© 2023, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1037/hea0001268 
Supplementary Materials
Supplementary Methods
R Packages Employed for Planned Analyses
All analyses were conducted with customized R codes (R Core Team, 2022). To implement analysis methods used in the present study, we imported packages lavaan (Rosseel, 2012) for CFA, sirt (Robitzsch, 2022) for measurement alignment, lmer (Bates et al., 2015) for frequentist MLM, brms (Bürkner, 2017) and bayestestR (Makowski et al., 2019) for Bayesian MLM.
Exploratory Bayesian Analysis of Random Slopes
To explore in which country the random slope of interest was significantly different from zero, we conducted additional exploratory Bayesian analysis of random slope models for testing H1, H2, and H3. In this process, we estimated the 95% highest density interval (HDI) of the posterior distribution of each random slope in each country. The 95% HDI indicates an interval that “any parameter value inside the HDI has higher probability density than any value outside the HDI, and the total probability of values in the 95% HDI is 95% (Kruschke, 2018; p. 271).” Once the 95% HDI was estimated, we examined to what extent the HDI overlapped with the region of practical equivalence (ROPE), which indicated the region of trivial effects within the context of the present study (Makowski et al. 2019). If 100% HDI overlaps with the predetermined ROPE, then the most credible (95%) values of the estimated effect size are likely to be within the ROPE of trivial effect, so it would be possible to assume that the examined effect is trivial and practically negligible. In the present study, we employed “-.10 < standardized effect size (e.g., D) < +.10” as a ROPE following guidelines suggested by (Kruschke, 2018). 
In the present study, by applying the aforementioned Bayesian approach, we examined which country demonstrated a significant random slope that was opposite to the general trend. For instance, in the case of H1, at the population level, vaccine attitude was positively associated with vaccine willingness. So, we examined in which country the random slope of vaccine attitude was significantly smaller than zero while demonstrating 0% HDI overlapping with the ROPE.
Supplementary Note
Relationship between the country-level mean and variance of trust and its effect size in predicting vaccine attitudes across different countries
Although we found that the random slopes of trust were significantly different across different countries, the differentiated association between trust and vaccine attitudes might be attributable to the differences in the variance of trust. More specifically, the extremely high or low mean trust or small variance in trust in a specific country might result in the small random slope of effect size of trust in that country due to the variance issue. To examine whether this is the case, we tested the association between the country-level mean and variance of trust and its effect size in predicting vaccine attitudes.
First, when we examined the correlation between the mean trust and its effect size across 43 countries, the correlation was not significant, r = .22, p = .17. This result supports the point that the country-level mean trust was not associated with the significant random slope difference. Second, we found that there was a significant association between the variance of trust and its effect size, r = .45, p < .01. As predicted, when a specific country demonstrates the relatively smaller variance in trust, then the association between trust and vaccine attitudes is likely to be smaller in that country. 
In addition to this simple correlation analysis which demonstrated the significant association between the variance and effect size, we conducted additional regression analysis to test whether the small vs. large variance in trust significantly altered the association between trust and vaccine attitudes. We classified each country into two different groups, i.e., small (variance < median) vs. large variance groups (variance ≥ median). When the regression analysis was conducted with data in the small variance group, the association between trust and vaccine attitude was significant, b = .15, t (3,973.00) = 10.54, p < .001. The similar result was found in the large variance group as well, b = .25, t (3,779.00) = 18.18, p < .001. Although the slope was smaller in the small variance group as expected, the association was still significant at p < .001.

Supplementary Tables
Table S1
Descriptive statistics of tested variables across different countries​​
	 
	Conspiratorial thinking
	Anti-expert sentiments
	Trust in government
	Trust in the scientific research community
	Vaccine attitudes
	Vaccine willingness

	
	M
	SD
	M
	SD
	M
	SD
	M
	SD
	M
	SD
	M
	SD

	All
	-.89
	1.25
	-.38
	1.39
	4.35
	3.22
	6.76
	2.75
	-.49
	1.64
	2.81
	1.54

	Belarus
	-.55
	.90
	.35
	1.16
	3.10
	3.33
	5.06
	2.80
	-1.83
	1.36
	1.64
	1.32

	Bolivia
	-.48
	1.03
	-.88
	1.19
	1.42
	1.95
	6.81
	2.56
	.40
	.97
	3.60
	1.05

	Bosnia and Herzegovina
	-1.37
	1.34
	-.44
	1.37
	1.20
	1.84
	5.65
	3.15
	-.31
	1.31
	3.12
	1.42

	Brazil
	-.51
	.82
	-.74
	.99
	1.24
	2.35
	8.98
	1.45
	.17
	.35
	3.89
	.56

	Bulgaria
	-.73
	1.35
	.41
	1.65
	2.38
	2.67
	5.12
	3.27
	-1.71
	1.81
	1.88
	1.46

	Colombia
	-.33
	1.08
	-.71
	1.02
	3.75
	2.94
	7.90
	2.15
	.54
	.99
	3.64
	1.02

	Costa Rica
	-.06
	1.05
	-.55
	1.00
	6.83
	2.78
	8.08
	1.93
	.41
	.97
	3.52
	1.13

	Czech Republic
	-.80
	1.14
	-.67
	1.29
	2.58
	2.62
	7.05
	2.85
	-1.14
	1.68
	2.77
	1.50

	Denmark
	-1.13
	.87
	-1.10
	.95
	7.81
	1.96
	8.88
	1.07
	.35
	1.28
	3.60
	.92

	Ecuador
	-.59
	1.11
	-.60
	1.14
	4.78
	2.80
	6.86
	2.28
	.33
	1.05
	3.45
	1.25

	Estonia
	-1.93
	1.04
	-.62
	.98
	5.99
	2.58
	7.98
	2.31
	.05
	1.23
	3.15
	1.38

	Finland
	-2.15
	1.22
	-1.15
	1.10
	6.61
	2.79
	8.24
	2.00
	.50
	1.31
	3.43
	1.12

	Germany
	-1.62
	1.24
	-.83
	1.23
	6.20
	2.83
	7.86
	2.53
	-.04
	1.52
	3.50
	1.09

	Guatemala
	.08
	1.06
	-.60
	1.27
	1.53
	2.09
	6.81
	2.77
	.22
	1.14
	3.30
	1.30

	Honduras
	-.09
	1.06
	-.19
	1.30
	1.27
	2.00
	5.48
	2.97
	.09
	1.03
	2.88
	1.67

	Hong Kong 
	.04
	1.02
	-.10
	.79
	4.90
	3.62
	7.25
	2.20
	-.51
	1.11
	2.68
	1.33

	Ireland
	-1.36
	.96
	-1.01
	1.22
	5.02
	2.92
	8.00
	1.98
	.25
	1.14
	3.53
	1.10

	Italy
	-1.10
	1.29
	-.55
	1.51
	4.97
	3.06
	6.94
	2.89
	-.23
	1.47
	3.02
	1.47

	Japan
	-.45
	.98
	-.21
	1.00
	3.22
	2.50
	5.08
	2.23
	-.73
	1.04
	2.54
	1.53

	Kazakhstan
	-.43
	1.15
	1.41
	1.48
	3.75
	3.19
	4.67
	3.06
	-2.39
	1.48
	1.97
	1.46

	Kyrgyzstan
	-.27
	.88
	.95
	1.31
	2.26
	2.24
	3.74
	2.72
	-2.38
	1.61
	1.41
	1.39

	Lebanon
	.11
	1.01
	.27
	1.40
	3.14
	2.65
	6.18
	2.73
	-.13
	1.08
	2.52
	1.68

	Malaysia
	-.45
	.98
	-.47
	1.23
	3.80
	3.23
	7.60
	2.27
	.55
	.87
	3.55
	1.04

	Maldives
	-.15
	.94
	-.04
	1.47
	3.09
	3.06
	6.40
	2.84
	.12
	1.03
	2.54
	1.80

	Nepal
	-.41
	.79
	-.19
	1.35
	2.80
	2.36
	6.40
	2.60
	.30
	.68
	3.26
	1.36

	New Zealand
	-1.28
	.88
	-1.11
	1.06
	8.86
	1.31
	8.61
	1.50
	.53
	.85
	3.83
	.38

	Norway
	-2.08
	1.04
	-1.37
	1.17
	6.11
	3.23
	8.10
	1.85
	.33
	1.22
	3.48
	1.09

	Other
	-.11
	1.09
	1.21
	1.36
	4.27
	3.23
	5.00
	2.78
	-2.37
	2.12
	1.43
	1.50

	Pakistan
	-.23
	.80
	.98
	1.21
	5.91
	2.78
	5.98
	2.39
	-.13
	1.02
	2.55
	1.79

	Portugal
	-1.34
	1.08
	-.35
	.90
	6.17
	2.54
	8.36
	1.66
	.35
	.82
	3.65
	.85

	Russian Federation
	-.63
	1.06
	.66
	1.44
	4.11
	2.83
	5.16
	2.68
	-2.54
	1.53
	1.18
	1.18

	Slovakia
	-.92
	1.12
	-.97
	1.30
	3.81
	2.86
	7.20
	2.66
	-.47
	1.43
	2.95
	1.44

	South Africa
	-1.47
	.83
	-.88
	1.62
	4.61
	2.60
	8.07
	1.25
	.34
	1.04
	3.48
	1.09

	Spain
	-.71
	1.19
	-.92
	1.22
	5.11
	3.04
	8.43
	1.87
	.49
	1.05
	3.61
	.97

	Sweden
	-2.05
	1.17
	-2.09
	1.21
	6.06
	2.76
	8.37
	1.63
	.63
	.93
	3.74
	.85

	Switzerland
	-1.86
	1.02
	-.65
	1.16
	7.40
	2.53
	8.02
	2.01
	-.21
	1.33
	3.28
	1.28

	Taiwan
	-1.21
	1.24
	.10
	.66
	8.37
	1.95
	8.00
	1.61
	.35
	.70
	3.60
	.94

	Turkey
	-.20
	1.26
	.09
	1.24
	3.38
	3.17
	7.90
	2.21
	.24
	1.08
	3.01
	1.44

	Uganda
	.27
	.87
	1.27
	1.54
	4.77
	2.82
	5.22
	2.47
	-.71
	1.19
	1.56
	1.69

	Ukraine
	-1.77
	1.22
	-1.94
	1.59
	3.09
	2.49
	7.60
	2.41
	.16
	1.36
	3.32
	1.34

	United Kingdom 
	-1.13
	1.08
	-.97
	1.37
	2.91
	2.82
	7.99
	2.04
	.32
	1.12
	3.52
	1.11

	United States of America
	-1.29
	1.10
	-.87
	1.25
	5.40
	3.09
	8.06
	1.96
	.34
	1.28
	3.53
	1.10

	Uruguay
	-1.31
	1.14
	-1.09
	1.06
	6.24
	3.35
	8.31
	1.91
	.22
	1.10
	3.37
	1.20


Note. Latent factor scores after alignment were used for conspiratorial thinking, anti-expert sentiments, and vaccine attitudes.

Table S2
Exploratory Bayesian analysis of the random slopes of vaccine attitude in H1​
	Country
	Median
(vaccine attitude)
	95% CI
	ROPE %

	Belarus
	-.16
	-.37
	.04
	25.36%

	Bolivia
	-.01
	-.18
	.15
	79.74%

	Bosnia and Herzegovina
	.09
	-.05
	.24
	55.56%

	Brazil
	.20
	.00
	.40
	14.94%

	Bulgaria
	-.13
	-.22
	-.04
	22.73%

	Colombia
	-.09
	-.20
	.02
	55.56%

	Costa Rica
	.00
	-.13
	.13
	90.48%

	Czech Republic
	.05
	-.04
	.14
	88.69%

	Denmark
	-.09
	-.23
	.05
	55.64%

	Ecuador
	.07
	-.06
	.19
	66.46%

	Estonia
	.09
	-.03
	.21
	56.35%

	Finland
	.00
	-.08
	.07
	100.00%

	Germany
	-.07
	-.20
	.06
	68.67%

	Guatemala
	.01
	-.11
	.11
	95.21%

	Honduras
	.20
	.04
	.37
	8.31%

	Hong Kong 
	.06
	-.16
	.26
	60.19%

	Ireland
	-.05
	-.17
	.08
	78.72%

	Italy
	.03
	-.07
	.14
	90.34%

	Japan
	.23
	.17
	.31
	.00%

	Kazakhstan
	.00
	-.15
	.18
	75.69%

	Kyrgyzstan
	-.10
	-.21
	.00
	44.25%

	Lebanon
	.09
	-.09
	.28
	53.09%

	Malaysia
	.06
	-.12
	.25
	64.64%

	Maldives
	.05
	-.16
	.27
	59.35%

	Nepal
	.00
	-.23
	.24
	62.51%

	New Zealand
	-.05
	-.27
	.15
	60.85%

	Norway
	.01
	-.10
	.11
	97.45%

	Other
	-.15
	-.32
	.01
	24.34%

	Pakistan
	.07
	-.13
	.26
	60.06%

	Portugal
	.03
	-.11
	.16
	83.79%

	Russian Federation
	-.36
	-.42
	-.30
	.00%

	Slovakia
	.08
	-.03
	.19
	64.69%

	South Africa
	.05
	-.14
	.26
	66.30%

	Spain
	-.03
	-.13
	.07
	92.13%

	Sweden
	-.01
	-.19
	.16
	75.56%

	Switzerland
	.05
	-.04
	.14
	86.42%

	Taiwan
	-.08
	-.26
	.09
	57.22%

	Turkey
	.06
	-.13
	.24
	64.72%

	Uganda
	-.11
	-.34
	.10
	44.75%

	Ukraine
	.00
	-.11
	.11
	97.21%

	United Kingdom 
	-.07
	-.25
	.09
	62.98%

	United States of America
	-.05
	-.21
	.11
	72.09%

	Uruguay
	.06
	-.05
	.18
	71.59%


Note. Countries reported a random slope that was opposite to the general trend with 0% HDI overlapping with the ROPE were highlighted.

Table S3
Exploratory Bayesian analysis of the random slopes of conspiratorial beliefs in H2
	Country
	Median
(conspiratorial beliefs)
	95% CI
	ROPE %

	Belarus
	.02
	-.19
	.26
	62.43%

	Bolivia
	.04
	-.11
	.20
	75.27%

	Bosnia and Herzegovina
	-.01
	-.16
	.14
	84.90%

	Brazil
	.15
	.03
	.27
	18.21%

	Bulgaria
	-.24
	-.33
	-.14
	.00%

	Colombia
	.13
	.04
	.22
	21.89%

	Costa Rica
	.09
	-.03
	.21
	56.25%

	Czech Republic
	-.29
	-.39
	-.19
	.00%

	Denmark
	-.05
	-.23
	.13
	69.56%

	Ecuador
	.08
	-.03
	.21
	60.64%

	Estonia
	-.19
	-.31
	-.05
	7.58%

	Finland
	-.06
	-.13
	.02
	87.32%

	Germany
	-.14
	-.28
	-.01
	23.18%

	Guatemala
	-.03
	-.14
	.10
	91.92%

	Honduras
	.29
	.14
	.43
	.00%

	Hong Kong 
	.01
	-.19
	.23
	66.75%

	Ireland
	-.03
	-.16
	.10
	85.00%

	Italy
	-.06
	-.17
	.05
	75.61%

	Japan
	.12
	.06
	.19
	22.18%

	Kazakhstan
	-.03
	-.26
	.19
	65.54%

	Kyrgyzstan
	-.01
	-.15
	.13
	85.82%

	Lebanon
	-.03
	-.20
	.13
	77.14%

	Malaysia
	.02
	-.16
	.19
	75.74%

	Maldives
	.07
	-.14
	.28
	55.93%

	Nepal
	.06
	-.19
	.30
	53.70%

	New Zealand
	.03
	-.20
	.26
	63.83%

	Norway
	-.03
	-.14
	.09
	90.63%

	Other
	-.05
	-.27
	.18
	60.56%

	Pakistan
	-.05
	-.26
	.17
	62.38%

	Portugal
	.17
	.06
	.29
	9.16%

	Russian Federation
	.04
	-.03
	.11
	97.13%

	Slovakia
	-.25
	-.39
	-.12
	.00%

	South Africa
	-.08
	-.30
	.14
	52.51%

	Spain
	.07
	-.02
	.16
	75.66%

	Sweden
	.09
	-.05
	.23
	53.49%

	Switzerland
	-.11
	-.21
	-.02
	42.49%

	Taiwan
	.17
	.05
	.28
	8.97%

	Turkey
	.13
	-.02
	.31
	34.68%

	Uganda
	-.01
	-.22
	.22
	67.11%

	Ukraine
	-.02
	-.13
	.09
	94.32%

	United Kingdom 
	.00
	-.17
	.16
	80.16%

	United States of America
	-.04
	-.20
	.10
	77.14%

	Uruguay
	-.03
	-.15
	.07
	87.53%


Note. Countries reported a random slope that was opposite to the general trend with 0% HDI overlapping with the ROPE were highlighted.

Table S4
Exploratory Bayesian analysis of the random slopes of trust in government in H2
	Country
	Median
(trust in government)
	95% CI
	ROPE %

	Belarus
	.01
	-.16
	.18
	78.32%

	Bolivia
	-.07
	-.21
	.08
	66.01%

	Bosnia and Herzegovina
	-.02
	-.17
	.14
	82.00%

	Brazil
	-.22
	-.33
	-.12
	.00%

	Bulgaria
	.11
	.01
	.23
	39.31%

	Colombia
	-.10
	-.18
	-.01
	51.07%

	Costa Rica
	-.04
	-.16
	.07
	86.61%

	Czech Republic
	.05
	-.06
	.16
	80.87%

	Denmark
	.15
	-.02
	.33
	26.15%

	Ecuador
	-.11
	-.23
	.00
	40.38%

	Estonia
	.05
	-.07
	.17
	82.11%

	Finland
	.17
	.10
	.26
	.00%

	Germany
	.05
	-.08
	.19
	75.48%

	Guatemala
	-.16
	-.30
	-.04
	13.97%

	Honduras
	-.09
	-.23
	.05
	55.72%

	Hong Kong 
	.12
	-.05
	.30
	40.09%

	Ireland
	-.02
	-.12
	.08
	95.97%

	Italy
	.21
	.10
	.32
	.00%

	Japan
	-.03
	-.09
	.03
	100.00%

	Kazakhstan
	.14
	-.05
	.32
	33.02%

	Kyrgyzstan
	.16
	.03
	.30
	14.92%

	Lebanon
	-.04
	-.19
	.12
	79.16%

	Malaysia
	-.12
	-.27
	.02
	39.38%

	Maldives
	-.08
	-.26
	.10
	59.56%

	Nepal
	-.11
	-.31
	.08
	44.20%

	New Zealand
	-.02
	-.24
	.23
	61.56%

	Norway
	.11
	.02
	.21
	40.59%

	Other
	.20
	.03
	.40
	12.10%

	Pakistan
	.01
	-.17
	.19
	76.40%

	Portugal
	.03
	-.10
	.15
	89.48%

	Russian Federation
	.17
	.10
	.23
	.00%

	Slovakia
	-.03
	-.15
	.09
	89.79%

	South Africa
	-.02
	-.19
	.15
	77.48%

	Spain
	-.02
	-.10
	.07
	99.16%

	Sweden
	-.12
	-.26
	.01
	35.86%

	Switzerland
	.12
	.02
	.22
	33.31%

	Taiwan
	-.07
	-.24
	.08
	62.40%

	Turkey
	-.17
	-.33
	.00
	19.57%

	Uganda
	.07
	-.10
	.23
	64.48%

	Ukraine
	-.13
	-.24
	-.01
	29.54%

	United Kingdom 
	-.10
	-.24
	.04
	49.78%

	United States of America
	.00
	-.14
	.14
	87.19%

	Uruguay
	-.05
	-.16
	.04
	82.37%


Note. Countries reported a random slope that was opposite to the general trend with 0% HDI overlapping with the ROPE were highlighted.

Table S5
Exploratory Bayesian analysis of the random slopes of anti-expert sentiments in H3
	Country
	Median
(anti-expert sentiments)
	95% CI
	ROPE %

	Belarus
	-.08
	-.25
	.06
	58.33%

	Bolivia
	.01
	-.13
	.14
	89.95%

	Bosnia and Herzegovina
	-.01
	-.14
	.12
	90.37%

	Brazil
	.04
	-.05
	.14
	90.34%

	Bulgaria
	-.12
	-.21
	-.03
	35.07%

	Colombia
	.01
	-.07
	.10
	99.92%

	Costa Rica
	.00
	-.11
	.10
	98.61%

	Czech Republic
	.01
	-.09
	.11
	98.40%

	Denmark
	-.10
	-.24
	.03
	47.67%

	Ecuador
	.07
	-.04
	.18
	69.38%

	Estonia
	.03
	-.08
	.14
	90.34%

	Finland
	-.08
	-.15
	.00
	72.19%

	Germany
	-.01
	-.12
	.11
	95.32%

	Guatemala
	-.04
	-.14
	.05
	91.34%

	Honduras
	.08
	-.02
	.20
	61.59%

	Hong Kong 
	.03
	-.12
	.19
	80.11%

	Ireland
	.01
	-.08
	.11
	97.68%

	Italy
	.01
	-.08
	.11
	97.29%

	Japan
	.02
	-.03
	.08
	100.00%

	Kazakhstan
	.01
	-.14
	.17
	82.93%

	Kyrgyzstan
	-.05
	-.15
	.04
	84.14%

	Lebanon
	.00
	-.13
	.12
	92.53%

	Malaysia
	.05
	-.09
	.18
	78.40%

	Maldives
	.02
	-.11
	.16
	88.11%

	Nepal
	.02
	-.14
	.17
	82.27%

	New Zealand
	.03
	-.12
	.18
	81.35%

	Norway
	-.16
	-.26
	-.06
	9.63%

	Other
	.05
	-.11
	.22
	72.03%

	Pakistan
	.07
	-.07
	.22
	63.64%

	Portugal
	-.01
	-.11
	.10
	97.45%

	Russian Federation
	.02
	-.03
	.07
	100.00%

	Slovakia
	-.15
	-.26
	-.04
	18.21%

	South Africa
	.06
	-.06
	.20
	70.93%

	Spain
	.05
	-.02
	.13
	89.79%

	Sweden
	-.01
	-.14
	.12
	91.58%

	Switzerland
	-.05
	-.14
	.02
	86.14%

	Taiwan
	.03
	-.10
	.15
	87.90%

	Turkey
	.00
	-.14
	.14
	88.19%

	Uganda
	.01
	-.11
	.13
	91.98%

	Ukraine
	.07
	-.01
	.14
	81.06%

	United Kingdom 
	.00
	-.12
	.12
	95.29%

	United States of America
	.06
	-.06
	.19
	72.43%

	Uruguay
	-.06
	-.16
	.05
	79.14%



Table S6
Exploratory Bayesian analysis of the random slopes of trust in the scientific research community in H3
	Country
	Median
(trust in science)
	95% CI
	ROPE %

	Belarus
	-.07
	-.28
	.14
	57.96%

	Bolivia
	-.23
	-.39
	-.10
	.34%

	Bosnia and Herzegovina
	-.16
	-.31
	-.03
	16.13%

	Brazil
	-.20
	-.35
	-.06
	6.34%

	Bulgaria
	.07
	-.04
	.17
	73.53%

	Colombia
	-.09
	-.19
	.02
	59.93%

	Costa Rica
	.04
	-.12
	.18
	77.58%

	Czech Republic
	.20
	.08
	.31
	2.10%

	Denmark
	.16
	-.07
	.43
	30.18%

	Ecuador
	-.07
	-.20
	.08
	67.32%

	Estonia
	.21
	.08
	.34
	2.45%

	Finland
	.20
	.11
	.29
	.00%

	Germany
	.19
	.04
	.36
	9.79%

	Guatemala
	-.08
	-.20
	.03
	61.27%

	Honduras
	-.22
	-.33
	-.10
	.00%

	Hong Kong 
	.13
	-.10
	.35
	39.02%

	Ireland
	.06
	-.08
	.21
	70.43%

	Italy
	.17
	.05
	.28
	9.71%

	Japan
	-.13
	-.20
	-.06
	18.71%

	Kazakhstan
	.07
	-.13
	.28
	58.46%

	Kyrgyzstan
	-.04
	-.15
	.08
	87.24%

	Lebanon
	-.11
	-.27
	.05
	43.83%

	Malaysia
	-.13
	-.31
	.06
	34.99%

	Maldives
	-.08
	-.27
	.12
	54.49%

	Nepal
	-.17
	-.40
	.05
	24.99%

	New Zealand
	-.14
	-.40
	.12
	36.75%

	Norway
	-.04
	-.18
	.09
	79.29%

	Other
	.30
	.06
	.54
	2.16%

	Pakistan
	-.06
	-.25
	.14
	63.67%

	Portugal
	-.03
	-.17
	.10
	82.79%

	Russian Federation
	.04
	-.02
	.11
	96.21%

	Slovakia
	.06
	-.07
	.17
	78.90%

	South Africa
	.02
	-.24
	.29
	54.93%

	Spain
	.17
	.05
	.29
	8.97%

	Sweden
	-.16
	-.37
	.04
	26.34%

	Switzerland
	.13
	.02
	.24
	26.68%

	Taiwan
	-.19
	-.35
	-.03
	9.76%

	Turkey
	-.05
	-.25
	.12
	65.32%

	Uganda
	-.03
	-.23
	.16
	70.56%

	Ukraine
	.18
	.06
	.29
	6.63%

	United Kingdom 
	-.05
	-.22
	.15
	70.30%

	United States of America
	.15
	-.05
	.39
	30.70%

	Uruguay
	.04
	-.10
	.17
	81.50%


Note. Countries reported a random slope that was opposite to the general trend with 0% HDI overlapping with the ROPE were highlighted.

Supplementary Figures
Figure S1
The full path model of Ms (simple mediation model)



Figure S2
The full path model of Mc (multiple mediation model)



COVIDiSTRESS II Consortium

Angélique M. Blackburn1, Hyemin Han2, Rebekah Gelpí3, Sabrina Stöckli4, Alma Jeftic5, Brendan Ch'ng6, Karolina Koszałkowska7, David Lacko8, Taciano L. Milfont9, Yookyung Lee10, Sara Vestergren11, Thao P. Tran12, Siobhán M. Griffin13, Evangelos Ntontis14, Stavroula Chrona15, Gözde Ikizer16, Douglas Parry17, Grace Byrne18, Mercedes Gómez-López19, Alida Acosta20, Marta Kowal21, Gabriel De Leon1, Aranza Gallegos1, Miles Perez1,  Mohamed, Abdelrahman 22, Elayne Ahern23, Ahmad Wali Ahmad Yar24, Oli Ahmed25, Nael H Alami26, Rizwana Amin27, Lykke E. Andersen28, Bráulio Oliveira Araújo29, Norah Aziamin Asongu30, Fabian Bartsch31, Jozef Bavoľár32, Khem Raj Bhatta33, Tuba Bircan24, Bita Shalani34, Hasitha Bombuwala35, Tymofii Brik36, Huseyin Cakal11, Marjolein C.J Caniëls37, Marcela Carballo38, Nathalia M. Carvalho39, Laura Cely 40, Sophie Chang41, Maria Chayinska42, Fang-Yu Chen43, John Bosco Chika Chukwuorji44, Ana Raquel Costa29, Vidijah Ligalaba Dalizu45, İlknur Dilekler Aldemir16, Anne M Doherty48, Rianne Doller35, Dmitrii Dubrov49, Salem Elegbede35, Jefferson Elizalde50, Eda Ermagan-Caglar51, Regina Fernández-Morales52, Juan Diego García-Castro53, Shagofah Ghafori24, Ximena Goldberg54,55, Catalina González-Uribe40, Alpízar-Rojas Harlen53, Christian Andres Palacios Haugestad56, Diana Higuera-Mendieta40, Kristof Hoorelbeke46, Evgeniya Hristova57, Barbora Hubená58, Hamidul Huq59, Keiko Ihaya60, Gosith Jayathilake35, Enyi Jen41, Amaani Jinadasa35, Jelena Joksimovic61, Pavol Kačmár32, Veselina Kadreva57, Kalina Nikolova Kalinova62, Huda Anter Abdallah Kandeel63,  Blerina Kellezi64, Sammyh Khan65, Maria Kontogianni64, Krzysztof Hanusz66,  Miguel Landa-Blanco67, Andreas Lieberoth68, Samuel Lins29, Liudmila Liutsko54,69,70, Amanda Londero-Santos71, Anne Lundahl-Mauritsen68, María Andrée Maegli72, Patience Magidie73, Roji Maharjan33, Tsvetelina Makaveeva74, Malose Makhubela75, María Gálvis Malagón40, Sergey Malykh76, Salomé Mamede29, Samuel Mandillah77,78, Mohammad Sabbir Mansoor79, Silvia Mari80, Inmaculada Marín-López19, Tiago A. Marot39, Sandra Martínez-Cabezas40, Juma Mauka81, Sigrun Marie Moss56, Asia Mushtaq82, Arian Musliu83, Daniel Mususa84, Arooj Najmussaqib82, Aishath Nasheeda85, Ramona Nasr26, Natalia Niño-Machado40, Jean Carlos Natividade39, Honest Prosper Ngowi86, Carolyne Nyarangi35, Charles Ogunbode87, Charles Onyutha88, K. Padmakumar89, Walter Paniagua90, Maria Caridad Pena91, Martin Pírko92, Mayda Portela38, Hamidreza Pouretemad93, Nikolay R. Rachev74,  Muhamad Ratodi94, Jason Reifler95, Saeid Sadeghi93, Harishanth Samuel Sahayanathan96, Eva Sanchez1, Ella Marie Sandbakken97, Dhakal Sandesh33, Shrestha Sanjesh33, Jana Schrötter32, Sabarjah Shanthakumar35, Pilleriin Sikka98,99,100, Konstantina Slaveykova101, Anna Studzinska102, Fadelia Deby Subandi103, Namita Subedi33, Gavin Brent Sullivan104, Benjamin Tag105, Takem Ebangha Agbor Delphine106, William Tamayo-Agudelo107, Giovanni A. Travaglino108, Jarno Tuominen98, Tuğba Türk-Kurtça109, Matutu Vakai110, Tatiana Volkodav111, Austin Horng-En Wang Wang112, Alphonsus Williams113, Charles Wu114, Yuki Yamada115, Teodora Yaneva74, Nicolás Yañez40, Yao-Yuan Yeh116, Emina Zoletic117

Consortia Affiliations

1Texas A&M International University, Laredo, TX, USA, 2University of Alabama, USA, 3University of Toronto, Canada, 4University of Bern, Switzerland, 5International Christian University, Japan, 6University of Malaya, Malaysia, 7University of Lodz, Poland, 8Masaryk University, Czechia, 9University of Waikato, New Zealand, 10The University of Texas at Austin, USA, 11Keele University, UK, 12Colorado State University, USA, 13University of Limerick, Ireland, 14The Open University, UK, 15Queens University Belfast, UK, 16TOBB University of Economics and Technology, Turkey, 17Stellenbosch University, South Africa, 18Vrije Universiteit Amsterdam, Netherlands, 19Universidad de Córdoba, Spain, 20Universidad Autónoma de Bucaramanga, Colombia, 21University of Wroclaw, Poland, 22Doha Institute for Graduate Studies, Qatar, 23Dublin City University, Ireland, 24Vrije Universiteit Brussel, Belgium, 25University of Chittagong, Bangladesh, 26Modern University for Business and Science, Lebanon, 27Bahria University Islamabad Campus, Pakistan, 28Sustainable Development Solutions Network, Bolivia, 29University of Porto, Portugal, 30National Centre for Education, Cameroon, 31Montpellier Business School, France, 32Pavol Jozef Safarik University in Kosice, Slovakia, 33Tribhuvan University, Nepal, 34Tarbiat Modares University, Iran, 35Icare Sustainably International, (Sri Lanka, Netherlands, Kenya, Switzerland), 36Kyiv School of Economics, Ukraine, 37Open Universiteit, Netherlands, 38Universidad Católica del Uruguay, Uruguay, 39Pontifical Catholic University of Rio de Janeiro, Brazil, 40Universidad de Los Andes, Colombia, 41Radboud University, Netherlands, 42University of Messina, Italy, 43Soochow University, Taiwan, 44University of Nigeria, Nigeria, 45The Hill School, Kenya, 46Ghent University, Belgium, 48University College Dublin, Ireland, 49National Research University Higher School of Economics, Russian Federation, 50Universidad del Azuay, Ecuador, 51The Embassy of Turkey in North Nicosia, Cyprus, 52Universidad Francisco Marroquin, Guatemala, 53Universidad de Costa Rica, Costa Rica, 54ISGlobal (Barcelona Institute for Global Health), Spain, 55CIBER Salud Mental, Spain, 56University of Oslo, Norway, 57New Bulgarian University, Bulgaria, 58Ministry of Health, Prague, Czechia, 59United International University, Bangladesh, 60Fukuoka Institute of Technology, Japan, 61University of Belgrade, Serbia, 62Leiden University, Netherlands, 63Assiut University, Egypt, 64Nottingham Trent University, UK, 65Örebro University, Sweden, 66Institute of Psychology Polish Academy of Sciences, Poland, 67National Autonomous University of Honduras (UNAH), Honduras, 68Aarhus University, Denmark, 69Lomonosov MSU, Russia, 70URFU Ekaterinburg, Russia, 71Federal University of Rio de Janeiro, Brazil, 72Universidad del Valle de Guatemala, Guatemala, 73Independent Researcher, South Africa, 74Sofia University St. Kliment Ohridski, Bulgaria, 75University of Limpopo, South Africa, 76Psychological Institute of Russian Academy of Education, Russia, 77Rathinam College of Arts and Science, India, 78Ajua Africa, Kenya, 79Tribhuvan University, Nepal, 80University of Milano-Bicocca, Italy, 81Rolffortress communications, Kenya, 82National University of Modern Languages, Pakistan, 83Ludwig Maximilian University, Germany, 84SIVIO Institute, Zimbabwe, 85Villa College, Maldives, 86Mzumbe University, Tanzania, 87University of Nottingham, UK, 88Kyambogo University, Uganda, 89Manipal Academy of Higher Education, India, 90Universidad Rafael Landivar, Guatemala, 91Universidad de Las Américas (UDLA), Ecuador, 92Mendel University, Czechia, 93Shahid Beheshti University, Iran, 94State Islamic University of Sunan Ampel, Indonesia, 95University of Exeter, UK, 96University of Kelaniya, Sri Lanka, 97Oslo New University, Norway, 98University of Turku, Finland, 99University of Skövde, Sweden, 100Stanford University, USA, 101University of Wellington, New Zealand, 102ICAM, France, 103Universitas Indonesia, Indonesia, 104International Psychoanalytic University Berlin, Germany, 105The University of Melbourne, Australia, 106National Institute of Cartography, Cameroon, 107Universidad Cooperativa de Colombia, Colombia, 108Royal Holloway, University of London, UK, 109Trakya University, Turkey, 110North - West University, South Africa, 111Kuban State University, Russia, 112University of Nevada, USA, 113Sustainable Environmental Solutions-Sweden, Sweden, 114University of South Alabama, USA, 115Kyushu University, Japan, 116University of St. Thomas, USA, 117University of Warsaw, Poland 



Supplementary References
Bates, D. (2005). Fitting linear mixed models in R. R News, 5(1), 27-30.
Bürkner, P. C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80, 1-28. https://doi.org/10.18637/jss.v080.i01 
Kruschke, J. K. (2018). Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270-280. https://doi.org/10.1177%2F2515245918771304 
Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2019). bayestestR: Describing effects and their uncertainty, existence and significance within the Bayesian framework. Journal of Open Source Software, 4(40), 1541. https://doi.org/10.21105/joss.01541 
R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 
Robitzsch, A. (2022). sirt: Supplementary Item Response Theory Models. R package version 3.12-66. https://CRAN.R-project.org/package=sirt.  
Rosseel Y. (2012). “lavaan: An R Package for Structural Equation Modeling.” Journal of Statistical Software, 48(2), 1–36. doi: 10.18637/jss.v048.i02.

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