# Information overload in the context of COVID-19 pandemic: A repeated cross-sectional study

Martin Breyton, Émilien Schultz, A. Smith, Alexandra Rouquette, J. Mancini

► To cite this version:

Martin Breyton, Émilien Schultz, A. Smith, Alexandra Rouquette, J. Mancini. Information overload in the context of COVID-19 pandemic: A repeated cross-sectional study. Patient Education and Counseling, 2023, 110, 10.1016/j.pec.2023.107672. hal-04122801

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<figure><img src="image_1.png" /></figure>
1 Information overload in the context of COVID-19 pandemic: a
2 repeated cross-sectional study.

3

4 Martin Breyton¹,², Émilien Schultz¹,³, Allan Ben Smith⁴,⁵, Alexandra Rouquette⁶,⁷, Julien

5 Mancini¹,⁸

6

7 1 - Aix Marseille Univ, INSERM, IRD, ISSPAM, SESSTIM, Cancer, Biomedicine & Society group,
8 Equipe Labellisée Ligue 2019, Marseille, France

9 2 - AP-HM, Marseille, France

10 3 - CEPED (UMR 196), Université de Paris, IRD, 75006 Paris, France

11 4 - Centre for Oncology Education and Research Translation (CONCERT), Ingham Institute for

12 Applied Medical Research & South Western Sydney Clinical School, University of New South
13 Wales, Liverpool, NSW, Australia

14 5 - Psycho-Oncology Co-operative Research Group (PoCoG), School of Psychology, University
15 of Sydney, Australia

16 6 - Public Health and Epidemiology Department, AP-HP Paris-Saclay, Le Kremlin-Bicêtre,
17 France

18 7 - Université Paris-Saclay, UVSQ, CESP, INSERM U1018, Villejuif, France

19 8 - AP-HM, BIOSTIC, Hop Timone, Marseille, France

20

21 **Corresponding author at:**

22 Julien Mancini

23 UMR1252 SESSTIM (Aix-Marseille Univ)

24 Institut Paoli-Calmettes

25 232 Bd Ste Marguerite, BP 156

26 13273 Marseille Cedex 9, FRANCE

27 E-mail: julien.mancini@univ-amu.fr

28 Telephone: +33 4 91 22 35 02

29 Facsimile: +33 4 91 22 35 04

30
31 Abstract

32 **Objectives:** To assess the psychometric properties of the Coronavirus Information Overload scale (CovIO) and explore relationships between CovIO, its predictors and several health behaviours related to the COVID-19 pandemic, using Cancer Information Overload (CIO) scale results as a reference for comparison.
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36 **Methods:** 2,003 participants representative of the French adult population answered a self-37
administered questionnaire over two waves of polling (N<sub>1(June 2020)</sub>=1,003, N<sub>2(January</sub>
2021)=1,000). Respondents were randomized to fill CovIO or CIO scale. Psychometric
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properties of scales were evaluated with Confirmatory Factor Analysis (CFA). Predictors were
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assessed using multivariate linear regression.
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41 **Results:** CovIO scale showed satisfactory psychometric properties (α=0.86, ω=0.86, 42 RMSEA=0.050) without any measurement invariance issue. CovIO increased between waves 43 of sampling and was significantly linked to education, health literacy and trust in institutions 44 among other variables. A negative relationship between information overload and 45 preventive behaviours was also observed.
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46 **Conclusion:** The CovIO scale is a valid tool for assessing COVID-19 information overload. The 47 dynamical formation of information overload and links with theorised predictors, especially, 48 health literacy are confirmed.
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49 **Practice implications:** Longitudinal designs could help better understand the potential 50 detrimental effect of information overload and improving public health campaigns. 51

Interventions to reduce the degree of overload are needed.
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53 **Keywords:** Cancer information overload; Coronavirus information overload; Health literacy; 54 Health behaviour; Validation studies; Psychometrics
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56 Highlights:

57 The CovIO scale is a valid tool to assess information overload relating to COVID-19

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* CovIO is linked to socioeconomic predictors but also related to trust in various media and health professionals

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* CovIO translates into negative health behaviours just like CIO does

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* There is a clear increase in CovIO across the time of the pandemic
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# 1 Introduction

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The COVID-19 pandemic hit the world at a global scale, with unprecedented media coverage.

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With it came a continuous flow of rapidly changing information: daily numbers of cases and deaths, updates about progression of the outbreak worldwide, promotion of countermeasures, barrier gestures, vaccines, lockdowns, etc. Citizens were asked to follow health recommendations, some of them enforced by law, that rapidly changed and for some of them were disputed in public space: as in France for lockdown or health pass[1].

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Recent research has investigated the impact of information overload on various chronic disease (e.g. cancer, atrial fibrillation)[2-4]. The COVID-19 pandemic offers an unmatched opportunity to study information overload in the context of an acute disease with a high information environment[5]. In this specific context, the huge amount of information available, the high level of uncertainty regarding its validity, and the frequent spread of misinformation[6] might lead to a very high level of information overload. Previous studies of COVID-19 information[7-9] have not validated a scale to capture COVID-19 information overload (CovIO) and studied its predictors in the same population sample. Existing attempts lacked representativeness or adequate methodological rigour, such as a control group.

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Furthermore, despite a growing body of evidence on health information overload on different topics, there are still discrepancies concerning key covariates, namely health literacy and education in various studies[4]. Our previous work on Cancer Information Overload (CIO)[10], demonstrated the possibility of valid assessment with a 5-item scale and found an association with poorer health related behaviours. We hypothesise that a similar pattern may occur for CovIO, even if little is known about information overload in the context of such a pandemic. In particular, CovIO might evolve rapidly as the epidemiological and informational context changes. A valid tool to measure CoVIO and identify associated health behaviours is needed to better assess and manage the impact of CoVIO in research and practice.

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The main objectives of our study were: to validate the psychometric properties of the CovIO scale, to study its predictors, and to explore the relationships between CovIO and several health behaviours over the first year of the COVID-19 pandemic in the general public. The same analyses were performed in parallel with the CIO for comparison.

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# 2 Methods

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## 2.1 Samples

98 We used data from large national repeated cross-sectional surveys conducted by IPSOS[11]
99 in the French general adult population[12,13]. Data was collected in two waves, the first
100 between May 27th and June 5th, 2020 (two weeks after the end of the first lockdown), and
101 the second between January 8th and January 18th 2021 (where a curfew was set at 6PM).
102 Participants were selected from an online nationally representative research panel of
103 households of the French general population, and quota sampling was used to match French
104 official census statistics for gender, age, size of the population in the area of residence, and
105 region. At each time of data collection, participants were randomly assigned to receive the
106 CIO scale (CIO group) or its adaptation to capture COVID-19 Information Overload (CovIO
group). The study was approved by the Ethics Evaluation Committee of the French national
107 biomedical research Institute (CEEI, IRB 00003888).
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## 2.2 Measures

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### 2.2.1 The Information Overload scales

113 The CIO group completed the French version of the 5-item CIO scale (CIO group)[10], while
114 the CovIO group completed an equivalent scale adapted to the context of the pandemic: the
115 Coronavirus Information Overload (CovIO) scale. This adaptation consisted of replacing
116 occurrences of the word “cancer” in the CIO scale with “coronavirus (COVID-19)”. For
117 example, the first item of the CIO scale: “There are so many different recommendations
118 about cancer, it’s hard to know which ones to follow”, became “There are so many different
recommendations about the coronavirus (COVID-19), it’s hard to know which one to follow”
119 (Table 1). The adaptation used both ‘coronavirus’ and ‘COVID-19’ terms due to the
120 polysemic use of those designations of the disease, and that SARS CoV2 was seldom
121 used[14]. A 4-point Likert scale (strongly disagree = 1, disagree = 2, agree = 3, strongly agree
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123 = 4) was used for each item and items were summed to produce a total score for each scale
124 ranging from 5 to 20, with higher scores indicating greater information overload.

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126 **2.2.2 Other measures**

127 The questionnaire comprised questions regarding: socio-demographics (age, gender,
128 education, rural or urban living area), chronic comorbidities, the occurrence of COVID-19
129 within the participant's household and level of preoccupation regarding the COVID-19
130 pandemic and its consequences. Health literacy was assessed using the French version of the
131 HLS19-Q12 questionnaire[15]. A score was computed by summing all twelve 4-point items
132 giving a range between 12 and 48, higher score indicating higher health literacy. Health
133 information seeking behaviour was assessed with a question about general health
134 information seeking in any source (books, magazines, internet, social media...). Trust in
135 institutions was measured with four questions regarding journalists, politicians, scientists,
136 and physicians. Finally, we inquired about several health-related behaviours linked to cancer
137 or COVID-19 prevention. For cancer prevention we asked for: the number of days per week
138 of tobacco use, alcohol consumption, physical exercise (more than 30 minutes), and healthy
139 diet. For COVID-19 we asked for the number of unsafe contacts, i.e. the number of social
140 interactions without respecting barrier gestures (excluding people within the household)
141 during the past week ("None, Only one, Two or three, Between Four and Eight, More than
142 Eight").

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145 **2.3 Statistical analyses**

146 Continuous and categorical variables were described using means (±standard deviation) and
147 counts (percentages) respectively. Univariate analyses were performed and significance was
148 measured using chi-square tests, Student t tests or ANOVAs for categorical and continuous
149 variables respectively. Tests were two-sided and the p-values were considered significant
150 when less than 0.05. All the following analyses were conducted for both scales, CovIO and
151 CIO, using R.3.5.2 and RStudio.

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2.3.1 Score and item responses description

Floor or ceiling effects at the scale level were considered to be present if more than 15% of respondents achieved the lowest or highest possible score, respectively[16]. At the item level, these effects were considered to be present if more than 95% endorsed if the lowest or highest response category[17].

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2.3.2 Internal consistency and structural validity

Items were considered redundant if the polychoric inter-item correlation > 0.7 and irrelevant if < 0.2[16]. Reliability was assessed using ordinal Cronbach's alpha (α)[18] and McDonald's Omega (ω)[19], and considered satisfactory if ≥ 0.7[20].

Structural validity was tested using confirmatory factor analysis (CFA) with the robust weighted least squares (WLSMV) estimator and Delta parameterization[21,22]. The initial factor structure explored was the unidimensional structure (as determined by Jensen and colleagues for the CIO scale) and modification indices were then examined, if necessary, to allow correlation between items until fit indices were satisfactory[2]. The following fit indices were used: the Root Mean Square Error of Approximation (indicating a good fit if RMSEA <0.05, poor fit if ≥0.10, acceptable elsewhere, the Comparative Fit Index and the Tucker-Lewis index (CFI and TLI, indicating a good fit if >0.95, poor fit if <0.90, acceptable elsewhere[23]).

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2.3.3 Measurement invariance

After assessing structural validity, measurement invariance was explored using multiple-group CFA and nested model comparisons. It was consecutively studied over the following variables: *age* (<50 and ≥50 years), *gender*, *education level* (more than high school or not), and the presence or absence of a *chronic condition*. We used the classic three-step sequence investigating configural, metric and scalar invariance consecutively by fitting and testing three different nested models with increasing constraints[21,24]. To explore gender invariance for example, the same model was initially hypothesized for both groups and the following sequence of nested model tests was constructed: 1) configural invariance: unconstrained factor loadings and item thresholds; 2) metric invariance: factor loadings constrained to be equal across gender groups and unconstrained item thresholds; 3) scalar invariance: factor loadings and item thresholds constrained to be equal across gender

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groups. Each level of invariance was considered to be met if the fit indices difference between two nested models (unconstrained – constrained) was equal or less than 0.01 for ΔCFI and equal or higher than -0.015 for ΔRMSEA[25,26]. When a level of invariance was not met, non-invariant items were identified by reviewing modification indices (essentially the χ² value) in order to release equality constraints concerning these items until partial invariance was met.

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## 2.3.4 CIO and CovIO predictors

Candidate variables to be screened as CIO and CovIO predictors were: polling wave, gender, age, education, living area, chronic disease, personal-history of COVID-19, history of COVID-19 within the household, preoccupation with COVID-19, health information seeking behaviour, health literacy, trust in journalists, politics, physicians, and scientists. CovIO and CIO predictors were identified across the two waves using linear regression models stratified with respect to the random assignment to the CovIO (N=1,003) or CIO group (N=1,000). The Akaike Information Criteria[27] and stepwise (forward and backward) methods were used for model selection[28]. Interaction terms between all candidate variables and the wave variable were tested using a global F-test comparing models with and without interaction terms. Stepwise regression was then initiated on the model with all candidate variables and, if the global F-test for interaction was significant, all the interaction terms.

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## 2.3.5 CIO, CovIO and Health behaviours

Lastly, relationships between CIO, CovIO and various health related behaviours were investigated by comparing mean levels of overload across several variables. We hypothesized that overload would be systematically associated with detrimental behaviours (e.g. higher levels of CIO would be linked to tobacco use or alcohol consumption, and higher levels of CovIO were expected to be related to higher number of social interactions without barrier gestures in the past week)[10,29]. Every hypothesis was either tested with an ANOVA or Pearson's correlation and considered significant when the p-value was below 0.05.

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217 **3 Results**

218 **3.1 Samples**

219 1,003 participants responded during the first wave of polling and 1,000 during the second. In
220 line with French census statistics, there was an almost equal distribution between men
221 (50.8%) and women (49.2%) in the 2,003 participants who responded to the surveys. The
222 mean age was 46.5 (±15.6) years old. The population was mostly urban (78.9%) and
223 educated with 1,209 participants (60.4%) with a degree higher than high school graduation.
224 Almost half the participants (44.9%) reported at least one chronic condition. Only 128 (6.4%)
225 respondents indicated having been infected by COVID-19, and 180 (9.0%) reported an
226 infection within the household. Those rates were higher in the second wave of the survey
227 (Appendix 1). Except for COVID-19 related variables, participants of the two waves were
228 similar. CovIO and CIO groups were also similar. CovIO was found to be significantly higher
229 than CIO (p<0.01) in both waves with mean CovIO of 14.1 (±3.4) and mean CIO of 13.2 (±3.0).
230 See Appendix 1 for a full description of the samples.

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232 **3.2 Score and item responses description**

233 There was no missing data for the CovIO and CIO scales. Item responses and score
distributions in the CovIO scale are shown in Figure 1. No floor or ceiling effect was detected
234 at the item level: the highest proportion of response for the lowest and highest response
235 category were respectively 12.7% (N=127) for item 4 and 30.1% (N=301) for item 5. This was
236 also the case at the scale level with a minimum and a maximum total score reached by 8.6%
237 (N=86) and 1.3% (N=13) of the sample respectively. Similar results were observed regarding
238 CIO (cf. Figure 1 and Appendix 3).
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241 **3.3 Reliability and Confirmatory Factor Analysis**

242 Internal consistency of CovIO was satisfactory with both Cronbach's alpha = 0.86 and
243 McDonald's Omega = 0.86. All inter-item polychoric correlations were between 0.2 and 0.7.
244 The initial Confirmatory Factor Analysis fitted poorly with a RMSEA=0.128, CFI=0.98 and
245 TLI=0.96, and it was necessary to allow for error-correlation between items 2 and 3 to obtain
246 satisfactory results: RMSEA=0.050, CFI=0.99, TLI=0.99. Similar adequate psychometric
247 properties were observed regarding CIO (Appendix 3).
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249 **3.4 Measurement Invariance**

250 No measurement invariance issue was found during MGCFA for the CovIO scale across
251 gender, education level, age, and the presence or absence of chronic comorbidity. Similar
252 invariance was observed regarding CIO (Appendix 2).

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254 **3.5 Study of predictors (Table 2)**

255 CovIO

256 The result of the global F-test for interaction was significant (F-statistic=2.27, p<0.01),
257 therefore interaction terms were kept in the stepwise procedure. In the retained model
258 (Table 2), CovIO was found to be significantly increased in wave 2 (beta=5.93, p<0.01). The
259 trust in physicians and a history of self-infection by COVID-19 were also significantly
260 associated with higher levels of CovIO (beta=1.08, p=0.014 and beta=2.31, p<0.01
261 respectively). Significant negative interactions were found between wave and health
262 information seeking and between wave and health literacy (Fig 2). Stratified analysis across
263 waves revealed that the negative effects of health information seeking and of health literacy
264 on CovIO were significant only in wave 2 with beta=-1.51 (p<0.01) and beta=-0.10 (p<0.01)
265 respectively. Higher age and higher education were globally associated with less CovIO
266 (beta=-0.02, p=0.006, and beta=-0.63, p=0.004 respectively), and so were the trust in
267 politicians (beta=-1.06, p<0.01) and scientists (beta=-1.28, p<0.01). Lastly, a negative linear
268 relationship between increasing levels of preoccupation for COVID-19 and CovIO was found
269 (beta=-0.65, p=0.047).

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271 CIO

272 The global F-test for interaction was not significant (p= 0.15). In the final model (Table 2),
273 higher age, education, and preoccupation with COVID-19 were all linked to lower CIO just
274 like with CovIO. Lastly, health literacy and health information seeking were significantly
275 associated with lower CIO, but independently from the sampling period (Fig 2).

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## 3.6 Health behaviours (Table 3)

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# 4 Discussion and conclusion

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## 4.1 Discussion

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In this study the CovIO scale was a reliable and valid tool (good internal consistency and structural validity) to measure the degree of information overload related to COVID-19, exhibiting measurement invariance across gender, education level, age, and presence of chronic illness.

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As expected, with an enormous amount of often changing and not always valid[30] COVID-19-related information being broadcast across all media channels during the study period and specific controversies over treatments (e.g., efficacy of chloroquine[31]), CovIO was higher than CIO and increased over time in our study (when adjusting for COVID-19 related variables such as personal infection and preoccupation). While CIO was higher in this sample compared to our previous study in 2016[10], it was stable across the two survey waves and this difference may reflect the fact that our previous study recruited a convenience sample of people specifically interested in cancer research and that participants not affected by cancer were found to have higher CIO than cancer patients or caregivers[10]. In contrast to our previous results[10] the present results suggest that exposure to an acute disease such as COVID-19 (through personal infection) increases information overload, however, this relationship tends to decrease over time, despite an increased number of infection. This can be due to the specific uncertainty about this virus and the fact that some of the available information became rapidly obsolete or learning that COVID-19 reinfection was possible.
The study of interactions suggests that the relationship between CovIO (and very likely information overload in general) and some of its predictors is not static. Indeed, the effects of health literacy and health information seeking behaviours became significant only during wave 2. These results might suggest that as the amount and complexity of information about the virus increased as the pandemic progressed, that health literacy helps navigate through it, or, that respondents with active interest for health information developed specific expertise to navigate this specific information. Similarly, the interaction between time and health seeking behaviours suggests that this mass information accumulating through the pandemic is also complex and sometimes contradictory (e.g debates around the vaccination in France) and more proactive information seeking reduces the amount of overload. This is in line with our previous study showing less information overload among intense information seekers[10]. As expected from previous results[10,32,33] and in accordance with the negative relationship with health literacy, higher education was also significantly associated with lower information overload. Both education level and health literacy are key factors to consider when developing health related messages and programs.

This is the first report of age being significantly negatively correlated with information overload[32,34] since its formalization by Jensen et al[2]. Although from a cognitive perspective, this results seems contradictory with the natural decline of cognitive processes and the limited capacity model[35], our samples were relatively young (only 15% over 65 years old) and it's likely that older people have more interest in health questions but may also be more accepting of official health messages. Such a positive relationship with official health messages would be consistent with the negative association between trust in politicians and overload and deemed important if not crucial in the context of a pandemic where the trust in public health measures appears to be central. But this encouraging result should be tempered by a the context of low trust in politicians in general (only 16.6%, Table S1), which could reflect the evolving political decisions throughout the pandemic (for example, masks were not deemed necessary by the minister of health of France at the beginning of the pandemic[36]). Changing political decisions may both increase overload and erode trust in politicians. On the other hand, the trust in scientists being associated with less CovIO with a very high rate of trust in scientists (88.2%) underlines the value of clear health messages emanating from the scientific community. This link to trust suggests that overload
depends on the way individuals select or reject sources of information as credible. However, the most surprising result was about the trust in physicians being linked to higher CovIO when most of the sample declared trusting their physicians (92.7%, Table S1). One speculative explanation could be that individual physicians were also prone to CovIO due to frequent changing recommendations about isolation protocols, systematic screening protocols for contact cases for example or, more generally, an overall feeling of overload (i.e work overload) about COVID-19 that was sensed by day-to-day patients. Alternatively, this could reveal that people who distrust physicians are engaged in an alternative view of the world with its own set of beliefs which, although false, are firm and less prone to information overload [37] or that people who trust physicians were confronted with conflicting information elsewhere, which caused them to feel overwhelmed.

Finally, we've shown that CovIO might translate into health behaviours, just like CIO in previous findings [10]. CovIO was related to risky behaviours such as the increase in the number of unsafe contacts (lacking recommended barrier gestures). This relationship was, again, strongest at wave 2 underpinning the dynamic nature of information overload.

Although, the desire to return to a normal life could be a confounding factor not measured in our study that could have interacted with CovIO and the compliance to social distancing. For more general health behaviours, CIO was confirmed to be linked with lower physical activity and less days of healthy diet in the week. The correlations with tobacco use and alcohol consumption were also expected but only close to significance. However, the significant relation between CovIO and non-healthy dieting wasn't expected and lacks any reasonable explanation except for a link to health information overload in general. But overall, these results reinforce the purpose of CovIO, CIO and health information overload in general, in capturing some of the reasons why the general population might not engage in healthy preventive behaviours upon clear health messages.

The major limitation of our study is that despite a repeated design to study the dynamics of information overload in a pandemic context, the participants in the two samples (in June 2020 and January 2021) are not the same. However, the sampling method was similar and both waves had good statistical power with large sample sizes. Moreover, the comparison between the characteristics of the two samples didn't reveal any major difference. The same limitation holds for the comparison between CovIO and CIO but to limit the burden of the
questionnaire it was not possible to ask sequentially for CovIO and CIO. The questionnaire
randomization compensated this limitation. Another limit would be the important number
of interactions between the predictors of CovIO discarded. We chose to focus on the
interaction with time since it was the main objective of our study. Exploratory analysis on
the data, not presented in the article suggested for example many interactions between
health literacy and other variables. These interactions should be addressed in later work
using, for instance, structural equation modelling with some meaningful constructs (e.g
health literacy, trust in various public actors...) yielding results that take in account
interactions without losing interpretability. Finally, the context of COVID-19 was marked by
specific national contexts of information, with governmental communication and media
debates. Especially for CovIO, a joint analysis with available public information at this time
would be useful to contextualise the results.

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## 4.2 Conclusion

We have shown that the CovIO scale is a valid instrument to measure the amount of
information saturation relating to COVID-19. The huge amount of often changing or
conflicting COVID-19 related information in all media appears to have led to greater CovIO
than CIO. The relationship to some of its predictors was better characterized and the
dynamic nature of its formation revealed.

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## 4.3 Practice implications

Even though monitoring COVID-19 information might be less relevant recently given the
evolution of the pandemic and public awareness, our study sheds light on the dynamics of
information overload in general. Therefore, longitudinal designs are needed to better
understand the potential detrimental effect of information overload and improve the
impact of public health campaigns. Future work should also consider interventional studies
to decrease health information overload on a selected topic (e.g. through educational
lectures, or dedicated medical sessions) and validate the effects on behaviours.

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## Conflict of interest statement
403 The authors have no conflicts to declare.

404 **Funding**

405 This work benefited from the support of La Ligue Contre le Cancer through the accreditation

406 (LIGUE 2019) of the CANBIOS team.

407 The sponsors had no role in study design; in the collection, analysis and interpretation of

408 data; in the writing of the report; and in the decision to submit the paper for publication.

409 **Authors' contribution**

410 MB: Conceptualization, Formal analysis, Writing – original draft.

411 JM: Conceptualization, Investigation, Writing – review and editing, Supervision, Project

412 administration, Funding acquisition.

413 ES: Investigation, Writing – review and editing

414 AR: Investigation, Writing – review and editing

415 BS: Investigation, Writing – review and editing

416 **Acknowledgements**

417 We thank the Réseau Francophone de la Littératie en Santé (REFLIS) network for their

418 collaboration in the European HLS<sub>19</sub> survey.

419
420 Figures and Tables

421 Figure 1: Item responses and score distributions of the COVID-19 Information overload

422 (CovIO) scale

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

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<figure><img src="image_5.png" /><figcaption>Figure 2: Interactions effects of Health Literacy and sampling Wave on CovIO</figcaption></figure>

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Figure 3: Univariate link between CovIO and the number of unsafe contacts for the different sampling periods

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<figure><img src="image_6.png" /></figure>

431
<table><caption>Table 1: CovIO scale translated from French</caption><thead><tr><td></td><td>CovIO scale adapted from the CIO scale after Costa et al. 2014 (DOI: 10.1016/j.pec.2015.04.020)</td><td>French version</td></tr></thead><tbody><tr><td>1</td><td>There are so many different recommendations about the coronavirus (COVID-19), it's hard to know which ones to follow.</td><td>Il y a tellement de recommandations différentes concernant le coronavirus (COVID-19) qu'il est difficile de savoir lesquelles suivre</td></tr><tr><td>2</td><td>There is so much coronavirus (COVID-19) information, I don't even care to hear new things about coronavirus (COVID-19)</td><td>Il y a tellement d'informations sur le coronavirus (COVID-19) qu'en avoir des nouvelles ne m'intéresse même plus</td></tr><tr><td>3</td><td>There is so much information about coronavirus (COVID-19), it all starts to sound the same after a while.</td><td>Il y a tellement d'informations sur le coronavirus (COVID-19), qu'au bout d'un moment elles commencent à toutes se ressembler</td></tr><tr><td>4</td><td>There is so much coronavirus (COVID-19) information, I forget most coronavirus (COVID-19) information right after I learn it.</td><td>Il y a tellement d'informations sur le coronavirus (COVID-19), que j'en oublie la plupart aussitôt après les avoir reçues</td></tr><tr><td>5</td><td>I feel overloaded by the amount of coronavirus (COVID-19) information I am supposed to know.</td><td>Je me sens submergé(e) par la quantité d'informations sur le coronavirus (COVID-19), que je suis censé(e) connaitre</td></tr></tbody></table>
<table><caption>Table 2: Linear regressions of CIO and CovIO on their predictors</caption><thead><tr><th></th><th colspan="2">CovIO</th><th colspan="2">CIO</th></tr><tr><th></th><th>β</th><th>p-value</th><th>β</th><th>p-value</th></tr></thead><tbody><tr><td>(Intercept)</td><td>14.62</td><td>&lt;0.001</td><td>18.46</td><td>&lt;0.001</td></tr><tr><td>Wave 2 (January 2021)</td><td>5.93</td><td>&lt;0.001</td><td>-</td><td>-</td></tr><tr><td>Male</td><td>0.30</td><td>0.159</td><td>-</td><td>-</td></tr><tr><td>Age (per 1-year increase)</td><td>-0.02</td><td>0.006</td><td>-0.02</td><td>0.007</td></tr><tr><td>Higher Education (&gt; Baccalaureate)</td><td>-0.64</td><td>0.004</td><td>-0.40</td><td>0.049</td></tr><tr><td>Self-history of COVID-19</td><td>2.31</td><td>0.001</td><td>0.83</td><td>0.065</td></tr><tr><td>Household-history of COVID-19</td><td>-</td><td>-</td><td>0.63</td><td>0.106</td></tr><tr><td>Health Information seeking</td><td>-0.23</td><td>0.585</td><td>-1.23</td><td>&lt;0.001</td></tr><tr><td>Health Literacy (per 1-point increase)</td><td>0.04</td><td>0.181</td><td>-0.08</td><td>&lt;0.001</td></tr><tr><td>COVID-19 preoccupation (per 1-category increase)</td><td>-0.65</td><td>0.047</td><td>-0.63</td><td>0.036</td></tr><tr><td>Urban area</td><td>-</td><td>-</td><td>0.39</td><td>0.096</td></tr><tr><td>Trust in:</td><td></td><td></td><td></td><td></td></tr><tr><td>Journalists</td><td>0.01</td><td>0.973</td><td>-</td><td>-</td></tr><tr><td>Politicians</td><td>-1.067</td><td>&lt;0.001</td><td>-</td><td>-</td></tr><tr><td>Physicians</td><td>1.08</td><td>0.014</td><td>-0.57</td><td>0.123</td></tr><tr><td>Scientists</td><td>-1.28</td><td>&lt;0.001</td><td>-</td><td>-</td></tr><tr><td>Selected interactions:</td><td></td><td></td><td></td><td></td></tr><tr><td>Wave 2 * Self-history of COVID-19</td><td>-1.47</td><td>0.095</td><td>-</td><td>-</td></tr><tr><td>Wave 2 * Health Information seeking</td><td>-1.27</td><td>0.026</td><td>-</td><td>-</td></tr><tr><td>Wave 2 * Health Literacy</td><td>-0.14</td><td>&lt;0.001</td><td>-</td><td>-</td></tr><tr><td>Wave 2 * Trust in journalists</td><td>-0.81</td><td>0.107</td><td>-</td><td>-</td></tr></tbody></table>
<table><caption>437 Table 3: Univariate links between CIO, CovIO and various health behaviours</caption><thead><tr><th colspan="2">Response</th><th>CovIO</th><th>p-value</th><th>CIO</th><th>p-value</th></tr><tr><th colspan="2"></th><th>Mean (SD)</th><th></th><th>Mean (SD)</th><th></th></tr></thead><tbody><tr><td rowspan="5">Number of unsafe contacts (within the past week)</td><td>None</td><td>13.4 (3.3) N=384</td><td rowspan="5">&lt;0.001</td><td>13.0 (2.9) N=401</td><td rowspan="5">0.15</td></tr><tr><td>1</td><td>14.1 (3.5) N=88</td><td>13.6 (2.9) N=88</td></tr><tr><td>2-3</td><td>14.3 (3.3) N=221</td><td>13.3 (2.9) N=221</td></tr><tr><td>4-8</td><td>14.6 (3.1) N=150</td><td>13.5 (2.9) N=153</td></tr><tr><td>&gt;8</td><td>14.8 (3.5) N=160</td><td>13.5 (3.3) N=137</td></tr><tr><td colspan="2">Number of days in a typical week with :</td><th>r*</th><th></th><th>r</th><th></th></tr><tr><td colspan="2">Physical activity</td><td>-0.039</td><td>0.21</td><td>-0.064</td><td>0.042</td></tr><tr><td colspan="2">Healthy diet</td><td>-0.129</td><td>&lt;0.01</td><td>-0.107</td><td>&lt;0.01</td></tr><tr><td colspan="2">Tobacco use</td><td>0.048</td><td>0.127</td><td>0.057</td><td>0.070</td></tr><tr><td colspan="2">Alcohol intake</td><td>0.019</td><td>0.544</td><td>0.058</td><td>0.067</td></tr></tbody></table>

438

439

<footnote>* Pearson's correlation coefficient</footnote>
<table><caption>440 Table S1: Sample characteristics between waves</caption><thead><tr><th></th><th></th><th>Both waves</th><th>Wave 1</th><th>Wave 2</th><th>p-value*</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Female</td><td>1017 (50.8)</td><td>506 (50.4)</td><td>511 (51.1)</td><td rowspan="2">0.805</td></tr><tr><td>Male</td><td>986 (49.2)</td><td>497 (49.6)</td><td>489 (48.9)</td></tr><tr><td>Age</td><td>Mean (SD)</td><td>46.5 (15.6)</td><td>46.5 (15.3)</td><td>46.5 (15.9)</td><td>0.946</td></tr><tr><td rowspan="2">Education level</td><td>Low**</td><td>794 (39.6)</td><td>411 (41.0)</td><td>383 (38.3)</td><td rowspan="2">0.238</td></tr><tr><td>High**</td><td>1209 (60.4)</td><td>592 (59.0)</td><td>617 (61.7)</td></tr><tr><td rowspan="2">Comorbidity</td><td>None</td><td>1103 (55.1)</td><td>538 (53.6)</td><td>565 (56.5)</td><td rowspan="2">0.214</td></tr><tr><td>At least 1</td><td>900 (44.9)</td><td>465 (46.4)</td><td>435 (43.5)</td></tr><tr><td rowspan="2">Work status</td><td>Active</td><td>1353 (67.5)</td><td>665 (66.3)</td><td>688 (68.8)</td><td rowspan="2">0.252</td></tr><tr><td>Not active</td><td>650 (32.5)</td><td>338 (33.7)</td><td>312 (31.2)</td></tr><tr><td rowspan="3">Self-history of COVID-19</td><td>No</td><td>1748 (87.3)</td><td>862 (85.9)</td><td>886 (88.6)</td><td rowspan="3">0.032</td></tr><tr><td>Yes</td><td>128 (6.4)</td><td>50 (5.0)</td><td>78 (7.8)</td></tr><tr><td>Don't know</td><td>127 (6.3)</td><td>91 (9.1)</td><td>36 (3.6)</td></tr><tr><td rowspan="3">History of COVID-19 within the household</td><td>No</td><td>1697 (84.7)</td><td>851 (84.8)</td><td>846 (84.6)</td><td rowspan="3">&lt; 0.001</td></tr><tr><td>Yes</td><td>180 (9.0)</td><td>58 (5.8)</td><td>122 (12.2)</td></tr><tr><td>Don't know</td><td>126 (6.3)</td><td>94 (9.4)</td><td>32 (3.2)</td></tr><tr><td rowspan="2">Living area</td><td>Rural</td><td>423 (21.1)</td><td>212 (21.1)</td><td>211 (21.1)</td><td rowspan="2">0.999</td></tr><tr><td>Urban</td><td>1580 (78.9)</td><td>791 (78.9)</td><td>789 (78.9)</td></tr><tr><td rowspan="2">Health information seeking behavior</td><td>No</td><td>296 (14.8)</td><td>136 (13.6)</td><td>160 (16.0)</td><td rowspan="2">0.14</td></tr><tr><td>Yes</td><td>1707 (85.2)</td><td>867 (86.4)</td><td>840 (84.0)</td></tr><tr><td>Health Literacy</td><td>Mean (SD)</td><td>35.7 (5.6)</td><td>36.0 (5.6)</td><td>35.4 (5.6)</td><td>0.009</td></tr><tr><td rowspan="2">Do you trust: Journalists</td><td>No</td><td>1476 (73.7)</td><td>744 (74.2)</td><td>732 (73.2)</td><td rowspan="2">0.656</td></tr><tr><td>Yes</td><td>527 (26.3)</td><td>259 (25.8)</td><td>268 (26.8)</td></tr><tr><td rowspan="2">Politicians</td><td>No</td><td>1671 (83.4)</td><td>824 (82.2)</td><td>847 (84.7)</td><td rowspan="2">0.141</td></tr><tr><td>Yes</td><td>332 (16.6)</td><td>179 (17.8)</td><td>153 (15.3)</td></tr><tr><td rowspan="2">Physicians</td><td>No</td><td>147 (7.3)</td><td>68 (6.8)</td><td>79 (7.9)</td><td rowspan="2">0.381</td></tr><tr><td>Yes</td><td>1856 (92.7)</td><td>935 (93.2)</td><td>921 (92.1)</td></tr><tr><td rowspan="2">Scientists</td><td>No</td><td>236 (11.8)</td><td>106 (10.6)</td><td>130 (13.0)</td><td rowspan="2">0.106</td></tr><tr><td>Yes</td><td>1767 (88.2)</td><td>897 (89.4)</td><td>870 (87.0)</td></tr><tr><td rowspan="4">Preoccupation for COVID-19 and its consequences</td><td>Not at all</td><td>114 (5.7)</td><td>53 (5.3)</td><td>61 (6.1)</td><td rowspan="4">0.018</td></tr><tr><td>A little</td><td>461 (23.0)</td><td>255 (25.4)</td><td>206 (20.6)</td></tr><tr><td>Somewhat</td><td>847 (42.3)</td><td>429 (42.8)</td><td>418 (41.8)</td></tr><tr><td>A lot</td><td>581 (29.0)</td><td>266 (26.5)</td><td>315 (31.5)</td></tr><tr><td>CIO score</td><td>Mean (SD)</td><td>13.2 (3.0)</td><td>13.3 (3.1)</td><td>13.2 (2.9)</td><td>0.865</td></tr><tr><td>CovIO score</td><td>Mean (SD)</td><td>14.1 (3.4)</td><td>14.2 (3.2)</td><td>14.0 (3.5)</td><td>0.329</td></tr></tbody></table>

*Test across waves, **High = degree higher than the Baccalaureate, ***Students, military

CIO: Cancer Information Overload; CovIO: Coronavirus Information Overload

441

442

443
<table><caption>444 Table S2: Group characteristics</caption><thead><tr><th></th><th></th><th>CIO-group</th><th>CovIO-group</th><th>p-value*</th></tr></thead><tbody><tr><td rowspan="2">Gender</td><td>Female</td><td>497 (49.7)</td><td>520 (51.8)</td><td rowspan="2">0.36</td></tr><tr><td>Male</td><td>503 (50.3)</td><td>483 (48.2)</td></tr><tr><td>Age</td><td>Mean (SD)</td><td>46.4 (15.6)</td><td>46.6 (15.5)</td><td>0.773</td></tr><tr><td rowspan="2">Education level</td><td>Low**</td><td>373 (37.3)</td><td>421 (42.0)</td><td rowspan="2">0.036</td></tr><tr><td>High**</td><td>627 (62.7)</td><td>582 (58.0)</td></tr><tr><td rowspan="2">Comorbidity</td><td>None</td><td>551 (55.1)</td><td>552 (55.0)</td><td rowspan="2">0.999</td></tr><tr><td>At least 1</td><td>449 (44.9)</td><td>451 (45.0)</td></tr><tr><td rowspan="2">Work status</td><td>Active</td><td>687 (68.7)</td><td>666 (66.4)</td><td rowspan="2">0.293</td></tr><tr><td>Not active</td><td>313 (31.3)</td><td>337 (33.6)</td></tr><tr><td rowspan="3">Self-history of COVID-19</td><td>No</td><td>870 (87.0)</td><td>878 (87.5)</td><td rowspan="3">0.711</td></tr><tr><td>Yes</td><td>61 (6.1)</td><td>67 (6.7)</td></tr><tr><td>Missing</td><td>69 (6.9)</td><td>58 (5.8)</td></tr><tr><td rowspan="3">History of COVID-19 within the household</td><td>No</td><td>849 (84.9)</td><td>848 (84.5)</td><td rowspan="3">0.436</td></tr><tr><td>Yes</td><td>84 (8.4)</td><td>96 (9.6)</td></tr><tr><td>Missing</td><td>67 (6.7)</td><td>59 (5.9)</td></tr><tr><td rowspan="2">Living area</td><td>Rural</td><td>208 (20.8)</td><td>215 (21.4)</td><td rowspan="2">0.769</td></tr><tr><td>Urban</td><td>792 (79.2)</td><td>788 (78.6)</td></tr><tr><td rowspan="2">Health information seeking behavior</td><td>No</td><td>134 (13.4)</td><td>162 (16.2)</td><td rowspan="2">0.095</td></tr><tr><td>Yes</td><td>866 (86.6)</td><td>841 (83.8)</td></tr><tr><td>Health Literacy</td><td>Mean (SD)</td><td>35.9 (5.7)</td><td>35.5 (5.5)</td><td>0.165</td></tr><tr><td rowspan="2">Do you trust: Journalists</td><td>No</td><td>729 (72.9)</td><td>747 (74.5)</td><td rowspan="2">0.453</td></tr><tr><td>Yes</td><td>271 (27.1)</td><td>256 (25.5)</td></tr><tr><td rowspan="2">Politicians</td><td>No</td><td>850 (85.0)</td><td>821 (81.9)</td><td rowspan="2">0.067</td></tr><tr><td>Yes</td><td>150 (15.0)</td><td>182 (18.1)</td></tr><tr><td rowspan="2">Physicians</td><td>No</td><td>73 (7.3)</td><td>74 (7.4)</td><td rowspan="2">0.999</td></tr><tr><td>Yes</td><td>927 (92.7)</td><td>929 (92.6)</td></tr><tr><td rowspan="2">Scientists</td><td>No</td><td>121 (12.1)</td><td>115 (11.5)</td><td rowspan="2">0.711</td></tr><tr><td>Yes</td><td>879 (87.9)</td><td>888 (88.5)</td></tr><tr><td rowspan="4">Preoccupation for COVID-19 and its consequences</td><td>Not at all</td><td>54 (5.4)</td><td>60 (6.0)</td><td rowspan="4">0.579</td></tr><tr><td>A little</td><td>219 (21.9)</td><td>242 (24.1)</td></tr><tr><td>Somewhat</td><td>433 (43.3)</td><td>414 (41.3)</td></tr><tr><td>A lot</td><td>294 (29.4)</td><td>287 (28.6)</td></tr><tr><td>CIO score</td><td>Mean (SD)</td><td>13.2 (3.0)</td><td>-</td><td>-</td></tr><tr><td>CovIO score</td><td>Mean (SD)</td><td>-</td><td>14.1 (3.4)</td><td>-</td></tr></tbody></table>

*Test across waves, **High = degree higher than the Baccalaureate, ***Students, military

445

446

CIO: Cancer Information Overload; CovIO: Coronavirus Information Overload
<table><caption>Table S3: Measurement invariance across age groups, education levels, and deprivation, for Cancer Information Overload scale (CIO)</caption><thead><tr><th colspan="5">AGE</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr></thead><tbody><tr><td>M1: Configural*</td><td>.997</td><td>.060 (.032-.091)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.997</td><td>.059 (.035-.085)</td><td>.000 (ΔM1-M2)</td><td>.001 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.995</td><td>.055 (.037-.074)</td><td>.002 (ΔM2-M3)</td><td>.004 (ΔM2-M3)</td></tr><tr><th colspan="5">GENDER</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr><tr><td>M1: Configural*</td><td>.999</td><td>.036 (.000-.070)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.999</td><td>.028 (.000-.059)</td><td>.000 (ΔM1-M2)</td><td>.008 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.999</td><td>.012 (.000-.016)</td><td>.000 (ΔM2-M3)</td><td>.012 (ΔM2-M3)</td></tr><tr><th colspan="5">CHRONIC ILLNESS</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr><tr><td>M1: Configural*</td><td>.998</td><td>.052 (.020-.082)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.997</td><td>.059 (.035-.085)</td><td>.001 (ΔM1-M2)</td><td>-.008 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.999</td><td>.022 (.000-.046)</td><td>-.002 (ΔM2-M3)</td><td>-.037 (ΔM2-M3)</td></tr><tr><th colspan="5">EDUCATION</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr><tr><td>M1: Configural*</td><td>.997</td><td>.066 (.038-.096)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.997</td><td>.057 (.032-.083)</td><td>.000 (ΔM1-M2)</td><td>.008 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.995</td><td>.051 (.032-.070)</td><td>.001 (ΔM2-M3)</td><td>.006 (ΔM2-M3)</td></tr></tbody><tfoot><tr><td colspan="5">* same factor structure across groups</td></tr><tr><td colspan="5">** loadings constrained to be equal across groups</td></tr><tr><td colspan="5">***loadings and thresholds constrained to be equal across groups</td></tr></tfoot></table>
<table><caption>Table S4: Measurement invariance across age groups, education levels, and deprivation, for Coronavirus Information Overload scale (CovIO)</caption><thead><tr><th colspan="5">AGE</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr></thead><tbody><tr><td>M1: Configural*</td><td>.996</td><td>.069 (.042-.099)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.996</td><td>.059 (.035-.085)</td><td>.000 (ΔM1-M2)</td><td>.01 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.991</td><td>.063 (.045-.081)</td><td>.005 (ΔM2-M3)</td><td>-.004 (ΔM2-M3)</td></tr><tr><th colspan="5">GENDER</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr><tr><td>M1: Configural*</td><td>.994</td><td>.082 (.055-.011)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.995</td><td>.065 (.042-.091)</td><td>-.001 (ΔM1-M2)</td><td>.017 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.990</td><td>.062 (.045-.081)</td><td>.005 (ΔM2-M3)</td><td>.003 (ΔM2-M3)</td></tr><tr><th colspan="5">CHRONIC ILLNESS</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr><tr><td>M1: Configural*</td><td>.997</td><td>.055 (.025-.086)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.995</td><td>.063 (.04-.089)</td><td>.002 (ΔM1-M2)</td><td>-.008 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.991</td><td>.061 (.043-.079)</td><td>.004 (ΔM2-M3)</td><td>.002 (ΔM2-M3)</td></tr><tr><th colspan="5">EDUCATION</th></tr><tr><th>Measurement Invariance model</th><th>CFI</th><th>RMSEA (90% CI)</th><th>ΔCFI</th><th>ΔRMSEA</th></tr><tr><td>M1: Configural*</td><td>.997</td><td>.058 (.029-.088)</td><td></td><td></td></tr><tr><td>M2: Metric**</td><td>.997</td><td>.055 (.029-.081)</td><td>.000 (ΔM1-M2)</td><td>.003 (ΔM1-M2)</td></tr><tr><td>M3: Scalar***</td><td>.994</td><td>.05 (.031-.069)</td><td>.003 (ΔM2-M3)</td><td>.005 (ΔM2-M3)</td></tr></tbody><tfoot><tr><td colspan="5">* same factor structure across groups</td></tr><tr><td colspan="5">** loadings constrained to be equal across groups</td></tr><tr><td colspan="5">***loadings and thresholds constrained to be equal across groups</td></tr></tfoot></table>
<table><caption>452 Table S5: CFA results for the CovIO and CIO scales</caption><thead><tr><th></th><th></th><th>CovIO scale<br>(N=1003)</th><th>CIO scale<br>(N=1000)</th></tr></thead><tbody><tr><td rowspan="2">Internal consistency</td><td>Cronbach's alpha</td><td>0.86</td><td>0.87</td></tr><tr><td>McDonald's omega</td><td>0.86</td><td>0.88</td></tr><tr><td rowspan="3">Structural validity</td><td>RMSEA</td><td>0.050 (0.024-0.080)*</td><td>0.045 (0.017-0.075)*</td></tr><tr><td>CFI</td><td>0.998</td><td>0.999</td></tr><tr><td>TLI</td><td>0.994</td><td>0.997</td></tr></tbody></table>

453 RMSEA = Root Mean Error of Approximation (95% confidence interval), CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, *With correlation between item 2 and 3

455

<table><caption>456 Table S6: Polychoric correlations for the CovIO scale</caption><thead><tr><th></th><th>COVIO1</th><th>COVIO2</th><th>COVIO3</th><th>COVIO4</th><th>COVIO5</th></tr></thead><tbody><tr><th>COVIO1</th><td>1</td><td></td><td></td><td></td><td></td></tr><tr><th>COVIO2</th><td>0.47</td><td>1</td><td></td><td></td><td></td></tr><tr><th>COVIO3</th><td>0.38</td><td>0.63</td><td>1</td><td></td><td></td></tr><tr><th>COVIO4</th><td>0.53</td><td>0.61</td><td>0.56</td><td>1</td><td></td></tr><tr><th>COVIO5</th><td>0.56</td><td>0.59</td><td>0.54</td><td>0.66</td><td>1</td></tr></tbody></table>

457

<table><caption>458 Tables S7: Polychoric correlations for the CIO scale</caption><thead><tr><th></th><th>CIO1</th><th>CIO2</th><th>CIO3</th><th>CIO4</th><th>CIO5</th></tr></thead><tbody><tr><th>CIO1</th><td>1</td><td></td><td></td><td></td><td></td></tr><tr><th>CIO2</th><td>0.41</td><td>1</td><td></td><td></td><td></td></tr><tr><th>CIO3</th><td>0.45</td><td>0.69</td><td>1</td><td></td><td></td></tr><tr><th>CIO4</th><td>0.49</td><td>0.66</td><td>0.64</td><td>1</td><td></td></tr><tr><th>CIO5</th><td>0.52</td><td>0.62</td><td>0.61</td><td>0.74</td><td>1</td></tr></tbody></table>

459

<table><caption>460 Table S8. Item responses for the CIO scale (N=1000)</caption><thead><tr><th></th><th>Strongly Disagree</th><th>Disagree</th><th>Agree</th><th>Strongly Agree</th></tr></thead><tbody><tr><th>CIO1</th><td>24 (2.4%)</td><td>186 (18.6%)</td><td>566 (56.6%)</td><td>224 (22.4%)</td></tr><tr><th>CIO2</th><td>95 (9.5%)</td><td>486 (48.6%)</td><td>325 (32.5%)</td><td>94 (9.4%)</td></tr><tr><th>CIO3</th><td>53 (5.3%)</td><td>367 (36.7%)</td><td>466 (46.6%)</td><td>114 (11.4%)</td></tr><tr><th>CIO4</th><td>60 (6%)</td><td>374 (37.4%)</td><td>453 (45.3%)</td><td>113 (11.3%)</td></tr><tr><th>CIO5</th><td>78 (7.8%)</td><td>376 (37.6%)</td><td>432 (43.2%)</td><td>114 (11.4%)</td></tr></tbody></table>

461

462
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