DOI: https://doi.org/10.21203/rs.3.rs-1807485/v1
The COVID-19 global crisis has put in danger more than our physical health. With containment measures people became more isolated, reducing drastically their daily social interactions. Many studies have already documented the negative impacts of these measures, highlighting fatalism. However, studies that linked the effect of this negative impact to well-being indicators are still limited. In this sense, the aim of this study is to explore the relationship between fatalism associated with COVID-19 and well-being indicators, as well as the moderating role of loneliness in this relationship. Data were collected from 1,036 adults living in Peru through an online survey that includes the Quality-of-life index, the Fatalism facing COVID-19 scale, the Loneliness Scale and the Scale for Mood Assessment. Three models were tested using linear regression and ordinary least squares with bias-corrected bootstrapping. The results confirm that fatalism has a negative effect on quality of life and a positive effect on negative affect, and that loneliness mediates both relationships, supporting the increase of fatalism the effect over well-being indicators and negative affect.
Since the first outbreak of COVID-19 in China in December 2019, the virus has spread exponentially across the world, causing an unprecedented crisis (Luchetti et al., 2020) and has had multiple effects on people’s health (Bogolyubova, et al., 2021). Local agencies and governments implemented multiple restrictive measures with different degrees of “stay-at-home” measures in order to detect and control cases of COVID-19 (Luchetti, et al. 2020). Overall, the virus and these measures caused adverse effects on people, causing drastic changes in their daily lives (changes in their routines, keeping safe distances, wearing a mask, closing educational and public centers, confinement at home, unemployment, etc.), and their well-being (Blasco-Belled, et al., 2020; Bogolyubova, et al., 2021; Chi et al., 2021). Publications about the effects of stay-at-home measures or social contact restrictions on indicators of well-being during COVID-19 have not been explored in depth (Benke, et al., 2020; Bogolyubova, et al., 2021).
As in previous pandemics, the mental health crisis related to COVID-19 has emerged due to the increase in negative psychological effects (Jacobson, et al., 2020; Rumas et al., 2021). This fact can lead to attitudes of resignation or discouragement, as people struggle to manage their daily activities and could give up or risk their health and safety to return to the previous normality (Hayes and Clerk, 2020). Frustration about the “new normality” has triggered passiveness, resignation and discouragement with respect to the situation, as well as fatalism, which has generated a sense of helplessness.
Fatalism is understood as a resignation attitude encompassed with a belief that daily actions have no effect on outcomes or inevitable life events (Bachem, et al., 2020; Bogolyubova, et al., 2021; Díaz et al., 2015; Hayes and Clerk, 2020; Torales, et al., 2020). According to different scholars (Bogolyubova, et al., 2021; Díaz et al., 2015; Hayes and Clerk, 2020), people with fatalistic beliefs exhibit increased levels of psychological distress, feelings of isolation and negative affect, and reduced levels of well-being and quality of life. Fatalistic beliefs are also a mechanism for reducing fear and anxiety. This occurs when people decide to stop controlling the uncontrollable, and therefore reduce or eliminate all the tensions created by uncertainty. The COVID-19 pandemic has led people to believe that they are unable to influence the situation (Bachem et al., 2020; Hayes and Clerk, 2020). In addition, this belief has been associated with low levels of well-being (Ngien and Jiang, 2021), subjective well-being (WHO-5; Sonderskov et al., 2020), satisfaction with life (Diaz et al., 2015); and quality of life, as well as less preventive efforts against COVID-19 (Bachem et al., 2020; Bogolyubova, et al., 2021; Hayes and Clerk, 2020).
Quality of life is understood as the convergence of several important elements for the individual (e.g., psychological well-being, emotional well-being, satisfaction with life) (Meda-Lara, et al., 2021). It is understood as the perception of one person has of its quality of life, in a general view or in relation to specific domains (Varela, et al., 2021). Mezzich et al. (2000) created the Quality-of-Life Index, which is a multidimensional subjective frame of reference for assessing quality of life that comprises elements like well-being (Meda-Lara, et al., 2021). Regarding well-being, it is important to understand it as a complex concept that is composed of remembered well-being and experienced well-being (Hervás and Vázquez, 2013). Remembered well-being is considered a cognitive component that is based on memories and judgments about people’s live. This component usually comprises life satisfaction and quality of life (Veenhoven, 2010). In turn, experienced well-being corresponds to all the appraisals that people make about affective states and feelings in real time, without considering memory. This component is usually analyzed with PANAS (Hervás and Vázquez, 2013).
According to Clair et al., (2021, p.2), the perception of loneliness is a personal measure of social isolation, which could be understood as “the inadequate quantity and/or quality of interactions with other people, including those interactions that occur at the individual, group or community level”. According to Mellor et al. (2008), loneliness is positively related to depression, suicidal ideation, and is implicated in different negative aspects of mental health. Loneliness and this social isolation due to confinement reduce satisfaction with life and well-being, while increasing psychological distress in different contexts during COVID 19 pandemic (Arslan, 2021; Benke et al., 2020; Clair et al., 2021; Labregue et al., 2021; Rossi et al., 2020; Saltzman, et al., 2020; Tull, et al., 2020).
Some scholars (Arslan, 2021; Özdemir and Tuncay, 2008; Peltzer and Pengpid, 2017) showed that university students experience loneliness and Bu et al., (2020) showed that loneliness could be a problem for students, who are at higher risk of isolation during the COVID-19 pandemic. According to Okruszek et al., (2021), loneliness increases the perception of threat, which makes the loneliest people have more negative affections against COVID-19. Arlsan (2021) states that loneliness has a negative impact on well-being, and that the loneliness of university students is associated with anxiety and depressive symptoms.
According to the above, people with high levels of loneliness have lower levels of well-being. Furthermore, a large body of evidence has shown that fatalism is correlated with lower levels of well-being and high levels of negative affection. Therefore, it is assumed that the effect of fatalism on well-being and affections will be stronger among individuals with high levels of loneliness.
Based on the literature noted above, this study has two objectives. First, to analyze the relationship between the fatalism associated with COVID-19 and indicators of well-being. Second, to determine the role of loneliness in this relationship. The relationship between fatalism and well-being indicators could be conditioned by the perception of loneliness. Therefore, the following hypotheses are proposed: (H1) Fatalism will have a relationship with well-being (negative with quality of life on specific domains, and positive with negative affect); (H2) loneliness will moderate the relationship between fatalism and quality of life on specific domains; and (H3) loneliness will moderate the relationship between fatalism and negative affect. To test the hypotheses, the variables sex and age were used as control variables.
Table 1 represents the descriptive results for the indicators of this study. As observed, the mean scores for positive emotions (M = 6.71) and quality of life (M = 7.63) present a mean above the median of the scale range assessed as opposed to negative emotions (M = 5.18), fatalism (M = 2.68) and loneliness (M = .58), whose values have a lower mean compared to the range median of these indicators. Comparing variable means according to the sex of participants, no significant differences are observed between both groups; except in the case of negative emotions, where the mean for females (M = 5.38) is statistically higher than for males (M = 4.82).
| Range | Overall Mean (SD) | Male Mean (SD) | Female Mean (SD) | p-value | ||||
|---|---|---|---|---|---|---|---|---|
| Quality of life | (1–10) | 7.63 (1.46) | 7.69 (1.41) | 7.59 (1.48) | .28 | |||
| Negative emotions | (1–10) | 5.18 (2.31) | 4.82 (2.30) | 5.38 (2.28) | .00 | |||
| Positive emotions | (1–10) | 6.71 (1.85) | 6.63 (1.85) | 6.75 (1.84) | .33 | |||
| Fatalism | (1–5) | 2.68 (.80) | 2.68 (.86) | 2.69 (0.77) | .95 | |||
| Loneliness | (1–3) | .58 (.28) | .59 (.29) | .57 (.28) | .47 |
Prior to the linear regression and moderation analyses, a correlation analysis was performed between the variables in the hypothesis of this study. The results of Pearson correlations are presented in Table 2. As observed, all the relationships between variables are significant. (p < .01). However, based on the standard interpretation of effect size (Cohen, 1988), this is high in the association between quality of life and positive emotions (r = .54), while correlation coefficients are moderate in the associations between loneliness and quality of life (r = − .49), loneliness and positive emotions (r = − .38), loneliness and negative emotions (r = .39), fatalism and negative emotions (r = .30), and quality of life and positive emotions (r = − .39). The rest of associations present a small association level.
| 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|
| 1. Loneliness | - | .23** | − .49** | − .38** | .39** |
| 2. Fatalism | - | − .26** | − .21** | .30** | |
| 3. Quality of life | - | .54** | − .39** | ||
| 4.Positive emotions | - | − .16** | |||
| 5. Negative emotions | - | ||||
| **. The correlation is significant at the 0.01 level (bilateral). | |||||
Table 3 presents the results of the analysis and control variable regressions over quality of life. Model 1 only comprises the control variables of sex and age. For this model, only the age variable results (β = .03, p < .05). In turn, Model 2 employs the variables of the first model but also included the independent variables of fatalism about COVID-19 and loneliness separately. In the case of fatalism, β = − .29 and p < .001 are reported, while for loneliness these values are β = -2.32, and p < .001.
Model 3 conducted calculations through Hayes’ PROCESS macro in order to assess the moderating effect of loneliness over the relationship between fatalism about COVID-19 and the quality-of-life indicator. The combined effect of fatalism and loneliness was negative (β = −.43, p < .01), and the regression coefficient for fatalism about COVID-19 indicator was not significant (β = −.03, p > .05) but it was for loneliness (β = −1.19, p < .01) The slope test in Table 4 indicates that the impact of fatalism on quality of life is strengthened as reported loneliness increases.
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| β | t | β | t | Β | t | |
| Age | .03* | 2.22 | .003 | .25 | .01 | .50 |
| Sex | .08 | .83 | .13 | 1.59 | .14 | 1.66 |
| Fatalism | − .29*** | -5.66 | − .03 | − .27 | ||
| Loneliness | -2.32*** | -16.35 | -1.19** | -2.61 | ||
| Fatalism vs loneliness | − .43** | -2.60 | ||||
| R2 | .04 | .26 | .27 | |||
| F(df1, df2) | 3.00(2,1029) | 91.79 (4,1021) | 75.2(5,1020) | |||
*p < .05, **p < .01, ***p < .001
| Conditional effects of moderator at M ± 1 SD (slope test) | Effect | SE | T | p |
|---|---|---|---|---|
| Loneliness Low − 1 SD (-0.28) | − .16 | .07 | -2.21 | .03 |
| Loneliness Medium M (0.00) | − .28 | .05 | -5.57 | .00 |
| Loneliness High + 1SD (0.28) | − .40 | .07 | -5.97 | .00 |
Likewise, the graphic analysis of the moderation in Fig. 1 indicates that the relationship between fatalism and quality of life presents a more pronounced slope with high levels of loneliness compared to the slope of this relationship when a low value of loneliness is used as a reference.
---------------------------------Please insert Fig. 1 here------------------------------------------
Table 5 presents the regression coefficients of the variables considered in the hypotheses of this study about the negative emotions’ indicator. For Model 1, which considers only the control variables, both variables are significant. In the case of age, β = − .07, p < .01, while for sex β = − .50, p < .01. Model 2 adds the variables of fatalism about COVID-19 and loneliness. The regression coefficient for the age variable in this model is β = − .04, p < .05, while for the sex variable is β = − .56, p < .001. Regarding independent variables, fatalism about COVID-19 exhibits a significant relationship (β = .62, p < .001), as well as loneliness (β = 2.69, p < .001).
Finally, Model 3, based on Hayes’ linear regression analysis, shows that most indicators are significant, except for the variable age (β = −.03, p > .05). This model incorporates the interaction variable of fatalism about COVID-19 vs. loneliness which yields a significant coefficient of β = − .53, p < .05. Table 6 presents the conditional effects of the moderating variable, where the impact of fatalism about COVID-19 on experienced negative emotions is weakened with the increase of the loneliness indicator.
| VD = negative emotions | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| β | t | β | t | Β | t | |
| Age | − .07** | -3.27 | − .04* | .02 | − .03 | -1.77 |
| Sex | − .50** | -3.38 | − .56*** | -4.17 | − .53** | -4.13 |
| Fatalism | .62*** | 7.61 | .66*** | 7.68 | ||
| Loneliness | 2.69*** | 11.60 | 2.58*** | 11.46 | ||
| Fatalism vs loneliness | − .53* | -1.40 | ||||
| R2 | .02 | .21 | .21 | |||
| F(df1, df2) | 12.13(2,1028) | 68.80(4,1021) | 55.48(5, 1020) | |||
*p < .05, **p < .01, ***p < .001
| Conditional effects of moderator at M ± 1 SD (slope test) | Effect | SE | t | p |
|---|---|---|---|---|
| Soledad Low − 1 SD (-0.29) | .81 | .12 | 7.03 | .00 |
| Soledad Medium M (0.00) | .66 | .08 | 8.12 | .00 |
| Soledad High + 1SD (0.29) | .51 | .11 | 4.74 | .00 |
Finally, Fig. 2 presents a moderation graph. The relationship between fatalism about COVID-19 and negative emotions has a steeper slope when lower levels of loneliness exist, compared with a straight line when there are higher levels of loneliness, for this case, the line is less steep.
---------------------------------Please insert Fig. 2 here------------------------------------------
The balance of affects presents both negative and positive emotions. Table 7 shows the results of the regression analyses when positive emotions are considered the dependent variable. When they are considered control variables (Model 1), both are not significant. Likewise, when incorporating the dependent variables separately into Model 1 (Model 2), the regression coefficient for the fatalism about COVID-19 indicator is negative and significant (β = − .29, p < .001), as is the case of loneliness (β = -2.27, p < .001).
Finally, Model 3, based on Hayes analysis, indicates that interaction of independent variables is not significant; however, the regression coefficients are still significant for fatalism about COVID-19 and loneliness at the individual level. In the case loneliness about COVID-19, these are β = -2.37, p < .001, while the values are β = − .31, p < .01 for fatalism about COVID-19.
| Model 1 | Model 2 | Model 3 | ||||
|---|---|---|---|---|---|---|
| Β | t | β | t | β | t | |
| Age | .02 | 0.72 | − .02 | − .89 | − .01 | − .89 |
| Sex | − .07 | -0.03 | − .07 | − .62 | − .07 | − .62 |
| Fatalism | − .29*** | -4.40 | − .31** | .04 | ||
| Loneliness | -2.27*** | -11.98 | -2.37*** | .00 | ||
| Fatalism vs loneliness | .03 | .91 | ||||
| R2 | .00 | .16 | .16 | |||
| F(df1, df2) | .75(2,1028) | 49.49(4,1021) | 39.56(5, 1020) | |||
*p < .05, **p < .01, ***p < .001
Descriptive results show average levels of fatalism due the possibility of COVID-19 infection on Peruvian adults. These results are similar to those of a previous study applied and released during the COVID-19 pandemic with the same population (Mejia et al 2020). According to the first hypothesis of this study, a significant relationship is observed between fatalism and indicators of well-being in both explored dimensions (quality of life and negative affects). These results agree with previous studies that related both variables and found significant variations on cognitive and affective components of well-being (Diaz, et al, 2015). However, more studies that explore this relationship are needed, especially if considered that it is not being explored enough during the COVID-19 pandemic and only few studies were found (Bachem et al., 2020; Bogolyubova, et al., 2021; Hayes and Clerk, 2020). Consequently, the results of this study constitute an important step to understand and measure well-being specific variations due to fatalism. Hopelessness has been previously associated with remembered well-being in depressed patients who perceived their quality of life as deteriorated (Duru-Asiret, et al, 2013). Fatalism could follow the same logic to affect this indicator of well-being. In addition, the threat of COVID-19 has been related to anxiety through negative affect as a mediator (Perez-Fuentes, et al, 2020). Fatalism implies the perception of an immediate threat that could explain the psychological distress that is not immediately expressed on anxiety symptoms but on the affective component of well-being.
Regarding the second hypothesis, the results show that loneliness moderates the effects of fatalism effect over indicators of perception about quality of life. Regarding the descriptive results, a strong negative relationship of these variables is observed in individuals with high levels of loneliness. The explanation for this result might be the change in the perception of quality of life’s domains and functioning due to confinement. Lonelier individuals— already affected by the negative consequences of loneliness associated with confinement—could perceive threats to economical and psychosocial well-being as more distressful than the direct impact of COVID-19 on physical health (Okruszek, et al,2020). In other words, loneliness could moderate fatalism effects due the possibility of further disconnection. A study examined trajectories of loneliness during lockdown due to COVID-19 pandemic, finding that loneliness levels increased in the highest loneliness group in the first six weeks of lockdown, and decreased in the lowest loneliness group (Bu, et al, 2020). Lonelier individuals could be more affected with confinement, increase their loneliness levels and get more disconnected from different domains of their lives.
Concerning the third hypothesis, results shows that loneliness moderates the effect of fatalism over negative affect. On descriptive results, a strong positive relationship between these variables is observed in individuals with high levels of loneliness. Loneliness has been demonstrated to explain a significant variance of psychiatric symptoms in individuals during the COVID-19 pandemic (Tso & Park, 2020); therefore, one explanation for the moderation role of loneliness may be that fatalism in less lonely individuals could act as a mechanism for reducing fear and anxiety that has indirect effects on experienced well-being (Bogolyubova, et al., 2021) but in lonelier individuals this same mechanism of resignation and helplessness could produce a further impact on this variable. Loneliness has been related to psychological functional affection and mainly to the possibility of developing depression (Erzen & Cikrikci, 2018). This condition of loneliness as risk factor could explain this moderation effect by increasing the possibility of developing psychological distress symptoms associated with fatalism.
Finally, due to the limitations of this study, further studies with longitudinal designs are necessary to observe effects of loneliness interventions on the trajectories of fatalism and well-being.
The results show the importance of loneliness in the relationship between fatalism and indicators of well-being. People with highest levels of loneliness may not handle confinement as well as people without this risk factor. Therefore, this group may need specific support on preventing further disconnection and further impact. Additionally, it is necessary to decrease levels of loneliness in the general population. Some treatments are proposed to mitigate loneliness (Cacioppo, et al, 2015): a first model that focuses on providing social support; a second model that increases opportunities for social interaction and a third one that is based on teaching lonely people to master social skills.
The participants of the study are adults living in Peru. Data collection was open and online. In total, 1,036 adults responded the survey. The mean age of participants was 22.08 years (SD = 3.27). Likewise, regarding the sex of participants, 63.7% (N = 658) were females and 36.3% (N = 375) were males. A web-based convenience sample was used. However over 1,000 observations were used for adequate estimation of the bias of the convenience sample estimator (Elliot & Haviland, 2007)
Participation was voluntary. An informed consent was attached to the survey. This document explained the objective and the voluntariness of the study. Once signed the informed consent, the participant could start the survey. The study was approved by the ethics committee of Universidad Continental (protocol number of the study’s ethical approval: N° 004-2020-CE-FH-UC)
The survey was applied through Google Forms. Data collection was conducted during the last months of 2020. Information was gathered in 14 of the 25 regions of Peru. To select participants, non-probabilistic sampling was employed, specifically the snowball technique. Therefore, respondents were asked to share the survey with their family and work connections to broaden the number of participants.
Quality of life: This scale adapted by Mezzich et al. (2000) assesses the quality of life of a person in terms of objective and subjective vital conditions. This scale measures 10 relevant aspects: psychological well-being, physical well-being, self-care, autonomous functioning, occupational functioning, interpersonal functioning, emotional and social support, community and services support, professional realization, spiritual satisfaction and an overall appraisal of quality of life. The scale has response interval that ranges from 1 to 10, (1 = bad to 10 = excellent). Concerning the reliability indexes of the scale, a Cronbach’s (α) of .94, and the correspondent omega (Ω) coefficient 0.94 are reported. Regarding the fitness indexes corresponding to the confirmatory factor analysis (CFA), the comparative fit index (CFI) is .94, the Tucker–Lewis index (TLI) is .93 and the root mean square error of approximation (RMSEA) is .09.
Fatalism about COVID-19: Scale developed by Mejía et al. (2020) that assesses through seven items the perceptions or beliefs about possible COVID-19 transmission situations (e.g., I believe that I will be admitted to hospital due to some complication). This scale is assessed through a 5-point Likert scale (1 = totally disagree to 5 = totally agree). The omega coefficient for this scale is Ω = .78, and Cronbach’s alpha is .78. Regarding the fitness indexes corresponding to the confirmatory factor analysis (CFA), the comparative fit index (CFI) is .97, the Tucker–Lewis index (TLI) is .94 and the root mean square error of approximation (RMSEA) is .07.
Loneliness: Based on De Jong Gierveld’ Loneliness Scale (De Jong Gierveld & Van Tilburg, 1999) adapted to Spanish (Buz, Urchaga and Polo, 2014). It includes eleven items with three response categories (0 = No, 1 = More or less and 2 = Yes). The scale assesses five items in a positive way (e.g., “I can count with my friends every time I need it”) and six, in a negative way (e.g., “I miss the company of other people”). Regarding the reliability indexes, a Cronbach’s alpha of .80 and an omega coefficient of .82 are reported. As for the reported adjustment indexes, CFI = .99, TLI .98 and RMSEA = .04.
Emotions: this indicator was assessed through the Scale for Mood Assessment (Sanz, 2001), which has 16 items to measure transient mood states. In this case, respondents were asked to evaluate these moods during the pandemic. The scale is Likert-type with 11 points (0 = nothing to 10 = a lot). The phrases used start with “I feel” and continue with an adjective that represents the mood (e.g., “I feel sad”, “I feel happy”). The scale presents four positive moods (joyful, happy, optimistic and cheerful), the rest of the moods are negative (e.g., mad, upset, sad). Positive and negative emotions were measured differently in this study, For the first indicator, Ω = .96 and α = .98. Regarding the adjustment indexes, CFI = .93, TLI .91 and RMSEA = .10. In the positive emotions sub-scale, the reported reliability indexes were Ω = .87 and α = .88. In the case of adjustment indexes of CFA, CFI = .99, TLI = .98 and RMSEA = .07.
First, data collected was analyzed using a descriptive analysis of means and standard deviation for each variable to be analyzed. Likewise, a T-student analysis was conducted to calculate the differences in the means of indicators by reported sex. In relation to the descriptive analysis, correlations between variables were calculated.
Prior to the descriptive analysis of variables, a confirmatory factor analysis (CFA) was calculated for each variable proposed. These calculations used the software AMOS v.22. For this analysis, the robust maximum likelihood estimator (MLR) was employed. Following Hu and Bentler (1998), the adjustment indexes of Tucker–Lewis index (TLI) and the comparative fit index (CFI), whose value should be above .90, were used, as well as the root mean square error of approximation (RMSEA), which should be below .08.
Complementarily, reliability was calculated for each indicator using Cronbach’s alpha and McDonald’s omega coefficient (Ω). For assessing the internal consistency of indicators, Kline’s criteria (2013) were followed, which consider a coefficient acceptable if higher than 0.66, and good from 0.80.
As for the linear regression models reported, and according to the hypotheses of the study, well-being indicators such as quality of life and positive and negative emotions were considered dependent variables and three models were calculated with them. In the first model, the control variables sex and age were selected as analysis variables. In the second model, the variables of fatalism about COVID-19 and loneliness were added independently. In the third model, the moderation variable of loneliness versus fatalism about COVID-19 was included.
The analysis of this last model was conducted using the PROCESS command (Darlington & Hayes, 2016) macro in SPSS v.23. PROCESS uses an ordinary least squares approach and a bias-corrected bootstrap method (with 5000 bootstrapped samples) to estimate the conditional (moderated) effect. To test the moderation model, variables were centered to the mean. Then, interactions were created from the calculation of the product of both variables. Linear regression analyses were conducted through the incorporation of two variables and the moderation variable. To determine significant interactions, a simple slope analysis was used at low (− 1 SD), and high (+ 1 SD) levels of the moderator, by means of the Johnson-Neyman technique (Spiller, Fitzsimmons, Lynch, & McClelland, 2013). Hypothesis were tested considering confidence intervals, effect size and significant interactions (p < 0.05)
For this analysis, the simple moderation model 1 proposed by Preacher, Rucker & Hayes (2007) was employed. Mild outliers in the dataset were detected for the four variables that were explored, however these were not eliminated because they were less than 5%.
All of the data and measures are available to download in an open repository at: https://osf.io/2m45x/?view_only=10abfba7596f44189fd32a88075411a0 (the link was blinded for peer review purposes).
Compliance of Ethical Standards
Disclosure of potential conflicts of interest: The authors have no conflicts of interest to disclose.
Research involving Human Participants and/or Animals: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.
Informed consent: Informed consent was obtained from all individual participants included in the study
Data availability: The datasets generated during and/or analysed during the current study are available in the OSF repository. The link was blinded for peer review purposes: https://osf.io/2m45x/?view_only=10abfba7596f44189fd32a88075411a0.
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Author’s contribution: conceptualization: GM; Methodology: GM and RM; Writing-original draft preparation: GM, RM, CTN, GF; Writing review and editing: GM, RM, CTN, GF.