Click, Confuse, Convert? Digital Health Information Seeking, Perceived Usefulness and Reliability of Online Health Information, and Alternative Medicine Use and Beliefs

Click, Confuse, Convert? 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Digital Health Information Seeking, Perceived Usefulness and Reliability of Online Health Information, and Alternative Medicine Use and Beliefs Daria Turavinina, Yot Amornkitvikai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6610060/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Using a large cross-national dataset (N ≈ 23,000), this study investigates the relationship between several aspects of online health-seeking and the use of complementary and alternative medicine (CAM), as well as the belief that CAM is better than conventional medicine in Western societies. It also examines how perceptions of the internet as a useful tool to guide health decisions, and perceived reliability of online information, relate to CAM use and beliefs about its superiority. Methods Ordinal logistic regression models were used to assess the association between online health-seeking behavior, perceived usefulness and reliability of online health information, and two outcomes: CAM use and belief in CAM superiority over conventional medicine. Analyses were based on data from the 2021 ISSP module on Health and Healthcare, restricted to Western countries. Results Findings reveal a significant, graded association between more frequent online health-seeking and both higher CAM use and stronger belief that CAM is better than conventional medicine. Those who perceived the internet as useful for verifying doctors’ advice or evaluating symptoms also had significantly higher odds of CAM use and belief in its superiority. Notably, those expressing uncertainty about the reliability of online health information were more likely to report CAM use and belief in CAM’s superiority. Conclusions These results suggest that the digital health landscape may simultaneously empower and confuse users, potentially facilitating engagement with complementary therapies in the absence of clear evaluative guidance. This study highlights the need to integrate CAM into institutional healthcare frameworks, develop legal standards for CAM use, promote digital health literacy, and improve doctor–patient communication. Trial registration Not applicable complementary and alternative medicine CAM digital health-seeking internet use healthcare Background Complementary and alternative medicine (CAM)—also increasingly referred to as complementary, alternative, and/or integrative medicine (CAIM), or traditional, complementary, and integrative medicine (TCIM)—represents a growing and evolving field ( 1 , 2 ). Despite its growth, the field of CAM remains understudied and underdefined in research contexts, with notable disconnects between lay and professional understandings ( 3 , 4 ). The term itself is undergoing debate and development, with, consequently, academic publications and surveys often referring to different ranges of practices ( 1 , 5 ). To ground this literature review and in line with the operational scope of the dataset used in this study, CAM is theoretically defined according to the Cochrane Collaboration as “a broad domain of healing resources that encompasses all health systems, modalities, and practices and their accompanying theories and beliefs, other than those intrinsic to the politically dominant health system of a particular society or culture in a given historical period” ( 6 ). There has been a documented increase in the prevalence of CAM in Western countries ( 7 – 9 ). The use of CAM in the Western world also includes patients diagnosed with serious conditions ( 10 ). CAM use is shaped by a number of socioeconomic, demographic, cultural, and attitudinal factors. Being female, of middle age, and highly educated are robust predictors of CAM use ( 11 , 12 ). Moreover, having chronic diseases and poor health was understandably documented as a factor associated with CAM use ( 13 , 14 ). Religiosity and spirituality, as well as trust, determine CAM use from a sociological perspective ( 15 – 19 ). Another important determinant of CAM use is cost and fairness – studies show that perceived inaccessibility and discrimination can determine CAM utilization ( 20 – 24 ). Tradition plays a larger role as a determinant of CAM use in Asia, Africa, and South America ( 4 ). Moreover, low cost drives CAM use in developing countries, unlike in Europe, where this factor is not commonly reported ( 4 ). Motivations for CAM usage include expectation of CAM benefits, dissatisfaction with conventional care, and perceived safety – in other words, CAM users believe that these therapies will help, and are often driven by disenchantment with standard care ( 25 ). While CAM therapies remain understudied, evidence exists to support the effectiveness of some ( 26 ). A significant proportion of migraine sufferers, for instance, found CAM treatments to be effective ( 27 ). Research indicates that health information received online influences people’s healthcare decisions ( 28 – 30 ). The availability of such information on the internet has helped users overcome structural barriers, reduce unnecessary and constant consultations, and achieve a more empowered and productive relationship with their healthcare providers ( 31 ). Importantly, medical issues that are perceived to be stigmatized make it more likely that patients will resort to seeking health information online ( 32 ). While the internet has greatly democratized health information availability, it may also become a vehicle for the spread of “fake news” and conspiracies, as exemplified during the COVID-19 pandemic, which can be harmful to the population’s health ( 33 , 34 ). Importantly, the nature of social media itself tends to amplify false claims, especially in times of crisis. Inaccurate information about COVID-19 was less likely to be shared than its accurate counterparts ( 35 ). While online misinformation is typically assumed to originate from unverified sources, respected sources such as WebMD are also vulnerable to inaccurate claims ( 36 ), making the internet a “wild west” regarding information on public health. The internet has also been documented to serve as a discussion forum about CAM therapies. While some CAM information online is accurate, a significant proportion was found to be misleading or incomplete across several studies ( 37 – 41 ). In some cases, social media becomes a debate stage for polarizing opinions and a home for unverified narratives on CAM ( 42 ). Research showed that both laypeople and underinformed professionals are vulnerable to misleading CAM information ( 43 ). Online health misinformation has serious and real-life consequences for public health( 36 ). Occasional reports exist of patients preferring CAM-only treatments in disease or applying CAM improperly, leading to adverse outcomes, including a higher risk of mortality ( 44 – 46 ). Another example of the implications of online health misinformation is vaccine hesitancy - a large percentage of vaccine-hesitant patients inhabit CAM-adjacent information bubbles ( 47 ). Additionally, the issue is compounded by patients often not informing doctors of their use of CAM ( 48 ). Healthcare professionals, on the other hand, can be skeptical or even dismissive about such treatments ( 8 , 49 , 50 ). Further complicating public trust in conventional healthcare is the increasing scrutiny of pharmaceutical industry practices. Evidence-supported concerns such as pricing, monopolization, and lobbying have fueled distrust ( 51 – 53 ). Online discourse often blurs the line between legitimate critique and conspiracy theory, entrenching anti-pharmaceutical sentiments that intersect with CAM use ( 54 ). This dynamic may foster a culture of secrecy around CAM, where patients may turn to the internet for guidance rather than to healthcare professionals. Against this backdrop, it is essential to examine how digital health information seeking behavior may influence CAM usage and beliefs about CAM effectiveness in Western societies. Research objective and questions This study investigates how online health information seeking behavior and perceived reliability of the internet for health decision-making are associated with the frequency of CAM use and belief in CAM superiority over conventional medicine across Western populations. To achieve that, the study aims to answer the following research questions: Is more frequent online health information seeking associated with greater use of CAM? Are positive perceptions of the internet as a resource for medical decision-making associated with more frequent CAM use? Is online health information seeking associated with a stronger belief that CAM is more effective than conventional medicine? Do perceptions of the internet’s usefulness and reliability for health decisions positively correlate with the belief in CAM’s superiority? Data This study uses data from the International Social Survey Programme, curated by the ISSP Research Group and distributed by GESIS – Leibniz Institute for the Social Sciences ( 55 ). The ISSP is a long-running cross-national collaboration that conducts harmonized annual surveys on topics of sociopolitical importance. The 2021 module on Health and Health Care II (ISSP 2021) explores themes including individual health status, trust in healthcare systems, online health information seeking, and the use of complementary and alternative medicine (CAM). The dataset consists of cross-sectional nationally representative samples of adults aged 18 and older (with minor country-specific exceptions). Data were collected between 2021 and 2024 via face-to-face interviews, telephone surveys, paper questionnaires, and online self-administered forms, depending on local fieldwork practices. The total analytic sample from Western countries included 25,741 respondents, allowing for precise multivariate modeling across sociodemographic and attitudinal dimensions. To enhance the reliability and contextual validity of the analysis, the present study focuses exclusively on Western countries characterized by high-income economies, well-established healthcare systems, and widespread internet penetration. These include: Australia, Austria, Croatia, the Czech Republic, Denmark, Finland, France, Germany, Hungary, Iceland, Italy, the Netherlands, New Zealand, Norway, Poland, Slovakia, Slovenia, Switzerland, and the United States. This regional focus was selected to ensure greater conceptual and infrastructural comparability across countries with similar patterns of internet accessibility and regulatory environments for CAM. Including countries from regions where digital access remains uneven or where CAM practices are more deeply rooted in traditional medicine systems could introduce heterogeneity that may confound the relationship between online health information seeking and CAM use. Imputation process The data had a substantial percentage of missing values, with rates ranging from 27.1–34.1% across models. To address item nonresponse and reduce potential bias due to missing data, multiple imputation by chained equations (MICE), which is a widely used and robust method, ( 56 , 57 ) was applied to the predictors with the highest missingness. To ensure model stability and avoid issues of separation and perfect prediction, an augmentation procedure was applied in all categorical models ( 57 ). A total of 20 imputed datasets were generated using chained equations, with sufficient iterations between imputations to ensure convergence. Imputed datasets were then pooled for subsequent analyses, and diagnostics indicated adequate convergence and consistency across imputations. Following imputation, between 86.1% and 90.2% of the total sample (n = 25,741) had complete data available for analysis across models, with analytic subsamples ranging from 22,155 to 23,220 individuals depending on variable availability. Methodology Analytical Approach Given the ordinal nature of all dependent variables, this study utilized ordinal logistic regression for all analyses. This modeling approach estimates the cumulative odds of being in or above a particular category of the dependent variable, assuming that the relationship between the predictors and the log-odds of the outcome is constant across thresholds ( 58 ). Ordered logistic regression was chosen over ordinary least squares (OLS) regression because OLS assumes equal intervals between response categories and homoscedastic, normally distributed errors—assumptions that are routinely violated when applied to ordinal outcomes ( 59 ). Ordinal logistic regression also allows for more efficient use of ordered data compared to collapsing outcomes into binary categories, preserving statistical power and interpretive nuance ( 59 ). As analysis was conducted on an imputed dataset, checks were made regarding the success of the imputational procedure and the influence of missing data on estimates. The largest fraction of missing information (FMI) across predictors remained at or below 0.2 ( 60 ), and the relative increase in variance (RVI) was low across models (below 0.03), indicating stable and reliable estimation. A model-wide Wald-type F-test consistently confirmed the joint significance of predictors across all outcomes ( p < .001). To ensure that results were not biased by collinearity among predictors, variance inflation factors (VIFs) were examined; all VIF values were well below the conventional threshold of 5, suggesting no serious multicollinearity. Odds ratios were employed for ease of interpretation. Variables Dependent variables The main outcome variables were derived from the following survey questions: “During the past 12 months, how often did you visit or were visited by an alternative/ traditional/ folk health care practitioner?” – used to measure CAM use frequency. “To what extent do you agree or disagree with the following statement: Alternative medicine provides better solutions for health problems than Western conventional medicine?” – used to measure perceived CAM efficacy. The ISSP Health and Health Care II (ISSP 2021) survey module defines CAM closely to the definition developed by the Cochrane Collaboration, which allowed each country to choose the most appropriate term referring to “medical and healthcare practices and products, which are not currently part of mainstream/Western conventional medicine.” Each country’s survey was tailored to “choose the term which most appropriately referred to allopathic mainstream Western medicine.” Key Predictors The first independent variable of interest was the frequency of online health information seeking behavior , based on the question: “During the past 12 months, how often, if at all, did you use the internet on any device (such as computers, tablets, and smartphones) to look for health or medical information for yourself or someone else?” Responses were categorized into six ordered levels: never or almost never, several times a year, several times a month, several times a week, once a day, and several times a day. The other set of independent variables of interest covered the perceived usefulness and reliability of the internet for health decision-making , rated on a five-point Likert scale: a) “The internet is useful to help people decide if their symptoms are serious enough to go to the doctor,” b) “The internet is useful to check that the doctor is giving people appropriate advice,” c) “It is not easy to distinguish between reliable and unreliable health information on the internet.” The study aimed to include a wide range of control variables to try and better isolate the potential relationship between internet health information seeking and the use of complementary and alternative medicine, as well as the belief in its superior efficacy. Control variables included age, age-squared, sex, education level, marital status, presence of chronic illness, subjective health rating, and subjective socioeconomic status. Additionally, religious affiliation and region were included to account for cultural and geographic heterogeneity in CAM attitudes. Trust-related variables were included to assess the influence of both institutional and interpersonal credibility on CAM attitudes. Perceived fairness of the healthcare system and trust in doctors were likewise included, guided by previous findings suggesting that distrust in conventional healthcare institutions may drive individuals toward alternative modalities ( 19 , 61 ). To account for psychological distress, the models also controlled for the frequency of depressive affect, as emotional distress has been consistently associated with CAM use ( 62 – 64 ). Results Descriptive statistics Descriptive statistics corresponding to the analytic samples used in the study are presented in Supplementary Tables 1 through 4 in the Appendix. Across all models, the analytic samples were broadly comparable. The gender distribution remained consistent across models (≈ 54% female). The mean age of the respondents was approximately 52 years (SD ≈ 17). Most participants rated their health as good or very good, and about one-third reported a chronic condition. The largest participants were from Western Europe (≈ 42% across models). This was followed by Central and Eastern Europe (≈ 26%), Northern Europe (≈ 19%), Australia and New Zealand (≈ 8%), and the United States (≈ 4% ). Respondents predominantly identified as Christian or non-religious. In Model 1 ( N = 23,220), which examined the frequency of online health information seeking as a predictor of visits to alternative healthcare practitioners, 22.0% reported CAM use in the past year (11.1% seldom, 7.9% sometimes, 2.4% often, and 0.6% very often. Online health information seeking behavior was very widespread, with 72.5% of surveyed adults reporting having engaged in online health information seeking. 31.4% reported going online for health advice several times a year, 19.7% several times a month, 8.8% several times a week, 2.8% once a day, and 9.9% several times a day. Models 2.1–2.3 ( N ≈ 23,000) retained CAM use as the outcome and examined its relationship with specific beliefs about the internet’s role in health decision-making. The dependent variable’s distribution remained consistent with Model 1. Perceptions of the internet’s usefulness for health decision-making varied considerably across models. In Model 2.1 , the independent variable of interest was whether the respondent believed that the internet helps assess the seriousness of symptoms. 39.6% of respondents agreed (35.3%) or strongly agreed (4.3%) that the internet was a useful tool for that purpose, 35.3% disagreed (23.6%) or strongly disagreed (11.7%), and 25.1% neither agreed nor disagreed. In Model 2.2 , focused on the belief that the internet’s usefulness for verifying doctors’ advice, agreement was lower (27.9%, with 24.8% agreeing and 3.1% strongly agreeing), and skepticism slightly higher, with 43.5% disagreeing or strongly disagreeing (29.5% and 14.0%, respectively), while 28.6% stayed neutral. Model 2.3 , where the main predictor of interest was the perceived difficulty in evaluating the reliability of online health information, showed the most critical responses: nearly 70% agreed that it is difficult to distinguish reliable information on the internet (44.7% agreed, 24.8% strongly agreed), compared to just 9.9% and 2.8% who disagreed or strongly disagreed (17.8% neither agreed nor disagreed). Model 3 ( N = 22,176) explored whether online health information seeking is positively correlated with believing CAM is superior to conventional medicine. Most respondents disagreed (49.0%) or held neutral views (37.1%) regarding the superiority of CAM, while just 13.9% expressed either agreement or strong agreement. Online health information seeking patterns closely resembled those in Model 1. Models 4.1–4.3 ( N ≈ 22,155) retained the belief in CAM superiority over conventional medicine as the outcome variable and examined its relationship to the same attitudes about the usefulness of the internet in health-decision making used in Models 2.1–2.3 (internet helps assess the seriousness of symptoms, internet is useful for verifying doctors’ advice, it is difficult to evaluate the reliability of online health information). Patterns of belief in CAM’s superiority remained stable across models. Perceptions of the internet as a reliable tool for medical information mirrored the variation observed in Models 2.1 to 2.3. Main results Table 1 Model 1. Online health information seeking and frequency of alternative medicine use Variable Odds Ratio (OR) 95% CI Frequency of online health information seeking (Ref. Never or almost never/No internet access) Several times a year 1.215*** [1.106, 1.335] Several times a month 1.523*** [1.375, 1.687] Several times a week 2.021*** [1.789, 2.283] Once a day 2.661*** [2.207, 3.209] Several times a day 1.597*** [1.410, 1.809] Age 1.044*** [1.031, 1.057] Age-squared 1.000*** [0.999, 1.000] Sex (Ref. Male) 1.267*** [1.225, 1.310] Education Level (Ref. Below Upper Secondary) Upper secondary 1.223*** [1.104, 1.356] Short-cycle tertiary 1.261*** [1.112, 1.430] Tertiary specialization and above 1.034 [0.925, 1.156] Marital status (Ref. Single) Married/Partnered 1.130*** [1.032, 1.238] Separated/Divorced 1.198*** [1.058, 1.356] Widowed 1.228** [1.037, 1.454] Self-placement on socioeconomic ladder ( 1 – 10 ) 1.023** [1.001, 1.045] Health status (Ref. Good) Fair 0.817** [0.687, 0.970] Good 0.904 [0.761, 1.074] Very good 0.997 [0.831, 1.196] Excellent 0.864 [0.692, 1.078] Chronic Illness (Ref. No) 1.136*** [1.052, 1.226] Perception doctors can be trusted(Ref. Strongly disagree) Disagree 1.028 [0.770, 1.372] Neutral 0.882 [0.671, 1.161] Agree 0.645*** [0.493, 0.844] Strongly agree 0.476*** [0.359, 0.631] Perception of healthcare system fairness (Ref. Very unfair) Somewhat unfair 1.083* [0.996, 1.176] Neutral 1.263*** [1.154, 1.382] Somewhat fair 1.248*** [1.119, 1.393] Very fair 1.260*** [1.065, 1.492] Western region (Ref. United States) Western Europe 1.075 [0.923, 1.253] Northern Europe 0.560*** [0.473, 0.662] Central and Eastern Europe 0.527*** [0.449, 0.619] Australia and New Zealand 1.417*** [1.185, 1.694] Religious group (Ref. No religion) Christian 1.017 [0.947, 1.093] Jewish 1.303 [0.706, 2.405] Muslim 1.335** [1.040, 1.715] Buddhist 1.790*** [1.148, 2.791] Hindu 1.671* [0.994, 2.809] Other 1.313 [0.935, 1.843] Frequency of feeling unhappy or depressed (Ref. Never) Seldom 1.497*** [1.377, 1.627] Sometimes 1.858*** [1.695, 2.035] Often 1.988*** [1.739, 2.274] Very often 1.883*** [1.558, 2.277] Note: n = 23,220 The findings reveal a significant and graded association between the frequency of online health information seeking and the use of complementary and alternative medicine (CAM) (Table 1 , Model 1). This relationship stands even accounting for a variety of socioeconomic characteristics, demographic indicators, region, health/chronic illness status, unhappiness or depressive affect, as well as trust in doctors and the respective country’s healthcare system. Individuals who sought health-related information online were significantly more likely to report visiting or being visited by CAM practitioners. The odds of using CAM increased with a higher frequency of online health information seeking. Compared to those who never or almost never used the internet for health purposes, individuals who searched for health or medical information several times a week exhibited more than twice the odds of CAM use, while those seeking information once a day demonstrated an even stronger association (OR = 2.661, p < 0.001) Although the odds ratio for the highest frequency category (“several times a day”) was slightly lower than that for daily users (OR = 1,597, p < 0.001), it remained statistically significant, suggesting that high-frequency engagement with online health content is positively associated with CAM use, though the relationship may diminish slightly at the highest levels of use. Table 2 Models 2.1–2.3. Frequency of CAM use and perceptions of the internet as a tool for health decision-making Variable Odds Ratio (OR) 95% CI Odds Ratio (OR) 95% CI Odds Ratio (OR) 95% CI Independent variable of interest: (Ref. Strongly disagree) Model 2.1 Independent variable of interest: Internet is useful to help people assess seriousness of symptoms Model 2.2 Independent variable of interest: Internet is useful to help verify advice from the doctor Model 2.3 Independent variable of interest: It is not easy to distinguish between reliable and unreliable health information on the internet Disagree 1.043 [0.924, 1.178] 1.031 [0.921, 1.153] 1.013 [0.797, 1.288] Neither agree nor disagree 1.197*** [1.062, 1.350] 1.178*** [1.052, 1.319] 1.329** [1.059, 1.669] Agree 1.103 [0.982, 1.239] 1.222*** [1.090, 1.370] 1.054 [0.844, 1.315] Strongly agree 1.355*** [1.125, 1.631] 1.791*** [1.467, 2.188] 1.024 [0.817, 1.284] Age 1.043*** [1.030, 1.056] 1.043*** [1.031, 1.056] 1.042*** [1.030, 1.055] Age-squared 0.999*** [0.999, 1.000] 0.999*** [0.999, 1.000] 0.999*** [0.999, 1.000] Sex (Ref. Male) 1.293*** [1.251, 1.337] 1.294*** [1.252, 1.338] 1.293*** [1.251, 1.337] Education Level (Ref. Below Upper Secondary) Upper secondary 1.259*** [1.136, 1.394] 1.244*** [1.123, 1.378] 1.265*** [1.142, 1.402] Short-cycle tertiary 1.304*** [1.151, 1.478] 1.293*** [1.141, 1.465] 1.307*** [1.152, 1.481] Tertiary Specialization and above 1.097 [0.982, 1.225] 1.089 [0.975, 1.216] 1.118** [1.001, 1.250] Marital status (Ref. Single) Married/Partnered 1.147*** [1.047, 1.256] 1.150*** [1.050, 1.260] 1.146*** [1.046, 1.255] Separated/Divorced 1.224*** [1.081, 1.386] 1.223*** [1.080, 1.385] 1.220*** [1.078, 1.382] Widowed 1.222** [1.032, 1.447] 1.226** [1.035, 1.452] 1.208** [1.020, 1.431] Self-placement on socioeconomic ladder ( 1 – 10 ) 1.025** [1.003, 1.047] 1.025** [1.003, 1.047] 1.025** [1.003, 1.048] Health status (Ref. Good) Fair 0.796*** [0.671, 0.946] 0.796*** [0.670, 0.945] 0.786*** [0.662, 0.934] Good 0.872 [0.734, 1.035] 0.876 [0.737, 1.040] 0.861* [0.726, 1.023] Very good 0.954 [0.796, 1.145] 0.957 [0.798, 1.148] 0.949 [0.792, 1.139] Excellent 0.807* [0.647, 1.007] 0.806* [0.646, 1.007] 0.810* [0.649, 1.010] Chronic Illness (Ref. No) 1.155*** [1.070, 1.247] 1.152*** [1.067, 1.244] 1.167*** [1.081, 1.260] Perception doctors can be trusted(Ref. Strongly disagree) Disagree 1.029 [0.771, 1.375] 1.043 [0.780, 1.394] 1.008 [0.755, 1.345] Neutral 0.858 [0.652, 1.130] 0.885 [0.672, 1.167] 0.829 [0.630, 1.090] Agree 0.614*** [0.469, 0.804] 0.642*** [0.489, 0.841] 0.601*** [0.459, 0.787] Strongly agree 0.456*** [0.344, 0.605] 0.476*** [0.358, 0.631] 0.447*** [0.337, 0.592] Perception of healthcare system fairness (Ref. Very unfair) Somewhat unfair 1.092** [1.005, 1.186] 1.092** [1.005, 1.187] 1.096** [1.009, 1.191] Neutral 1.252*** [1.144, 1.370] 1.264*** [1.155, 1.383] 1.251*** [1.144, 1.369] Somewhat fair 1.282*** [1.149, 1.430] 1.277*** [1.144, 1.425] 1.276*** [1.144, 1.424] Very fair 1.278*** [1.081, 1.512] 1.275*** [1.077, 1.509] 1.284*** [1.085, 1.520] Western region (Ref. United States) Western Europe 1.049 [0.900, 1.224] 1.079 [0.925, 1.258] 1.055 [0.905, 1.229] Northern Europe 0.544*** [0.460, 0.642] 0.551*** [0.467, 0.652] 0.546*** [0.462, 0.645] Central and Eastern Europe 0.542*** [0.462, 0.636] 0.544*** [0.463, 0.638] 0.546*** [0.465, 0.641] Australia and New Zealand 1.346*** [1.126, 1.609] 1.357*** [1.136, 1.622] 1.356*** [1.134, 1.620] Religious group (Ref. No religion) Christian 1.044 [0.973, 1.121] 1.043 [0.972, 1.120] 1.040 [0.969, 1.117] Jewish 1.289 [0.689, 2.414] 1.315 [0.713, 2.424] 1.346 [0.732, 2.474] Muslim 1.392*** [1.085, 1.787] 1.343** [1.045, 1.727] 1.379** [1.074, 1.772] Buddhist 1.795*** [1.151, 2.799] 1.782*** [1.142, 2.781] 1.780** [1.141, 2.777] Hindu 1.827** [1.099, 3.039] 1.813** [1.088, 3.021] 1.873** [1.121, 3.129] Other 1.338* [0.954, 1.877] 1.306* [0.931, 1.833] 1.338* [0.954, 1.877] Frequency of feeling unhappy or depressed (Ref. Never) Seldom 1.526*** [1.404, 1.659] 1.536*** [1.413, 1.669] 1.539*** [1.416, 1.673] Sometimes 1.927*** [1.759, 2.111] 1.932*** [1.763, 2.116] 1.943*** [1.774, 2.129] Often 2.088*** [1.827, 2.387] 2.095*** [1.833, 2.395] 2.111*** [1.847, 2.412] Very often 2.031*** [1.681, 2.453] 2.028*** [1.679, 2.450] 2.066*** [1.710, 2.495] Note: Model 2.1 n = 23,057, Model 2.2 n = 23,013, Model 2.3 n = 23,046. The analysis further indicates that stronger beliefs in the internet’s usefulness in health decision-making are significantly associated with CAM use (Table 2 , Models 2.1–2.2). In Model 2.1, respondents who strongly agreed that the internet is useful for assessing whether symptoms are serious enough to visit a doctor had significantly higher odds of CAM use (OR = 1.355, p < 0.001) compared to those who strongly disagreed. Similarly, the belief that the internet is useful for verifying their doctors’ advice was also significantly associated with CAM use (Model 2.2), with the strongest association observed among those who strongly agreed (OR = 1.791, p < 0.001). These findings suggest that individuals who endorse the internet as a valuable tool for evaluating health conditions or medical authority are more likely to engage in CAM. Interestingly, when examining the relationship between perceptions of difficulty in distinguishing reliable from unreliable health information on the internet and CAM practitioner use (Model 2.3), a significant association was observed primarily among those who neither agreed nor disagreed with the statement (OR = 1.329, p < 0.01). Individuals who expressed stronger agreement did not show a statistically significant increase in CAM use. This adds nuance to the interpretation of the findings and may indicate that feeling unsure and confused in the online space may also be predictive of engagement with CAM practices. Table 3 Model 3. Believing that alternative medicine is better than conventional medicine and online health information seeking Variable Odds Ratio (OR) 95% CI Frequency of online health information seeking (Ref. Never or almost never/No internet access) Several times a year 1.037 [0.970, 1.109] Several times a month 1.059 [0.981, 1.142] Several times a week 1.108** [1.004, 1.223] Once a day 1.404*** [1.192, 1.653] Several times a day 1.138*** [1.033, 1.254] Age 1.014*** [1.005, 1.023] Age-squared 0.999*** [1.000, 1.000] Sex (Ref. Male) 1.170*** [1.141, 1.200] Education Level (Ref. Below Upper Secondary) Upper secondary 1.034 [0.960, 1.115] Short-cycle tertiary 0.915* [0.832, 1.007] Tertiary Specialization and above 0.635*** [0.585, 0.689] Marital status (Ref. Single) Married/Partnered 1.030 [0.960, 1.104] Separated/Divorced 1.146*** [1.039, 1.263] Widowed 0.989 [0.873, 1.120] Self-placement on socioeconomic ladder ( 1 – 10 ) 0.987 [0.970, 1.004] Health status (Ref. Good) Fair 1.144* [0.995, 1.314] Good 1.176** [1.024, 1.352] Very good 1.262*** [1.090, 1.461] Excellent 1.294*** [1.088, 1.539] Chronic Illness (Ref. No) 0.901*** [0.849, 0.955] Perception doctors can be trusted(Ref. Strongly disagree) Disagree 0.630*** [0.479, 0.829] Neutral 0.492*** [0.379, 0.639] Agree 0.300*** [0.232, 0.389] Strongly agree 0.153*** [0.117, 0.199] Perception of healthcare system fairness (Ref. Very unfair) Somewhat unfair 1.038 [0.976, 1.104] Neutral 1.126*** [1.052, 1.205] Somewhat fair 1.058 [0.973, 1.151] Very fair 0.936 [0.821, 1.068] Western region (Ref. United States) Western Europe 0.735*** [0.651, 0.831] Northern Europe 0.357*** [0.314, 0.407] Central and Eastern Europe 0.475*** [0.419, 0.538] Australia and New Zealand 0.671*** [0.581, 0.775] Religious group (Ref. No religion) Christian 1.167*** [1.105, 1.232] Jewish 1.273 [0.770, 2.106] Muslim 1.920*** [1.548, 2.382] Buddhist 2.176*** [1.458, 3.248] Hindu 2.656*** [1.636, 4.310] Other 2.315*** [1.741, 3.077] Frequency of feeling unhappy or depressed (Ref. Never) Seldom 1.029 [0.969, 1.093] Sometimes 1.090** [1.018, 1.168] Often 1.042 [0.936, 1.160] Very often 1.312*** [1.118, 1.540] Note n = 22,176 The relationship between online health information seeking frequency and belief in CAM’s superiority over conventional medicine was observed to be statistically significant (Table 3 , Model 3). Individuals who searched for health-related information once a day had 40.4% greater odds of endorsing the belief that CAM provides better solutions for health problems than conventional medicine (OR = 1.404, p < 0.001). Likewise, those seeking information several times a day (OR = 1.138, p < 0.001) and several times a week (OR = 1.108, p < 0.05) also exhibited significantly greater odds of holding such beliefs. Lower-frequency users (e.g., several times a year or month) did not differ significantly from non-users in their attitudes toward CAM efficacy. The association appears to be strongest among daily users rather than occasional searchers, and, again, is slightly lower with reported use of multiple times a day, indicating a potential saturation effect. Table 4 Models 4.1–4.3. Believing alternative medicine is better than conventional medicine, and perceptions of the internet as a tool for health decision-making Variable Odds Ratio (OR) 95% CI Odds Ratio (OR) 95% CI Odds Ratio (OR) 95% CI Independent variable of interest: (Ref. Strongly disagree) Model 4.1 Independent variable of interest: Internet is useful to help assess seriousness of symptoms Model 4.2 Independent variable of interest: Internet is useful to help verify advice from the doctor Model 4.3 Independent variable of interest: It is not easy to distinguish between reliable and unreliable health information on the internet Disagree 1.211*** [1.106, 1.326] 1.261*** [1.160, 1.371] 1.344*** [1.122, 1.610] Neither agree nor disagree 1.621*** [1.482, 1.774] 1.800*** [1.655, 1.958] 1.957*** [1.645, 2.328] Agree 1.495*** [1.371, 1.631] 1.834*** [1.681, 2.002] 1.593*** [1.347, 1.883] Strongly agree 1.652*** [1.420, 1.921] 2.085*** [1.749, 2.484] 1.346*** [1.136, 1.595] Age 1.014*** [1.005, 1.023] 1.012*** [1.003, 1.021] 1.012*** [1.003, 1.022] Age-squared 1.000*** [1.000, 1.000] 1.000*** [1.000, 1.000] 1.000*** [1.000, 1.000] Sex (Ref. Male) 1.174*** [1.145, 1.204] 1.176*** [1.147, 1.206] 1.172*** [1.143, 1.202] Education Level (Ref. Below Upper Secondary) Upper secondary 1.043 [0.967, 1.124] 1.040 [0.965, 1.121] 1.055 [0.979, 1.138] Short-cycle tertiary 0.926 [0.843, 1.018] 0.928 [0.844, 1.020] 0.937 [0.852, 1.031] Tertiary Specialization and above 0.641*** [0.591, 0.695] 0.650*** [0.599, 0.705] 0.662*** [0.610, 0.718] Marital status (Ref. Single) Married/Partnered 1.030 [0.960, 1.105] 1.030 [0.960, 1.105] 1.029 [0.959, 1.103] Separated/Divorced 1.147*** [1.040, 1.264] 1.144*** [1.038, 1.262] 1.151*** [1.044, 1.269] Widowed 0.997 [0.880, 1.129] 0.995 [0.878, 1.128] 0.996 [0.879, 1.129] Self-placement on socioeconomic ladder ( 1 – 10 ) 0.985* [0.968, 1.002] 0.986* [0.969, 1.003] 0.987 [0.970, 1.005] Health status (Ref. Good) Fair 1.138* [0.990, 1.308] 1.154** [1.004, 1.327] 1.127* [0.980, 1.295] Good 1.164** [1.013, 1.338] 1.184** [1.030, 1.361] 1.150** [1.000, 1.322] Very good 1.253*** [1.082, 1.452] 1.273*** [1.099, 1.475] 1.243*** [1.073, 1.440] Excellent 1.280*** [1.076, 1.524] 1.286*** [1.081, 1.531] 1.274*** [1.070, 1.516] Chronic Illness (Ref. No) 0.904*** [0.852, 0.958] 0.906*** [0.855, 0.961] 0.913*** [0.861, 0.968] Perception doctors can be trusted(Ref. Strongly disagree) Disagree 0.627*** [0.476, 0.827] 0.606*** [0.460, 0.800] 0.613*** [0.465, 0.808] Neutral 0.480*** [0.369, 0.624] 0.474*** [0.364, 0.617] 0.463*** [0.355, 0.602] Agree 0.294*** [0.227, 0.382] 0.301*** [0.232, 0.390] 0.287*** [0.221, 0.372] Strongly agree 0.151*** [0.116, 0.198] 0.161*** [0.123, 0.210] 0.150*** [0.115, 0.197] Perception of healthcare system fairness (Ref. Very unfair) Somewhat unfair 1.023 [0.962, 1.089] 1.018 [0.957, 1.083] 1.029 [0.967, 1.094] Neutral 1.099*** [1.027, 1.177] 1.097*** [1.025, 1.175] 1.102*** [1.029, 1.180] Somewhat fair 1.039 [0.955, 1.130] 1.034 [0.951, 1.125] 1.055 [0.970, 1.148] Very fair 0.942 [0.826, 1.075] 0.951 [0.834, 1.086] 0.948 [0.831, 1.081] Western region (Ref. United States) Western Europe 0.781*** [0.691, 0.883] 0.820*** [0.726, 0.927] 0.754*** [0.667, 0.851] Northern Europe 0.360*** [0.317, 0.410] 0.375*** [0.330, 0.427] 0.359*** [0.315, 0.408] Central and Eastern Europe 0.496*** [0.437, 0.563] 0.507*** [0.447, 0.574] 0.492*** [0.434, 0.558] Australia and New Zealand 0.683*** [0.591, 0.789] 0.697*** [0.603, 0.805] 0.678*** [0.587, 0.783] Religious group (Ref. No religion) Christian 1.181*** [1.118, 1.247] 1.172*** [1.110, 1.237] 1.171*** [1.109, 1.236] Jewish 1.311 [0.794, 2.166] 1.291 [0.782, 2.132] 1.327 [0.804, 2.190] Muslim 1.914*** [1.543, 2.373] 1.856*** [1.497, 2.302] 1.926*** [1.553, 2.388] Buddhist 2.101*** [1.407, 3.136] 1.957*** [1.311, 2.921] 2.110*** [1.414, 3.148] Hindu 2.628*** [1.622, 4.256] 2.564*** [1.581, 4.158] 2.688*** [1.652, 4.375] Other 2.337*** [1.758, 3.107] 2.251*** [1.692, 2.993] 2.362*** [1.776, 3.142] Frequency of feeling unhappy or depressed (Ref. Never) Seldom 1.013 [0.954, 1.076] 1.009 [0.950, 1.071] 1.028 [0.968, 1.092] Sometimes 1.076** [1.004, 1.153] 1.065* [0.994, 1.141] 1.091** [1.018, 1.169] Often 1.024 [0.919, 1.140] 1.010 [0.907, 1.125] 1.055 [0.948, 1.175] Very often 1.310*** [1.116, 1.537] 1.301*** [1.108, 1.528] 1.327*** [1.131, 1.557] Note: Model 4.1 n = 22,157, Model 4.2 n = 22,155, Model 4.3 n = 22,158. Beliefs about the usefulness and reliability of online health information were also significantly associated with thinking that CAM is better than conventional medicine (Table 4 , Models 4.1–4.3). Those who strongly agreed that the internet is useful for assessing seriousness of symptoms had significantly greater odds of believing CAM is more effective than conventional medicine (OR = 1.652, p < 0.001), with a similarly elevated association among those who simply agreed (OR = 1.495, p < 0.001) (Model 4.1). Individuals who strongly agreed that the internet is useful for verifying doctors’ advice exhibited more than double the odds of endorsing CAM superiority over conventional medicine (OR = 2.085, p < 0.001). The belief that it is difficult to distinguish reliable from unreliable online health information was also positively associated with beliefs in CAM’s superiority over conventional medicine (Model 4.3). The strongest effect was observed among those who neither agreed nor disagreed (OR = 1.957, p < 0.001), though significant associations were also observed for those who agreed (OR = 1.593, p < 0.001) and strongly agreed (OR = 1.346, p < 0.001). These results, again, continue to expand on the nuanced picture observed in the previous model and point to the potential link between uncertainty in online health spaces and CAM endorsement. Discussion The findings suggest that more frequent online health information seeking is positively linked to higher use of complementary and alternative medicine (CAM), as well as beliefs in the superiority of CAM over conventional medicine. Moreover, beliefs about the usefulness of online health information for making healthcare decisions (ie. believing it is useful to help assess the seriousness of symptoms / verify doctor’s advice) are positively associated with higher use of CAM and stronger beliefs that CAM is better than conventional medicine. However, at the highest levels of health information seeking (everyday), the relationship with CAM use and belief in CAM efficacy slightly diminished. This may be due to multiple reasons, one of which is information overload. Social media overload has been linked to reduced health self-efficacy – the confidence that one can address health challenges ( 65 ). As a result, individuals who engage in very frequent online health information seeking may feel overwhelmed rather than empowered and motivated to seek unconventional treatments. Moreover, users who most frequently seek out online health information may present a more informed internet health seeker profile and be more mistrustful of CAM, as many of these therapies lack structured clinical evidence. Notably, respondents who selected “neither agree nor disagree” in response to the statement “It is not easy to distinguish reliable health information online” were the most likely to use complementary and alternative medicine (CAM) and to believe in its superiority over conventional treatments. This finding suggests that uncertainty and confusion when navigating online health information, rather than outright mistrust or full confidence, may be most strongly associated with CAM use and belief in its effectiveness. Additionally, individuals who agreed that it is difficult to distinguish between reliable and unreliable online health information were also more likely to believe that CAM is superior to conventional medicine. These results may point to the role of digital literacy as an important dimension in CAM decision-making. The study’s implications are twofold – on one hand, the internet is increasingly enabling people to more equitably access medical information ( 31 ). On the other hand, the online space may also provide a pathway to non-mainstream health narratives ( 66 ). These, in turn, are at risk of verging into misinformation ( 42 ) and conspiracy theories ( 34 , 35 ). This is particularly important when studying complementary and alternative medicine, as CAM use often occurs without the knowledge of healthcare providers ( 67 , 68 ). Results also reflect agreement with studies reporting underlying skepticism toward conventional medical authority, a phenomenon increasingly documented in Western countries, especially since the COVID-19 pandemic ( 69 – 71 ). In summary, these findings suggest that: Individuals who rely on the internet as a mechanism for searching health information, as well as for questioning or supplementing medical authority, may be especially likely to believe in the superiority of alternative medicine; Individuals who were unsure whether it was easy to distinguish reliable from unreliable online health information were both more likely to use CAM and believe that it is better than conventional medicine; Finally, individuals who admitted that it is difficult to identify reliable health information online were also found to hold stronger beliefs in CAM efficacy than those who reported greater confidence in their ability to evaluate health information. Limitations The first limitation of the present study is the use of cross-sectional data, which does not allow us to confidently determine a causal link between relying on the internet for health information and agreeing that it is useful in health decision-making, and the use of CAM. While this study included depressive affect and unhappiness, as well as the respondents’ trust in the healthcare system and doctors as control variables, it may not be enough to account for reverse causality. Frequently searching for online health information may be a manifestation of unhealthy preoccupation that is not captured by our available mental health variable, which is then a determinant of CAM use. Secondly, we do not know what websites are being used by the study participants and the quality of information they provide. Some CAM websites, for instance, were reported to have less balanced representation and fewer external links ( 72 ). Importantly, people who use traditional and complementary medicine out of aversion to conventional medicine are less likely to be influenced by doctors’ advice and scientific studies ( 73 ). This necessitates that future research to focus on specific cohorts of online health seekers and the varying quality of information pathways that they undertake. Future research should pursue qualitative interviews and longitudinal design, as well as more specific and literature-supported survey questions before building a case that online health information seeking shapes CAM use and beliefs. Conclusion and Policy Recommendations This study contributes to the growing literature supporting the connection between internet use, online health information seeking behavior, and the use of complementary and alternative medicine (CAM). To the best of the author’s knowledge, the present study provides the first large-scale cross-national evidence that online health information seeking behavior is strongly associated with both CAM use and belief in its superiority over conventional medicine in the general Western population. While being cautious of causal interpretation, these findings support additional research and conceptualization of the internet as a potential facilitator of CAM use. Additionally, the findings provide evidence of a positive association between online health information seeking, perceptions of the internet’s reliability for making medical decisions, and the belief that CAM is better than conventional medicine. In line with these findings, the present study provides a discussion of potential policy recommendations to better respond to the population’s increasing interest in CAM. The majority of European WHO member states lack policies and programs governing traditional and complementary medicine ( 74 ). European states have been documented to experience regulatory issues regarding traditional and complementary medicine, as well as a lack of research and financial support, a lack of expertise, and a lack of mechanisms to monitor safety ( 74 ). This regulatory vacuum leaves patients navigating CAM practices without sufficient institutional oversight or guidance. As the demand for CAM is rising in the West ( 7 – 9 ), governments should work to integrate evidence-based CAM therapies into the mainstream, to the extent that they do not interfere with highly proven conventional treatments. Such integration is not only a matter of legitimacy but one of patient safety. Other countries’ good practices can be used as a model in Western societies that lack a clear framework of CAM integration. In Japan, complementary medicine is more integrated with the mainstream healthcare complex, and some of it is covered by national insurance, with one of the most popular forms being Kampo (Japanese traditional herbal) medicines, a significant proportion of which is prescribed by doctors ( 75 , 76 ). On a micro-level, doctors and healthcare providers should not scorn complementary and alternative medicine. Having healthcare providers who are more empathetic, as well as the quality of time spent with the physician, has been negatively associated with online health information seeking behaviors ( 77 ). On the contrary, dissatisfaction with the healthcare received was shown to correlate with patients preferring the internet as a better information source ( 77 ). Vulnerable populations, particularly older adults, are likely to especially benefit from education about online health information ( 78 ). Moreover, women are more likely to resort to online health information seeking than men ( 79 ). Efforts to increase access to reliable online health information should consider these intersectionalities and approach the development of training programs accordingly. Abbreviations CAM Complementary and Alternative Medicine CAIM Complementary, Alternative, and Integrative Medicine TCIM Traditional, Complementary, and Integrative Medicine ISSP International Social Survey Programme FMI Fraction of Missing Information RVI Relative Increase in Variance VIF Variance Inflation Factor OR Odds Ratio CI Confidence Interval MICE Multiple Imputation by Chained Equations OLS Ordinary Least Squares SD Standard Deviation Declarations Ethics approval and consent to participate This study utilized publicly available data from the International Social Survey Programme (ISSP Research Group, 2024). The original data collection was conducted according to ethical standards in each participating country. No additional ethical approval was required for this analysis. Consent for publication Not applicable. This manuscript does not contain data from any individual person that requires consent for publication as it uses publicly available, de-identified secondary data. Availability of data and materials The datasets analyzed during the current study are available from the ISSP Research Group, distributed by GESIS - Leibniz Institute for the Social Sciences (ISSP Research Group, 2024). The dataset is available at: https://doi.org/10.4232/5.ZA8000.2.0.0 Competing interests The authors declare that they have no competing interests. Trial registration: Not applicable Funding This research did not receive any specific grant funding. However, Daria Turavinina is a recipient of Chulalongkorn University's Graduate Scholarship Program for ASEAN or Non-ASEAN Countries. Authors' contributions Daria Turavinina conceptualized the study, performed the data analysis, and wrote the original draft. 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Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–9. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99. Long JS, Freese J. Regression models for categorical dependent variables. using Stata: Stata; 2006. Agresti A. Analysis of ordinal categorical data. Wiley; 2010. Savalei V, Rhemtulla M. On obtaining estimates of the fraction of missing information from full information maximum likelihood. Struct Equation Modeling: Multidisciplinary J. 2012;19(3):477–94. Soveri A, Karlsson LC, Mäki O, Antfolk J, Waris O, Karlsson H, et al. Trait reactance and trust in doctors as predictors of vaccination behavior, vaccine attitudes, and use of complementary and alternative medicine in parents of young children. PLoS ONE. 2020;15(7):e0236527. Montazeri A, Sajadian A, Ebrahimi M, Akbari ME. Depression and the use of complementary medicine among breast cancer patients. Support Care Cancer. 2005;13(5):339–42. Druss BG, Rohrbaugh R, Kosten T, Hoff R, Rosenheck RA. Datapoints: Use of Alternative Medicine in Major Depression. Psychiatric Serv. 1998;49(11):1397. Unützer J, Klap R, Sturm R, Young AS, Marmon T, Shatkin J, et al. Mental disorders and the use of alternative medicine: results from a national survey. Am J Psychiatry. 2000;157(11):1851–7. Li K, Jiang S, Yan X, Li J. Mechanism study of social media overload on health self-efficacy and anxiety. Heliyon. 2024;10(1). Ng JY, Verhoeff N, Steen J. What are the ways in which social media is used in the context of complementary and alternative medicine in the health and medical scholarly literature? a scoping review. BMC Complement Med Ther. 2023;23(1):32. Robinson A, McGrail MR. Disclosure of CAM use to medical practitioners: a review of qualitative and quantitative studies. Complement Ther Med. 2004;12(2–3):90–8. Zhang Y, Peck K, Spalding M, Jones BG, Cook RL. Discrepancy between patients’ use of and health providers’ familiarity with CAM. Patient Educ Couns. 2012;89(3):399–404. Hardy LJ, Mana A, Mundell L, Neuman M, Benheim S, Otenyo E. Who is to blame for COVID-19? Examining politicized fear and health behavior through a mixed methods study in the United States. PLoS ONE. 2021;16(9):e0256136. Geiger N. Do people actually listen to the experts? A cautionary note on assuming expert credibility and persuasiveness on public health policy advocacy. Health Commun. 2022;37(6):677–84. Choi Y, Fox AM. Mistrust in public health institutions is a stronger predictor of vaccine hesitancy and uptake than Trust in Trump. Soc Sci Med. 2022;314:115440. Chen AT, Taylor-Swanson L, Buie RW, Park A, Conway M. Characterizing websites that provide information about complementary and integrative health: systematic search and evaluation of five domains. Interact J Med Res. 2018;7(2):e9803. Trübner M, Patzina A, Lehmann J, Brinkhaus B, Kessler CS, Hoffmann R. Health information-seeking behavior among users of traditional, complementary and integrative medicine (TCIM). BMC Complement Med Ther. 2025;25(1):111. WHO. WHO global report on traditional and complementary medicine 2019. World Health Organization; 2019. Motoo Y, Yukawa K, Hisamura K, Tsutani K, Arai I. Internet survey on the provision of complementary and alternative medicine in Japanese private clinics: a cross-sectional study. J Integr Med. 2019;17(1):8–13. Motoo Y, Yukawa K, Arai I, Hisamura K, Tsutani K. Use of complementary and alternative medicine in Japan: a cross-sectional internet survey using the Japanese version of the International Complementary and Alternative Medicine Questionnaire. JMA J. 2019;2(1):35–46. Tustin N. The role of patient satisfaction in online health information seeking. J health communication. 2010;15(1):3–17. Miller LMS, Bell RA. Online health information seeking: the influence of age, information trustworthiness, and search challenges. J Aging Health. 2012;24(3):525–41. Hallyburton A, Evarts LA. Gender and online health information seeking: A five survey meta-analysis. J consumer health Internet. 2014;18(2):128–42. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6610060","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463553030,"identity":"6b0d159e-2eb8-4473-badc-c798dc821d5f","order_by":0,"name":"Daria Turavinina","email":"data:image/png;base64,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","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":true,"prefix":"","firstName":"Daria","middleName":"","lastName":"Turavinina","suffix":""},{"id":463553031,"identity":"5081110a-fe06-4869-bb06-c3509fe6b137","order_by":1,"name":"Yot Amornkitvikai","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Yot","middleName":"","lastName":"Amornkitvikai","suffix":""}],"badges":[],"createdAt":"2025-05-07 08:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6610060/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6610060/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83801844,"identity":"1c1893c0-6224-4c97-9ca8-64cb39cf4430","added_by":"auto","created_at":"2025-06-03 03:19:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1717395,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6610060/v1/8ac0eb76-3802-4bb8-a8e3-cc8971399d62.pdf"},{"id":83801240,"identity":"04dac2ba-b289-4f16-aae0-a317beeb542b","added_by":"auto","created_at":"2025-06-03 03:03:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":55718,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials070525.docx","url":"https://assets-eu.researchsquare.com/files/rs-6610060/v1/cd953f5f76a645cc5361de4d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Click, Confuse, Convert? Digital Health Information Seeking, Perceived Usefulness and Reliability of Online Health Information, and Alternative Medicine Use and Beliefs","fulltext":[{"header":"Background","content":"\u003cp\u003eComplementary and alternative medicine (CAM)\u0026mdash;also increasingly referred to as complementary, alternative, and/or integrative medicine (CAIM), or traditional, complementary, and integrative medicine (TCIM)\u0026mdash;represents a growing and evolving field (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite its growth, the field of CAM remains understudied and underdefined in research contexts, with notable disconnects between lay and professional understandings (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The term itself is undergoing debate and development, with, consequently, academic publications and surveys often referring to different ranges of practices (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). To ground this literature review and in line with the operational scope of the dataset used in this study, CAM is theoretically defined according to the Cochrane Collaboration as \u0026ldquo;a broad domain of healing resources that encompasses all health systems, modalities, and practices and their accompanying theories and beliefs, other than those intrinsic to the politically dominant health system of a particular society or culture in a given historical period\u0026rdquo; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere has been a documented increase in the prevalence of CAM in Western countries (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The use of CAM in the Western world also includes patients diagnosed with serious conditions (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). CAM use is shaped by a number of socioeconomic, demographic, cultural, and attitudinal factors. Being female, of middle age, and highly educated are robust predictors of CAM use (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Moreover, having chronic diseases and poor health was understandably documented as a factor associated with CAM use (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Religiosity and spirituality, as well as trust, determine CAM use from a sociological perspective (\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Another important determinant of CAM use is cost and fairness \u0026ndash; studies show that perceived inaccessibility and discrimination can determine CAM utilization (\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTradition plays a larger role as a determinant of CAM use in Asia, Africa, and South America (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Moreover, low cost drives CAM use in developing countries, unlike in Europe, where this factor is not commonly reported (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Motivations for CAM usage include expectation of CAM benefits, dissatisfaction with conventional care, and perceived safety \u0026ndash; in other words, CAM users believe that these therapies will help, and are often driven by disenchantment with standard care (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). While CAM therapies remain understudied, evidence exists to support the effectiveness of some (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A significant proportion of migraine sufferers, for instance, found CAM treatments to be effective (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch indicates that health information received online influences people\u0026rsquo;s healthcare decisions (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The availability of such information on the internet has helped users overcome structural barriers, reduce unnecessary and constant consultations, and achieve a more empowered and productive relationship with their healthcare providers (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Importantly, medical issues that are perceived to be stigmatized make it more likely that patients will resort to seeking health information online (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the internet has greatly democratized health information availability, it may also become a vehicle for the spread of \u0026ldquo;fake news\u0026rdquo; and conspiracies, as exemplified during the COVID-19 pandemic, which can be harmful to the population\u0026rsquo;s health (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Importantly, the nature of social media itself tends to amplify false claims, especially in times of crisis. Inaccurate information about COVID-19 was less likely to be shared than its accurate counterparts (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). While online misinformation is typically assumed to originate from unverified sources, respected sources such as WebMD are also vulnerable to inaccurate claims (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), making the internet a \u0026ldquo;wild west\u0026rdquo; regarding information on public health.\u003c/p\u003e \u003cp\u003eThe internet has also been documented to serve as a discussion forum about CAM therapies. While some CAM information online is accurate, a significant proportion was found to be misleading or incomplete across several studies (\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In some cases, social media becomes a debate stage for polarizing opinions and a home for unverified narratives on CAM (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Research showed that both laypeople and underinformed professionals are vulnerable to misleading CAM information (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnline health misinformation has serious and real-life consequences for public health(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Occasional reports exist of patients preferring CAM-only treatments in disease or applying CAM improperly, leading to adverse outcomes, including a higher risk of mortality (\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Another example of the implications of online health misinformation is vaccine hesitancy - a large percentage of vaccine-hesitant patients inhabit CAM-adjacent information bubbles (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the issue is compounded by patients often not informing doctors of their use of CAM (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Healthcare professionals, on the other hand, can be skeptical or even dismissive about such treatments (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Further complicating public trust in conventional healthcare is the increasing scrutiny of pharmaceutical industry practices. Evidence-supported concerns such as pricing, monopolization, and lobbying have fueled distrust (\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Online discourse often blurs the line between legitimate critique and conspiracy theory, entrenching anti-pharmaceutical sentiments that intersect with CAM use (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This dynamic may foster a culture of secrecy around CAM, where patients may turn to the internet for guidance rather than to healthcare professionals.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, it is essential to examine how digital health information seeking behavior may influence CAM usage and beliefs about CAM effectiveness in Western societies.\u003c/p\u003e"},{"header":"Research objective and questions","content":"\u003cp\u003eThis study investigates how online health information seeking behavior and perceived reliability of the internet for health decision-making are associated with the frequency of CAM use and belief in CAM superiority over conventional medicine across Western populations. To achieve that, the study aims to answer the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIs more frequent online health information seeking associated with greater use of CAM?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAre positive perceptions of the internet as a resource for medical decision-making associated with more frequent CAM use?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIs online health information seeking associated with a stronger belief that CAM is more effective than conventional medicine?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDo perceptions of the internet\u0026rsquo;s usefulness and reliability for health decisions positively correlate with the belief in CAM\u0026rsquo;s superiority?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData\u003c/h2\u003e \u003cp\u003eThis study uses data from the International Social Survey Programme, curated by the ISSP Research Group and distributed by GESIS \u0026ndash; Leibniz Institute for the Social Sciences (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The ISSP is a long-running cross-national collaboration that conducts harmonized annual surveys on topics of sociopolitical importance. The 2021 module on Health and Health Care II (ISSP 2021) explores themes including individual health status, trust in healthcare systems, online health information seeking, and the use of complementary and alternative medicine (CAM). The dataset consists of cross-sectional nationally representative samples of adults aged 18 and older (with minor country-specific exceptions). Data were collected between 2021 and 2024 via face-to-face interviews, telephone surveys, paper questionnaires, and online self-administered forms, depending on local fieldwork practices. The total analytic sample from Western countries included 25,741 respondents, allowing for precise multivariate modeling across sociodemographic and attitudinal dimensions.\u003c/p\u003e \u003cp\u003eTo enhance the reliability and contextual validity of the analysis, the present study focuses exclusively on Western countries characterized by high-income economies, well-established healthcare systems, and widespread internet penetration. These include: Australia, Austria, Croatia, the Czech Republic, Denmark, Finland, France, Germany, Hungary, Iceland, Italy, the Netherlands, New Zealand, Norway, Poland, Slovakia, Slovenia, Switzerland, and the United States. This regional focus was selected to ensure greater conceptual and infrastructural comparability across countries with similar patterns of internet accessibility and regulatory environments for CAM. Including countries from regions where digital access remains uneven or where CAM practices are more deeply rooted in traditional medicine systems could introduce heterogeneity that may confound the relationship between online health information seeking and CAM use.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImputation process\u003c/h3\u003e\n\u003cp\u003eThe data had a substantial percentage of missing values, with rates ranging from 27.1\u0026ndash;34.1% across models. To address item nonresponse and reduce potential bias due to missing data, multiple imputation by chained equations (MICE), which is a widely used and robust method, (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e) was applied to the predictors with the highest missingness. To ensure model stability and avoid issues of separation and perfect prediction, an augmentation procedure was applied in all categorical models (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). A total of 20 imputed datasets were generated using chained equations, with sufficient iterations between imputations to ensure convergence. Imputed datasets were then pooled for subsequent analyses, and diagnostics indicated adequate convergence and consistency across imputations. Following imputation, between 86.1% and 90.2% of the total sample (n\u0026thinsp;=\u0026thinsp;25,741) had complete data available for analysis across models, with analytic subsamples ranging from 22,155 to 23,220 individuals depending on variable availability.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical Approach\u003c/h2\u003e \u003cp\u003eGiven the ordinal nature of all dependent variables, this study utilized ordinal logistic regression for all analyses. This modeling approach estimates the cumulative odds of being in or above a particular category of the dependent variable, assuming that the relationship between the predictors and the log-odds of the outcome is constant across thresholds (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Ordered logistic regression was chosen over ordinary least squares (OLS) regression because OLS assumes equal intervals between response categories and homoscedastic, normally distributed errors\u0026mdash;assumptions that are routinely violated when applied to ordinal outcomes (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Ordinal logistic regression also allows for more efficient use of ordered data compared to collapsing outcomes into binary categories, preserving statistical power and interpretive nuance (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). As analysis was conducted on an imputed dataset, checks were made regarding the success of the imputational procedure and the influence of missing data on estimates. The largest fraction of missing information (FMI) across predictors remained at or below 0.2 (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), and the relative increase in variance (RVI) was low across models (below 0.03), indicating stable and reliable estimation. A model-wide Wald-type F-test consistently confirmed the joint significance of predictors across all outcomes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). To ensure that results were not biased by collinearity among predictors, variance inflation factors (VIFs) were examined; all VIF values were well below the conventional threshold of 5, suggesting no serious multicollinearity. Odds ratios were employed for ease of interpretation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDependent variables\u003c/h2\u003e \u003cp\u003eThe main outcome variables were derived from the following survey questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e\u0026ldquo;During the past 12 months, how often did you visit or were visited by an alternative/ traditional/ folk health care practitioner?\u0026rdquo; \u0026ndash; used to measure CAM use frequency.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e\u0026ldquo;To what extent do you agree or disagree with the following statement: Alternative medicine provides better solutions for health problems than Western conventional medicine?\u0026rdquo; \u0026ndash; used to measure perceived CAM efficacy.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe ISSP Health and Health Care II (ISSP 2021) survey module defines CAM closely to the definition developed by the Cochrane Collaboration, which allowed each country to choose the most appropriate term referring to \u0026ldquo;medical and healthcare practices and products, which are \u003cb\u003enot\u003c/b\u003e currently part of mainstream/Western conventional medicine.\u0026rdquo; Each country\u0026rsquo;s survey was tailored to \u0026ldquo;choose the term which most appropriately referred to allopathic mainstream Western medicine.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eKey Predictors\u003c/h3\u003e\n\u003cp\u003eThe first independent variable of interest was the \u003cb\u003efrequency of online health information seeking behavior\u003c/b\u003e, based on the question: \u0026ldquo;During the past 12 months, how often, if at all, did you use the internet on any device (such as computers, tablets, and smartphones) to look for health or medical information for yourself or someone else?\u0026rdquo; Responses were categorized into six ordered levels: never or almost never, several times a year, several times a month, several times a week, once a day, and several times a day.\u003c/p\u003e \u003cp\u003eThe other set of independent variables of interest covered the \u003cb\u003eperceived usefulness and reliability of the internet for health decision-making\u003c/b\u003e, rated on a five-point Likert scale: a) \u0026ldquo;The internet is useful to help people decide if their symptoms are serious enough to go to the doctor,\u0026rdquo; b) \u0026ldquo;The internet is useful to check that the doctor is giving people appropriate advice,\u0026rdquo; c) \u0026ldquo;It is not easy to distinguish between reliable and unreliable health information on the internet.\u0026rdquo;\u003c/p\u003e \u003cp\u003eThe study aimed to include a wide range of control variables to try and better isolate the potential relationship between internet health information seeking and the use of complementary and alternative medicine, as well as the belief in its superior efficacy. Control variables included age, age-squared, sex, education level, marital status, presence of chronic illness, subjective health rating, and subjective socioeconomic status. Additionally, religious affiliation and region were included to account for cultural and geographic heterogeneity in CAM attitudes. Trust-related variables were included to assess the influence of both institutional and interpersonal credibility on CAM attitudes. Perceived fairness of the healthcare system and trust in doctors were likewise included, guided by previous findings suggesting that distrust in conventional healthcare institutions may drive individuals toward alternative modalities (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). To account for psychological distress, the models also controlled for the frequency of depressive affect, as emotional distress has been consistently associated with CAM use (\u003cspan additionalcitationids=\"CR63\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics\u003c/p\u003e \u003cp\u003eDescriptive statistics corresponding to the analytic samples used in the study are presented in Supplementary Tables\u0026nbsp;1 through 4 in the Appendix. Across all models, the analytic samples were broadly comparable. The gender distribution remained consistent across models (\u0026asymp;\u0026thinsp;54% female). The mean age of the respondents was approximately 52 years (SD\u0026thinsp;\u0026asymp;\u0026thinsp;17). Most participants rated their health as good or very good, and about one-third reported a chronic condition. The largest participants were from Western Europe (\u0026asymp;\u0026thinsp;42% across models). This was followed by Central and Eastern Europe (\u0026asymp;\u0026thinsp;26%), Northern Europe (\u0026asymp;\u0026thinsp;19%), Australia and New Zealand (\u0026asymp;\u0026thinsp;8%), and the United States (\u0026asymp;\u0026thinsp;4% ). Respondents predominantly identified as Christian or non-religious.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn Model 1\u003c/b\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23,220), which examined the frequency of online health information seeking as a predictor of visits to alternative healthcare practitioners, 22.0% reported CAM use in the past year (11.1% seldom, 7.9% sometimes, 2.4% often, and 0.6% very often. Online health information seeking behavior was very widespread, with 72.5% of surveyed adults reporting having engaged in online health information seeking. 31.4% reported going online for health advice several times a year, 19.7% several times a month, 8.8% several times a week, 2.8% once a day, and 9.9% several times a day.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModels 2.1\u0026ndash;2.3\u003c/b\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;23,000) retained CAM use as the outcome and examined its relationship with specific beliefs about the internet\u0026rsquo;s role in health decision-making. The dependent variable\u0026rsquo;s distribution remained consistent with Model 1. Perceptions of the internet\u0026rsquo;s usefulness for health decision-making varied considerably across models. In \u003cb\u003eModel 2.1\u003c/b\u003e, the independent variable of interest was whether the respondent believed that the internet helps assess the seriousness of symptoms. 39.6% of respondents agreed (35.3%) or strongly agreed (4.3%) that the internet was a useful tool for that purpose, 35.3% disagreed (23.6%) or strongly disagreed (11.7%), and 25.1% neither agreed nor disagreed. In \u003cb\u003eModel 2.2\u003c/b\u003e, focused on the belief that the internet\u0026rsquo;s usefulness for verifying doctors\u0026rsquo; advice, agreement was lower (27.9%, with 24.8% agreeing and 3.1% strongly agreeing), and skepticism slightly higher, with 43.5% disagreeing or strongly disagreeing (29.5% and 14.0%, respectively), while 28.6% stayed neutral. \u003cb\u003eModel 2.3\u003c/b\u003e, where the main predictor of interest was the perceived difficulty in evaluating the reliability of online health information, showed the most critical responses: nearly 70% agreed that it is difficult to distinguish reliable information on the internet (44.7% agreed, 24.8% strongly agreed), compared to just 9.9% and 2.8% who disagreed or strongly disagreed (17.8% neither agreed nor disagreed).\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 3\u003c/b\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22,176) explored whether online health information seeking is positively correlated with believing CAM is superior to conventional medicine. Most respondents disagreed (49.0%) or held neutral views (37.1%) regarding the superiority of CAM, while just 13.9% expressed either agreement or strong agreement. Online health information seeking patterns closely resembled those in Model 1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModels 4.1\u0026ndash;4.3\u003c/b\u003e (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;22,155) retained the belief in CAM superiority over conventional medicine as the outcome variable and examined its relationship to the same attitudes about the usefulness of the internet in health-decision making used in Models 2.1\u0026ndash;2.3 (internet helps assess the seriousness of symptoms, internet is useful for verifying doctors\u0026rsquo; advice, it is difficult to evaluate the reliability of online health information). Patterns of belief in CAM\u0026rsquo;s superiority remained stable across models. Perceptions of the internet as a reliable tool for medical information mirrored the variation observed in Models 2.1 to 2.3.\u003c/p\u003e \u003cp\u003eMain results\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\u003eModel 1. Online health information seeking and frequency of alternative medicine use\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of online health information seeking (Ref. Never or almost never/No internet access)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eSeveral times a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.106, 1.335]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeveral times a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.523***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.375, 1.687]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeveral times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.021***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.789, 2.283]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.661***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[2.207, 3.209]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeveral times a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.410, 1.809]\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.044***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.031, 1.057]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.999, 1.000]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Ref. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.267***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.225, 1.310]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level (Ref. Below Upper Secondary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eUpper secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.104, 1.356]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-cycle tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.112, 1.430]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary specialization and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.925, 1.156]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Ref. Single)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.130***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.032, 1.238]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.198***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.058, 1.356]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.228**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.037, 1.454]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-placement on\u003c/p\u003e \u003cp\u003esocioeconomic ladder (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.023**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.001, 1.045]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status (Ref. Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.817**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.687, 0.970]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.761, 1.074]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.831, 1.196]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.692, 1.078]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Illness (Ref. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.052, 1.226]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception doctors can be trusted(Ref. Strongly disagree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.770, 1.372]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.671, 1.161]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.645***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.493, 0.844]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.476***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.359, 0.631]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception of healthcare system fairness (Ref. Very unfair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eSomewhat unfair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.083*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.996, 1.176]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.154, 1.382]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.248***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.119, 1.393]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.260***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.065, 1.492]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern region (Ref. United States)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.923, 1.253]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.560***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.473, 0.662]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Eastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.527***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.449, 0.619]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia and New Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.417***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.185, 1.694]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligious group (Ref. No religion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.947, 1.093]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJewish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.706, 2.405]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.335**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.040, 1.715]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.790***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.148, 2.791]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.671*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.994, 2.809]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.935, 1.843]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of feeling unhappy or depressed (Ref. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.497***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.377, 1.627]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.858***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.695, 2.035]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.988***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.739, 2.274]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.883***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.558, 2.277]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNote: n\u0026thinsp;=\u0026thinsp;23,220\u003c/h2\u003e \u003cp\u003eThe findings reveal a significant and graded association between the frequency of online health information seeking and the use of complementary and alternative medicine (CAM) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Model 1). This relationship stands even accounting for a variety of socioeconomic characteristics, demographic indicators, region, health/chronic illness status, unhappiness or depressive affect, as well as trust in doctors and the respective country\u0026rsquo;s healthcare system. Individuals who sought health-related information online were significantly more likely to report visiting or being visited by CAM practitioners. The odds of using CAM increased with a higher frequency of online health information seeking. Compared to those who never or almost never used the internet for health purposes, individuals who searched for health or medical information several times a week exhibited more than twice the odds of CAM use, while those seeking information once a day demonstrated an even stronger association (OR\u0026thinsp;=\u0026thinsp;2.661, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) Although the odds ratio for the highest frequency category (\u0026ldquo;several times a day\u0026rdquo;) was slightly lower than that for daily users (OR\u0026thinsp;=\u0026thinsp;1,597, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), it remained statistically significant, suggesting that high-frequency engagement with online health content is positively associated with CAM use, though the relationship may diminish slightly at the highest levels of use.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModels 2.1\u0026ndash;2.3. Frequency of CAM use and perceptions of the internet as a tool for health decision-making\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003cp\u003eof interest:\u003c/p\u003e \u003cp\u003e(Ref. Strongly disagree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 2.1\u003c/p\u003e \u003cp\u003eIndependent variable of interest: Internet is useful to help people assess seriousness of symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2.2\u003c/p\u003e \u003cp\u003eIndependent variable of interest: Internet is useful to help verify advice from the doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 2.3\u003c/p\u003e \u003cp\u003eIndependent variable of interest: It is not easy to distinguish between reliable and unreliable health information on the internet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.924, 1.178]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.921, 1.153]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.797, 1.288]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.197***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.062, 1.350]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.178***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.052, 1.319]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.329**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.059, 1.669]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.982, 1.239]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.222***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.090, 1.370]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.844, 1.315]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.355***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.125, 1.631]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.791***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.467, 2.188]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.817, 1.284]\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\u003e1.043***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.030, 1.056]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.043***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.031, 1.056]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.030, 1.055]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.999, 1.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.999***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.999, 1.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.999***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.999, 1.000]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Ref. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.251, 1.337]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.252, 1.338]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.251, 1.337]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level (Ref. Below Upper Secondary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.259***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.136, 1.394]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.244***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.123, 1.378]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.265***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.142, 1.402]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-cycle tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.304***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.151, 1.478]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.141, 1.465]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.307***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.152, 1.481]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Specialization and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.982, 1.225]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.975, 1.216]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.118**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.001, 1.250]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Ref. Single)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.047, 1.256]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.050, 1.260]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.046, 1.255]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.224***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.081, 1.386]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.080, 1.385]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.220***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.078, 1.382]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.222**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.032, 1.447]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.226**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.035, 1.452]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.020, 1.431]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-placement on\u003c/p\u003e \u003cp\u003esocioeconomic ladder (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.003, 1.047]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.003, 1.047]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.025**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.003, 1.048]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status (Ref. Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.796***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.671, 0.946]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.796***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.670, 0.945]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.786***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.662, 0.934]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.734, 1.035]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.737, 1.040]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.861*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.726, 1.023]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.796, 1.145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.798, 1.148]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.792, 1.139]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.807*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.647, 1.007]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.806*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.646, 1.007]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.810*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.649, 1.010]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Illness (Ref. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.155***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.070, 1.247]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.152***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.067, 1.244]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.081, 1.260]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception doctors can be trusted(Ref. Strongly disagree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.771, 1.375]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.780, 1.394]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.755, 1.345]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.652, 1.130]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.672, 1.167]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.630, 1.090]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.614***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.469, 0.804]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.642***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.489, 0.841]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.601***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.459, 0.787]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.456***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.344, 0.605]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.476***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.358, 0.631]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.447***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.337, 0.592]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception of healthcare system fairness (Ref. Very unfair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat unfair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.092**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.005, 1.186]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.092**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.005, 1.187]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.096**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.009, 1.191]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.252***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.144, 1.370]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.155, 1.383]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.144, 1.369]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.282***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.149, 1.430]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.277***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.144, 1.425]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.276***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.144, 1.424]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.278***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.081, 1.512]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.077, 1.509]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.284***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.085, 1.520]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern region (Ref. United States)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.900, 1.224]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.925, 1.258]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.905, 1.229]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.544***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.460, 0.642]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.551***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.467, 0.652]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.546***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.462, 0.645]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Eastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.542***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.462, 0.636]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.544***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.463, 0.638]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.546***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.465, 0.641]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia and New Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.346***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.126, 1.609]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.357***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.136, 1.622]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.356***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.134, 1.620]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligious group (Ref. No religion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.973, 1.121]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.972, 1.120]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.969, 1.117]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJewish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.689, 2.414]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.713, 2.424]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.732, 2.474]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.392***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.085, 1.787]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.343**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.045, 1.727]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.379**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.074, 1.772]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.795***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.151, 2.799]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.782***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.142, 2.781]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.780**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.141, 2.777]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.827**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.099, 3.039]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.813**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.088, 3.021]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.873**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.121, 3.129]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.338*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.954, 1.877]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.306*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.931, 1.833]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.338*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.954, 1.877]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of feeling unhappy or depressed (Ref. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.526***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.404, 1.659]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.536***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.413, 1.669]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.539***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.416, 1.673]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.927***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.759, 2.111]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.932***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.763, 2.116]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.943***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.774, 2.129]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.827, 2.387]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.095***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.833, 2.395]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.111***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.847, 2.412]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.681, 2.453]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.679, 2.450]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.066***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.710, 2.495]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote: Model 2.1 n\u0026thinsp;=\u0026thinsp;23,057, Model 2.2 n\u0026thinsp;=\u0026thinsp;23,013, Model 2.3 n\u0026thinsp;=\u0026thinsp;23,046.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis further indicates that stronger beliefs in the internet\u0026rsquo;s usefulness in health decision-making are significantly associated with CAM use (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Models 2.1\u0026ndash;2.2). In Model 2.1, respondents who strongly agreed that the internet is useful for assessing whether symptoms are serious enough to visit a doctor had significantly higher odds of CAM use (OR\u0026thinsp;=\u0026thinsp;1.355, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those who strongly disagreed. Similarly, the belief that the internet is useful for verifying their doctors\u0026rsquo; advice was also significantly associated with CAM use (Model 2.2), with the strongest association observed among those who strongly agreed (OR\u0026thinsp;=\u0026thinsp;1.791, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings suggest that individuals who endorse the internet as a valuable tool for evaluating health conditions or medical authority are more likely to engage in CAM.\u003c/p\u003e \u003cp\u003eInterestingly, when examining the relationship between perceptions of difficulty in distinguishing reliable from unreliable health information on the internet and CAM practitioner use (Model 2.3), a significant association was observed primarily among those who neither agreed nor disagreed with the statement (OR\u0026thinsp;=\u0026thinsp;1.329, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Individuals who expressed stronger agreement did not show a statistically significant increase in CAM use. This adds nuance to the interpretation of the findings and may indicate that feeling unsure and confused in the online space may also be predictive of engagement with CAM practices.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel 3. Believing that alternative medicine is better than conventional medicine and online health information seeking\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of online health information seeking (Ref. Never or almost never/No internet access)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eSeveral times a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.970, 1.109]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeveral times a month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.981, 1.142]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeveral times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.004, 1.223]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.404***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.192, 1.653]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeveral times a day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.033, 1.254]\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.005, 1.023]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.999***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.000, 1.000]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Ref. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.170***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.141, 1.200]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level (Ref. Below Upper Secondary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eUpper secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.960, 1.115]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-cycle tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.915*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.832, 1.007]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Specialization and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.635***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.585, 0.689]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Ref. Single)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.960, 1.104]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.039, 1.263]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.873, 1.120]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-placement on\u003c/p\u003e \u003cp\u003esocioeconomic ladder (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.970, 1.004]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status (Ref. Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.144*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.995, 1.314]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.176**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.024, 1.352]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.262***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.090, 1.461]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.088, 1.539]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Illness (Ref. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.901***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.849, 0.955]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception doctors can be trusted(Ref. Strongly disagree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.630***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.479, 0.829]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.492***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.379, 0.639]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.300***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.232, 0.389]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.117, 0.199]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception of healthcare system fairness (Ref. Very unfair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eSomewhat unfair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.976, 1.104]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.126***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.052, 1.205]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.973, 1.151]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.821, 1.068]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern region (Ref. United States)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.735***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.651, 0.831]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.357***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.314, 0.407]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Eastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.475***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.419, 0.538]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia and New Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.671***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.581, 0.775]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligious group (Ref. No religion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.105, 1.232]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJewish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.770, 2.106]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.920***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.548, 2.382]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.458, 3.248]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.656***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.636, 4.310]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.315***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.741, 3.077]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of feeling unhappy or depressed (Ref. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.969, 1.093]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.090**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.018, 1.168]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.936, 1.160]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.312***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.118, 1.540]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNote n\u0026thinsp;=\u0026thinsp;22,176\u003c/h2\u003e \u003cp\u003eThe relationship between online health information seeking frequency and belief in CAM\u0026rsquo;s superiority over conventional medicine was observed to be statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Model 3). Individuals who searched for health-related information once a day had 40.4% greater odds of endorsing the belief that CAM provides better solutions for health problems than conventional medicine (OR\u0026thinsp;=\u0026thinsp;1.404, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, those seeking information several times a day (OR\u0026thinsp;=\u0026thinsp;1.138, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and several times a week (OR\u0026thinsp;=\u0026thinsp;1.108, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) also exhibited significantly greater odds of holding such beliefs. Lower-frequency users (e.g., several times a year or month) did not differ significantly from non-users in their attitudes toward CAM efficacy. The association appears to be strongest among daily users rather than occasional searchers, and, again, is slightly lower with reported use of multiple times a day, indicating a potential saturation effect.\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModels 4.1\u0026ndash;4.3. Believing alternative medicine is better than conventional medicine, and perceptions of the internet as a tool for health decision-making\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003cp\u003eof interest:\u003c/p\u003e \u003cp\u003e(Ref. Strongly disagree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 4.1\u003c/p\u003e \u003cp\u003eIndependent variable of interest: Internet is useful to help assess seriousness of symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 4.2\u003c/p\u003e \u003cp\u003eIndependent variable of interest: Internet is useful to help verify advice from the doctor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 4.3\u003c/p\u003e \u003cp\u003eIndependent variable of interest: It is not easy to distinguish between reliable and unreliable health information on the internet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.211***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.106, 1.326]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.160, 1.371]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.344***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.122, 1.610]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeither agree nor disagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.621***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.482, 1.774]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.800***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.655, 1.958]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.957***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.645, 2.328]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.495***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.371, 1.631]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.834***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.681, 2.002]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.593***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.347, 1.883]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.652***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.420, 1.921]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.085***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.749, 2.484]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.346***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.136, 1.595]\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\u003e1.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.005, 1.023]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.003, 1.021]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.003, 1.022]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.000, 1.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.000, 1.000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.000, 1.000]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Ref. Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.174***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.145, 1.204]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.147, 1.206]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.143, 1.202]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level (Ref. Below Upper Secondary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper secondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.967, 1.124]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.965, 1.121]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.979, 1.138]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-cycle tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.843, 1.018]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.844, 1.020]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.852, 1.031]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary Specialization and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.641***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.591, 0.695]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.650***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.599, 0.705]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.662***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.610, 0.718]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (Ref. Single)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.960, 1.105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.960, 1.105]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.959, 1.103]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.040, 1.264]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.144***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.038, 1.262]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.151***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.044, 1.269]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.880, 1.129]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.878, 1.128]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.879, 1.129]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-placement on\u003c/p\u003e \u003cp\u003esocioeconomic ladder (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.985*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.968, 1.002]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.986*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.969, 1.003]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.970, 1.005]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status (Ref. Good)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.138*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.990, 1.308]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.154**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.004, 1.327]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.127*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.980, 1.295]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.164**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.013, 1.338]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.184**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.030, 1.361]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.150**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.000, 1.322]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.253***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.082, 1.452]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.273***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.099, 1.475]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.243***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.073, 1.440]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.280***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.076, 1.524]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.286***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.081, 1.531]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.274***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.070, 1.516]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Illness (Ref. No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.904***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.852, 0.958]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.906***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.855, 0.961]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.913***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.861, 0.968]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception doctors can be trusted(Ref. Strongly disagree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.627***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.476, 0.827]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.606***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.460, 0.800]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.613***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.465, 0.808]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.480***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.369, 0.624]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.474***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.364, 0.617]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.463***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.355, 0.602]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.227, 0.382]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.301***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.232, 0.390]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.287***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.221, 0.372]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.151***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.116, 0.198]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.123, 0.210]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.115, 0.197]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerception of healthcare system fairness (Ref. Very unfair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat unfair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.962, 1.089]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.957, 1.083]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.967, 1.094]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.099***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.027, 1.177]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.097***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.025, 1.175]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.029, 1.180]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.955, 1.130]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.951, 1.125]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.970, 1.148]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.826, 1.075]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.834, 1.086]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.831, 1.081]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern region (Ref. United States)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.781***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.691, 0.883]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.726, 0.927]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.754***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.667, 0.851]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.360***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.317, 0.410]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.375***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.330, 0.427]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.359***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.315, 0.408]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Eastern Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.496***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.437, 0.563]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.507***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.447, 0.574]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.492***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.434, 0.558]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia and New Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.683***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.591, 0.789]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.697***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.603, 0.805]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.678***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.587, 0.783]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligious group (Ref. No religion)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.118, 1.247]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.110, 1.237]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.109, 1.236]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJewish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.794, 2.166]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.782, 2.132]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.804, 2.190]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.914***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.543, 2.373]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.856***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.497, 2.302]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.926***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.553, 2.388]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuddhist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.101***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.407, 3.136]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.957***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.311, 2.921]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.414, 3.148]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.628***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.622, 4.256]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.564***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.581, 4.158]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.688***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.652, 4.375]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.337***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.758, 3.107]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.692, 2.993]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.362***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.776, 3.142]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of feeling unhappy or depressed (Ref. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.954, 1.076]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.950, 1.071]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.968, 1.092]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.076**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.004, 1.153]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.065*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.994, 1.141]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.091**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.018, 1.169]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.919, 1.140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.907, 1.125]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.948, 1.175]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery often\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.310***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[1.116, 1.537]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.301***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[1.108, 1.528]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.327***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.131, 1.557]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote: Model 4.1 n\u0026thinsp;=\u0026thinsp;22,157, Model 4.2 n\u0026thinsp;=\u0026thinsp;22,155, Model 4.3 n\u0026thinsp;=\u0026thinsp;22,158.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBeliefs about the usefulness and reliability of online health information were also significantly associated with thinking that CAM is better than conventional medicine (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Models 4.1\u0026ndash;4.3). Those who strongly agreed that the internet is useful for assessing seriousness of symptoms had significantly greater odds of believing CAM is more effective than conventional medicine (OR\u0026thinsp;=\u0026thinsp;1.652, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a similarly elevated association among those who simply agreed (OR\u0026thinsp;=\u0026thinsp;1.495, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Model 4.1). Individuals who strongly agreed that the internet is useful for verifying doctors\u0026rsquo; advice exhibited more than double the odds of endorsing CAM superiority over conventional medicine (OR\u0026thinsp;=\u0026thinsp;2.085, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eThe belief that it is difficult to distinguish reliable from unreliable online health information was also positively associated with beliefs in CAM\u0026rsquo;s superiority over conventional medicine (Model 4.3). The strongest effect was observed among those who neither agreed nor disagreed (OR\u0026thinsp;=\u0026thinsp;1.957, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), though significant associations were also observed for those who agreed (OR\u0026thinsp;=\u0026thinsp;1.593, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and strongly agreed (OR\u0026thinsp;=\u0026thinsp;1.346, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results, again, continue to expand on the nuanced picture observed in the previous model and point to the potential link between uncertainty in online health spaces and CAM endorsement.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings suggest that more frequent online health information seeking is positively linked to higher use of complementary and alternative medicine (CAM), as well as beliefs in the superiority of CAM over conventional medicine. Moreover, beliefs about the usefulness of online health information for making healthcare decisions (ie. believing it is useful to help assess the seriousness of symptoms / verify doctor\u0026rsquo;s advice) are positively associated with higher use of CAM and stronger beliefs that CAM is better than conventional medicine.\u003c/p\u003e \u003cp\u003eHowever, at the highest levels of health information seeking (everyday), the relationship with CAM use and belief in CAM efficacy slightly diminished. This may be due to multiple reasons, one of which is information overload. Social media overload has been linked to reduced health self-efficacy \u0026ndash; the confidence that one can address health challenges (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). As a result, individuals who engage in very frequent online health information seeking may feel overwhelmed rather than empowered and motivated to seek unconventional treatments. Moreover, users who most frequently seek out online health information may present a more informed internet health seeker profile and be more mistrustful of CAM, as many of these therapies lack structured clinical evidence.\u003c/p\u003e \u003cp\u003eNotably, respondents who selected \u0026ldquo;neither agree nor disagree\u0026rdquo; in response to the statement \u0026ldquo;It is not easy to distinguish reliable health information online\u0026rdquo; were the most likely to use complementary and alternative medicine (CAM) and to believe in its superiority over conventional treatments. This finding suggests that uncertainty and confusion when navigating online health information, rather than outright mistrust or full confidence, may be most strongly associated with CAM use and belief in its effectiveness. Additionally, individuals who agreed that it is difficult to distinguish between reliable and unreliable online health information were also more likely to believe that CAM is superior to conventional medicine. These results may point to the role of digital literacy as an important dimension in CAM decision-making.\u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s implications are twofold \u0026ndash; on one hand, the internet is increasingly enabling people to more equitably access medical information (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). On the other hand, the online space may also provide a pathway to non-mainstream health narratives (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). These, in turn, are at risk of verging into misinformation (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) and conspiracy theories (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). This is particularly important when studying complementary and alternative medicine, as CAM use often occurs without the knowledge of healthcare providers (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Results also reflect agreement with studies reporting underlying skepticism toward conventional medical authority, a phenomenon increasingly documented in Western countries, especially since the COVID-19 pandemic (\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, these findings suggest that:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals who rely on the internet as a mechanism for searching health information, as well as for questioning or supplementing medical authority, may be especially likely to believe in the superiority of alternative medicine;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndividuals who were unsure whether it was easy to distinguish reliable from unreliable online health information were both more likely to use CAM and believe that it is better than conventional medicine;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFinally, individuals who admitted that it is difficult to identify reliable health information online were also found to hold stronger beliefs in CAM efficacy than those who reported greater confidence in their ability to evaluate health information.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe first limitation of the present study is the use of cross-sectional data, which does not allow us to confidently determine a causal link between relying on the internet for health information and agreeing that it is useful in health decision-making, and the use of CAM. While this study included depressive affect and unhappiness, as well as the respondents\u0026rsquo; trust in the healthcare system and doctors as control variables, it may not be enough to account for reverse causality. Frequently searching for online health information may be a manifestation of unhealthy preoccupation that is not captured by our available mental health variable, which is then a determinant of CAM use. Secondly, we do not know what websites are being used by the study participants and the quality of information they provide. Some CAM websites, for instance, were reported to have less balanced representation and fewer external links (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Importantly, people who use traditional and complementary medicine out of aversion to conventional medicine are less likely to be influenced by doctors\u0026rsquo; advice and scientific studies (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). This necessitates that future research to focus on specific cohorts of online health seekers and the varying quality of information pathways that they undertake. Future research should pursue qualitative interviews and longitudinal design, as well as more specific and literature-supported survey questions before building a case that online health information seeking shapes CAM use and beliefs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConclusion and Policy Recommendations\u003c/h2\u003e \u003cp\u003eThis study contributes to the growing literature supporting the connection between internet use, online health information seeking behavior, and the use of complementary and alternative medicine (CAM). To the best of the author\u0026rsquo;s knowledge, the present study provides the first large-scale cross-national evidence that online health information seeking behavior is strongly associated with both CAM use and belief in its superiority over conventional medicine in the general Western population.\u003c/p\u003e \u003cp\u003eWhile being cautious of causal interpretation, these findings support additional research and conceptualization of the internet as a potential facilitator of CAM use. Additionally, the findings provide evidence of a positive association between online health information seeking, perceptions of the internet\u0026rsquo;s reliability for making medical decisions, and the belief that CAM is better than conventional medicine.\u003c/p\u003e \u003cp\u003eIn line with these findings, the present study provides a discussion of potential policy recommendations to better respond to the population\u0026rsquo;s increasing interest in CAM. The majority of European WHO member states lack policies and programs governing traditional and complementary medicine (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). European states have been documented to experience regulatory issues regarding traditional and complementary medicine, as well as a lack of research and financial support, a lack of expertise, and a lack of mechanisms to monitor safety (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). This regulatory vacuum leaves patients navigating CAM practices without sufficient institutional oversight or guidance.\u003c/p\u003e \u003cp\u003eAs the demand for CAM is rising in the West (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), governments should work to integrate evidence-based CAM therapies into the mainstream, to the extent that they do not interfere with highly proven conventional treatments. Such integration is not only a matter of legitimacy but one of patient safety. Other countries\u0026rsquo; good practices can be used as a model in Western societies that lack a clear framework of CAM integration. In Japan, complementary medicine is more integrated with the mainstream healthcare complex, and some of it is covered by national insurance, with one of the most popular forms being Kampo (Japanese traditional herbal) medicines, a significant proportion of which is prescribed by doctors (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn a micro-level, doctors and healthcare providers should not scorn complementary and alternative medicine. Having healthcare providers who are more empathetic, as well as the quality of time spent with the physician, has been negatively associated with online health information seeking behaviors (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). On the contrary, dissatisfaction with the healthcare received was shown to correlate with patients preferring the internet as a better information source (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVulnerable populations, particularly older adults, are likely to especially benefit from education about online health information (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Moreover, women are more likely to resort to online health information seeking than men (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Efforts to increase access to reliable online health information should consider these intersectionalities and approach the development of training programs accordingly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplementary and Alternative Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplementary, Alternative, and Integrative Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTraditional, Complementary, and Integrative Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Social Survey Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFraction of Missing Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRVI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Increase in Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMICE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple Imputation by Chained Equations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrdinary Least Squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study utilized publicly available data from the International Social Survey Programme (ISSP Research Group, 2024). The original data collection was conducted according to ethical standards in each participating country. No additional ethical approval was required for this analysis.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain data from any individual person that requires consent for publication as it uses publicly available, de-identified secondary data.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the ISSP Research Group, distributed by GESIS - Leibniz Institute for the Social Sciences (ISSP Research Group, 2024). The dataset is available at: https://doi.org/10.4232/5.ZA8000.2.0.0\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research did not receive any specific grant funding. However, Daria Turavinina is a recipient of Chulalongkorn University\u0026apos;s Graduate Scholarship Program for ASEAN or Non-ASEAN Countries.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eDaria Turavinina conceptualized the study, performed the data analysis, and wrote the original draft. Yot Amornkitvikai supervised the research, provided methodological guidance, and reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to acknowledge the International Social Survey Programme (ISSP) for providing the data used in this study.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; information\u003c/h2\u003e\n\u003cp\u003eDaria Turavinina is a PhD Candidate at the College of Population Studies, Chulalongkorn University, Thailand.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYot Amornkitvikai is an Associate Professor (Economics) at the College of Population Studies, Chulalongkorn University, Thailand.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNg JY, Dhawan T, Dogadova E, Taghi-Zada Z, Vacca A, Wieland LS, et al. Operational definition of complementary, alternative, and integrative medicine derived from a systematic search. BMC Complement Med Ther. 2022;22(1):104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, Traditional, Complementary and Integrative Medicine. 2025 [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/health-topics/traditional-complementary-and-integrative-medicine#tab=tab_1\u003c/span\u003e\u003cspan address=\"https://www.who.int/health-topics/traditional-complementary-and-integrative-medicine#tab=tab_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErnst E. The prevalence of complementary/alternative medicine in cancer: a systematic review. Cancer: Interdisciplinary Int J Am Cancer Soc. 1998;83(4):777\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTangkiatkumjai M, Boardman H, Walker D-M. 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ZA8000 International Social Survey Programme: Health and Health Care II - ISSP 2021. GESIS, Cologne. ZA8000 Data file Version 2.0.0. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong JS, Freese J. Regression models for categorical dependent variables. using Stata: Stata; 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgresti A. Analysis of ordinal categorical data. Wiley; 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavalei V, Rhemtulla M. 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World Health Organization; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotoo Y, Yukawa K, Hisamura K, Tsutani K, Arai I. Internet survey on the provision of complementary and alternative medicine in Japanese private clinics: a cross-sectional study. J Integr Med. 2019;17(1):8\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotoo Y, Yukawa K, Arai I, Hisamura K, Tsutani K. Use of complementary and alternative medicine in Japan: a cross-sectional internet survey using the Japanese version of the International Complementary and Alternative Medicine Questionnaire. JMA J. 2019;2(1):35\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTustin N. The role of patient satisfaction in online health information seeking. J health communication. 2010;15(1):3\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller LMS, Bell RA. Online health information seeking: the influence of age, information trustworthiness, and search challenges. J Aging Health. 2012;24(3):525\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHallyburton A, Evarts LA. Gender and online health information seeking: A five survey meta-analysis. J consumer health Internet. 2014;18(2):128\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-complementary-medicine-and-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcam","sideBox":"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Complementary Medicine and Therapies","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"complementary and alternative medicine, CAM, digital health-seeking, internet use, healthcare","lastPublishedDoi":"10.21203/rs.3.rs-6610060/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6610060/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUsing a large cross-national dataset (N\u0026thinsp;\u0026asymp;\u0026thinsp;23,000), this study investigates the relationship between several aspects of online health-seeking and the use of complementary and alternative medicine (CAM), as well as the belief that CAM is better than conventional medicine in Western societies. It also examines how perceptions of the internet as a useful tool to guide health decisions, and perceived reliability of online information, relate to CAM use and beliefs about its superiority.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eOrdinal logistic regression models were used to assess the association between online health-seeking behavior, perceived usefulness and reliability of online health information, and two outcomes: CAM use and belief in CAM superiority over conventional medicine. Analyses were based on data from the 2021 ISSP module on Health and Healthcare, restricted to Western countries.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFindings reveal a significant, graded association between more frequent online health-seeking and both higher CAM use and stronger belief that CAM is better than conventional medicine. Those who perceived the internet as useful for verifying doctors\u0026rsquo; advice or evaluating symptoms also had significantly higher odds of CAM use and belief in its superiority. Notably, those expressing uncertainty about the reliability of online health information were more likely to report CAM use and belief in CAM\u0026rsquo;s superiority.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese results suggest that the digital health landscape may simultaneously empower and confuse users, potentially facilitating engagement with complementary therapies in the absence of clear evaluative guidance. This study highlights the need to integrate CAM into institutional healthcare frameworks, develop legal standards for CAM use, promote digital health literacy, and improve doctor\u0026ndash;patient communication.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"Click, Confuse, Convert? Digital Health Information Seeking, Perceived Usefulness and Reliability of Online Health Information, and Alternative Medicine Use and Beliefs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 03:03:06","doi":"10.21203/rs.3.rs-6610060/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-30T04:40:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T02:45:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T12:50:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132859914121625385558957020830632273423","date":"2025-07-03T13:07:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57306003864049657834740241986001014159","date":"2025-07-02T23:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-27T09:28:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-27T09:30:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-19T11:31:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-19T11:29:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Complementary Medicine and Therapies","date":"2025-05-07T08:42:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-complementary-medicine-and-therapies","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcam","sideBox":"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Complementary Medicine and Therapies","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb218891-3b58-4e1e-b176-031824cac246","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:02:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 03:03:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6610060","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6610060","identity":"rs-6610060","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}