fatal to me” (0 = Not fatal at all; 10 = Completely fatal). The responses to these two items were averaged into a composite score, with higher scores indicating a higher perceived severity when getting the vaccination (Cronbach’s α = .87, M = 5.25, SD = 1.91).

Perceived Benefits of Vaccination

Vaccination benefits were measured using three items based on previous studies (Al-Metwali et al., 2021; Wong et al., 2021): “The COVID-19 bivalent vaccine will be helpful to my life and health,” “The COVID-19 bivalent vaccine booster dose will reduce the susceptibility to COVID-19,” and “The COVID-19 bivalent vaccine will reduce the severity of COVID-19.” The responses to these three items were averaged into a composite score, with higher scores indicating a higher perceived severity when getting the vaccination (Cronbach’s α = .89, M = 4.96, SD = 1.81).

Perceived Costs of Vaccination

The two dimensions of perceived costs, susceptibility and severity of vaccination of side-effects, were also measured. Susceptibility to vaccination side-effects was measured with two items based on previous studies (Chung et al., 2022; Mo & Lau, 2015; P. J. Smith et al., 2011): “If I get a COVID-19 bivalent vaccine booster dose, I will experience side-effects” and “If I get a COVID-19 bivalent vaccine booster dose, the possibility that I will experience side-effects is [ ]%” Participants indicated the perceived likelihood of experiencing side-effects on a 10% interval scale. The responses to these two items were averaged into a composite score, with higher scores indicating higher perceived susceptibility to vaccination side-effects (Cronbach’s α = .94, M = 5.20, SD = 2.12).

Severity of vaccination side-effects was measured with two items based on previous studies (Badr et al., 2021; Chung et al., 2022): “The severity of side-effects if you get a COVID-19 bivalent vaccine” (0 = Not severe at all; 10 = Completely severe), and “If I have side-effects after getting the COVID-19 bivalent vaccine, it will be very fatal to me” (0 = Not fatal at all; 10 = Completely fatal). The responses to these two items were averaged into a composite score, with higher scores indicating higher perceived severity to vaccination side-effects (Cronbach’s α = .93, M = 5.44, SD = 2.11).

Self-Efficacy for Vaccination

In both types of questionnaires, self-efficacy for COVID-19 vaccination was measured with two items based on items that were previously used by Ling et al. (2019): “I could get the COVID-19 bivalent vaccine if I wanted to,” and “It is easy for me to get the COVID-19 bivalent vaccine”. The responses to these two items were averaged into a composite score, with higher scores indicating higher self-efficacy for vaccination (Cronbach’s α = .65, M = 5.90, SD = 2.48).

Hypotheses Testing: Modified HBM (H1 to H8)

Proposed hypotheses about the modified HBM (H1 to H8) were tested using structural equation modeling (AMOS 27.0). Bivariate correlations among the observable variables are presented in Table 1. In testing the model, we controlled for the effects of age, sex, COVID-19 infection history, and vaccination history on the variables.

Table 1.

Means, Standard Deviations, and Correlations among the Study Variables(N =266)

Model Fit

The measurement model was tested only for latent variables with multiple indicators, χ² (91, N = 266) = 228.54, CFI = .95, RMSEA = .08, with 90% CI [.06, .09]. The measurement model showed a reasonable fit (Bentler & Raykov, 2000; Browne & Cudeck, 1993). The full model that includes both measurement and structural models was tested, with the results shown in Figure 2. The direct link between susceptibility without vaccination and vaccination intention was not specified in the proposed model, but it was included to test whether the mediation relationship was either full or partial. The direct links to vaccination intention from susceptibility with vaccination, severity without vaccination, and severity with vaccination were also specified in the model. The full model was tested and found to have a good fit with the data, χ² (103, N = 266) = 255.65, NFI = .92, CFI = .95, RMSEA = .08, with 90% CI [.06, .09], TLI = .92 (Bentler & Raykov, 2000; Browne & Cudeck, 1993).

Figure 2.

Results of Structural Equation Modeling Analysis of the Modified HBMNote. All latent variables aside from vaccination benefits were measured using two observed indicators, and vaccination benefits was measured using three indicators (standardized factors loadings = [.57, .97]). Vaccination intention was a single-indicator variable. The model fit was χ2 (103, N = 266) = 255.65, NFI = .92, CFI = .95, RMSEA = .08, with 90% CI [.06, .09], TLI = .92 (Bentler & Raykov, 2000; Browne & Cudeck, 1993). The parameters shown on the paths are the standardized regression weights. These parameters were estimated using bootstrapping procedures (2,000 bootstrap samples) and bias-corrected confidence intervals (Hayes & Scharkow, 2013).* p < .05. *** p < .001.

Hypotheses Testing

H1 predicted that vaccination intention is positively associated with (a) susceptibility without vaccination increases, but (b) negatively associated with susceptibility with vaccination. Susceptibility without vaccination was positively associated with vaccination intention (β = .45, SE = 0.18, p <.001). Susceptibility with vaccination had no significant effect on vaccination intention (β = -.14, SE = 0.11, p = .063). H1a was supported but H1b was not supported. H2 predicted that vaccination intention is positively associated with (a) severity without vaccination increases, but (b) negatively associated with severity with vaccination. Neither severity without vaccination (β = .08, SE = 0.12, p = .372) nor severity with vaccination (β = .10, SE = 0.07, p = .069) had a significant effect on vaccination intention. H2a and H2b were not supported.

Supporting, H3, vaccination benefits was positively associated with vaccination intention (β = .28, SE = 0.12, p < .001). To test the mediating effects of perceived vaccination benefits proposed in H4 and H5, we used bootstrapping procedures with 2,000 resamples to generate 95% bias-corrected confidence intervals. H4 predicted the mediating role of vaccination benefits in the relationship (a) between susceptibility without vaccination and vaccination intention and (b) between susceptibility with vaccination and vaccination intention. The indirect effect of susceptibility without vaccination on vaccination intention through vaccination benefits was statistically significant (β = .18, bootstrap SE = .11, 95%, bootstrap CI [0.13, 0.50], p = .010). Given that susceptibility without vaccination had a significant direct effect on vaccination intention, vaccination benefits partially mediated this relationship. The indirect effect of susceptibility with vaccination on vaccination intention through vaccination benefits was statistically significant (β = -.08, bootstrap SE = .06, 95%, bootstrap CI [-0.24, -0.04], p = .010). Given that susceptibility with vaccination did not have a direct effect on vaccination intention, vaccination benefits fully mediated this relationship. Both H4 and H4b were supported.

H5 predicted the mediating role of vaccination benefits in the relationship (a) between severity without vaccination and vaccination intention and (b) between severity with vaccination and vaccination intention. The indirect effect of severity without vaccination on vaccination intention through vaccination benefits was statistically not significant (β = .03, bootstrap SE = .07, 95%, bootstrap CI [-0.05, 0.19], p = .475). H5a was not supported. The indirect effect of severity with vaccination on vaccination intentions through vaccination benefits was not statistically significant (β = -.02, bootstrap SE = .04, 95%, bootstrap CI [-0.11, 0.004], p = .165). H5b was not supported.

H6 predicted that vaccination intention is negatively associated with perceived susceptibility to vaccination side effects. Susceptibility to side effects had no significant effect on vaccination intention (β = -.02, SE = 0.09, p = .732). H6 was not supported. H7 predicted that vaccination intention is negatively associated with the perceived severity of vaccination side effects increases. The severity of side effects was negatively associated with vaccination intention (β = -.16, SE = 0.08, p = .024). H7 was supported. Lastly, self-efficacy had a positive effect on vaccination intention (β = .26, SE = 0.12, p < .001). H8 was supported.

Limitations and Suggestions for Future Study

This study has several limitations that suggest directions for future research. First, the context of the current study has some unique characteristics. For instance, the data was collected during a specific time period when the general public may have already formed beliefs about risk perceptions and vaccination. Collecting data at the onset of the COVID-19 pandemic or during the initial vaccine rollout could have yielded more significant results. This study also focused on vaccination, which is a form of preventive health behavior. Future studies that apply modified HBM could test health promotion behaviors.

Next, this study focused on five individual health beliefs (i.e., perceived susceptibility, perceived severity, perceived benefits, perceived costs, and self-efficacy; Glanz et al., 2015). In addition to these individual beliefs, cues to action, which is a term that refers to strategies to activate health behavior, have also been used in many HBM studies as a predictor of health behaviors (Glanz et al., 2015). Future studies could measure cues to action as a predictor of vaccination intention in the modified HBM to enhance the predictive power.

Another important limitation of this study is the exclusion of vaccine-related information exposure and message effects, which could significantly influence individuals’ risk perception and vaccination intention. Although the modified HBM was applied to predict COVID-19 vaccination intention based on perceived risk, the present study did not account for exposure to relevant messages or information. In particular, the extent of exposure to vaccine-promoting messages disseminated by government or public health agencies through mass media or social media, as well as exposure to messages highlighting potential side effects of the vaccine, may shape how individuals perceive the risks associated with COVID-19 and their intention to be vaccinated. Interpersonal communication about such topics may also play a crucial role in shaping beliefs and behavioral intentions. Future research should apply the modified HBM while incorporating variables that measure the degree and nature of message exposure—both supportive and critical—to better understand their impact on perceived risk and vaccination behavior.

As observed in many other countries, the decision to receive a COVID-19 vaccine in South Korea was not solely based on medical or scientific considerations but was, in part, shaped by political orientations. The pandemic and associated public health measures, including vaccination, became subjects of political discourse, with different political groups advancing contrasting arguments. Consequently, some individuals formed vaccine-related beliefs and attitudes based on their political orientation or their level of trust in the government (Baek et al., 2022; Fridman et al., 2021). Political orientation has been shown to influence not only vaccine attitudes but also compliance with public health guidelines (Barrios & Hochberg, 2021). Given this context, studies employing the modified health belief model to predict vaccination intentions should consider political orientation as a potential confounding variable. However, the present study did not include any measure of political orientation and thus was unable to statistically control its potential influence. Future studies applying the modified HBM to COVID-19 or other pandemic-related behaviors should incorporate political orientation to enhance the robustness of their findings.

The HBM was first proposed in the 1950s and has continued to make significant contributions to health communication research and the design of health campaigns for over six decades. Like all scientific models, the HBM has evolved over time, shaped by new empirical findings and developments in adjacent theoretical frameworks. This process of ongoing refinement through self-reflection and scholarly debate is not only natural but also desirable—and indeed essential—for the advancement of knowledge (Chung, 2024; Kuhn, 1960). Within this context, Ronis’s (1992) revision of the HBM stands out as particularly noteworthy. We applied Ronis’s model to behavioral responses to infectious diseases and found empirical evidence supporting its validity. We look forward to meaningful scholarly dialogue comparing Ronis’s model to the traditional HBM as such engagement can deepen our understanding of health-related behaviors and enhance the model’s predictive utility.

Ethics Approval Statement

This study was approved by the first author’s University Institutional Review Board.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

Participant consent statement

Before taking the survey, participants were presented with informed consent form in which we explained the purpose, procedures, benefits, and risk of the survey.