From Intolerance of Uncertainty to Cyberchondria through Information Overload: The Italian Validation of the Cyberchondria Severity Scale 12 (CSS-12) and a Mediation Model

From Intolerance of Uncertainty to Cyberchondria through Information Overload: The Italian Validation of the Cyberchondria Severity Scale 12 (CSS-12) and a Mediation Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From Intolerance of Uncertainty to Cyberchondria through Information Overload: The Italian Validation of the Cyberchondria Severity Scale 12 (CSS-12) and a Mediation Model Anna Panzeri, Gioia Bottesi, Alessio Vieno, Marcantonio M. Spada, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7196510/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The present two-study research aimed to validate the Italian version of the Cyberchondria Severity Scale-12 (CSS-12), assess its psychometric properties, and explore the relationships among intolerance of uncertainty (IU), cyberchondria, online information overload (OIO), and online information trust (OIT). Confirmatory factor analysis was used in Study 1 and a path analysis mediation model was tested in Study 2. Online self-report questionnaires were administered to samples from the Italian general population. Study 1 confirmed the robust factorial structure of the CSS-12, supporting both a hierarchical second-order model and four first-order dimensions (excessiveness, compulsivity, reassurance, distress), with strong internal consistency, convergent-divergent validity, and test-retest reliability. Study 2 demonstrated that IU is directly associated with cyberchondria and that this relationship is partially mediated by OIO, but not by OIT. Psychological distress also emerged as a significant variable associated with cyberchondria. The findings highlight OIO as a critical mechanism linking IU to maladaptive health-related online behaviors, while the role of OIT appears more nuanced and context-dependent. Results underscore the value of the CSS-12 as a brief, reliable tool for research and clinical use in Italy and suggest that interventions targeting IU and information overload may be effective in mitigating cyberchondria. cyberchondria CSS-12 intolerance of uncertainty online information overload validation mediation model Figures Figure 1 Figure 2 Introduction The term "cyberchondria" comes from combining the words "cyber", a prefix referring to cyberspace, the internet, or computers, and "hypochondria", referring to people who are preoccupied with fears of having or acquiring a serious illness. In psychology, the term cyberchondria describes the rise in anxiety resulting from engaging in online health information searches (Starcevic, 2017 ; White & Horvitz, 2009 ). Cyberchondria significantly affects individuals on psychological, cognitive, and behavioral levels, often resulting in functional impairment. Those affected may suspend normal activities (such as work or school) to focus obsessively on searching for health information online (Balyan & Srivastava, 2024 ; Doherty-Torstrick et al., 2016; Mathes et al., 2018). While some individuals with cyberchondria report satisfaction in various life areas, research shows that they experience psychosocial impairment and a reduced quality of life, becoming trapped in a cycle of anxiety, fear, and loneliness (Balyan & Srivastava, 2024 ; Doherty-Torstrick et al., 2016; Mathes et al., 2018; Vismara et al., 2020 ). Cyberchondria can lead to premature and often inaccurate health conclusions, causing unnecessary worry, emotional distress, and increased, sometimes unwarranted, medical consultations—placing additional strain and costs on healthcare systems (Mathes et al., 2018). Over the last few years, a growing body of research has focused on this phenomenon with the twofold aim of identifying the psychological mechanisms and correlates underlying cyberchondria (e.g., Yang et al., 2025 ) and testing the validity of a scale designed to assess it in several cultural contexts (Arnáez et al., 2023 ; Hallit et al., 2023 ; McElroy & Shevlin, 2014 ; Robles-Mariños et al., 2023 ; Uzun & Zencir, 2021 ). However, the extant knowledge is limited and support for the short form of the Cyberchondria Severity Scale (CSS-12; McElroy et al., 2019 ) is needed. Therefore, the aim of the current study is twofold: providing the Italian validation of the CSS-12 (Study 1) and modeling the mediating role of online information overload (OIO) and online information trust (OIT) in the association between intolerance of uncertainty (IU) and cyberchondria (Study 2). Cyberchondria manifests as a “complex combination of cognitive, emotional, and behavioral components” (Balyan & Srivastava, 2024 , p. 781) and encompasses both a behavioral component consisting in the excessive searching of health-related content online and an associated emotional response of heightened health-related worry and anxiety. In a nutshell, cyberchondria is the compulsive search for health-related information online aimed at reducing health-related anxiety. While cyberchondria shares similarities with related constructs like health anxiety and hypochondria, it constitutes a distinct clinical phenomenon (Starcevic, 2017 ). Cyberchondria not only refers to the excessive seeking of medical information online, but also to the compulsive nature of such behavior, which persists despite the distress it generates and its interference with daily functioning (Starcevic & Berle, 2013 ). Indeed, a central characteristic of cyberchondria is the tendency toward escalation and excessiveness, with individuals devoting an inordinate and progressively increasing amount of time to searching for health-related information (Starcevic, 2017 ; White & Horvitz, 2009 ). Several studies have identified potential correlates of cyberchondria and its adverse sequelae, including health anxiety symptoms (Fergus, 2015 ; Norr et al., 2015 ), reduced quality of life, increased use of healthcare services (Mathes, Norr, Allan, Albanese, & Schmidt, 2018), generalized problematic internet use (Fergus & Spada, 2017 ), and obsessive–compulsive tendencies (e.g., Fergus & Russell, 2016 ). Research has largely focused on analyzing, identifying, and understanding the risk factors and antecedents that predispose to dysfunctional online behaviors up the development of cyberchondria. Fergus ( 2015 ) and Starcevic and Berle ( 2013 ) examined dispositional and psychological variables, suggesting that difficulties in managing anxiety or tolerating uncertainty may play a significant role in the cyberchondria onset. Several studies (X. Yang et al., 2025 )indicated that compulsive web searches may contribute to form a dysfunctional maintenance cycle. Among the risk-factors related to cyberchondria, Intolerance of Uncertainty (IU) appears to have a prominent role (Bottesi et al., 2022 ; Vujić et al., 2024 ). IU is the dispositional incapacity to endure the aversive response triggered by the perception of lacking salient, key, or sufficient information, accompanied by the subjective experience of uncertainty. In other words, IU is a psychological construct that reflects an individual’s tendency to perceive uncertain or ambiguous situations as unacceptable, stressful, or threatening (Carleton, 2016 ). Such difficulty in tolerating ambiguous, unpredictable, or unresolved situations is often associated with emotional distress and dysfunctional coping strategies aiming to manage it (Carleton, 2016 ). The literature has highlighted IU as a transdiagnostic factor associated with various anxiety and mood disorders (Bajcar & Babiak, 2020 ; Bottesi et al., 2022 ; Shihata et al., 2016 ; Starcevic et al., 2020 ), as well as with cyberchondria, which tends to strengthen and maintain it (Bottesi et al., 2022 ; Starcevic et al., 2020 ). Several experimental findings have confirmed that individuals with high levels of IU are more likely to engage in dysfunctional patterns of health information seeking, leading to a progressive escalation of anxiety rather than its reduction (Fergus, 2015 ; Zheng, Chen, et al., 2020). Although the link between IU and cyberchondria has been largely documented (Bajcar & Babiak, 2020 ; Bottesi et al., 2022 ; Fergus, 2015 ; Fergus & Spada, 2017 , 2018 ; Starcevic et al., 2019 , 2020 ; Vismara et al., 2020 ; Zheng, Chen, et al., 2020; Zheng, Sin, et al., 2020 ), the underlying mechanisms of this relationship remain not fully understood with some researchers suggesting that the relationship between IU and cyberchondria could be mediated by cognitive and informational variables that contribute to the process of online health information seeking (Fergus & Spada, 2017 , 2018 ). The role of online information overload and online information trust Recently, two constructs relevant to digital information management within this process have been identified: online information overload (OIO), referring to the cognitive burden resulting from excessive online content; and online information trust (OIT), referring to users’ trust in online health information (Laato et al., 2020 ; Starcevic, 2023 ; Weng et al., 2023 ; Zheng et al., 2023 ). OIO may represent a potential mediating variable in the relationship between IU and cyberchondria in that, in the context of online health information seeking, IU is linked to the need to obtain certainty regarding one’s health status. Some studies (Bajcar & Babiak, 2020 ; Starcevic et al., 2019 ) have shown that when confronted with a high volume of information, individuals with elevated IU may struggle to organize contents, resulting in a paradoxical increase in uncertainty rather than its reduction. Additionally, Starcevic ( 2023 ) suggested that OIO may lead some individuals to experience a “loss of control during the search” (p. 233) for health information online. OIT may also have a role in the relationship between IU and cyberchondria. However, the literature has yet to clarify whether high levels of OIT help reduce uncertainty and promote more informed processing of health information (Weng et al., 2023 ; Zheng et al., 2023 ), or conversely, whether they increase the risk of cyberchondria by encouraging acritical acceptance of alarmist and unreliable content (Laato et al., 2020 ; Zheng et al., 2023 ). In addition, demographic factors such as gender and age, and general psychological distress significantly influence the development and severity of cyberchondria. Studies consistently show that women are more likely to experience higher levels of cyberchondria compared to men, possibly because of gender differences in health anxiety and information-seeking behaviors (Maftei & Holman, 2020 ; Sezer et al., 2022 ). Younger individuals tend to engage more in online health searches due to their digital fluency and comfort with diverse online tools, but older adults may experience greater psychological impacts from cyberchondria due to factors like resource depletion, social isolation, and compensatory internet use (Durak Batıgün et al., 2021 ; Hansen et al., 2003 ; Liu et al., 2023 ; Maftei & Holman, 2020 ). General psychological distress, encompassing symptoms of anxiety and depression, plays a key role in exacerbating cyberchondria (Fang et al., 2024 ; Xu et al., 2025 ). These elements interact dynamically with individual characteristics, highlighting the complexity of cyberchondria. Research gaps Based on the previously reviewed literature, there remains a notable gap in research specifically aimed at uncovering the psychological processes that may foster and maintain cyberchondria, considering the role of IU, OIO and OIT. Despite several findings in the literature highlighting the associations among these constructs, no research study to date has examined these relationships simultaneously within a single confirmatory path analysis model. To the authors’ knowledge, only one study by Zheng et al. ( 2023 ) considered the types of online information sources and through a path model showed that using online search engines to seek health information increases OIO, while obtaining health information through social media platforms and specialized health websites enhances OIT. Furthermore, a significant negative association between OIO and OIT emerged and both of them increased cyberchondria. However, no mediation effects were tested and IU was not considered. At the same time, a validation and study of the psychometric properties of the Cyberchondria Severity Scale-12 (CSS-12) – currently considered the gold standard tool to measure cyberchondria (Arnáez et al., 2023 ; Hallit et al., 2023 ; Marino et al., 2020 ; McElroy et al., 2019 ), CSS-12 in the Italian context is lacking. To date, the CSS is the most widely used self-report instrument for measuring cyberchondria. There are two versions of the scale: the original 33-item version (Marino et al., 2020 ; McElroy & Shevlin, 2014 ) and a shorter, more flexible version, the CSS-12 (McElroy et al., 2019 ), which is quicker to administer yet equally precise and reliable, if not superior. In fact, the CSS-12 excludes a dimension (i.e., medical mistrust) that showed weak correlations with the other factors. The retained dimensions are excessiveness, compulsiveness, reassurance seeking, and distress. However, despite its growing use, a validated Italian version of the CSS-12 is not yet available, despite the well-known need for validated measures in psychological sciences (Flake et al., 2022 ). Aim of the present research In light of this theoretical background, the present research had a twofold aim addressed by two consequential Studies. First, Study 1 aimed to provide the Italian validation of the CSS-12, to study its construct validity and psychometric properties. In line with other studies on the CSS-12 (McElroy et al., 2019 ), also its Italian version is expected to show strong structural validity, good convergent validity, and good test-retest reliability. Second, using the CSS-12, Study 2 aimed to provide empirical insights into the relationship between IU and cyberchondria, highlighting the mediating mechanisms through which OIO and OIT might contribute to the management of IU in the health domain, potentially leading to increased levels of cyberchondria in the general population. In line with previous evidence briefly reviewed above, all these constructs are expected to be positively associated. In particular, OIT and especially OIO are expected to partially mediate the influence of IU to cyberchondria. Specifically, it is hypothesized that OIT may function as a mechanism through which IU contributes to increased levels of cyberchondria by fostering less critical processing of online health information, potentially reinforcing compulsive search behaviors (Laato et al., 2020 ; Zheng et al., 2023 ). Moreover, it is hypothesized that higher levels of IU would lead to greater engagement in online search behaviors, thereby increasing exposure to OIO, which in turn would contribute to higher levels of cyberchondria (Bajcar & Babiak, 2020 ; Starcevic, 2023 ; Starcevic et al., 2019 ). Indeed, information overload directly contributes to the unpleasant emotional states (worry, anxiety, distress) linked to cyberchondria. However, trust in online health information does not inherently mitigate or worsen these emotional outcomes or cyberchondria itself. Trust operates within a broader cognitive appraisal process that includes unexamined factors like information valence (positive/negative), source credibility, and perceived relevance. Thus, while overload fuels distress, trust alone cannot predict emotional impacts without considering these additional contextual variables. All these relationships are expected to hold even when controlling these effects for the covariates of age, gender and general psychological distress which are known to be associated with cyberchondria (e.g., Bottesi et al., 2022 ). Study 1. Validation of the Italian version of the CSS-12 Study 1 aimed at providing the validation of the Italian version of the CSS-12, examining its validity in its various facets, structural and convergent-divergent, and test-retest reliability. Methods A cross-sectional research design was used. Participants were enrolled online and in person through advertisements, and convenience snowball sampling was used. The inclusion criteria were: being above 18 years old, being proficient in Italian language, and providing informed consent. Exclusion criteria: reporting severe medical condition (e.g., cancer, multiple sclerosis); not completing the CSS-12. Participants completed an online self-report survey administered via Qualtrics. Being part of a larger project, part of this data was used in previous studies (blinded). The Ethical committee of the University of [blinded for review], psychological area, approved this study (protocol number: 194-a). Measures A socio-demographic form collected general information about participants’ age, gender, nationality, education, civil status, occupational status. Cyberchondria Severity Scale, 12 items (CSS-12; McElroy et al., 2019 ) comprises four subscales, namely, “compulsion” (CM), “distress” DS, “excessiveness” (EX), and “reassurance” (RE). These dimensions provide an overall score for the cyberchondria construct, higher scores indicate higher levels of cyberchondria. As the same 12 items from McElroy’s short version were selected from the Italian CSS, no retranslation was necessary. Sample items included “If I notice an unexplained bodily sensation I will search for it on the internet” and “Researching symptoms or perceived medical conditions online interrupts my work (e.g. writing emails, working on word documents or spreadsheets” . In this study, the CSS-12 demonstrated high internal consistency, both for the general total score (McDonald’s Omega of .91; Cronbach's alpha of 0.89) and the subscales (CM: omega = .87, alpha = .87; DS: omega = .85, alpha = .84; EX: omega = .71, alpha = .70; RE: omega = .67, alpha = .67). Depression Anxiety Stress Scales-21 (DASS-21; Bottesi et al., 2015 ) is a self-report measure designed to assess general psychological distress across three dimensions: depression, anxiety, and stress. It consists of 21 items, with seven items per subscale, and participants rate each item on a 4-point scale ranging from 0 ("did not apply to me at all") to 3 ("applied to me very much or most of the time"). Sample items included “ I found it difficult to relax ” and “ I felt down-hearted and blue ”. The Italian DASS-21 demonstrated strong psychometric properties, including good internal consistency and temporal stability, supporting its validity for use in both community and clinical samples. In this study, DASS-21 demonstrated high internal consistency, with a McDonald’s Omega of .95 and a Cronbach's alpha of 0.95. Generalized Problematic Internet Use Scale 2 (GPIUS2; Italian adaptation by Fioravanti et al., 2013 ) consists of 15 items designed to evaluate problematic Internet use. It measures five dimensions: preference for online social interactions, mood regulation, compulsive use, cognitive preoccupation, and negative consequences associated with Internet use. Participants respond to each item using an 8-point Likert scale ranging from 1 ("definitely disagree") to 8 ("definitely agree"). Sample items included “ I have difficulty controlling the amount of time I spend online ” and “ When offline, I have a hard time trying to resist the urge to go online ”. In the current study, the GPIUS2 demonstrated high internal consistency, with a McDonald’s Omega of .91 and a Cronbach's alpha of 0.89. Health Anxiety Questionnaire (HAQ; Italian adaptation by Melli et al., 2007 ), originally developed by Lucock & Morley ( 1996 ), includes 21 items to evaluate health anxiety. Participants respond to each item using a 4-point scale, ranging from 1 ("never or rarely") to 4 ("almost always"). Sample items included “ Do you ever worry about your health? ” and “ Does the thought of a serious illness ever scare you? ”. In this study, the HAQ demonstrated excellent internal consistency, with a McDonald’s Omega of .94 and a Cronbach's alpha of 0.93. Intolerance of Uncertainty Scale Revised (IUS-R; Bottesi et al., 2019 ) is a widely used self-report tool designed to capture the extent to which individuals find uncertain situations distressing and difficult to tolerate. It consists of 12 items rated on a 5-point Likert scale, ranging from "not at all characteristic of me" to "entirely characteristic of me." The scale is unidimensional, with a single total score representing general IU. The IUS-R has demonstrated excellent psychometric properties, including high internal consistency, with Cronbach’s alpha values typically exceeding 0.90. In this study the internal consistency was optimal, with Cronbach’s alpha = .89 and McDonald omega = .91 Statistical analysis Descriptive statistics were used to describe sample socio-demographic characteristics and the psychological variables. Correlations were used to evaluate the associations among items of the CSS. Confirmatory factor analysis was used to test the factorial structure of the CSS-12. Considering the items’ distribution, the CFA model was estimated using the diagonally-weighted least squares estimator (DWLS) (Brown, 2015 ). Following the guidelines of Hu and Bentler (Hu & Bentler, 1999 ), the measurement model fit to the observed data was evaluated based on these criteria: Comparative Fit Index (CFI), both requiring values greater than 0.95 for a good fit, the Root Mean Square Error of Approximation (RMSEA), which should be less than 0.05 for a good fit, and the Standardized Root Mean Square Residual (SRMR), which should be below 0.08 for a good fit. Additionally, the χ2 statistic was reported. McDonald omega and Cronbach’s alpha evaluated the scale internal consistency. Correlations explored the convergent-divergent validity of the scale. The statistical analysis was conducted using the R software (R Core Team, 2023 ) and the "lavaan" package (Rosseel, 2012 ). Results of Study 1 The sample of Study 1 consisted of 2411 participants (54.65% females) who provided complete responses to the CSS-12. Age ranged from 18 to 77 years, with a mean of 28.27 and a SD of 9.88. Participants were single (71.41%), in a relationship or married (25.15%), separated or divorced (2.84%), or widow (0.60%). About education, most participants had a high school diploma (40.32%), a bachelor's degree (27.36%), a master’s degree (23.91%), a PhD or a post-degree specialization (6.04%), or middle school license (2.22%). Regarding the occupational status, most participants were employed (39.26%), university students (36.55%), unemployed (4.91%), working students (2.89%), housekeepers (2.45%), self-employed (2.10%), retired (1.84%), or other (9.99%). All participants were fluent in Italian and almost all declared Italian nationality (98.33%). Item descriptive statistics and correlations Table 1 shows the descriptive statistics of the items of the CSS-12 and their correlations. In some cases, the values of skewness and kurtosis of the CSS-12 fell beyond the desired thresholds (i.e., |2|), thus an estimator for categorical items is preferable and DWLS was chosen. The bivariate correlations among the CSS-12 items were all positively associated and revealed no problematic associations, as the highest was 0.67 between DS13 and DS12 and 0.65 between CM6 and CM4. The lowest correlation was between item RE27 and EX17 (rho = 0.14). Table 1 Descriptive statistics and correlations between items Descriptives Correlations Variable M SD skewness kurtosis 1 2 3 4 5 6 7 8 9 10 11 1. item cm#4 1.61 0.95 1.60 1.94 2. item cm#6 1.48 0.86 1.97 3.57 .68 3. item cm#7 1.39 0.78 2.28 5.14 .68 .69 4. item ds#12 1.88 1.11 1.12 0.33 .48 .45 .42 5. item ds#13 1.97 1.13 1.01 0.09 .48 .46 .43 .70 6. item ds#14 1.69 1.04 1.51 1.50 .41 .40 .45 .55 .64 7. item ex#17 2.04 1.32 0.96 -0.39 .40 .32 .26 .54 .36 .23 8. item ex#19 1.72 1.05 1.45 1.33 .51 .48 .48 .44 .48 .44 .36 9. item ex#23 2.16 1.18 0.72 -0.50 .51 .44 .39 .48 .47 .39 .46 .48 10. item re#27 1.73 1.10 1.40 0.94 .25 .23 .23 .25 .32 .30 .11 .30 .23 11. item re#28 1.76 1.05 1.28 0.78 .30 .29 .26 .38 .33 .24 .39 .30 .34 .41 12. item re#30 1.38 0.76 2.17 4.41 .39 .39 .40 .37 .37 .34 .25 .37 .33 .39 .42 Note. M and SD are used to represent mean and standard deviation, respectively. All p are < .001. N = 2411. Factorial structure of the CSS-12 A hierarchical factor structure was specified, with four first-order factors and one general factor of higher order (Fig. 1 ). The hierarchical model provided a good fit to the data as shown by the following indices (Table 2 ): X2 = 407.871, df = 50, CFI = .995, RMSEA = .054, RMSEA 95%CI[.050, .059], SRMR = .046. Moreover, the item loadings of the model were inspected and were all high and statistically significant. There were no negative variances. Table 2 Fit indices of hierarchical model and its items’ factor loadings X 2 df CFI RMSEA 95%CI SRMR Hierarchical model 407.871 50 .995 .054 [.050, .059] .046 Loadings Compulsivity Distress Excessiveness Reassurance General factor R2 item cm#4 0.907 0 0 0 0 0.823 item cm#6 0.890 0 0 0 0 0.791 item cm#7 0.893 0 0 0 0 0.797 item ds#12 0 0.883 0 0 0 0.780 item ds#13 0 0.883 0 0 0 0.780 item ds#14 0 0.791 0 0 0 0.626 item ex#17 0 0 0.664 0 0 0.441 item ex#19 0 0 0.778 0 0 0.605 item ex#23 0 0 0.748 0 0 0.559 item re#27 0 0 0 0.644 0 0.415 item re#28 0 0 0 0.746 0 0.557 item re#30 0 0 0 0.837 0 0.701 Compulsivity - - - - 0.847 0.718 Distress - - - - 0.865 0.749 Excessiveness - - - - 0.977 0.954 Reassurance - - - - 0.763 0.582 Note : X 2 : chi-square; df: degrees of freedom; CFI: comparative fit index; RMSEA 95%CI: root mean square error of approximation and its 95% confidence interval; SRMR = standardized root mean square residual. Internal consistency of CSS-12 The internal consistency for the overall scale and the subscales was good, as measured with Cronbach’s alpha and McDonald’s Omega (general CSS: alpha = .88, omega = .91; CM: alpha = .86, omega = .87; DS: alpha = .84, omega = .85; EX: alpha = .69, omega = .71; RE: alpha = .66, omega = .67). Test-retest reliability In a sample of 280 participants (males = 17.14%, females = 82.86%; mean age 30.03, SD 11.90), the CSS-12 was readministered after six months to evaluate test-retest reliability through the Pearson’ r coefficient that was equal to .77 with a satisfying 95%CI[0.70; 0.83], meaning that the test-retest reliability of the Italian version of the CSS-12 was good. This reflects the reliability of the Italian version of the CSS-12 over time. Correlations between CSS-12 subscales The associations of the total score of the CSS-12 and its subscales were explored through observed Spearman correlation. As expected, the total score of the CSS-12 was strongly associated with excessiveness (rho = .85), with DS (rho = .83), and CM (rho = .74). Among the subscales, the highest association was between EX and DS (rho = .60), followed by EX and CM (rho = .59), and EX and RE (rho = .47). Convergent-divergent validity of CSS-12 Convergent-divergent validity of the CSS-12 was evaluated through observed Spearman correlations with the total scores of other scales. Regarding convergent validity, the total CSS-12 score was strongly associated with HAQ (rho = .65). Still, regarding discriminant validity, the CSS-12 total score was positive and moderate with the related constructs measured by IUS-R (rho = .33) and DASS-21 (rho = .33). About divergent validity, the CSS-12 shoved a small-moderate positive association with GPIUS2 measuring problematic internet use (rho = .28, with 95% CI[.24; .32]). Study 2. Mediation model Study 2 aimed at testing a model in which IU is the independent variable, cyberchondria is the dependent variable, and OIO and OIT are mediators in the relationships between IU and cyberchondria in a sample from the general Italian population. Methods The same criteria of Study 1 were used to recruit the participants in Study 2, using a cross-sectional design, and the same inclusion and exclusion criteria. Convenience snowball sampling was used and the survey was administered online. The Ethical committee of the [blinded for review], psychological area, approved this study (protocol number: 194-a). Measures The following self-report tools were administered through an online Qualtrics survey. A socio-demographic survey gathered general information about participants, including age, gender, nationality, education level, civil status, and occupational status. The Cyberchondria Severity Scale (CSS-12; McElroy et al., 2019 ; Italian version used in Study 1, see the dedicated measures section). In this study, the omega was .93 and alpha was .90. The Depression Anxiety Stress Scales-21 (DASS-21;Bottesi et al., 2015 ; Italian version used in Study 1, see the dedicated measures section). In the current study, the DASS-21 showed optimal internal consistency (omega = .95, alpha = .94). Online Information Overload it consists in three items adapted from Laato et al. ( 2020 ) to assess perceived online information overload. Respondents rated their agreement with three statements on a 5-point Likert scale. Sample items included “I am often distracted by the excessive amount of health information on the internet” and “I receive too much online health information to form a coherent picture of what’s happening.” This scale exhibited satisfactory reliability in the current study, with a McDonald’s omega of .82 Cronbach's alpha of 0.80. Online Information Trust it consists of four items adapted from Griffin et al. ( 1999 ) to measure trust in online information. Sample items included statements such as “ I find most online health information is useful” and “ I find most online health information is believable .” This scale has been employed in prior research to evaluate trust in specific information sources. For instance, Yang (Z. Yang et al., 2015 ) utilized it to assess the perceived trustworthiness and validity of news media disseminating information about the H1N1 vaccine. In the current study, the scale demonstrated satisfactory internal consistency, with omega = .83 and alpha = .73. The Intolerance of Uncertainty Scale-Revised (IUS-R; Bottesi et al., 2019 ; Italian version used in Study 1, see the dedicated measures section) is a commonly used self-report instrument that evaluates how distressing and difficult individuals find uncertain situations to tolerate. It includes 12 items rated on a 5-point Likert scale, from "not at all characteristic of me" to "entirely characteristic of me." The scale is typically treated as unidimensional, with a single total score reflecting overall intolerance of uncertainty. The IUS-R has demonstrated strong psychometric qualities, notably high internal consistency with Cronbach’s alpha values often above 0.90, and solid construct validity evidenced by significant correlations with anxiety, worry, and depression measures. In Study, the CSS-12 internal consistency was optimal, with Cronbach’s alpha = .90 and McDonald’s omega = .93. Statistical analyses Initially, descriptive statistics and zero-order correlations were computed. Next, a path analysis model with observed variables was used to test the relationship pattern specified according to the theoretical framework (See Fig. 2 ). Each construct in the model was represented by a single observed score. The maximum likelihood method estimator was employed, and bias-corrected bootstrap confidence intervals with 5000 iterations were used to calculate indirect effects. These effects were deemed significant if their 95% confidence intervals did not include zero. To assess model fit, we examined both the explained variance (R²) for each endogenous variable and the Total Coefficient of Determination (TCD) (Bollen, 1989 ; Jöreskog & Sörbom, 1996 ). The TCD, an established fit measure for path analysis models (a type of structural equation modeling using observed variables), quantifies the combined influence of all predictors on dependent variables. Higher TCD values reflect greater overall explanatory power of the model, capturing the proportion of variance jointly explained across all outcomes. In the tested model, the CSS-12 served as outcome variables, while IUS-R acted as independent variable, and OIO and OIT were mediators in such association. Age, gender, and general psychological distress were included as control variables in the model, with effects on OIO, OIT, and CSS-12. The R software (R Core Team, 2023 ) was used with the lavaan package (Rosseel, 2012 ). Results of Study 2 Below are shown the results of Study 2, including descriptive statistics of the sample, correlations among variables, and the hypothesized mediation model. Sample descriptive statistics The sample consisted in 381 participants (22.05% males, 77.95% females) with mean age 29.92 and SD 11.91. Most had a bachelor’s degree (36.48%), a high-school license (32.28%), a master’s degree (22.83%), a PhD or specialization (6.30%), or middle-school license (2.10%). Regarding the civil status, most participants were single (71.39%), in a relationship or married (24.41%), separated or divorced (2.89%), or widow (1.31%). Regarding the occupational status, most participants were employed (26.25%), university students (36.55%), unemployed (4.72%), working students (11.01%), housekeepers (0.52%), self-employed (6.56%), or other (7.35%). Almost all participants declared Italian nationality (98.43%). Table 3 shows the descriptive statistics of variables in Study 2 and their correlations. Correlations Correlations among the total scores of the continuous variables in Study 2 (Table 3 ) were used to evaluate the variables associations. The variables we not too highly correlated, the highest correlation was between IUS-R and DASS-21 (r = .47) while the lowest was between age and OIT (r = − .04). Table 3 Descriptive statistics and correlations with confidence intervals between constructs in Study 2 Variable M SD minimum maximum 1 2 3 4 5 1. Intolerance of uncertainty 30.59 9.56 14 60 2. Online Information Overload 7.53 2.96 3 15 .26*** [.17, .35] 3. Online Information Trust 12.03 1.74 4 19 .15** .27*** [.05, .25] [.17, .36] 4. Cyberchondria 23.42 8.32 12 51 .33*** .47*** .18*** [.23, .41] [.38, .54] [.08, .27] 5. Age 29.92 11.91 19 71 − .23*** − .09 − .05 − .15** [-.32, − .13] [-.18, .01] [-.15, .05] [-.24, − .05] 6. General psychological distress 39.70 11.14 22 75 .47*** .20*** .12* .37*** − .21*** [.39, .55] [.10, .29] [.02, .22] [.28, .45] [-.31, − .12] Note. M and SD are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation. The confidence interval is a plausible range of population correlations that could have caused the sample correlation. * indicates p < .05. ** indicates p < .01. *** indicates p < .001. N = 381 Mediation model Table 4 shows the results of the parallel mediation model that is represented in Fig. 2 where dashed lines represent non-statistically significant paths and continuous lines the statistically significant ones. All coefficients in the figure are standardized. Table 4 Results of the parallel mediation model. est se p 95%CI std CSS-12 ~ OIO (b1) 1.039 0.129 < .001*** 0.786 1.292 0.370 OIT (b2) 0.145 0.211 .492 -0.269 0.559 0.030 IUS-R (c) 0.089 0.043 .041* 0.004 0.173 0.102 DASS-21 (ed1) 0.167 0.037 < .001*** 0.096 0.239 0.224 Age (ed2) -0.021 0.031 .507 -0.081 0.040 -0.029 Gender (ed3) 1.623 0.878 .065 -0.099 3.344 0.081 OIO ~ est se p 95%CI std IUS-R (a1) 0.064 0.017 < .001*** 0.030 0.098 0.206 DASS-21 (emo1) 0.021 0.015 .165 -0.008 0.050 0.078 Age (emo2) 0.000 0.013 .978 -0.024 0.025 0.001 Gender (emo3) 1.091 0.354 .002** 0.397 1.786 0.153 OIT ~ est se p 95%CI std IUS-R (a2) 0.021 0.011 .044* 0.001 0.042 0.117 DASS-21 (emt1) 0.009 0.009 .314 -0.009 0.027 0.058 Age (emt2) -0.000 0.008 .971 -0.015 0.015 -0.002 Gender (emt3) 0.140 0.216 .517 -0.284 0.564 0.033 Covariances est se p 95%CI std OIO ~ ~ OIT (cor) 1.124 0.253 < .001*** 0.627 1.621 0.233 Indirect effects est se p 95%CI std IU ◊OIO◊ CSS (a1*b1) 0.066 0.020 .001** 0.027 0.105 0.076 IU ◊OIT◊ CSS (a2*b2) 0.003 0.005 .515 -0.006 0.012 0.004 Note : CSS-12: cyberchondria severity scale 12 items; OIO: online information overload; OIT: online information trust; IUS-R: intolerance of uncertainty scale revised; DASS-21: depression anxiety stress scale 21 items; est: estimate; se: standard error; p: p-value; 95%CI: confidence interval at 95%; std: standardized effect. The results of the mediation model indicated a complex pattern of direct and indirect relationships among the predictors and the outcome variable of cyberchondria measured with the CSS-12. The independent variable IUS (c) yielded a modest but significant positive effect on CSS-12 (b = 0.089, SE = 0.043, p = .041, 95%CI = [0.004; 0.173], std = 0.102). This indicates that higher levels of IU are associated with higher scores in CSS-12. About mediators, IUS was associated with OIO with a positive and statistically significant effect (a1: b = 0.064, SE = 0.017, p < .001, 95%CI = [0.030; 0.098], std = 0.206). Moreover, OIO was positively associated with CSS 12 as the unstandardized coefficient (b1) is 1.039 (SE = 0.129, p < .001) with a 95%CI of [0.786; 1.292] and a standardized coefficient of 0.370. This strong and significant positive effect suggests that as OIO increases, CSS-12 also increases considerably. In contrast, IUS was positively associated with OIT showing a statistically significant, albeit smaller, effect (a2: b = 0.021, SE = 0.011, p = .044, 95% CI = [0.001, 0.042], std = 0.117). Moreover, OIT (b2) showed a non-significant effect (b = 0.145, SE = 0.211, p = .492, 95%CI = [–0.269; 0.559], std = 0.030), implying that changes in OIT were not reliably associated with changes in CSS-12. Regarding covariates, the measure of general psychological distress (DASS-21, ed1) also was statistically significantly associated with CSS12(b = 0.167, SE = 0.037, p < .001, 95%CI = [0.096; 0.239], std = 0.224). Considering demographic variables as covariates, age was not significantly associated with CSS-12 (age: b = − 0.021, p = .507, 95%CI = [–0.081; 0.040], std = − 0.029) and neither with gender (ed3) (Gender: b = 1.623, p = .065, 95%CI = [–0.099; 3.344], std = 0.081). With regards to the association between covariates and the two mediator variables, OIO and OIT, were differently associated with DASS-21, age, and Gender. OIO was positively associated only with gender (emo3: b = 1.091, SE = 0.354, p = .002, 95% CI = [0.397, 1.786], std = 0.153) but not with DASS-21 (emo1) nor with age (emo2) which did not reach statistical significance., OIT was not associated with any of the covariates [DASS-21 (emt1), age (emt2), gender (emt3)]. OIO and OIT showed a statistically significant covariance (b = 1.124, SE = 0.253, p < .001, 95% CI = [0.627, 1.621], std = 0.233) indicating that these mediators were moderately correlated. Regarding the two indirect effects, the indirect pathway from IUS-R to CSS-12 through OIO was statistically significant (ind_a1b1: estimate = 0.066, SE = 0.020, p = .001, 95%CI = [0.027; 0.105], std = 0.076), suggesting that part of the effect of IUS-R on CSS-12 was mediated by OIO. The other indirect effect via OIT was not statistically significant (ind_a2b2: estimate = 0.003, SE = 0.005, p = .515, 95%CI = [–0.006; 0.012], std = 0.004). Overall, the model explained 31.4% of the variance in CSS-12, with lower explained variances for OIO (9.8%) and OIT (2.7%). With respect to model fit, the total variance explained by the model (TCD = 0.21) suggests a good fit to the observed data. In terms of effect size, this value corresponds to a correlation of r = 0.46, which represents a moderately large effect according to Cohen’s (Cohen, Jacob, 1988 ) conventional benchmarks. Discussion The present research aimed to validate (in a general population) the Italian validation of the CSS-12, to examine its construct validity and psychometric properties, and to investigate the relationship between IU and cyberchondria, considering the mediating roles of OIO and OIT. Our findings suggest that OIO is a critical mediator in the relationship between IU and the outcome variable of cyberchondria. While IU was directly associated with CSS-12, its effect was partly channeled through its significant associations with OIO. DASS-21 also emerged as an important direct statistical predictor, highlighting the important role of emotional distress. The non-significant paths involving OIT suggested that not all aspects of the measured constructs contribute equally to the outcome. These insights provide a nuanced understanding of the underlying processes and highlight potential targets for further research or intervention. Findings of Study 1 showed that the Italian version of the CSS-12 has good factorial validity with a hierarchical second-order factorial structure with four first order dimensions (i.e., excessiveness, compulsivity, reassurance, distress) and an overarching one of general cyberchondria. These results support treating cyberchondria as a single, overarching construct, allowing for a straightforward total score calculation to represent severity. Nonetheless, the specific subscales provide nuanced information when considered, offering potential avenue to explore the distinct relationships between various aspects of cyberchondria and relevant clinical outcomes or risk factors. The CSS-12 also has strong psychometric properties, good internal consistency, good convergent-divergent validity, and good test-retest reliability over time. Thus, the CSS-12 represent a valuable brief tool to measure cyberchondria in the Italian context, making it a suitable tool both for research and clinical purposes. Findings of Study 2 showed that the relationship from IU to cyberchondria is partially mediated by OIO but not by OIT. The partial mediation supported the hypothesis that elevated IU levels may increase online health information-seeking behavior, thereby amplifying cyberchondria. Moreover, IU was directly associated with cyberchondria, suggesting that IU remains a robust transdiagnostic vulnerability factor for this maladaptive behavior, consistent with findings by Bottesi et al. ( 2022 ). These findings align with prior research which identified weak direct IU-cyberchondria associations and posited indirect pathways (Starcevic et al., 2019 , 2020 ). Additionally, the association between IU and OIO is consistent with recent findings showing that individuals with both high prospective IU and, in particular, high inhibitory IU (Fergus & Spada, 2017 , 2018 ; Starcevic et al., 2019 , 2020 ) tend to engage in dysfunctional anxiety-reduction strategies to manage uncertainty, such as excessive health information seeking online, thereby heightening their risk for increased information overload, which in turn can increase cyberchondria and anxiety-related outcomes. Beyond the direct association, the relationship between IU and cyberchondria was found to be partially mediated by OIO, strengthening and maintaining cyberchondria. This finding reinforces the notion that information overload resulting from health information seeking is pivotal in understanding cyberchondria-related behaviors. This aligns with several studies which have highlighted a significant association between OIO and cyberchondria, emphasizing how excessive exposure to online health content can foster confusion, uncertainty, and compulsive searching (Hong & Kim, 2020 ; Laato et al., 2020 ; Starcevic, 2023 ; White & Horvitz, 2009 ; Zheng et al., 2023 ). Conversely, OIT did not significantly mediate the relationship between IU and cyberchondria, suggesting that its role is not straightforward but needs to be considered within a wider framework including other factors related to the appraisal process of information (e.g., valence, relevance). In this study, no evidence of a direct positive association between OIT and cyberchondria emerged, consistent with recent findings by Weng et al. (Weng et al., 2023 ). Furthermore, the limited relevance of OIT observed here aligns with findings by Laato et al. (Laato et al., 2020 ), reporting that OIT is more closely associated with other factors, such as misinformation, rather than directly with cyberchondria. In line with Laato et al. ( 2020 ) and Zheng et al. ( 2023 ), information trust is a complex construct plausibly influenced by individual and contextual factors that can take both an adaptive form, associated with the ability to select reliable sources, and a maladaptive form characterized by undermined critical evaluation and indiscriminate trust that may increase exposure to misleading digital content and contribute to cyberchondria (Laato et al., 2020 ; Zheng et al., 2023 ). If both adaptive and maladaptive forms of OIT are balanced within the sample, their overall association with OIO may be unclear, potentially reducing the statistical significance of the direct and mediated effects. Another potential explanation is that the lack of a significant role for OIT in this sample may reflect specific characteristics, such as adequate digital health literacy and a balanced level of trust and critical judgment among participants, which could limit OIT’s role in cyberchondria. This suggests that in models where OIT mediates the IU–cyberchondria relationship, additional individual factors—such as digital literacy or critical evaluation skills—may be more influential in mitigating the effects of IU. In other words, the relationship between IU, OIT, and cyberchondria may be more complex than initially hypothesized. OIT and OIO were positively associated as it is reasonable that greater OIT may lead to broader exposure to digital content and, consequently, a higher risk of OIO – and viceversa. This finding is consistent with Laato et al. (Laato et al., 2020 ; Zheng et al., 2023 ) and Zheng et al. ( 2023 ), who noted that excessive OIT can increase the likelihood of accepting inaccurate or misleading information. Such dynamics not only elevate OIO but also perpetuate compulsive information seeking, as users - confronted with unsatisfying or alarming content - continue searching for more reassuring information (Laato et al., 2020 ; Zheng et al., 2023 ). In this research the effects remained consistent even after adjusting for gender and age covariates which had no statistically significant effects, despite the sample's female majority – thus overcoming the findings by Laato et al. (Laato et al., 2020 ). General psychological distress emerged as a critical direct variable to be taken into account, underscoring the influence of emotional distress the hypothesized model. The result suggests that individuals experiencing psychological distress may engage in reassurance-seeking behavior via online platforms, up to excessive and compulsive behaviors of seeking health related information typical of cyberchondria. Limitations and strengths The present studies have some limitations that need to be considered. The sole reliance on self-report measures, despite these being well-validated, may introduce response bias. Convenience sampling may reduce representativeness, as in our case they were predominantly young participants, educated, and tech-literate which may not be reflective of the broader Italian population. The female-skewed sample could also influence psychological variables like IU or OIT, given prior reports of higher cyberchondria in women (Laato et al., 2020 ). Although the path model tested in Study 2 was informed by a cognitive-behavioral perspective suggesting potential directions of association (Fergus & Spada, 2018 ; Laato et al., 2020 ; Starcevic, 2023 ; Starcevic et al., 2020 ; Weng et al., 2023 ; Zheng et al., 2023 ), the cross-sectional design restricts the interpretation of findings to correlations rather than causal effects. To overcome these limitations, future studies should prioritize larger, gender-balanced samples to enhance generalizability and integrate direct behavioral measures (e.g., tracking time spent on health-related searches or social media scrolling) to objectively assess online information-seeking patterns and clarify IU-OIO-OIT relationships. Expanding models to include variables like digital literacy, critical evaluation skills, and health literacy - measured using validated tools such as the - could improve theoretical frameworks’ explanatory power. Future studies should employ longitudinal and experimental designs to examine the emotional impacts of online health information seeking, for example by manipulating the levels of OIO (amount and complexity of information) and OIT (source reliability) to assess their effects on cyberchondria. The present research also has important strengths. Key strengths include the strong methodology, the two-study design, the large samples, and the accurate and well-consolidated statistical techniques used. Standardized measures, rigorous methodology, and validated statistical analyses further bolstered validity and replicability of the study. As one of the few studies testing a mediation model from IU to cyberchondria via OIO and OIT, it highlights the indirect role of IU via OIO, offering insights for clinical research and practice. Understanding these psychological dynamics is important not only from a theoretical perspective but also for informing practical approaches to the development of psychological intervention strategies (Zheng, Sin, et al., 2020 ). Clinical implications From a clinical standpoint, this research suggests key directions for developing targeted interventions to prevent and treat cyberchondria. Indeed, identifying factors and antecedents of cyberchondria is crucial for devising effective strategies (Starcevic, 2023 ; Starcevic et al., 2020 ; Zheng, Sin, et al., 2020 ), but given its multifaceted nature and complex conceptualization, therapeutic approaches remain under study (Starcevic, 2023 ). A recent systematic review and meta-analysis by Schenkel et al. (Schenkel et al., 2021 ) examined 25 studies (n = 3,069 participants) investigating cyberchondria's treatment approaches and cognitive-behavioral therapy (CBT) emerged as the most evidence-supported intervention. CBT-based programs are widely supported as an effective treatment to mitigate cyberchondria by helping patients modifying dysfunctional beliefs, develop coping strategies to reduce excessive health uncertainty, information overload, and compulsive online searches (Balyan & Srivastava, 2024 ; Fergus, 2015 ; Starcevic, 2023 ). Building on findings from this study, interventions targeting IU should integrate educational and psychotherapeutic approaches addressing OIO, since it partially mediated the effect of IU on cyberchondria. Notably, only Newby and McElroy’s (Newby & McElroy, 2020 ) randomized controlled trial has empirically validated a CBT protocol adapted for cyberchondria, demonstrating symptom reduction mediated by decreased health anxiety, enhanced digital health literacy, and reducing maladaptive online health information-seeking and tolerating uncertainty (Newby & McElroy, 2020 ; Starcevic, 2023 ; Starcevic et al., 2020 ). Within the CBT framework, the Uncertainty Distress Model (Freeston et al., 2020 ) recommends clinical strategies that increase tolerance to uncertainty and reduce the perceived need for certainty through exposure, behavioral experiments, and cognitive restructuring (Bottesi et al., 2023 ). Thus, preliminary patient assessment should include dispositional traits like IU (Starcevic, 2023 ; Starcevic et al., 2020 ), information-related variables (e.g., OIO, OIT), and associated psychopathologies (e.g., health anxiety, obsessive-compulsive disorder) - as addressing these through existing psychotherapies may reduce cyberchondria (Fergus, 2015 ; Fergus & Dolan, 2014 ; Fergus & Russell, 2016 ; Fergus & Spada, 2018 ; McManus et al., 2012 ; Starcevic, 2023 ; Starcevic et al., 2020 ; Vismara et al., 2020 ). Mindfulness-Based Interventions may also be beneficial by fostering nonjudgmental awareness, helping manage distress and intrusive health-related thoughts, enhancing tolerance of uncertainty, and preventing repetitive dysfunctional thought cycles that exacerbate cyberchondria (Balyan & Srivastava, 2024 ; Fergus & Spada, 2017 ; McManus et al., 2012 ). Digital health literacy (i.e., the ability to identify reliable information and avoid misleading or alarmist content) emerges as a major protective factor for cyberchondria as it promotes critical use of online platforms, counters unrealistic expectations about search engines, highlights the harms of OIO, supports uncertainty management, and enables discernment between trustworthy and unreliable sources (Balyan & Srivastava, 2024 ; Kwon et al., 2015; Peng et al., 2021; Siebenhaar et al., 2020; Starcevic, 2023 ; Starcevic et al., 2020 ; Vismara et al., 2020 ; Weng et al., 2023 ; Zheng, Chen, et al., 2020; Zheng et al., 2023 ). Beyond user skills, platform-level interventions are vital, as the implementation of health information filters can help users find reliable content and improve search engine prioritization of institutional sources. Enhancing platform design with clear, user-friendly layouts, comprehensive and authoritatively sourced content, and simple language increases user trust and protects against maladaptive OIT and OIO (Bottesi et al., 2022 ; Hong & Kim, 2020 ; Laato et al., 2020 ; Rowley et al., 2015; Starcevic, 2023 ; Swar et al., 2017). Taking together all this information, a multidisciplinary strategic approach combining expertise from information technology, medicine, psychology, and healthcare administration is essential to effectively address cyberchondria (Starcevic & Berle, 2013 ). Conclusion This study details the validation of the Italian version of the CSS-12. 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The short-form of the Cyberchondria Severity Scale (CSS-12): Adaptation and validation of the Spanish version in young Peruvian students. PLOS ONE , 18 (10), e0292459. https://doi.org/10.1371/journal.pone.0292459 Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software , 48 , 1–36. https://doi.org/10.18637/jss.v048.i02 Schenkel, S. K., Jungmann, S. M., Gropalis, M., & Witthöft, M. (2021). Conceptualizations of Cyberchondria and Relations to the Anxiety Spectrum: Systematic Review and Meta-analysis. Journal of Medical Internet Research , 23 (11), e27835. https://doi.org/10.2196/27835 Sezer, Ö., Başoğlu, M. A., & Dağdeviren, H. N. (2022). An examination of cyberchondria’s relationship with trait anxiety and psychological well-being in women of reproductive age: A cross-sectional study. Medicine , 101 (46), e31503. https://doi.org/10.1097/MD.0000000000031503 Shihata, S., McEvoy, P. M., Mullan, B. A., & Carleton, R. N. (2016). Intolerance of uncertainty in emotional disorders: What uncertainties remain? Journal of Anxiety Disorders , 41 , 115–124. https://doi.org/10.1016/j.janxdis.2016.05.001 Starcevic, V. (2017). Cyberchondria: Challenges of Problematic Online Searches for Health-Related Information. Psychotherapy and Psychosomatics , 86 (3), 129–133. https://doi.org/10.1159/000465525 Starcevic, V. (2023). Keeping Dr. Google under control: How to prevent and manage cyberchondria. World Psychiatry , 22 (2), 233–234. https://doi.org/10.1002/wps.21076 Starcevic, V., Baggio, S., Berle, D., Khazaal, Y., & Viswasam, K. (2019). Cyberchondria and its Relationships with Related Constructs: A Network Analysis. Psychiatric Quarterly , 90 (3), 491–505. https://doi.org/10.1007/s11126-019-09640-5 Starcevic, V., & Berle, D. (2013). Cyberchondria: Towards a better understanding of excessive health-related Internet use. Expert Review of Neurotherapeutics , 13 (2), 205–213. https://doi.org/10.1586/ern.12.162 Starcevic, V., Berle, D., & Arnáez, S. (2020). Recent Insights Into Cyberchondria. Current Psychiatry Reports , 22 (11), 56. https://doi.org/10.1007/s11920-020-01179-8 Uzun, S. U., & Zencir, M. (2021). Reliability and validity study of the Turkish version of cyberchondria severity scale. Current Psychology , 40 (1), 65–71. https://doi.org/10.1007/s12144-018-0001-x Vismara, M., Caricasole, V., Starcevic, V., Cinosi, E., Dell’Osso, B., Martinotti, G., & Fineberg, N. A. (2020). Is cyberchondria a new transdiagnostic digital compulsive syndrome? A systematic review of the evidence. Comprehensive Psychiatry , 99 , 152167. https://doi.org/10.1016/j.comppsych.2020.152167 Vujić, A., Volarov, M., Latas, M., Demetrovics, Z., Kiraly, O., & Szabo, A. (2024). Are Cyberchondria and Intolerance of Uncertainty Related to Smartphone Addiction? International Journal of Mental Health and Addiction , 22 (6), 3361–3379. https://doi.org/10.1007/s11469-023-01054-6 Weng, Z., Zheng, H., & Yang, M. (2023). A Research on the Factors Influencing Cyberchondria from the Perspective of Online Health Information Seeking. 2023 10th International Conference on Dependable Systems and Their Applications (DSA) , 658–667. https://doi.org/10.1109/DSA59317.2023.00097 White, R. W., & Horvitz, E. (2009). Cyberchondria: Studies of the escalation of medical concerns in Web search. ACM Transactions on Information Systems , 27 (4), 1–37. https://doi.org/10.1145/1629096.1629101 Xu, R. H., Liang, X., & Starcevic, V. (2025). Exploring the Relationship Between Cyberchondria and Suicidal Ideation: Cross-Sectional Mediation Analysis. Journal of Medical Internet Research , 27 (1), e72414. https://doi.org/10.2196/72414 Yang, X., Luo, C., Xu, Y., He, Y., & Zhao, R. (2025). Unpacking cyberchondria: The roles of online health information seeking, health information overload, and health misperceptions. Telematics and Informatics , 97 , 102225. https://doi.org/10.1016/j.tele.2024.102225 Yang, Z., Wang, R., Chen, H., & Ding, J. (2015). Personality and Worry: The Role of Intolerance of Uncertainty. Social Behavior and Personality: An International Journal , 43 (10), 1607–1616. https://doi.org/10.2224/sbp.2015.43.10.1607 Zheng, H., Chen, X., & Fu, S. (2020). An exploration of determinants of cyberchondria: A moderated mediation analysis. Proceedings of the Association for Information Science and Technology , 57 (1), e214. https://doi.org/10.1002/pra2.214 Zheng, H., Chen, X., Jiang, S., & Sun, L. (2023). How does health information seeking from different online sources trigger cyberchondria? The roles of online information overload and information trust. Information Processing & Management , 60 (4), 103364. https://doi.org/10.1016/j.ipm.2023.103364 Zheng, H., Sin, S.-C. J., Kim, H. K., & Theng, Y.-L. (2020). Cyberchondria: A systematic review. Internet Research , 31 (2), 677–698. https://doi.org/10.1108/INTR-03-2020-0148 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7196510","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489768489,"identity":"bd9b188f-765f-4171-86ef-26df2b80bbac","order_by":0,"name":"Anna Panzeri","email":"","orcid":"","institution":"Department of General Psychology, University of Padova, Padova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Panzeri","suffix":""},{"id":489768490,"identity":"9ad5df5c-34a6-4ed5-bc3a-f75dec9b0eb2","order_by":1,"name":"Gioia Bottesi","email":"","orcid":"","institution":"Department of General Psychology, University of Padova, Padova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Gioia","middleName":"","lastName":"Bottesi","suffix":""},{"id":489768491,"identity":"b413d6ce-b37a-4331-9a74-b6eb35df1efd","order_by":2,"name":"Alessio Vieno","email":"","orcid":"","institution":"Department of Developmental and Social Psychology, University of Padova, Padova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Alessio","middleName":"","lastName":"Vieno","suffix":""},{"id":489768492,"identity":"524b297b-325c-4e23-84fa-86b1069bd96b","order_by":3,"name":"Marcantonio M. Spada","email":"","orcid":"","institution":"School of Applied Sciences, London South Bank University, London, UK","correspondingAuthor":false,"prefix":"","firstName":"Marcantonio","middleName":"M.","lastName":"Spada","suffix":""},{"id":489768493,"identity":"ac7042d8-4bdc-4913-8587-106557416b3a","order_by":4,"name":"Marta Ghisi","email":"","orcid":"","institution":"Department of General Psychology, University of Padova, Padova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Ghisi","suffix":""},{"id":489768494,"identity":"d4c2a914-4e65-4835-b2ce-1d23bde07220","order_by":5,"name":"Veronica Fin","email":"","orcid":"","institution":"Department of General Psychology, University of Padova, Padova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Veronica","middleName":"","lastName":"Fin","suffix":""},{"id":489768495,"identity":"e1eb060b-4ef4-41a8-ac11-8ff67b2c5010","order_by":6,"name":"Claudia Marino","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACdiDmMYCwDzAw2DAwMDMwSDAY4NHCjKolDaYFjx6wFgT3MJiUYMBjDX8z8zOJNwV2DLrtZx8eLqg4n7idnffgDYaCPzi1SBxmM5OcY5DMYHYm3eDwjDO3E3c28yVb4HOYATODmTQPiDyQxnCYt+124obDPGZ4/WLAzP4NqKWewez8M5CWc8Ro4QHZcpjB7AbYlgOEtUgc5im2nGNwnMfsBtAWnjPJxhsOA/2SYGCMUwt/e/vGG2/+VMuZnU9j/sxTYSe74fzZgzc+/JHDqQUGeFDZCQQ14NY+CkbBKBgFo4CBAQDU9kqX97ad0wAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Developmental and Social Psychology, University of Padova, Padova, Italy","correspondingAuthor":true,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Marino","suffix":""}],"badges":[],"createdAt":"2025-07-23 12:45:25","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7196510/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7196510/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87470107,"identity":"12ad02c7-270d-47bf-9849-594c12ab3c78","added_by":"auto","created_at":"2025-07-24 08:28:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical representation of the second-order hierarchical model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7196510/v1/00eee65d58bc7248664e0e85.png"},{"id":87470105,"identity":"fa190ee8-666a-4897-97a3-b5bbd88bb7e9","added_by":"auto","created_at":"2025-07-24 08:28:24","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":270709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraph of the mediation model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7196510/v1/eefe1e60ab0c6bc17a65947c.jpeg"},{"id":87472930,"identity":"69dcc977-7d2d-4367-a5b7-1177e9dacd6b","added_by":"auto","created_at":"2025-07-24 08:38:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1646156,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7196510/v1/360234f8-7edf-4f68-9958-074945d6ef6c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eFrom Intolerance of Uncertainty to Cyberchondria through Information Overload: The Italian Validation of the Cyberchondria Severity Scale 12 (CSS-12) and a Mediation Model\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe term \"cyberchondria\" comes from combining the words \"cyber\", a prefix referring to cyberspace, the internet, or computers, and \"hypochondria\", referring to people who are preoccupied with fears of having or acquiring a serious illness. In psychology, the term \u003cem\u003ecyberchondria\u003c/em\u003e describes the rise in anxiety resulting from engaging in online health information searches (Starcevic, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; White \u0026amp; Horvitz, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCyberchondria significantly affects individuals on psychological, cognitive, and behavioral levels, often resulting in functional impairment. Those affected may suspend normal activities (such as work or school) to focus obsessively on searching for health information online (Balyan \u0026amp; Srivastava, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Doherty-Torstrick et al., 2016; Mathes et al., 2018). While some individuals with cyberchondria report satisfaction in various life areas, research shows that they experience psychosocial impairment and a reduced quality of life, becoming trapped in a cycle of anxiety, fear, and loneliness (Balyan \u0026amp; Srivastava, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Doherty-Torstrick et al., 2016; Mathes et al., 2018; Vismara et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cyberchondria can lead to premature and often inaccurate health conclusions, causing unnecessary worry, emotional distress, and increased, sometimes unwarranted, medical consultations\u0026mdash;placing additional strain and costs on healthcare systems (Mathes et al., 2018).\u003c/p\u003e\u003cp\u003eOver the last few years, a growing body of research has focused on this phenomenon with the twofold aim of identifying the psychological mechanisms and correlates underlying cyberchondria (e.g., Yang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and testing the validity of a scale designed to assess it in several cultural contexts (Arn\u0026aacute;ez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hallit et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; McElroy \u0026amp; Shevlin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Robles-Mari\u0026ntilde;os et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Uzun \u0026amp; Zencir, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the extant knowledge is limited and support for the short form of the Cyberchondria Severity Scale (CSS-12; McElroy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is needed. Therefore, the aim of the current study is twofold: providing the Italian validation of the CSS-12 (Study 1) and modeling the mediating role of online information overload (OIO) and online information trust (OIT) in the association between intolerance of uncertainty (IU) and cyberchondria (Study 2).\u003c/p\u003e\u003cp\u003eCyberchondria manifests as a \u0026ldquo;complex combination of cognitive, emotional, and behavioral components\u0026rdquo; (Balyan \u0026amp; Srivastava, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, p. 781) and encompasses both a behavioral component consisting in the excessive searching of health-related content online and an associated emotional response of heightened health-related worry and anxiety. In a nutshell, cyberchondria is the compulsive search for health-related information online aimed at reducing health-related anxiety. While cyberchondria shares similarities with related constructs like health anxiety and hypochondria, it constitutes a distinct clinical phenomenon (Starcevic, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Cyberchondria not only refers to the excessive seeking of medical information online, but also to the compulsive nature of such behavior, which persists despite the distress it generates and its interference with daily functioning (Starcevic \u0026amp; Berle, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Indeed, a central characteristic of cyberchondria is the tendency toward escalation and excessiveness, with individuals devoting an inordinate and progressively increasing amount of time to searching for health-related information (Starcevic, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; White \u0026amp; Horvitz, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral studies have identified potential correlates of cyberchondria and its adverse sequelae, including health anxiety symptoms (Fergus, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Norr et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), reduced quality of life, increased use of healthcare services (Mathes, Norr, Allan, Albanese, \u0026amp; Schmidt, 2018), generalized problematic internet use (Fergus \u0026amp; Spada, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and obsessive\u0026ndash;compulsive tendencies (e.g., Fergus \u0026amp; Russell, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Research has largely focused on analyzing, identifying, and understanding the risk factors and antecedents that predispose to dysfunctional online behaviors up the development of cyberchondria. Fergus (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Starcevic and Berle (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined dispositional and psychological variables, suggesting that difficulties in managing anxiety or tolerating uncertainty may play a significant role in the cyberchondria onset. Several studies (X. Yang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2025\u003c/span\u003e)indicated that compulsive web searches may contribute to form a dysfunctional maintenance cycle.\u003c/p\u003e\u003cp\u003eAmong the risk-factors related to cyberchondria, Intolerance of Uncertainty (IU) appears to have a prominent role (Bottesi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vujić et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). IU is the dispositional incapacity to endure the aversive response triggered by the perception of lacking salient, key, or sufficient information, accompanied by the subjective experience of uncertainty. In other words, IU is a psychological construct that reflects an individual\u0026rsquo;s tendency to perceive uncertain or ambiguous situations as unacceptable, stressful, or threatening (Carleton, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such difficulty in tolerating ambiguous, unpredictable, or unresolved situations is often associated with emotional distress and dysfunctional coping strategies aiming to manage it (Carleton, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The literature has highlighted IU as a transdiagnostic factor associated with various anxiety and mood disorders (Bajcar \u0026amp; Babiak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bottesi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shihata et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as with cyberchondria, which tends to strengthen and maintain it (Bottesi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Several experimental findings have confirmed that individuals with high levels of IU are more likely to engage in dysfunctional patterns of health information seeking, leading to a progressive escalation of anxiety rather than its reduction (Fergus, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zheng, Chen, et al., 2020). Although the link between IU and cyberchondria has been largely documented (Bajcar \u0026amp; Babiak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bottesi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fergus, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fergus \u0026amp; Spada, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vismara et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng, Chen, et al., 2020; Zheng, Sin, et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the underlying mechanisms of this relationship remain not fully understood with some researchers suggesting that the relationship between IU and cyberchondria could be mediated by cognitive and informational variables that contribute to the process of online health information seeking (Fergus \u0026amp; Spada, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe role of online information overload and online information trust\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRecently, two constructs relevant to digital information management within this process have been identified: online information overload (OIO), referring to the cognitive burden resulting from excessive online content; and online information trust (OIT), referring to users\u0026rsquo; trust in online health information (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Weng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). OIO may represent a potential mediating variable in the relationship between IU and cyberchondria in that, in the context of online health information seeking, IU is linked to the need to obtain certainty regarding one\u0026rsquo;s health status. Some studies (Bajcar \u0026amp; Babiak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have shown that when confronted with a high volume of information, individuals with elevated IU may struggle to organize contents, resulting in a paradoxical increase in uncertainty rather than its reduction. Additionally, Starcevic (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) suggested that OIO may lead some individuals to experience a \u0026ldquo;loss of control during the search\u0026rdquo; (p. 233) for health information online. OIT may also have a role in the relationship between IU and cyberchondria. However, the literature has yet to clarify whether high levels of OIT help reduce uncertainty and promote more informed processing of health information (Weng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), or conversely, whether they increase the risk of cyberchondria by encouraging acritical acceptance of alarmist and unreliable content (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition, demographic factors such as gender and age, and general psychological distress significantly influence the development and severity of cyberchondria. Studies consistently show that women are more likely to experience higher levels of cyberchondria compared to men, possibly because of gender differences in health anxiety and information-seeking behaviors (Maftei \u0026amp; Holman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sezer et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Younger individuals tend to engage more in online health searches due to their digital fluency and comfort with diverse online tools, but older adults may experience greater psychological impacts from cyberchondria due to factors like resource depletion, social isolation, and compensatory internet use (Durak Batıg\u0026uuml;n et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hansen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maftei \u0026amp; Holman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). General psychological distress, encompassing symptoms of anxiety and depression, plays a key role in exacerbating cyberchondria (Fang et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These elements interact dynamically with individual characteristics, highlighting the complexity of cyberchondria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch gaps\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBased on the previously reviewed literature, there remains a notable gap in research specifically aimed at uncovering the psychological processes that may foster and maintain cyberchondria, considering the role of IU, OIO and OIT. Despite several findings in the literature highlighting the associations among these constructs, no research study to date has examined these relationships simultaneously within a single confirmatory path analysis model. To the authors\u0026rsquo; knowledge, only one study by Zheng et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) considered the types of online information sources and through a path model showed that using online search engines to seek health information increases OIO, while obtaining health information through social media platforms and specialized health websites enhances OIT. Furthermore, a significant negative association between OIO and OIT emerged and both of them increased cyberchondria. However, no mediation effects were tested and IU was not considered.\u003c/p\u003e\u003cp\u003eAt the same time, a validation and study of the psychometric properties of the Cyberchondria Severity Scale-12 (CSS-12) \u0026ndash; currently considered the gold standard tool to measure cyberchondria (Arn\u0026aacute;ez et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hallit et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Marino et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McElroy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), CSS-12 in the Italian context is lacking. To date, the CSS is the most widely used self-report instrument for measuring cyberchondria. There are two versions of the scale: the original 33-item version (Marino et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; McElroy \u0026amp; Shevlin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and a shorter, more flexible version, the CSS-12 (McElroy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is quicker to administer yet equally precise and reliable, if not superior. In fact, the CSS-12 excludes a dimension (i.e., medical mistrust) that showed weak correlations with the other factors. The retained dimensions are excessiveness, compulsiveness, reassurance seeking, and distress. However, despite its growing use, a validated Italian version of the CSS-12 is not yet available, despite the well-known need for validated measures in psychological sciences (Flake et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAim of the present research\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn light of this theoretical background, the present research had a twofold aim addressed by two consequential Studies. First, Study 1 aimed to provide the Italian validation of the CSS-12, to study its construct validity and psychometric properties. In line with other studies on the CSS-12 (McElroy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), also its Italian version is expected to show strong structural validity, good convergent validity, and good test-retest reliability.\u003c/p\u003e\u003cp\u003eSecond, using the CSS-12, Study 2 aimed to provide empirical insights into the relationship between IU and cyberchondria, highlighting the mediating mechanisms through which OIO and OIT might contribute to the management of IU in the health domain, potentially leading to increased levels of cyberchondria in the general population. In line with previous evidence briefly reviewed above, all these constructs are expected to be positively associated. In particular, OIT and especially OIO are expected to partially mediate the influence of IU to cyberchondria. Specifically, it is hypothesized that OIT may function as a mechanism through which IU contributes to increased levels of cyberchondria by fostering less critical processing of online health information, potentially reinforcing compulsive search behaviors (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, it is hypothesized that higher levels of IU would lead to greater engagement in online search behaviors, thereby increasing exposure to OIO, which in turn would contribute to higher levels of cyberchondria (Bajcar \u0026amp; Babiak, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Indeed, information overload directly contributes to the unpleasant emotional states (worry, anxiety, distress) linked to cyberchondria. However, trust in online health information does not inherently mitigate or worsen these emotional outcomes or cyberchondria itself. Trust operates within a broader cognitive appraisal process that includes unexamined factors like information valence (positive/negative), source credibility, and perceived relevance. Thus, while overload fuels distress, trust alone cannot predict emotional impacts without considering these additional contextual variables.\u003c/p\u003e\u003cp\u003eAll these relationships are expected to hold even when controlling these effects for the covariates of age, gender and general psychological distress which are known to be associated with cyberchondria (e.g., Bottesi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Study 1. Validation of the Italian version of the CSS-12","content":"\u003cp\u003eStudy 1 aimed at providing the validation of the Italian version of the CSS-12, examining its validity in its various facets, structural and convergent-divergent, and test-retest reliability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA cross-sectional research design was used. Participants were enrolled online and in person through advertisements, and convenience snowball sampling was used.\u003c/p\u003e\u003cp\u003eThe inclusion criteria were: being above 18 years old, being proficient in Italian language, and providing informed consent. Exclusion criteria: reporting severe medical condition (e.g., cancer, multiple sclerosis); not completing the CSS-12. Participants completed an online self-report survey administered via Qualtrics. Being part of a larger project, part of this data was used in previous studies (blinded). The Ethical committee of the University of [blinded for review], psychological area, approved this study (protocol number: 194-a).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA socio-demographic form collected general information about participants\u0026rsquo; age, gender, nationality, education, civil status, occupational status.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCyberchondria Severity Scale, 12 items\u003c/em\u003e (CSS-12; McElroy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) comprises four subscales, namely, \u0026ldquo;compulsion\u0026rdquo; (CM), \u0026ldquo;distress\u0026rdquo; DS, \u0026ldquo;excessiveness\u0026rdquo; (EX), and \u0026ldquo;reassurance\u0026rdquo; (RE). These dimensions provide an overall score for the cyberchondria construct, higher scores indicate higher levels of cyberchondria. As the same 12 items from McElroy\u0026rsquo;s short version were selected from the Italian CSS, no retranslation was necessary. Sample items included \u003cem\u003e\u0026ldquo;If I notice an unexplained bodily sensation I will search for it on the internet\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;Researching symptoms or perceived medical conditions online interrupts my work (e.g. writing emails, working on word documents or spreadsheets\u0026rdquo;\u003c/em\u003e. In this study, the CSS-12 demonstrated high internal consistency, both for the general total score (McDonald\u0026rsquo;s Omega of .91; Cronbach's alpha of 0.89) and the subscales (CM: omega\u0026thinsp;=\u0026thinsp;.87, alpha\u0026thinsp;=\u0026thinsp;.87; DS: omega\u0026thinsp;=\u0026thinsp;.85, alpha\u0026thinsp;=\u0026thinsp;.84; EX: omega\u0026thinsp;=\u0026thinsp;.71, alpha\u0026thinsp;=\u0026thinsp;.70; RE: omega\u0026thinsp;=\u0026thinsp;.67, alpha\u0026thinsp;=\u0026thinsp;.67).\u003c/p\u003e\u003cp\u003e\u003cem\u003eDepression Anxiety Stress Scales-21\u003c/em\u003e (DASS-21; Bottesi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) is a self-report measure designed to assess general psychological distress across three dimensions: depression, anxiety, and stress. It consists of 21 items, with seven items per subscale, and participants rate each item on a 4-point scale ranging from 0 (\"did not apply to me at all\") to 3 (\"applied to me very much or most of the time\"). Sample items included \u0026ldquo;\u003cem\u003eI found it difficult to relax\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eI felt down-hearted and blue\u003c/em\u003e\u0026rdquo;. The Italian DASS-21 demonstrated strong psychometric properties, including good internal consistency and temporal stability, supporting its validity for use in both community and clinical samples. In this study, DASS-21 demonstrated high internal consistency, with a McDonald\u0026rsquo;s Omega of .95 and a Cronbach's alpha of 0.95.\u003c/p\u003e\u003cp\u003e\u003cem\u003eGeneralized Problematic Internet Use Scale 2\u003c/em\u003e (GPIUS2; Italian adaptation by Fioravanti et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) consists of 15 items designed to evaluate problematic Internet use. It measures five dimensions: preference for online social interactions, mood regulation, compulsive use, cognitive preoccupation, and negative consequences associated with Internet use. Participants respond to each item using an 8-point Likert scale ranging from 1 (\"definitely disagree\") to 8 (\"definitely agree\"). Sample items included \u0026ldquo;\u003cem\u003eI have difficulty controlling the amount of time I spend online\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eWhen offline, I have a hard time trying to resist the urge to go online\u003c/em\u003e\u0026rdquo;. In the current study, the GPIUS2 demonstrated high internal consistency, with a McDonald\u0026rsquo;s Omega of .91 and a Cronbach's alpha of 0.89.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHealth Anxiety Questionnaire\u003c/em\u003e (HAQ; Italian adaptation by Melli et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), originally developed by Lucock \u0026amp; Morley (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), includes 21 items to evaluate health anxiety. Participants respond to each item using a 4-point scale, ranging from 1 (\"never or rarely\") to 4 (\"almost always\"). Sample items included \u0026ldquo;\u003cem\u003eDo you ever worry about your health?\u003c/em\u003e\u0026rdquo; and \u0026ldquo;\u003cem\u003eDoes the thought of a serious illness ever scare you?\u003c/em\u003e\u0026rdquo;. In this study, the HAQ demonstrated excellent internal consistency, with a McDonald\u0026rsquo;s Omega of .94 and a Cronbach's alpha of 0.93.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIntolerance of Uncertainty Scale Revised\u003c/em\u003e (IUS-R; Bottesi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is a widely used self-report tool designed to capture the extent to which individuals find uncertain situations distressing and difficult to tolerate. It consists of 12 items rated on a 5-point Likert scale, ranging from \"not at all characteristic of me\" to \"entirely characteristic of me.\" The scale is unidimensional, with a single total score representing general IU. The IUS-R has demonstrated excellent psychometric properties, including high internal consistency, with Cronbach\u0026rsquo;s alpha values typically exceeding 0.90. In this study the internal consistency was optimal, with Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;.89 and McDonald omega\u0026thinsp;=\u0026thinsp;.91\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to describe sample socio-demographic characteristics and the psychological variables. Correlations were used to evaluate the associations among items of the CSS. Confirmatory factor analysis was used to test the factorial structure of the CSS-12. Considering the items\u0026rsquo; distribution, the CFA model was estimated using the diagonally-weighted least squares estimator (DWLS) (Brown, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Following the guidelines of Hu and Bentler (Hu \u0026amp; Bentler, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the measurement model fit to the observed data was evaluated based on these criteria: Comparative Fit Index (CFI), both requiring values greater than 0.95 for a good fit, the Root Mean Square Error of Approximation (RMSEA), which should be less than 0.05 for a good fit, and the Standardized Root Mean Square Residual (SRMR), which should be below 0.08 for a good fit. Additionally, the χ2 statistic was reported. McDonald omega and Cronbach\u0026rsquo;s alpha evaluated the scale internal consistency. Correlations explored the convergent-divergent validity of the scale. The statistical analysis was conducted using the R software (R Core Team, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the \"lavaan\" package (Rosseel, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults of Study 1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe sample of Study 1 consisted of 2411 participants (54.65% females) who provided complete responses to the CSS-12. Age ranged from 18 to 77 years, with a mean of 28.27 and a SD of 9.88. Participants were single (71.41%), in a relationship or married (25.15%), separated or divorced (2.84%), or widow (0.60%). About education, most participants had a high school diploma (40.32%), a bachelor's degree (27.36%), a master\u0026rsquo;s degree (23.91%), a PhD or a post-degree specialization (6.04%), or middle school license (2.22%). Regarding the occupational status, most participants were employed (39.26%), university students (36.55%), unemployed (4.91%), working students (2.89%), housekeepers (2.45%), self-employed (2.10%), retired (1.84%), or other (9.99%). All participants were fluent in Italian and almost all declared Italian nationality (98.33%).\u003c/p\u003e\u003cp\u003e\u003cb\u003eItem descriptive statistics and correlations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics of the items of the CSS-12 and their correlations. In some cases, the values of skewness and kurtosis of the CSS-12 fell beyond the desired thresholds (i.e., |2|), thus an estimator for categorical items is preferable and DWLS was chosen. The bivariate correlations among the CSS-12 items were all positively associated and revealed no problematic associations, as the highest was 0.67 between DS13 and DS12 and 0.65 between CM6 and CM4. The lowest correlation was between item RE27 and EX17 (rho\u0026thinsp;=\u0026thinsp;0.14).\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\u003eDescriptive statistics and correlations between items\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eDescriptives\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"11\" nameend=\"c16\" namest=\"c6\"\u003e\u003cp\u003eCorrelations\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eskewness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ekurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. item cm#4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. item cm#6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. item cm#7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. item ds#12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. item ds#13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. item ds#14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7. item ex#17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. item ex#19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9. item ex#23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10. item re#27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11. item re#28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12. item re#30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNote. M\u003c/em\u003e and \u003cem\u003eSD\u003c/em\u003e are used to represent mean and standard deviation, respectively. All \u003cem\u003ep\u003c/em\u003e are \u0026lt;\u0026thinsp;.001. N\u0026thinsp;=\u0026thinsp;2411.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFactorial structure of the CSS-12\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA hierarchical factor structure was specified, with four first-order factors and one general factor of higher order (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The hierarchical model provided a good fit to the data as shown by the following indices (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): X2\u0026thinsp;=\u0026thinsp;407.871, df\u0026thinsp;=\u0026thinsp;50, CFI\u0026thinsp;=\u0026thinsp;.995, RMSEA\u0026thinsp;=\u0026thinsp;.054, RMSEA 95%CI[.050, .059], SRMR\u0026thinsp;=\u0026thinsp;.046. Moreover, the item loadings of the model were inspected and were all high and statistically significant. There were no negative variances.\u003c/p\u003e\u003cp\u003e\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\u003eFit indices of hierarchical model and its items\u0026rsquo; factor loadings\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHierarchical model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e407.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[.050, .059]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoadings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompulsivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExcessiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReassurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGeneral factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eR2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem cm#4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem cm#6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem cm#7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem ds#12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem ds#13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem ds#14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem ex#17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem ex#19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.605\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem ex#23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem re#27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem re#28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.557\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eitem re#30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompulsivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcessiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.954\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReassurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: X\u003csup\u003e2\u003c/sup\u003e: chi-square; df: degrees of freedom; CFI: comparative fit index; RMSEA 95%CI: root mean square error of approximation and its 95% confidence interval; SRMR\u0026thinsp;=\u0026thinsp;standardized root mean square residual.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eInternal consistency of CSS-12\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe internal consistency for the overall scale and the subscales was good, as measured with Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s Omega (general CSS: alpha\u0026thinsp;=\u0026thinsp;.88, omega\u0026thinsp;=\u0026thinsp;.91; CM: alpha\u0026thinsp;=\u0026thinsp;.86, omega\u0026thinsp;=\u0026thinsp;.87; DS: alpha\u0026thinsp;=\u0026thinsp;.84, omega\u0026thinsp;=\u0026thinsp;.85; EX: alpha\u0026thinsp;=\u0026thinsp;.69, omega\u0026thinsp;=\u0026thinsp;.71; RE: alpha\u0026thinsp;=\u0026thinsp;.66, omega\u0026thinsp;=\u0026thinsp;.67).\u003c/p\u003e\u003cp\u003e\u003cb\u003eTest-retest reliability\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn a sample of 280 participants (males\u0026thinsp;=\u0026thinsp;17.14%, females\u0026thinsp;=\u0026thinsp;82.86%; mean age 30.03, SD 11.90), the CSS-12 was readministered after six months to evaluate test-retest reliability through the Pearson\u0026rsquo; r coefficient that was equal to .77 with a satisfying 95%CI[0.70; 0.83], meaning that the test-retest reliability of the Italian version of the CSS-12 was good. This reflects the reliability of the Italian version of the CSS-12 over time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelations between CSS-12 subscales\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe associations of the total score of the CSS-12 and its subscales were explored through observed Spearman correlation. As expected, the total score of the CSS-12 was strongly associated with excessiveness (rho\u0026thinsp;=\u0026thinsp;.85), with DS (rho\u0026thinsp;=\u0026thinsp;.83), and CM (rho\u0026thinsp;=\u0026thinsp;.74). Among the subscales, the highest association was between EX and DS (rho\u0026thinsp;=\u0026thinsp;.60), followed by EX and CM (rho\u0026thinsp;=\u0026thinsp;.59), and EX and RE (rho\u0026thinsp;=\u0026thinsp;.47).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConvergent-divergent validity of CSS-12\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConvergent-divergent validity of the CSS-12 was evaluated through observed Spearman correlations with the total scores of other scales. Regarding convergent validity, the total CSS-12 score was strongly associated with HAQ (rho\u0026thinsp;=\u0026thinsp;.65). Still, regarding discriminant validity, the CSS-12 total score was positive and moderate with the related constructs measured by IUS-R (rho\u0026thinsp;=\u0026thinsp;.33) and DASS-21 (rho\u0026thinsp;=\u0026thinsp;.33). About divergent validity, the CSS-12 shoved a small-moderate positive association with GPIUS2 measuring problematic internet use (rho\u0026thinsp;=\u0026thinsp;.28, with 95% CI[.24; .32]).\u003c/p\u003e"},{"header":"Study 2. Mediation model","content":"\u003cp\u003eStudy 2 aimed at testing a model in which IU is the independent variable, cyberchondria is the dependent variable, and OIO and OIT are mediators in the relationships between IU and cyberchondria in a sample from the general Italian population.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe same criteria of Study 1 were used to recruit the participants in Study 2, using a cross-sectional design, and the same inclusion and exclusion criteria. Convenience snowball sampling was used and the survey was administered online. The Ethical committee of the [blinded for review], psychological area, approved this study (protocol number: 194-a).\u003c/p\u003e\u003cp\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe following self-report tools were administered through an online Qualtrics survey. A socio-demographic survey gathered general information about participants, including age, gender, nationality, education level, civil status, and occupational status.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eCyberchondria Severity Scale\u003c/em\u003e (CSS-12; McElroy et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Italian version used in Study 1, see the dedicated measures section). In this study, the omega was .93 and alpha was .90.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eDepression Anxiety Stress Scales-21\u003c/em\u003e (DASS-21;Bottesi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Italian version used in Study 1, see the dedicated measures section). In the current study, the DASS-21 showed optimal internal consistency (omega\u0026thinsp;=\u0026thinsp;.95, alpha\u0026thinsp;=\u0026thinsp;.94).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOnline Information Overload\u003c/strong\u003e\u003cp\u003eit consists in three items adapted from Laato et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) to assess perceived online information overload. Respondents rated their agreement with three statements on a 5-point Likert scale. Sample items included \u003cem\u003e\u0026ldquo;I am often distracted by the excessive amount of health information on the internet\u0026rdquo;\u003c/em\u003e and \u003cem\u003e\u0026ldquo;I receive too much online health information to form a coherent picture of what\u0026rsquo;s happening.\u0026rdquo;\u003c/em\u003e This scale exhibited satisfactory reliability in the current study, with a McDonald\u0026rsquo;s omega of .82 Cronbach's alpha of 0.80.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOnline Information Trust\u003c/strong\u003e\u003cp\u003eit consists of four items adapted from Griffin et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) to measure trust in online information. Sample items included statements such as \u0026ldquo;\u003cem\u003eI find most online health information is useful\u0026rdquo;\u003c/em\u003e and \u0026ldquo;\u003cem\u003eI find most online health information is believable\u003c/em\u003e.\u0026rdquo; This scale has been employed in prior research to evaluate trust in specific information sources. For instance, Yang (Z. Yang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) utilized it to assess the perceived trustworthiness and validity of news media disseminating information about the H1N1 vaccine. In the current study, the scale demonstrated satisfactory internal consistency, with omega\u0026thinsp;=\u0026thinsp;.83 and alpha\u0026thinsp;=\u0026thinsp;.73.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eIntolerance of Uncertainty Scale-Revised\u003c/em\u003e (IUS-R; Bottesi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Italian version used in Study 1, see the dedicated measures section) is a commonly used self-report instrument that evaluates how distressing and difficult individuals find uncertain situations to tolerate. It includes 12 items rated on a 5-point Likert scale, from \"not at all characteristic of me\" to \"entirely characteristic of me.\" The scale is typically treated as unidimensional, with a single total score reflecting overall intolerance of uncertainty. The IUS-R has demonstrated strong psychometric qualities, notably high internal consistency with Cronbach\u0026rsquo;s alpha values often above 0.90, and solid construct validity evidenced by significant correlations with anxiety, worry, and depression measures. In Study, the CSS-12 internal consistency was optimal, with Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;.90 and McDonald\u0026rsquo;s omega\u0026thinsp;=\u0026thinsp;.93.\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/p\u003e\u003cp\u003eInitially, descriptive statistics and zero-order correlations were computed. Next, a path analysis model with observed variables was used to test the relationship pattern specified according to the theoretical framework (See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Each construct in the model was represented by a single observed score. The maximum likelihood method estimator was employed, and bias-corrected bootstrap confidence intervals with 5000 iterations were used to calculate indirect effects. These effects were deemed significant if their 95% confidence intervals did not include zero. To assess model fit, we examined both the explained variance (R\u0026sup2;) for each endogenous variable and the Total Coefficient of Determination (TCD) (Bollen, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; J\u0026ouml;reskog \u0026amp; S\u0026ouml;rbom, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The TCD, an established fit measure for path analysis models (a type of structural equation modeling using observed variables), quantifies the combined influence of all predictors on dependent variables. Higher TCD values reflect greater overall explanatory power of the model, capturing the proportion of variance jointly explained across all outcomes. In the tested model, the CSS-12 served as outcome variables, while IUS-R acted as independent variable, and OIO and OIT were mediators in such association. Age, gender, and general psychological distress were included as control variables in the model, with effects on OIO, OIT, and CSS-12. The R software (R Core Team, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was used with the lavaan package (Rosseel, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults of Study 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBelow are shown the results of Study 2, including descriptive statistics of the sample, correlations among variables, and the hypothesized mediation model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample descriptive statistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe sample consisted in 381 participants (22.05% males, 77.95% females) with mean age 29.92 and SD 11.91. Most had a bachelor\u0026rsquo;s degree (36.48%), a high-school license (32.28%), a master\u0026rsquo;s degree (22.83%), a PhD or specialization (6.30%), or middle-school license (2.10%). Regarding the civil status, most participants were single (71.39%), in a relationship or married (24.41%), separated or divorced (2.89%), or widow (1.31%). Regarding the occupational status, most participants were employed (26.25%), university students (36.55%), unemployed (4.72%), working students (11.01%), housekeepers (0.52%), self-employed (6.56%), or other (7.35%). Almost all participants declared Italian nationality (98.43%). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the descriptive statistics of variables in Study 2 and their correlations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCorrelations among the total scores of the continuous variables in Study 2 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were used to evaluate the variables associations. The variables we not too highly correlated, the highest correlation was between IUS-R and DASS-21 (r\u0026thinsp;=\u0026thinsp;.47) while the lowest was between age and OIT (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.04).\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\u003eDescriptive statistics and correlations with confidence intervals between constructs in Study 2\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\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\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eminimum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003emaximum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Intolerance of uncertainty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Online Information Overload\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.26***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003cp\u003e[.17, .35]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Online Information Trust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.15**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.27***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003cp\u003e[.05, .25]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[.17, .36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Cyberchondria\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.33***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.47***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.18***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003cp\u003e[.23, .41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[.38, .54]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[.08, .27]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.15**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\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\u003cp\u003e[-.32, \u0026minus;\u0026thinsp;.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[-.18, .01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[-.15, .05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[-.24, \u0026minus;\u0026thinsp;.05]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. General psychological distress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.47***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.20***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.37***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.21***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003cp\u003e[.39, .55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e[.10, .29]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[.02, .22]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e[.28, .45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[-.31, \u0026minus;\u0026thinsp;.12]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote. M\u003c/em\u003e and \u003cem\u003eSD\u003c/em\u003e are used to represent mean and standard deviation, respectively. Values in square brackets indicate the 95% confidence interval for each correlation. The confidence interval is a plausible range of population correlations that could have caused the sample correlation. * indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05. ** indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01. *** indicates \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;381\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMediation model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of the parallel mediation model that is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e where dashed lines represent non-statistically significant paths and continuous lines the statistically significant ones. All coefficients in the figure are standardized.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the parallel mediation model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003estd\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCSS-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(b1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\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\u003e1.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(b2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.492\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(c)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.041*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(ed1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(ed2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(ed3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOIO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003estd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(a1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(emo1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(emo2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(emo3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.002**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e~\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003estd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUS-R\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(a2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.044*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDASS-21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(emt1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(emt2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(emt3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCovariances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003estd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOIO\u0026thinsp;~\u0026thinsp;~\u0026thinsp;OIT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(cor)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003estd\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIU \u0026loz;OIO\u0026loz; CSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(a1*b1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.001**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIU \u0026loz;OIT\u0026loz; CSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(a2*b2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNote\u003c/b\u003e: CSS-12: cyberchondria severity scale 12 items; OIO: online information overload; OIT: online information trust; IUS-R: intolerance of uncertainty scale revised; DASS-21: depression anxiety stress scale 21 items; est: estimate; se: standard error; p: p-value; 95%CI: confidence interval at 95%; std: standardized effect.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results of the mediation model indicated a complex pattern of direct and indirect relationships among the predictors and the outcome variable of cyberchondria measured with the CSS-12. The independent variable IUS (c) yielded a modest but significant positive effect on CSS-12 (b\u0026thinsp;=\u0026thinsp;0.089, SE\u0026thinsp;=\u0026thinsp;0.043, p\u0026thinsp;=\u0026thinsp;.041, 95%CI = [0.004; 0.173], std\u0026thinsp;=\u0026thinsp;0.102). This indicates that higher levels of IU are associated with higher scores in CSS-12. About mediators, IUS was associated with OIO with a positive and statistically significant effect (a1: b\u0026thinsp;=\u0026thinsp;0.064, SE\u0026thinsp;=\u0026thinsp;0.017, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95%CI = [0.030; 0.098], std\u0026thinsp;=\u0026thinsp;0.206). Moreover, OIO was positively associated with CSS 12 as the unstandardized coefficient (b1) is 1.039 (SE\u0026thinsp;=\u0026thinsp;0.129, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) with a 95%CI of [0.786; 1.292] and a standardized coefficient of 0.370. This strong and significant positive effect suggests that as OIO increases, CSS-12 also increases considerably. In contrast, IUS was positively associated with OIT showing a statistically significant, albeit smaller, effect (a2: b\u0026thinsp;=\u0026thinsp;0.021, SE\u0026thinsp;=\u0026thinsp;0.011, p\u0026thinsp;=\u0026thinsp;.044, 95% CI = [0.001, 0.042], std\u0026thinsp;=\u0026thinsp;0.117). Moreover, OIT (b2) showed a non-significant effect (b\u0026thinsp;=\u0026thinsp;0.145, SE\u0026thinsp;=\u0026thinsp;0.211, p\u0026thinsp;=\u0026thinsp;.492, 95%CI = [\u0026ndash;0.269; 0.559], std\u0026thinsp;=\u0026thinsp;0.030), implying that changes in OIT were not reliably associated with changes in CSS-12.\u003c/p\u003e\u003cp\u003eRegarding covariates, the measure of general psychological distress (DASS-21, ed1) also was statistically significantly associated with CSS12(b\u0026thinsp;=\u0026thinsp;0.167, SE\u0026thinsp;=\u0026thinsp;0.037, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95%CI = [0.096; 0.239], std\u0026thinsp;=\u0026thinsp;0.224). Considering demographic variables as covariates, age was not significantly associated with CSS-12 (age: b = \u0026minus;\u0026thinsp;0.021, p\u0026thinsp;=\u0026thinsp;.507, 95%CI = [\u0026ndash;0.081; 0.040], std = \u0026minus;\u0026thinsp;0.029) and neither with gender (ed3) (Gender: b\u0026thinsp;=\u0026thinsp;1.623, p\u0026thinsp;=\u0026thinsp;.065, 95%CI = [\u0026ndash;0.099; 3.344], std\u0026thinsp;=\u0026thinsp;0.081).\u003c/p\u003e\u003cp\u003eWith regards to the association between covariates and the two mediator variables, OIO and OIT, were differently associated with DASS-21, age, and Gender. OIO was positively associated only with gender (emo3: b\u0026thinsp;=\u0026thinsp;1.091, SE\u0026thinsp;=\u0026thinsp;0.354, p\u0026thinsp;=\u0026thinsp;.002, 95% CI = [0.397, 1.786], std\u0026thinsp;=\u0026thinsp;0.153) but not with DASS-21 (emo1) nor with age (emo2) which did not reach statistical significance., OIT was not associated with any of the covariates [DASS-21 (emt1), age (emt2), gender (emt3)].\u003c/p\u003e\u003cp\u003eOIO and OIT showed a statistically significant covariance (b\u0026thinsp;=\u0026thinsp;1.124, SE\u0026thinsp;=\u0026thinsp;0.253, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% CI = [0.627, 1.621], std\u0026thinsp;=\u0026thinsp;0.233) indicating that these mediators were moderately correlated.\u003c/p\u003e\u003cp\u003eRegarding the two indirect effects, the indirect pathway from IUS-R to CSS-12 through OIO was statistically significant (ind_a1b1: estimate\u0026thinsp;=\u0026thinsp;0.066, SE\u0026thinsp;=\u0026thinsp;0.020, p\u0026thinsp;=\u0026thinsp;.001, 95%CI = [0.027; 0.105], std\u0026thinsp;=\u0026thinsp;0.076), suggesting that part of the effect of IUS-R on CSS-12 was mediated by OIO. The other indirect effect via OIT was not statistically significant (ind_a2b2: estimate\u0026thinsp;=\u0026thinsp;0.003, SE\u0026thinsp;=\u0026thinsp;0.005, p\u0026thinsp;=\u0026thinsp;.515, 95%CI = [\u0026ndash;0.006; 0.012], std\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003cp\u003eOverall, the model explained 31.4% of the variance in CSS-12, with lower explained variances for OIO (9.8%) and OIT (2.7%). With respect to model fit, the total variance explained by the model (TCD\u0026thinsp;=\u0026thinsp;0.21) suggests a good fit to the observed data. In terms of effect size, this value corresponds to a correlation of r\u0026thinsp;=\u0026thinsp;0.46, which represents a moderately large effect according to Cohen\u0026rsquo;s (Cohen, Jacob, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) conventional benchmarks.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present research aimed to validate (in a general population) the Italian validation of the CSS-12, to examine its construct validity and psychometric properties, and to investigate the relationship between IU and cyberchondria, considering the mediating roles of OIO and OIT. Our findings suggest that OIO is a critical mediator in the relationship between IU and the outcome variable of cyberchondria. While IU was directly associated with CSS-12, its effect was partly channeled through its significant associations with OIO. DASS-21 also emerged as an important direct statistical predictor, highlighting the important role of emotional distress. The non-significant paths involving OIT suggested that not all aspects of the measured constructs contribute equally to the outcome. These insights provide a nuanced understanding of the underlying processes and highlight potential targets for further research or intervention.\u003c/p\u003e\u003cp\u003eFindings of Study 1 showed that the Italian version of the CSS-12 has good factorial validity with a hierarchical second-order factorial structure with four first order dimensions (i.e., excessiveness, compulsivity, reassurance, distress) and an overarching one of general cyberchondria. These results support treating cyberchondria as a single, overarching construct, allowing for a straightforward total score calculation to represent severity. Nonetheless, the specific subscales provide nuanced information when considered, offering potential avenue to explore the distinct relationships between various aspects of cyberchondria and relevant clinical outcomes or risk factors. The CSS-12 also has strong psychometric properties, good internal consistency, good convergent-divergent validity, and good test-retest reliability over time. Thus, the CSS-12 represent a valuable brief tool to measure cyberchondria in the Italian context, making it a suitable tool both for research and clinical purposes.\u003c/p\u003e\u003cp\u003eFindings of Study 2 showed that the relationship from IU to cyberchondria is partially mediated by OIO but not by OIT. The partial mediation supported the hypothesis that elevated IU levels may increase online health information-seeking behavior, thereby amplifying cyberchondria. Moreover, IU was directly associated with cyberchondria, suggesting that IU remains a robust transdiagnostic vulnerability factor for this maladaptive behavior, consistent with findings by Bottesi et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings align with prior research which identified weak direct IU-cyberchondria associations and posited indirect pathways (Starcevic et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the association between IU and OIO is consistent with recent findings showing that individuals with both high prospective IU and, in particular, high inhibitory IU (Fergus \u0026amp; Spada, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) tend to engage in dysfunctional anxiety-reduction strategies to manage uncertainty, such as excessive health information seeking online, thereby heightening their risk for increased information overload, which in turn can increase cyberchondria and anxiety-related outcomes. Beyond the direct association, the relationship between IU and cyberchondria was found to be partially mediated by OIO, strengthening and maintaining cyberchondria. This finding reinforces the notion that information overload resulting from health information seeking is pivotal in understanding cyberchondria-related behaviors. This aligns with several studies which have highlighted a significant association between OIO and cyberchondria, emphasizing how excessive exposure to online health content can foster confusion, uncertainty, and compulsive searching (Hong \u0026amp; Kim, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; White \u0026amp; Horvitz, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, OIT did not significantly mediate the relationship between IU and cyberchondria, suggesting that its role is not straightforward but needs to be considered within a wider framework including other factors related to the appraisal process of information (e.g., valence, relevance). In this study, no evidence of a direct positive association between OIT and cyberchondria emerged, consistent with recent findings by Weng et al. (Weng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the limited relevance of OIT observed here aligns with findings by Laato et al. (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), reporting that OIT is more closely associated with other factors, such as misinformation, rather than directly with cyberchondria. In line with Laato et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Zheng et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), information trust is a complex construct plausibly influenced by individual and contextual factors that can take both an adaptive form, associated with the ability to select reliable sources, and a maladaptive form characterized by undermined critical evaluation and indiscriminate trust that may increase exposure to misleading digital content and contribute to cyberchondria (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). If both adaptive and maladaptive forms of OIT are balanced within the sample, their overall association with OIO may be unclear, potentially reducing the statistical significance of the direct and mediated effects.\u003c/p\u003e\u003cp\u003eAnother potential explanation is that the lack of a significant role for OIT in this sample may reflect specific characteristics, such as adequate digital health literacy and a balanced level of trust and critical judgment among participants, which could limit OIT\u0026rsquo;s role in cyberchondria. This suggests that in models where OIT mediates the IU\u0026ndash;cyberchondria relationship, additional individual factors\u0026mdash;such as digital literacy or critical evaluation skills\u0026mdash;may be more influential in mitigating the effects of IU. In other words, the relationship between IU, OIT, and cyberchondria may be more complex than initially hypothesized.\u003c/p\u003e\u003cp\u003eOIT and OIO were positively associated as it is reasonable that greater OIT may lead to broader exposure to digital content and, consequently, a higher risk of OIO \u0026ndash; and viceversa. This finding is consistent with Laato et al. (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Zheng et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who noted that excessive OIT can increase the likelihood of accepting inaccurate or misleading information. Such dynamics not only elevate OIO but also perpetuate compulsive information seeking, as users - confronted with unsatisfying or alarming content - continue searching for more reassuring information (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this research the effects remained consistent even after adjusting for gender and age covariates which had no statistically significant effects, despite the sample's female majority \u0026ndash; thus overcoming the findings by Laato et al. (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). General psychological distress emerged as a critical direct variable to be taken into account, underscoring the influence of emotional distress the hypothesized model. The result suggests that individuals experiencing psychological distress may engage in reassurance-seeking behavior via online platforms, up to excessive and compulsive behaviors of seeking health related information typical of cyberchondria.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and strengths\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe present studies have some limitations that need to be considered. The sole reliance on self-report measures, despite these being well-validated, may introduce response bias. Convenience sampling may reduce representativeness, as in our case they were predominantly young participants, educated, and tech-literate which may not be reflective of the broader Italian population. The female-skewed sample could also influence psychological variables like IU or OIT, given prior reports of higher cyberchondria in women (Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although the path model tested in Study 2 was informed by a cognitive-behavioral perspective suggesting potential directions of association (Fergus \u0026amp; Spada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Weng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the cross-sectional design restricts the interpretation of findings to correlations rather than causal effects.\u003c/p\u003e\u003cp\u003eTo overcome these limitations, future studies should prioritize larger, gender-balanced samples to enhance generalizability and integrate direct behavioral measures (e.g., tracking time spent on health-related searches or social media scrolling) to objectively assess online information-seeking patterns and clarify IU-OIO-OIT relationships. Expanding models to include variables like digital literacy, critical evaluation skills, and health literacy - measured using validated tools such as the - could improve theoretical frameworks\u0026rsquo; explanatory power. Future studies should employ longitudinal and experimental designs to examine the emotional impacts of online health information seeking, for example by manipulating the levels of OIO (amount and complexity of information) and OIT (source reliability) to assess their effects on cyberchondria.\u003c/p\u003e\u003cp\u003eThe present research also has important strengths. Key strengths include the strong methodology, the two-study design, the large samples, and the accurate and well-consolidated statistical techniques used. Standardized measures, rigorous methodology, and validated statistical analyses further bolstered validity and replicability of the study. As one of the few studies testing a mediation model from IU to cyberchondria via OIO and OIT, it highlights the indirect role of IU via OIO, offering insights for clinical research and practice. Understanding these psychological dynamics is important not only from a theoretical perspective but also for informing practical approaches to the development of psychological intervention strategies (Zheng, Sin, et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom a clinical standpoint, this research suggests key directions for developing targeted interventions to prevent and treat cyberchondria. Indeed, identifying factors and antecedents of cyberchondria is crucial for devising effective strategies (Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zheng, Sin, et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but given its multifaceted nature and complex conceptualization, therapeutic approaches remain under study (Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA recent systematic review and meta-analysis by Schenkel et al. (Schenkel et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined 25 studies (n\u0026thinsp;=\u0026thinsp;3,069 participants) investigating cyberchondria's treatment approaches and cognitive-behavioral therapy (CBT) emerged as the most evidence-supported intervention. CBT-based programs are widely supported as an effective treatment to mitigate cyberchondria by helping patients modifying dysfunctional beliefs, develop coping strategies to reduce excessive health uncertainty, information overload, and compulsive online searches (Balyan \u0026amp; Srivastava, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fergus, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuilding on findings from this study, interventions targeting IU should integrate educational and psychotherapeutic approaches addressing OIO, since it partially mediated the effect of IU on cyberchondria. Notably, only Newby and McElroy\u0026rsquo;s (Newby \u0026amp; McElroy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) randomized controlled trial has empirically validated a CBT protocol adapted for cyberchondria, demonstrating symptom reduction mediated by decreased health anxiety, enhanced digital health literacy, and reducing maladaptive online health information-seeking and tolerating uncertainty (Newby \u0026amp; McElroy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Within the CBT framework, the Uncertainty Distress Model (Freeston et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) recommends clinical strategies that increase tolerance to uncertainty and reduce the perceived need for certainty through exposure, behavioral experiments, and cognitive restructuring (Bottesi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, preliminary patient assessment should include dispositional traits like IU (Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), information-related variables (e.g., OIO, OIT), and associated psychopathologies (e.g., health anxiety, obsessive-compulsive disorder) - as addressing these through existing psychotherapies may reduce cyberchondria (Fergus, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fergus \u0026amp; Dolan, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fergus \u0026amp; Russell, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fergus \u0026amp; Spada, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McManus et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vismara et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMindfulness-Based Interventions may also be beneficial by fostering nonjudgmental awareness, helping manage distress and intrusive health-related thoughts, enhancing tolerance of uncertainty, and preventing repetitive dysfunctional thought cycles that exacerbate cyberchondria (Balyan \u0026amp; Srivastava, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fergus \u0026amp; Spada, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McManus et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDigital health literacy (i.e., the ability to identify reliable information and avoid misleading or alarmist content) emerges as a major protective factor for cyberchondria as it promotes critical use of online platforms, counters unrealistic expectations about search engines, highlights the harms of OIO, supports uncertainty management, and enables discernment between trustworthy and unreliable sources (Balyan \u0026amp; Srivastava, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kwon et al., 2015; Peng et al., 2021; Siebenhaar et al., 2020; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Starcevic et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vismara et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Weng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zheng, Chen, et al., 2020; Zheng et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond user skills, platform-level interventions are vital, as the implementation of health information filters can help users find reliable content and improve search engine prioritization of institutional sources. Enhancing platform design with clear, user-friendly layouts, comprehensive and authoritatively sourced content, and simple language increases user trust and protects against maladaptive OIT and OIO (Bottesi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hong \u0026amp; Kim, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Laato et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rowley et al., 2015; Starcevic, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Swar et al., 2017).\u003c/p\u003e\u003cp\u003eTaking together all this information, a multidisciplinary strategic approach combining expertise from information technology, medicine, psychology, and healthcare administration is essential to effectively address cyberchondria (Starcevic \u0026amp; Berle, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study details the validation of the Italian version of the CSS-12. While the structure of the CSS-12 reflects four previously identified factors, hierarchical second-order modeling suggests that it can also be scored as a unidimensional scale. Nevertheless, subscale scores may offer additional insights for clinical and research purposes. Furthermore, this research provided interesting insights into the complex relationship between intolerance of uncertainty and cyberchondria, which is partially mediated by information overload, thereby disclosing fruitful avenues for clinical intervention to promote individuals\u0026rsquo; health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest:\u003c/h2\u003e\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArn\u0026aacute;ez, S., Garc\u0026iacute;a-Soriano, G., Castro, J., Berle, D., \u0026amp; Starcevic, V. (2023). 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The roles of online information overload and information trust. \u003cem\u003eInformation Processing \u0026amp; Management\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(4), 103364. https://doi.org/10.1016/j.ipm.2023.103364\u003c/li\u003e\n \u003cli\u003eZheng, H., Sin, S.-C. J., Kim, H. K., \u0026amp; Theng, Y.-L. (2020). Cyberchondria: A systematic review. \u003cem\u003eInternet Research\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(2), 677\u0026ndash;698. https://doi.org/10.1108/INTR-03-2020-0148\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Padua","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cyberchondria, CSS-12, intolerance of uncertainty, online information overload, validation, mediation model","lastPublishedDoi":"10.21203/rs.3.rs-7196510/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7196510/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present two-study research aimed to validate the Italian version of the Cyberchondria Severity Scale-12 (CSS-12), assess its psychometric properties, and explore the relationships among intolerance of uncertainty (IU), cyberchondria, online information overload (OIO), and online information trust (OIT). Confirmatory factor analysis was used in Study 1 and a path analysis mediation model was tested in Study 2. Online self-report questionnaires were administered to samples from the Italian general population. Study 1 confirmed the robust factorial structure of the CSS-12, supporting both a hierarchical second-order model and four first-order dimensions (excessiveness, compulsivity, reassurance, distress), with strong internal consistency, convergent-divergent validity, and test-retest reliability. Study 2 demonstrated that IU is directly associated with cyberchondria and that this relationship is partially mediated by OIO, but not by OIT. Psychological distress also emerged as a significant variable associated with cyberchondria. The findings highlight OIO as a critical mechanism linking IU to maladaptive health-related online behaviors, while the role of OIT appears more nuanced and context-dependent. Results underscore the value of the CSS-12 as a brief, reliable tool for research and clinical use in Italy and suggest that interventions targeting IU and information overload may be effective in mitigating cyberchondria.\u003c/p\u003e","manuscriptTitle":"From Intolerance of Uncertainty to Cyberchondria through Information Overload: The Italian Validation of the Cyberchondria Severity Scale 12 (CSS-12) and a Mediation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 08:03:43","doi":"10.21203/rs.3.rs-7196510/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1b7aeae-cd64-417f-ad9c-3e735d806e3c","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T08:03:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 08:03:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7196510","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7196510","identity":"rs-7196510","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}