# Social and temporal disorientation during the Covid-19 pandemic: An analysis of 3306 responses to a quantitative questionnaire

Pablo Fernandez Velasco, Bastien Perroy, Umer Gurchani, Roberto Casati

<figure><img src="image_1.png" /></figure>

## To cite this version:

Pablo Fernandez Velasco, Bastien Perroy, Umer Gurchani, Roberto Casati. Social and temporal disorientation during the Covid-19 pandemic: An analysis of 3306 responses to a quantitative questionnaire. British Journal of Psychology, 2024, 115 (4), pp.577 - 598. 10.1111/bjop.12704. hal-04865019

HAL Id: hal-04865019

https://ens.hal.science/hal-04865019v1

Submitted on 5 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

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

Distributed under a Creative Commons Attribution 4.0 International License
ARTICLE

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

the british
psychological society
promoting excellence in psychology

# Social and temporal disorientation during the Covid-19 pandemic: An analysis of 3306 responses to a quantitative questionnaire

Pablo Fernandez Velasco<fnref n="1" /> ID
Roberto Casati<fnref n="2" />

Bastien Perroy<fnref n="2" />

Umer Gurchani<fnref n="2" />

Correspondence

Pablo Fernandez Velasco, Department of
Philosophy, University of York, York, UK.
Email: p.fernandezvelasco@gmail.com

Funding information

Agence Nationale de la Recherche, Grant/
Award Number: ANR-17- EURE-0017 and
RA-COVID-19 V11; Irish Research Council,
Grant/Award Number: GOIPD/2021/570;
British Academy, Grant/Award Number:
PFSS23\,230053

Abstract

The societal hallmark of the Covid-19 pandemic was a set of mitigation measures such as lockdowns and curfews. The cognitive impact on the public of the resulting spatial, social and temporal constraints is still being investigated. While pandemic time has been extensively studied and mostly described as slowed down and elongated, opposite experimental patterns across national and social contexts leave open an important explanatory gap in order to understand which factor has been causally fundamental in determining the phenomenology of the crisis. In this paper, we use a quantitative questionnaire developed for measuring temporal and social disorientation on a sample of 3306 respondents during an acute phase of restrictions in France. We show that social disorientation greatly contributed to the temporal disruptions experienced during the pandemic. This result reinforces the importance for public authorities to address the compounding effect of feeling isolated during crises.

KEYWORDS

Covid-19, disorientation, pandemic, social isolation, temporal disruption

BACKGROUND

By now, there are a multitude of studies about the multiplicity of experiential disruptions and distortions caused by the Covid-19 crisis. Disorientation offers a helpful lens through which to make sense of the documented phenomenological heterogeneity of the pandemic (Benedikter & Fathi, 2022; di Friedberg, 2021; Henk, 2020; Means & Slater, 2021; Nelson, 2020; Oinas, 2021; Ratcliffe, 2021; van

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited.

© 2024 The Authors. *British Journal of Psychology* published by John Wiley & Sons Ltd on behalf of The British Psychological Society.

<footnote n="1">¹Department of Philosophy, University of York,
York, UK</footnote>

<footnote n="2">²Institut Jean Nicod (ENS, EHESS, CNRS),
Paris, France</footnote>
Gils-Schmidt et al., 2020). At the onset of the pandemic, our team set out to study the Covid-19 crisis using disorientation as a conceptual framework, focusing in particular on its social and temporal dimensions. To achieve this goal, we developed a quantitative questionnaire spanning 59 questions: the instrument to measure temporal and social disorientation (ITSD). In this paper, we analyse 3306 responses to the ITSD.

To contextualize our study, we will first review existing research on the social and temporal disruptions caused by the pandemic, highlighting some key conceptual distinctions as well as some overarching patterns.

Regarding time distortions, something that has particularly struck researchers is the perdurance of these distortions throughout the different stages of the pandemic (Droit-Volet et al., 2021; Ogden, 2021). The most investigated phenomenon has been the alteration of *passage of time judgements* (e.g. ‘time passes faster’). These are noticeably different from duration judgements (e.g. ‘it has been 5 minutes since I left the room’), as the former are judgements about a *feeling* of the passage of time, while the latter are about an interval production task, whether prospective or retrospective (Wearden, 2015). In the context of the pandemic, we dub ‘slow’ or ‘fast’ the feelings in terms of their absolute mean across the slow–fast spectrum of passage of time judgements, while ‘slower’ or ‘faster’ refers to the relative evolution compared to pre-pandemic times. Both slow and fast feelings of passage of time have been documented across countries: slow in France (Droit-Volet et al., 2020, 2021) slow in Italy (Cellini et al., 2020), slow in Iraq (Alatrany et al., 2022), both fast and slow in the UK (Ogden, 2020), fast in Uruguay (Loose et al., 2021) and fast in Argentina (Brenlla et al., 2022).

The varying directions of the distortions have left researchers puzzled when it comes to advancing a unified picture of the temporal experience of the pandemic. In Germany, the biggest study to date has found a fast feeling of passage of time (i.e. in absolute terms) but also a feeling of slower passage compared to pre-pandemic level. This suggests that the pandemic has had a significant impact on the experience of time—mostly in slowing time down, whether or not the feeling ends up being fast or slow—while also corroborating the importance of complex cognitive patterns found across different social environments (e.g. significant gendered differences in Argentina (Brenlla et al., 2022)), or cultural contexts (e.g. a polychronic time culture emphasized in Iraq (Alatrany et al., 2022)).

Another important and similarly studied cognitive aspect of the temporal experience of the pandemic has been subjective temporal distances. They have been labelled as such in some studies (Chaumon et al., 2021), while others referred to them as duration judgements (Kosak et al., 2022), 8-month passage of time judgements (Ogden, 2021) or 11-month passage of time judgements (Alatrany et al., 2022), with 8 and 11 months being the objective temporal distance from the beginning of the first lockdown at the time of questioning. Although the terms differ, all the studies mentioned refer to short/long apprehension of time between one's temporal situation and a temporal landmark/period. These judgements are suspected to rely more on memory processes compared to passage of time judgements (Ogden, 2021; Zauberman et al., 2010).

Pandemic time was reported as overall longer than normal in most of the studies that concomitantly found an overall slow time (Chaumon et al., 2021; Ogden, 2021). However, it was reported as shorter in the only study that tested them alongside fast passage of time judgements, which makes sense considering that both are known to align despite their different cognitive underpinnings (Kosak et al., 2022). Other noteworthy time distortions of the pandemic include confusion in temporal self-location (i.e. recalling what day of the week it is (Cellini et al., 2020)), disruptions of prospective memory abilities (i.e. recalling action intentions (Pisano et al., 2021)) or shifts in temporal perspectives (Loose et al., 2021; Micillo et al., 2021), which have been found to be related to compliance with health restrictions (Barnes, 2021; Sobol et al., 2020) and overall mental health (Holman & Grisham, 2020; Micillo et al., 2021).

These key determinants of the temporal phenomenology of the Covid-19 crisis are closely modulated by several negative affects known to be on the rise during the pandemic, such as anxiety (Micillo et al., 2021; Pisano et al., 2021; Santomauro et al., 2021), boredom (Martinelli et al., 2021; Tutzer et al., 2021; Wessels et al., 2022) and, most importantly, loneliness. Accordingly, loneliness will be the
main affect we tackle in this paper, which we capture as part of the broader notion of social disorientation. It is important to distinguish loneliness (or perceived isolation) from social isolation (or objective isolation): Loneliness was already widespread before the pandemic, especially for older adults, and may not coincide totally with (objective) social isolation (Hwang et al., 2020). During the pandemic, loneliness was associated with poor life satisfaction across domains and increased work-related stress (Clair et al., 2021).

When it comes to its interplay with time distortions, while (objective) social isolation has been found to be significantly associated with alterations of duration judgements (i.e. time production tasks (Chaumon et al., 2021; Ingram et al., 2021)), (perceived) social isolation is distinctively the most influential factor for the temporal disruptions mentioned above: It is the most noteworthy factor for slower passage of time judgements and for longer subjective temporal distances (Chaumon et al., 2021; Kosak et al., 2022; Ogden, 2021); as well as for time expansion (Cravo et al., 2022).

While the studies mentioned above emphasize the significant association between social disorientation and time distortions (especially in the direction mostly described for the pandemic: a general slowing and elongation of time), none of them really manages to elucidate this association. This is one of the main gaps in the literature that we address in the present study.

To understand the relationship between social disorientation and time disruptions, it is helpful to look at the phenomenology of secluded lonely elders in residential care during Covid-19. In the study by Aho (Aho, 2022), loneliness is described as a mood insofar as it is ‘affectively shading the things we're involved with in daily life' (p. 4) and 'directed at everything, to existence as a whole' (p. 8, original emphasis). In the author's own words, 'loneliness is often expressed in terms of existing without a future' (p. 10), suggesting that loneliness precedes time disruptions, which are described as instances in which time becomes 'shapeless', 'drags on' and in which days become indistinguishable from one another due to 'no meaningful human contact' (p. 10). This leaves people 'confused and abandoned' (p. 3) and with 'a blanketing sense of disorientation and estrangement that makes possible an array of different and oftentimes conflicting emotions' (p. 7).

The above description of loneliness as a disorientation stemming from the social world and transforming temporal phenomenology with a wide wealth of conflicting temporal affects corresponds to our own previous results. A thematic and a phenomenological analysis of qualitative reports about the experience of both social and temporal distortions (retrieved during restriction phases in France and in the UK) showed a heterogeneity of temporal disruptions, especially when compared to social disorientation, which was a more unified phenomenon (Perroy et al., 2022; Velasco et al., 2023).

In a recent review of isolation studies and time perception studies, van Wassenhove (2022) conceives different instances of temporal disorientation as resulting from a disruption of the key orientational mechanisms underpinning temporal experience, most notably what she terms 'temporal cognitive maps'. This is in line with recent neuroscientific findings that the hippocampus maps not only physical but also semantic (Solomon et al., 2019; Viganò & Piazza, 2020), conceptual (Constantinescu et al., 2016), social (Tavares et al., 2015) and temporal structures (MacDonald et al., 2011). In this light, disorientation emerges from the disruptions of cognitive maps in a variety of domains (Velasco et al., 2021). In what concerns us, the disruptions to the social and temporal maps caused by the pandemic elicited widespread social and temporal disorientation. Disorientation here is to be understood as a sustained existential feeling (akin to depression (Ratcliffe, 2005; Stephan, 2012)), which in turn encompasses a variety of affectively charged disruptions.

This conception of social and temporal disorientation offers a theoretical framework through which to approach the experienced distortions of the pandemic. With this framework in mind, we set out to clarify the experienced temporal and social disruptions caused by the Covid-19 crisis. We followed a multistage process to create a quantitative questionnaire, the ITSD. Our distribution efforts resulted in a sample of 3306 respondents in France in Spring 2021.

As we have seen, time distortions have been documented in opposite directions across national and social contexts. Previous research has hypothesized a connection between social and temporal disruptions. The aim of the present study is to clarify the relationship between the different overarching
components of the social and temporal disruptions experienced during the pandemic. Based on the existing literature and on theoretical considerations, our hypothesis is that, during the Covid-19 pandemic, social disorientation had a strong impact on temporal disorientation. In other words, a disruption of the processes people use to orient within social frameworks cascaded into a disruption of the processes people use to orient within temporal frameworks.

## METHODS

To conduct this study, we created an instrument (ITSD) through a multiple-stage development process. We first created and distributed an open-ended, qualitative questionnaire. We then ran a thematic analysis of the responses to that qualitative questionnaire. Based on the thematic analysis, we developed the ITSD, an instrument that could measure social and temporal disorientation and we modified it based on feedback from four external experts in different relevant fields (cognitive sciences, phenomenology, geography and psychology). The survey distribution strategy, demographic details and component structure for this instrument have been discussed in detail in a previous paper validating the ITSD (Fernandez Velasco, Gurchani et al., 2022). In that paper, we validated the ITSD through the 3-faced construct validation process (Kyriazos, 2018), using exploratory and then confirmatory factor analysis.

The ITSD questionnaire was distributed through both email (4724 respondents) and Twitter (727 respondents) in May and June 2021. This was just during a 6-month-old curfew in France, which was preceded by the 7 weeks of the second national lockdown in late 2020. There was no financial compensation for completing the questionnaire. The sample size was 5453 participants, of which 3306 participants completed the full survey comprising 59 questions. The questionnaire was fully anonymous, and participants completed a written consent form online. All research involving human participants has followed the ethical procedures for approval at our institution. The Pôle Éthique of the Institut des Sciences Biologiques (INSB) of the Centre national de la recherche scientifique (CNRS) waived all ethical approval for fully anonymous questionnaires. The study has been conducted according to the principles expressed in the Declaration of Helsinki.

The ITSD includes nine demographic questions, the MacArthur Scale of Subjective Social Status, 11 questions on lifestyle changes (pre-pandemic vs. post-pandemic), 6 social disorientation questions, 16 temporal disorientation questions (divided into three blocks) and 13 questions from the Global Psychotrauma Screening. The questionnaire was hosted on Qualtrics.

The factor analysis in our validation paper revealed a series of latent variables. Each variable corresponds to a set of questions which together form a unified component. Not all questions were associated with a latent variable during the validation process; consequently, these questions were excluded from subsequent analyses. Social disorientation forms a unified component (SD; Questions 20, 21, 22, 23, 24, 27), with the exceptions of Q25 and Q26, which pertain to the capabilities of creating and maintaining social relationships using digital tools. The same is not true for temporal disorientation. This, once again, points to the heterogeneity of temporal disruptions. Temporal disorientation thus becomes an umbrella term for the following temporal disruption components:

1. POT: Passage of Time (Questions 48, 49, 50)

2. TD: Temporal Distance (Questions 45, 46, 47)

3. TOE: Temporal Order of Events (Questions 42, 43, 54)

4. FO: Future Orientation (Questions 52, 56, 57)

5. TSL: Temporal Self Location (Questions 40, 41)

6. TR: Temporal Rupture (Questions 44, 58)

Our review of existing literature in the introduction covered passage of time (POT) and temporal distance (TD). Temporal order of events (TOE) refers to the subject's ability to remember the order in which events happened. Future orientation (FO) is a participant's ease to imagine the future and the
extent to which she feels anxious or in control. Temporal self-location (TSL) corresponds to the partic-
ipant knowing what day of the week, or what month of the year it currently is. Finally, temporal rupture
(TR) refers to the sense that there is a rift separating the events before and after the pandemic as well
as time feeling unreal.

The lifestyle changes component, which captures changes in routine between pre-pandemic and pandemic times, was created by adding three different components. These components measure the change between pre-pandemic and pandemic times:

1. Week activity: This component is calculated by subtracting values between questions 16 and 12, reflecting changes in physical activity levels during the week.

2. Weekend activity: This component is calculated by subtracting values between questions 17 and 13, indicating changes in physical activity levels over the weekends.

3. Activity changes: This component is calculated by subtracting values between questions 15 and 11, representing changes in overall activity levels.

In addition, the questions from the Global Psychotrauma Screening decomposed into two com-
ponents. The first one (questions 61–69), we refer to as the Global Psychotrauma Screening (GPS)
component, and the second one (questions 70, 71 and 72), we refer to as the extreme trauma (ET)
component. The two components originally come from the previous existing global psychotrauma
screening, but they appear as separate in the result of our factor analysis. Finally, question 25 forms
its own component—*ability to create relationships online*—, and it serves in our analysis as an instru-
mental variable.

With as many as 59 questions, we can gain a more detailed insight than existing studies on the tempo-
ral and social disruptions of the Covid-19 crisis. Clustering questions into components gives us a further
understanding of the underlying nature of the disruptions. Moreover, the high number of participants
in our study allowed us to run more intricate statistical analyses in order to understand the relationship
between the different variables.

Our data set consists of ordinal-scale measurements. Previous research has stressed bi-modality in
people's judgements about COVID-related temporal distortions, particularly concerning the passage
of time component (Ogden, 2020; Ogden & Piovesan, 2022). Owing to these considerations, we forgo
the normality assumption, opting for a non-parametric method to scrutinize group differences. We
employed Kruskal-Wallis tests as omnibus tests in conjunction with post hoc Mann-Whitney *U*-tests
to investigate group differences on each component. In light of the numerous tests and post hoc tests,
as well as our important statistical power with *n* >3000, we set the alpha threshold at the more demand-
ing .01 value. Most importantly, our analysis focused on effect sizes, computing Vandekar's *S* (Vandekar
et al., 2020) for significant differences. *S*, a robust version of Cohen's *d*, denotes a small effect size with
*S* ≈ 0.1, a medium effect size with *S* ≈ 0.25 and a large effect size with *S* ≈ 0.40.

As mentioned in the introduction, previous studies had found an association between affective states that belong to social disorientation (e.g. loneliness) and time disruptions that fall under the umbrella of temporal disorientation. However, no study to date has corrected the measured effect with a 2SLS regression (assuming the causal direction). In our analysis, we take two key steps: first, we assess the potential presence of multicollinearity to establish social disorientation as an independent variable. Subsequently, we aim to elucidate and quantify the relationship between social and temporal disorientation.

We begin by giving an overview of the responses to the questionnaire. There, we note the bi-modal distribution in both passage of time judgements and temporal distance judgements. We then contrast the responses coming from individuals belonging to different demographic groups. Next, and as a first step in our stated goal of teasing out the relationship among existing variables, we create a correlation matrix. Our final goal, however, was to shine a light on the connections between variables. To this end, we start with establishing the endogeneity of the social disorientation variable and we then run a two-stage least squares regression analysis (2SLS) to estimate the effect of the social disorientation variable on temporal disorientation variables.
# RESULTS

## Overview

At first glance, several demographic effects seem to be present. Table 1 displays deviations from the mean with either positive or negative values for distinct, interesting groups within our sample. Values across various components were cautiously aligned so that negative values correspond to disruptions of one's orientation compared to the mean. Correspondingly, component values are standardized as predicted component value, minus the mean, divided by the standard deviation. Difficulties such as emotional connection with others, self-projection into the future, temporal self-placement, chronological ordering of events and feeling a connection with one's past, as well as trauma, are all viewed as overall disruptions in one's orientation. Past research has also underscored that a slower (as opposed to faster) perception of time and longer (not shorter) temporal distances are associated with critical disruptions (Chaumon et al., 2021), such as depression (Martinelli et al., 2021), thereby informing our decision to frame the directionalities accordingly.

The younger the people in our sample were, the more disoriented they seemed to be across both social and temporal components. Female respondents seemed more disoriented than male respondents. Students seemed more disoriented than employed respondents. We assess the significance of these mean differences with Kruskal-Wallis as omnibus tests in conjunction with post hoc Mann-Whitney *U*-tests in a later subsection. Among temporal components, passage of time showed the highest level of standard deviation, across all demographic groups. As this component has been the most extensively studied during the crisis, we focus on it in the next section.

## Effect of demographics

Table 2 shows effect sizes associated with significant group differences. We now summarize the effects from most important to only noteworthy. Complete test statistics for both Kruskal-Wallis and post hoc Mann-Whitney *U*-tests are available in the Data S1. All tests within this section are Holm-Bonferroni corrected to prevent alpha error inflation, with the corresponding corrected alpha thresholds reported in the Data S1.

The most important difference was whether the respondent was a student ($n=2791$) or not ($n=515$), for which all components appeared significantly different with Kruskal-Wallis tests, except for passage of time ($\chi^2(2) = 4.25, p=.039$). Post hoc Mann-Whitney *U*-tests confirmed a more disoriented phenomenology for students across components ($p<.001$), except for passage of time, with the two noteworthy largest effect sizes concerning the global psycho trauma component ($S=0.30$) and future orientation ($S=0.26$).

We tested age groups by clustering respondents into three categories (inferior 25 years old; in between 25 and 60; superior to 60) and comparing young with middle-aged and middle-aged with older respondents). Kruskal-Wallis tests revealed that middle-aged respondents ($n=704$) compared to older respondents ($n=89$) showed significant differences across all components ($p<.001$), except for passage of time ($\chi^2(2) = 0.26, p=.61$). Due to the important disruption of the job market, which mainly impacts middle-aged respondents, this makes it possible that this age group would be more disoriented. Post hoc Mann-Whitney confirmed a more disoriented phenomenology across components except for passage of time and social disorientation for middle-aged respondents compared to those who were older ($p<.001$), with the strongest effect sizes on global psycho trauma ($S=0.29$) and future orientation ($S=0.25$). Kruskal-Wallis tests also revealed that young ($n=2508$) and middle-aged respondents ($n=704$) showed significant differences across all components ($p<.001$). Post hoc Mann-Whitney *U*-tests showed that young respondents compared to middle-aged ones presented a more disoriented phenomenology across all components ($p<.001$), except for lifestyle changes. This included a slightly slower passage of time
<table><caption>TABLE 1 Descriptive statistics of the mean values of the components from the ITSD and GPS per demographic groups after CFA.</caption><thead><tr><th>Characteristics</th><th>Count</th><th>Mean social disorientation (SD)</th><th>Mean future orientation (SD)</th><th>Mean temporal self-location (SD)</th><th>Mean temporal order of events (SD)</th><th>Mean temporal rupture (SD)</th><th>Mean temporal distance (SD)</th><th>Mean passage of time (SD)</th><th>Mean global psychotrauma screen (SD)</th><th>Mean extreme trauma (SD)</th></tr></thead><tbody><tr><td>All</td><td>3306</td><td>0 (0.79)</td><td>0 (0.55)</td><td>0 (0.4)</td><td>0 (0.8)</td><td>0 (0.36)</td><td>0 (0.66)</td><td>0 (0.89)</td><td>0 (0.9)</td><td>0 (0.45)</td></tr><tr><td>Age</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>From 18 to 25</td><td>2508</td><td>-0.07 (0.78)</td><td>-0.07 (0.52)</td><td>-0.04 (0.4)</td><td>-0.1 (0.78)</td><td>-0.04 (0.35)</td><td>-0.08 (0.62)</td><td>-0.03 (0.91)</td><td>-0.14 (0.85)</td><td>-0.04 (0.45)</td></tr><tr><td>From 25 to 60</td><td>715</td><td>0.18 (0.77)</td><td>0.19 (0.55)</td><td>0.13 (0.39)</td><td>0.26 (0.81)</td><td>0.11 (0.37)</td><td>0.23 (0.69)</td><td>0.09 (0.83)</td><td>0.37 (0.92)</td><td>0.11 (0.42)</td></tr><tr><td>Over 60</td><td>78</td><td>0.48 (0.72)</td><td>0.55 (0.43)</td><td>0.25 (0.25)</td><td>0.63 (0.61)</td><td>0.32 (0.31)</td><td>0.56 (0.52)</td><td>0.04 (0.61)</td><td>1.03 (0.68)</td><td>0.29 (0.3)</td></tr><tr><td>Gender</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>990</td><td>0.12 (0.8)</td><td>0.16 (0.55)</td><td>0.07 (0.41)</td><td>0.21 (0.83)</td><td>0.11 (0.37)</td><td>0.14 (0.7)</td><td>0.09 (0.87)</td><td>0.3 (0.93)</td><td>0.1 (0.43)</td></tr><tr><td>Female</td><td>2273</td><td>-0.05 (0.77)</td><td>-0.07 (0.53)</td><td>-0.03 (0.39)</td><td>-0.08 (0.77)</td><td>-0.05 (0.35)</td><td>-0.06 (0.62)</td><td>-0.04 (0.89)</td><td>-0.12 (0.86)</td><td>-0.04 (0.45)</td></tr><tr><td>Other</td><td>43</td><td>-0.03 (0.83)</td><td>-0.03 (0.6)</td><td>-0.06 (0.42)</td><td>-0.29 (0.82)</td><td>-0.05 (0.43)</td><td>-0.03 (0.78)</td><td>0.12 (0.94)</td><td>-0.29 (0.94)</td><td>-0.27 (0.55)</td></tr><tr><td>Main activity</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Student</td><td>2392</td><td>-0.06 (0.78)</td><td>-0.07 (0.53)</td><td>-0.04 (0.4)</td><td>-0.07 (0.78)</td><td>-0.04 (0.35)</td><td>-0.07 (0.63)</td><td>-0.02 (0.91)</td><td>-0.13 (0.85)</td><td>-0.03 (0.45)</td></tr><tr><td>Employed</td><td>342</td><td>0.25 (0.75)</td><td>0.29 (0.5)</td><td>0.17 (0.35)</td><td>0.35 (0.81)</td><td>0.15 (0.36)</td><td>0.31 (0.67)</td><td>0.05 (0.75)</td><td>0.58 (0.86)</td><td>0.17 (0.4)</td></tr><tr><td>The higher the mean the...</td><td></td><td>More oriented socially</td><td>Easier to project oneself in the future</td><td>Easier to locate oneself in time</td><td>Easier to order events</td><td>More connected time felt</td><td>Shorter time felt</td><td>Faster time felt</td><td>Lesser trauma</td><td>Lesser extreme trauma</td></tr></tbody></table>

Note: Scales have been normalized around a mean of 0 during factor analysis with Python's psych library. Demographic groups are exclusive (respondents with multiple belongings are not presented). Only the most noteworthy main activity categories are displayed. An exhaustive and inclusive activity table is present in the Data S1. The values of some questions in POT, TID, FO, TSF framed in opposite directions have been inverted when integrated into a single component during the validation process.

Abbreviation: SD, for standard deviation.
(S = 0.06). The most important difference between these two age groups concerned the global psycho trauma component (S = 0.23).

Gender differences were significant with Kruskal-Wallis tests across all components (*p* < .001). A series of post hoc Mann-Whitney *U*-tests showed that women displayed a more disoriented phenomenology across the board (*p* < .001). The top three most noteworthy effect sizes were for global psycho trauma (S = 0.21), Future orientation (S = 0.19) as well as temporal rupture (S = 0.19).

A final noteworthy variable was whether our respondents held intimate weekly journals (n = 311) or not (n = 2995). All components were found to be significantly different through Kruskal-Wallis tests (*p* < .001) except for passage of time (*p* = .35) and changes in life (*p* = .08). Respondents feeling the need to use a journal on a regular basis were plausibly more disoriented than other respondents, which post hoc Mann-Whitney *U*-tests confirmed across all components (*p* < .001), except for passage of time, temporal distances and lifestyle changes. The most noteworthy effect sizes concerned extreme trauma (S = 0.16), global psycho trauma (S = 0.14), temporal rupture (S = 0.11) and temporal order of events (S = 0.10).

Two variables were found to be associated with significant differences but only with weak effect sizes: Whether one was living alone (n = 585) and whether one reported being located average or below average on the MacArthur scale of subjective social status (n = 1127). As a result of the weak effect sizes, we do not elaborate on them any further. Finally, several variables were not associated with significant differences. These include whether our respondents had received vaccine shots (n = 984), whether they reported being vulnerable to Covid-19 (n = 281) or whether they were recently diagnosed with Covid-19 (n = 326). We refer the reader to the Data S1 for complete test statistics.

To better understand how age, gender and student status affect our findings, we conducted an additional stratified analysis (Table 3). We stratified the data by gender to study age effects; by age to study gender effects; and specifically looked at the role of student status among younger respondents while stratifying for gender.

Table 3 reaffirms that the two most polarizing factors were future orientation and trauma, which particularly disadvantaged our female and younger participants. It is also noteworthy that passage of time was not influenced by sociodemographic factors, other than a small difference between young women compared to young men, which stands in contrast with all other temporal components. Because we do not have many young people who are not students in our sample, it is hard to see any specific effects on students (after considering age and gender). We did not find any significant differences between men and women over 60, but our ability to detect such differences was limited.

## Passage of time and temporal distances

We confirm previous findings—both in France (Droit-Volet et al., 2021) and across several countries (Alatrany et al., 2022; Cellini et al., 2020; Ogden, 2021)—showing that during the pandemic, people have largely experienced time as slow and elongated. Most importantly, we find a tendency for both slow and fast *passage of time judgements* (at times) in Q48 and Q49. We also find a tendency for both long and short *temporal distance judgements* (at times) in Q45 and Q46 (although note that Q47 shows the majority, 73.4%, felt the time before the pandemic felt further away, compared to just 6.8% who perceived it as closer; Figure 1). A similar distribution has been found and stressed in the UK (Ogden, 2020) and in Germany (Kosak et al., 2022).

We designed the ITSD with three rather than a single judgement for each of these two components. Our design with three questions allows us to precisely pinpoint disorientation effects in time disruptions: Among our respondents, 13.9% strongly agreed or agreed to both fast and slow experiences of time (at times), and 14.1% agreed or strongly agreed to both long and short experiences of temporal distance (at times). This counterintuitive aspect of the temporal phenomenology of the crisis outlines not only that time was lived as disrupted, but that it was a temporally disorienting one.
<table><caption>TABLE 3 Stratified analysis showing effect sizes (Vandekar's S) associated with features forming significant group differences after post hoc Mann-Whitney U-tests with Holm-Bonferroni correction.</caption><thead><tr><th>Variables of interest</th><th>Comparisons</th><th>n1</th><th>n2</th><th>TSL</th><th>TR</th><th>TOE</th><th>TD</th><th>POT</th><th>SD</th><th>FO</th><th>CIL</th><th>GPS</th><th>ET</th></tr></thead><tbody><tr><td>Age</td><td>Men: &lt;25 versus in between 25-60</td><td>647</td><td>297</td><td>0.20</td><td>0.14</td><td>0.19</td><td>0.21</td><td>0.15</td><td>0.18</td><td>0.22</td><td></td><td>0.23</td><td>0.14</td></tr><tr><td>Age</td><td>Men: in between 25-60 versus &gt; 60</td><td>297</td><td>46</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.22</td><td></td><td>0.24</td><td></td></tr><tr><td>Age</td><td>Men: &lt;25 versus &gt; 60</td><td>647</td><td>46</td><td>0.25</td><td>0.21</td><td>0.24</td><td>0.25</td><td>0.18</td><td>0.27</td><td>0.17</td><td>0.32</td><td>0.21</td><td></td></tr><tr><td>Age</td><td>Women: &lt;25 versus in between 25-60</td><td>1830</td><td>398</td><td>0.15</td><td>0.15</td><td>0.15</td><td>0.15</td><td>0.11</td><td>0.18</td><td>0.26</td><td>0.20</td><td>0.19</td><td>0.11</td></tr><tr><td>Age</td><td>Women: in between 25-60 versus &gt; 60</td><td>398</td><td>42</td><td></td><td>0.20</td><td></td><td>0.24</td><td></td><td></td><td>0.26</td><td></td><td>0.32</td><td></td></tr><tr><td>Age</td><td>Women &lt;25 versus &gt; 60</td><td>1830</td><td>42</td><td>0.15</td><td>0.19</td><td>0.18</td><td>0.21</td><td>0.09</td><td>0.25</td><td>0.12</td><td>0.28</td><td>0.16</td><td></td></tr><tr><td>Gender</td><td>Aged &lt;25; women versus men</td><td>1830</td><td>647</td><td>0.08</td><td>0.17</td><td>0.13</td><td>0.09</td><td>0.07</td><td></td><td>0.16</td><td></td><td>0.17</td><td>0.12</td></tr><tr><td>Gender</td><td>Aged In between 25-60; women versus men</td><td>398</td><td>297</td><td>0.12</td><td>0.14</td><td>0.15</td><td>0.13</td><td></td><td></td><td>0.14</td><td></td><td>0.18</td><td>0.14</td></tr><tr><td>Gender</td><td>Aged &gt; 60; women versus men</td><td>42</td><td>46</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Student status</td><td>Men &lt; 25; student versus not-student</td><td>630</td><td>17</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Student status</td><td>Women &lt; 25; student versus not-student</td><td>1806</td><td>24</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: No effect size is reported when the difference was not found statistically significant.
<figure><img src="image_4.png" /><figcaption>FIGURE 1 Distribution of answers for questions subtending temporal distance (in blue, Q45, Q46, Q47) and passage of time (in red, Q48, Q49, Q50).</figcaption></figure>

## Correlation matrix

As a first step to understand the connections among the different components in our study, we plotted the following correlation matrix (see Figure 2).

The weakest correlations in the matrix are between the lifestyle changes component and all of the components of temporal and social disorientation. The second weakest correlations are between passage of time and all other components, ranging between a correlation below .25 with temporal self-location, temporal order of events and extreme trauma (.23, .16, .19, respectively), and a correlation of .35 with temporal distance. All of the other components in the matrix are correlated with each other above .33, with the majority of those correlations above .5.

## Two-stage least squares regression analysis

As we saw in the Background section, previous studies had identified the affective elements of social disorientation (e.g. loneliness, perceived isolation...) to be strongly associated with time disruptions, while the same was not the case for objective measurements of social isolation. We obtain a similar finding in a study, with lifestyle changes not being a strong predictor of temporal disorientation (Fernandez Velasco, Perroy et al., 2022). Thus, from a theoretical standpoint, social disorientation made the strongest candidate for an independent variable.

In our previous work, we have established a distinction between social disorientation, GPS and ET using exploratory factor analysis (Fernandez Velasco, Gurchani et al., 2022). While social disorientation is conceptually different from GPS and ET, there is a potential for a relationship between them, and this can lead to multi-collinearity in our models (as both GPS and ET are included as independent variables in our analysis). We found it essential to elucidate the quantitative relationship between SD, GPS and ET. For this purpose, we ran a regression model in which social disorientation was considered a dependent variable and ET and GPS were considered independent variables.

This regression model (Figure 3) confirms that despite being conceptually different from social disorientation, GPS is highly correlated with it. Therefore, to check for multi-collinearity, we calculated the variance inflation factor for each variable. In general, a VIF value of 1 indicates that there is no
<figure><img src="image_5.png" /><figcaption>FIGURE 2 Correlation matrix for latent variables. All correlation coefficients are significant (p < .0001).</figcaption></figure>

multicollinearity, while a VIF value of greater than 1 indicates the presence of some degree of multi-collinearity (Neter et al., 1989). As a rule of thumb, a VIF value greater than 5 or 10 indicates a high degree of multicollinearity and suggests that the variable should be removed from the model. As seen in Table 4 below, VIF values are indicative of some level of multi-collinearity, but they are not high enough to exclude any of the independent variables from the analysis.

## Regression analysis

We created regression models for each of the temporal components and evaluated the initial parameters for independent variables. These results were then corrected using 2SLS regression and a justifiable instrument variable to estimate the impact of *social disorientation* on temporal disruptions. The following are the linear models used for six of the temporal disruption variables, with social disorientation, global psychotrauma screen and extreme trauma as dependent variables:

$$TOE = \beta_0 + \beta_1(SD) + \beta_2(GPS) + \beta_3(ET) + \beta_4(CIL) + \epsilon$$

$$TSL = \beta_0 + \beta_1(SD) + \beta_2(GPS) + \beta_3(ET) + \beta_4(CIL) + \epsilon$$

$$POT = \beta_0 + \beta_1(SD) + \beta_2(GPS) + \beta_3(ET) + \beta_4(CIL) + \epsilon$$
<table><caption>FIGURE 3 GPS represents global psychotrauma screen, whereas ET represents extreme trauma experience (as listed in Questions 70, 71 and 72).</caption><thead><tr><th colspan="2">Dependent variable:</th></tr><tr><th colspan="2">SD</th></tr></thead><tbody><tr><td>GPS</td><td>0.626***<br>(0.015)</td></tr><tr><td>ET</td><td>0.021<br>(0.031)</td></tr><tr><td>Constant</td><td>0.000<br>(0.009)</td></tr><tr><td colspan="2">Observations 3,306</td></tr><tr><td colspan="2">R2 0.530</td></tr><tr><td colspan="2">Adjusted R2 0.530</td></tr><tr><td colspan="2">Residual Std. Error 0.538 (df = 3303)</td></tr><tr><td colspan="2">F Statistic 1,863.646*** (df = 2; 3303)</td></tr><tr><td colspan="2">Note: *p&lt;0.1; **p&lt;0.05; ***p&lt;0.01</td></tr></tbody></table>

$$TD = \beta_0 + \beta_1(SD) + \beta_2(GPS) + \beta_3(ET) + \beta_4(CIL) + \epsilon$$

$$TR = \beta_0 + \beta_1(SD) + \beta_2(GPS) + \beta_3(ET) + \beta_4(CIL) + \epsilon$$

$$FO = \beta_0 + \beta_1(SD) + \beta_2(GPS) + \beta_3(ET) + \beta_4(CIL) + \epsilon$$

We use a Wooldridge test to show that there are endogeneity issues with the above regression models leading to biased results in the OLS models, and that a 2SLS regression step will help us alleviate
<table><caption>TABLE 4 VIF checks if there is multi-collinearity among the independent variables.</caption><thead><tr><td>Independent variable</td><td>Variance inflation factor</td></tr></thead><tbody><tr><td>Global psych trauma (GPS)</td><td>3.32</td></tr><tr><td>Extreme trauma (ET)</td><td>2.18</td></tr><tr><td>Changes in life (CIL)</td><td>1.08</td></tr><tr><td>Social disorientation (SD)</td><td>2.15</td></tr></tbody></table>

<table><caption>TABLE 5 Correlations between the instrument variable and all other variables.</caption><thead><tr><th rowspan="2">Instrument variable</th><th colspan="6">Dependent variables</th><th rowspan="2">Independent variable<br>SD</th></tr><tr><th>TOE</th><th>TSL</th><th>POT</th><th>TD</th><th>TR</th><th>FO</th></tr></thead><tbody><tr><td>Correlation</td><td>-.069</td><td>-.1203</td><td>-.114</td><td>-.097</td><td>-.12531</td><td>-.17701</td><td>-.2195</td></tr><tr><td>p-value</td><td>.00001</td><td>.0000</td><td>.0000</td><td>.000001</td><td>.000498</td><td>.0000</td><td>.000</td></tr></tbody></table>

these issues. Wooldridge's test for endogeneity checks if an independent variable is correlated with the error term in a regression model. By including the residuals from an auxiliary regression as an additional explanatory variable, the test helps identify whether OLS estimates are biased due to endogeneity (Wooldridge, 2013). However, to check for suspected endogeneity, we will need an instrument variable. To find a valid instrument variable, the following two conditions for instrument selection must be met (Stock & Watson, 2011):

Relevance: For an instrument not to be weak, it must have a reasonable correlation with the endogenous independent variable.

Exogeneity: For the instrument to be valid, it should not have any direct relationship with the dependent variable, except through the endogenous variable (which in our case is *Social Disorientation*).

Using the above two criteria, a potential instrument variable was identified, namely the ‘individual’s ability to create relationships online’. In our survey, this component was covered by Question 25: ‘I was able to build and maintain new relationships through digital tools’.

To establish the relevance condition, we checked for correlation between the chosen instrument variable and the endogenous variable. Table 5 shows the results of the correlation between the instrument variable (Q25) and all variables of interest (including the suspected endogenous variable SD). As noticed in the results, the strongest correlation exists between the instrument variable and the independent variable (–.2195), which makes it likely that the relevance condition is met.

Evaluating the exogeneity condition for an instrumental variable (IV) is more complex than verifying its relevance due to the unobservable nature of the true error term in regression models. An indirect approach, such as the Durbin–Wu–Hausman test, can be helpful in this case. This test does not directly check for exogeneity but by comparing ordinary least squares (OLS) and two-stage least squares (2SLS) estimates, it helps us infer whether the instrument variable is uncorrelated with the error term (Hausman, 1978). A significant difference between OLS and 2SLS estimates (*p*-value lower than .05) suggests that the instrument variable is potentially endogenous whereas the converse can establish exogeneity condition of instrument variable. Table 6 shows the results of the Durbin–Wu–Hausman test.

The Durbin–Wu–Hausman (DWH) test does not directly assess exogeneity. However, based on the results presented in Table 6, it is noteworthy that there are significant differences observed between OLS and 2SLS estimates in two out of six models. While this finding does not conclusively establish exogeneity, it provides suggestive evidence that the exogeneity condition may be satisfied. This observation
<table><caption>TABLE 6 Results from Durbin-Wu-Hausman test.</caption><thead><tr><td>Models</td><td>Dw_hausman_p</td><td>Exogeneity_pass</td></tr></thead><tbody><tr><td>TOE = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>.764</td><td>True</td></tr><tr><td>TSL = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>.00006</td><td>False</td></tr><tr><td>POT = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>.00000</td><td>False</td></tr><tr><td>TD = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>.135</td><td>True</td></tr><tr><td>TR = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>.00068</td><td>False</td></tr><tr><td>FO = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>.00000</td><td>False</td></tr></tbody></table>

<table><caption>TABLE 7 The results of endogeneity where the suspected endogenous variable is 'SD' and Question 25 is taken as instrument variable.</caption><thead><tr><td>Model</td><td>Wooldridge's test statistic (t-value)</td><td>p-value</td><td>Endogeneity present</td></tr></thead><tbody><tr><td>TOE = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>12.043</td><td>1.01e-32</td><td>Strong evidence</td></tr><tr><td>TSL = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>-23.8267</td><td>6.42e-116</td><td>Strong evidence</td></tr><tr><td>POT = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>-18.973</td><td>2.85e-76</td><td>Strong evidence</td></tr><tr><td>TD = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>-5.4887</td><td>4.35e-08</td><td>Strong evidence</td></tr><tr><td>TR = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>-4.3167</td><td>1.63e-05</td><td>Strong evidence</td></tr><tr><td>FO = β<sub>0</sub> + β<sub>1</sub>(SD) + β<sub>2</sub>(GPS) + β<sub>3</sub>(ET) + β<sub>4</sub>(CIL) + ε</td><td>-18.8801</td><td>1.4e-75</td><td>Strong evidence</td></tr></tbody></table>

can be further supported by the argument that an individual's ability to engage in online socialization should theoretically have no direct connection to any of the temporal factors or their associated error terms, reinforcing the plausibility of the exogeneity assumption in this context.

After identifying the instrument variable, we ran the Wooldridge's test for endogeneity which showed the following results (see Table 7).

As argued earlier, one of the direct results of multiple confinements (due to lockdown, quarantine and curfew) during the Covid-19 crisis was the widespread experience of social disorientation. While social disorientation correlates with many components of temporal disorientation, there are many confounding variables, which complicated our aim of determining the nature of the relationship between social disorientation and temporal disorientation components.

One of these confounding variables could be the change in daily routines. Due to lockdown-related restrictions, it was no longer possible for people to perform routine activities such as going to the office or school, going to the gym or meeting friends after work. Lockdown meant a break from all such tasks and instead shifted many workers and students to perform work-related tasks using the internet. This change of routine can impact temporal experience as well as diminish the social life of an individual, therefore acting as a potential confounding variable when assessing the relationship between social and temporal disorientation. However, this confounding variable should be independent from our chosen instrument variable *individual's ability to create relationships online*, as lockdown-related restrictions only applied to physical socialization and did not have a direct impact on the online socialization behaviour. Thus, the choice of our instrument variable allows us to isolate the impact of social disorientation on temporal disorientation.

Coefficients shown in Table 8 can be interpreted as corrected coefficients for the impact of social disorientation variable on independent variables. From the comparison of coefficients, we can see that social disorientation is one of the main factors contributing to temporal disorientation factors. Note that for each pair of components (e.g. SD-PoT), there is a notable contrast between correlations in the matrix (e.g. .26) and coefficients in the regressions (e.g. .627), which highlights the importance of carrying out the 2SLS analysis.
number of participants described time as passing quickly as well as time as passing slowly (at times, not overall). Regarding temporal distance, most participants describe the beginning of the pandemic as feeling farther away overall.

A final factor that contributes to temporal experience is demographics. Having a large sample allows us to compare groups across a variety of demographic variables in a reliable way. What we find is that comparing groups that vary in gender, occupation and perceived class results in statistically significant differences in a social disorientation, temporal rupture, temporal order of events and future orientation. As was the case in previous studies (Pierce et al., 2020), we found that women and younger participants experienced stronger disruptions than male and older participants, respectively. In general, the younger participants were more disoriented than older participants, particularly when it came to the global psy-chotrauma and the future orientation components. Many researchers have documented the struggle of students regarding mental health during COVID restrictions (Jehi et al., 2022), and this is something that we also find in our sample: Students showed higher levels of disorientation for all components except for passage of time. Passage of time emerged as the component exhibiting the least consensus regarding the direction of distortion (i.e. slower or faster) in our sample, evidenced by possessing the highest standard deviation among all temporal components. Furthermore, it was the aspect of disorientation least influ-enced by sociodemographic factors. In this context, our results are consistent with previous research, which identified emotions—rather than sociodemographic characteristics—as the predominant predic-tors of perceptions related to the passage of time during the pandemic (Martinelli et al., 2021).

Thanks to its large number of questions, the ITSD provides a very detailed picture of the effects on social and temporal experience. Our goal, however, goes beyond reporting those effects. We aimed to gain an understanding of how the main components of social and temporal disorientation interact. A first step in this direction was creating a correlation matrix of the components that had been revealed during the validation process of the ITSD.

Through lockdowns, curfews, homeworking, social distancing and quarantines, Covid-19 mea-sures brought about a radical restructuring of routines and patterns of movement. Nevertheless, pre-vious studies found that perceived social isolation was a better predictor than objective measures of isolation when it came to explaining distortions in temporal experience (Chaumon et al., 2021). Our correlation matrix points in a similar direction: The lifestyle changes component, which captures changes in routine between pre-pandemic and pandemic times (i.e. it was constructed by subtracting the level of physical activity (known through Likert scale) and time spent at home during Covid-19 from pre-COVID physical activity and time spent at home), shows the weakest correlations with all of the components of temporal and social disorientation. In fact, notwithstanding lifestyle changes, the only correlations below .25 are between passage of time and three components: temporal self-location, temporal order of events and extreme trauma (.23, .16, and .19, respectively). This might indicate that the feeling of time passing is somewhat independent of both the ability to locate events with respect to each other and the ability to locate oneself with respect to events (or larger frame-works, such as time of the day or day of the month). Moreover, temporal self-location and temporal order of events are strongly correlated (.68), so it is likely that there is substantial overlap in the cognitive processes underlying both abilities but little overlap with the cognitive processes contributing to the sense of time passing. The strongest correlation for passage of time is with temporal distance (.35). This indicates that whether one feels time as short or long (temporal distance) partly overlaps with the feeling that the flow of time is fast or slow.

Temporal rupture is a novel addition to the literature, and it seems to be an important component in the experience of the pandemic. It shows a substantial correlation with temporal order of events, with temporal distance and with future orientation (.7, .69, .76, respectively), as well as with social disorienta-tion (.6) and with temporal self-location (.66). This indicates that the larger the experienced rift between pre-pandemic and pandemic times, the more difficulty people have when it comes to remembering the order of past events and the distance since the beginning of the pandemic. Locating oneself presently within larger temporal structures (day, week, month) becomes harder as well. And projecting oneself into the future—ostensibly, beyond pandemic time—becomes particularly difficult.
Social disorientation shows a correlation above .33 with each of the other components (except for the aforementioned CIL and PoT, .28 and .32, respectively). All of the questions in the social disorientation component asked for participants to compare their current social situation to the one before the start of the pandemic. Social disorientation encompasses both loneliness (Q20: 'I have lost contact with people with whom I used to be close') and perceived social isolation (Q21: 'I feel more isolated from others'), which were elements that had been found to be associated with temporal disruptions in previous studies. However, we also wanted to elucidate the orientational aspects of social life with questions that pertain to the ability to turn towards others (Q21), to the feeling of belonging to social groups (Q23), to the ability of judging interpersonal closeness (Q26) and to the feeling that one's social world has shrunk (Q27). Overall, the social disorientation component captures an existential feeling of finding oneself out of place and not knowing how to proceed within one's social world.

Our further analysis using a 2SLS regression helps us clarify the relationship between social and temporal disorientation. Social disorientation appears to be a predictor of temporal distance (TOE) and, especially, of passage of time (POT), which are the two main disruptions examined in existing research. Social disorientation is also a good predictor of temporal rupture (TR), temporal self-location (TSL) and future orientation (FO). Only the model in which temporal order of events (TOE) is the dependent variable shows a relatively low coefficient (-.058). It is important to note that this is the case despite the correlation of social disorientation and temporal order of events being .144. Contrast this with passage of time (POT): It has a .32 correlation with social disorientation, but the coefficient in the corresponding regression is .627. This double divergence between correlations in the matrix and coefficients in the regressions reiterates the need for the 2SLS analysis. Correlation by itself fails to elucidate the connection between the relevant variables. What we manage to show with the analysis of the responses to the ITSD questionnaire is that the disorientation emerging within the social realm results in a variety of disruptions to temporal phenomenology.

The above analysis shows social disorientation to be a strong predictor of temporal disorientation, and in this aspect, the present work already goes a step further than previous studies in clarifying the relationship between social and temporal disorientation. Now, while it is always difficult to infer causality from panel data, we believe that a causal relationship might be the best explanation for our results. That is, we believe that social disorientation caused temporal disorientation. Our interpretation of the results follows an abductive argument. We consider the two other possibilities, that is that temporal disorientation caused social disorientation and that temporal and social disorientation were caused by a third factor, and we find the proposed causal direction to be much more likely based on our results.

First, we find it unlikely that the causal relationship goes in the temporal to social disorientation direction. The reason is that there is no clear theoretical explanation of that effect. Moreover, in our results, social disorientation is a predictor of temporal disorientation. The possibility of confounding factors that caused both forms of disorientation is somewhat more likely, but still not a fully convincing explanation of our findings. The clearest potential candidates are anxiety, depression and the objective aspects of physical confinement during the pandemic (e.g. lockdowns, curfews, etc.). We aimed to cover anxiety and depression with our inclusion of elements in the GPS component, and we included questions on lifestyle changes that served as proxies for objective aspects of physical confinement. In our 2SLS model, both of these elements were weaker predictors than social disorientation for passage of time and for temporal self-location. Moreover, previous work had found perceived social isolation, as opposed to objective social isolation (i.e. the objective aspects of physical confinement), to be distinctively the most influential factor for temporal disruptions (Chaumon et al., 2021; Ingram et al., 2021). In contrast, careful phenomenological analysis of the experience of isolation during the pandemic suggests the social to temporal disorientation direction. As we covered in the introduction, previous research exploring the phenomenology of secluded elder people in residential care during the pandemic suggested that social disorientation has effects
on temporal disorientation (Aho, 2022). The predictive power of social disorientation in our own
analysis further cements this causal connection.

CONCLUSION

While there is a consensus that the Covid-19 pandemic had a consequential effect on people's experience of the social and temporal domains, there is a divergence in existing findings, particularly when it comes to temporal phenomenology. Here, we approach the issue under the conceptual framework of disorientation, an existential feeling with social and temporal ramifications. We conceive social disorientation as a unified phenomenon encompassing loneliness and isolation into a coherent phenomenal whole. In contrast, temporal disorientation is an umbrella term for a variety of temporal disruptions that hinder the subject's ability to orient within a temporal framework.

To clarify the phenomenological impact of the pandemic, we developed a quantitative questionnaire, the ITSD, which was completed by 3306 respondents. During the validation process, a factor analysis of the responses to its 59 questions revealed a unified social disorientation component and a series of key components of temporal disorientation. In the present work, we clarify the relationship between these components. Employing a 2SLS regression analysis, we show social disorientation to be a strong predictor of temporal disorientation during the Covid-19 pandemic.

The main exception to this connection was the component *temporal order of events*. Future studies should investigate what is the main driver of this particular disturbance to temporal cognition. The effect of social disorientation on temporal rupture was also quite low. Another limitation is that the instrumental variable we employ might not be a very strong one. The instrumental variable (ability to build and maintain relationships through digital tools) could potentially also be influenced by the pandemic restrictions, given that the pandemic forced people to build and maintain contacts online, since all other means were blocked. In this way, changes in routines probably had an impact on online socialization behaviour, and perhaps on people's ability to maintain relationships digitally in a way that interfered with the other variables. Further limitations have to do with the nature of our sample. We employ panel data, with responses from a subset of the population in France, at a particular period within the larger event that is the Covid-19 pandemic. Future work could take our results further by employing longitudinal and cross-cultural data to explore the connection between the different phenomenological components investigated in the present work. It would be interesting to explore in more detail whether social disorientation affects the traumatic symptoms captured by the global psychotrauma screening, which in turn may affect time perception (mediating the connection between the two). Another venue of further analysis is the remediation of social disorientation and its effects on temporal phenomenology.

The results of this study can inform both clinical psychology and public health policy. Disturbances
across the social and temporal dimensions of experience often co-occur. What this study shows is that
addressing the disturbances in the social dimension should be the top priority. Moreover, the emphasis
should not be just on objective measures of social isolation but on the experiential aspects of social dis-
orientation. Helping people from becoming socially disoriented is of prime importance in times of crisis.

AUTHOR CONTRIBUTIONS

**Pablo Fernandez Velasco:** Conceptualization; funding acquisition; methodology; project administra-
tion; writing – original draft; writing – review and editing. **Bastien Perroy:** Conceptualization; meth-
odology; writing – original draft; writing – review and editing. **Umer Gurchani:** Conceptualization;
data curation; formal analysis. **Roberto Casati:** Funding acquisition; project administration; supervi-
sion; writing – review and editing.

CONFLICT OF INTEREST STATEMENT

None declared.
DATA AVAILABILITY STATEMENT

The anonymized data collected and the analysis supporting the findings are available as open data via the following online data repository link: https://osf.io/tqn6k/?view_only=d264a1d61c164173ac69 871e9beacfc3.

ORCID

Pablo Fernandez Velasco https://orcid.org/0000-0001-7563-8170

TWITTER

Bastien Perroy bastienperroy

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

Umer Gurchani umergurchani5

<figure><img src="image_7.png" /></figure>

REFERENCES

Aho, K. (2022). “We’re protecting them to death”—A Heideggerian interpretation of loneliness among older adults in long-term care facilities during COVID-19: Contact details. *Phenomenology and the Cognitive Sciences*, 22(7), 1–14. https://doi.org/10.1007/s11097-022-09803-z

Alatrany, S. S. J., Ogden, R., Falaiyah, A. M., ALdrraji, H. A. S., & Alatrany, A. S. S. (2022). The passage of time in Iraq during the covid-19 pandemic. *PLoS One*, 17(4), e0266877. https://doi.org/10.1371/journal.pone.0266877

Barnes, S. J. (2021). Stuck in the past or living in the present? Temporal focus and the spread of COVID-19. *Social Science & Medicine*, 280, 114057. https://doi.org/10.1016/j.socscimed.2021.114057

Benedikter, R., & Fathi, K. (2022). *Corona: The once-in-a-century health crisis and its teachings: Towards a more multi-resilient post-Corona world*. Brill. https://doi.org/10.1163/9789004469686

Brenlla, M. E., Germano, G., Seivane, M. S., da Lama, R. F., & Ogden, R. (2022). Experiences of distortions to the passage of time during the Argentinian Covid-19 pandemic. *PLoS One*, 17(3), e0266261. https://doi.org/10.1371/journal.pone.0266261

Cellini, N., Canale, N., Mioni, G., & Costa, S. (2020). Changes in sleep pattern, sense of time and digital media use during COVID-19 lockdown in Italy. *Journal of Sleep Research*, 29(4), e13074. https://doi.org/10.1111/jsr.13074

Chaumon, M., Rioux, P.-A., Herbst, S., Spiousas, I., Kübel, S., Hiroyasu, E. G., Runyun, S., Micillo, L., Thanopoulos, V., Mendoza-Durán, E., Wagelmans, A., Mudumba, R., Tachmatzidou, R., Cellini, N., D'Argembeau, A., Giersch, A., Grondin, S., Gronfier, C., Igarzábal, F. A., ... van Wassenhove, V. (2021). The Blursday database: Individuals' temporalities in Covid times. [preprint]. In Review. https://doi.org/10.21203/rs.3.rs-1098637/v1

Clair, R., Gordon, M., Kroon, M., & Reilly, C. (2021). The effects of social isolation on well-being and life satisfaction during pandemic. *Humanities and Social Sciences Communications*, 8(1), 28. https://doi.org/10.1057/s41599-021-00710-3

Constantinescu, A. O., O'Reilly, J. X., & Behrens, T. E. J. (2016). Organizing conceptual knowledge in humans with a gridlike code. *Science*, 352(6292), 1464–1468. https://doi.org/10.1126/science.aaf0941

Cravo, A. M., de Azevedo, G. B., Azarias, M. B., C., Barne, L. C., Bueno, F. D., de Camargo, R. Y., Morita, V. C., Sirius, E. V. P., Recio, R. S., Silvestrin, M., & de Azevedo Neto, R. M. (2022). Time experience during social distancing: A longitudinal study during the first months of COVID-19 pandemic in Brazil. *Science Advances*, 8(15), eabj7205. https://doi.org/10.1126/sciadv.abj7205

di Friedberg, M. S. (2021). Orientation, disorientation, reorientation: A reply to Fernández Velasco, Perroy and Casati. *Global Discourse*, 11(3), 467–470. https://doi.org/10.1332/204378921X16190010665450

Droit-Volet, S., Gil, S., Martinelli, N., Andant, N., Clinchamps, M., Parreira, L., Rouffiac, K., Dambrun, M., Huguet, P., Dubuis, B., Pereira, B., Bouillon, J.-B., Dutheil, F., & COVISTRESS network. (2020). Time and Covid-19 stress in the lockdown situation: Time free, «dying» of boredom and sadness. *PLoS One*, 15(8), e0236465. https://doi.org/10.1371/journal.pone.0236465

Droit-Volet, S., Martinelli, N., Chevalère, J., Belletier, C., Dezecache, G., Gil, S., & Huguet, P. (2021). The persistence of slowed time experience during the COVID-19 pandemic: Two longitudinal studies in France. *Frontiers in Psychology*, 12, 721716. https://doi.org/10.3389/fpsyg.2021.721716

Fernandez Velasco, P., Gurchani, U., Perroy, B., Pelletreau-Duris, T., & Casati, R. (2022). Development and validation of a quantitative instrument for measuring temporal and social disorientation in the Covid-19 crisis. *PLoS One*, 17(11), e0264604.

Fernandez Velasco, P., Perroy, B., Gurchani, U., & Casati, R. (2022). Experiencing a slow passage of time was an indicator of social and temporal disorientation during the Covid-19 pandemic. *Scientific Reports*, 12(1), 22338.

Hausman, J. A. (1978). Specification tests in econometrics. *Econometrica*, 46(6), 1251–1271. https://doi.org/10.2307/1913827

Henk, J. V. G. S. (2020). Practical disorientation & transformative experience as a framework for understanding & exploring the covid-19 pandemic's impact. *Revista de Filosofia Aplicată*, 3(Summer), 52–68. https://filosofieaplicata.ro/index.php/filap/article/view/48

Holman, E. A., & Grisham, E. L. (2020). When time falls apart: The public health implications of distorted time perception in the age of COVID-19. *Psychological Trauma Theory Research Practice and Policy*, 12(S1), S63–S65. https://doi.org/10.1037/tra000756
Hwang, T.-J., Rabheru, K., Peisah, C., Reichman, W., & Ikeda, M. (2020). Loneliness and social isolation during the COVID-19 pandemic. *International Psychogeriatrics*, **32**(10), 1217–1220. https://doi.org/10.1017/S1041610220000988

Ingram, J., Hand, C. J., & Maciejewski, G. (2021). Social isolation during COVID-19 lockdown impairs cognitive function. *Applied Cognitive Psychology*, **35**(4), 935–947. https://doi.org/10.1002/acp.3821

Jehi, T., Khan, R., Dos Santos, H., & Majzoub, N. (2022). Effect of COVID-19 outbreak on anxiety among students of higher education; A review of literature. *Current Psychology*, **42**(5), 1–15. https://doi.org/10.1007/s12144-021-02587-6

Kosak, F., Schelhorn, I., & Wittmann, M. (2022). The subjective experience of time during the pandemic in Germany: The big slowdown. *PLoS One*, **17**(5), e0267709. https://doi.org/10.1371/journal.pone.0267709

Kyriazos, T. A. (2018). Applied psychometrics: The 3-faced construct validation method, a routine for evaluating a factor structure. *Psychology*, **9**(8), 2044–2072. https://doi.org/10.4236/psych.2018.98117

Loose, T., Wittmann, M., & Vásquez-Echeverría, A. (2021). Disrupting times in the wake of the pandemic: Dispositional time attitudes, time perception and temporal focus. *Time & Society*, **31**, 961463X2110274. https://doi.org/10.1177/0961463X211027420

MacDonald, C. J., Lepage, K. Q., Eden, U. T., & Eichenbaum, H. (2011). Hippocampal “time cells” bridge the gap in memory for discontiguous events. *Neuron*, **71**(4), 737–749. https://doi.org/10.1016/j.neuron.2011.07.012

Martinelli, N., Gil, S., Belletier, C., Chevalère, J., Dezecache, G., Huguet, P., & Droit-Volet, S. (2021). Time and emotion during lockdown and the Covid-19 epidemic: Determinants of our experience of time? *Frontiers in Psychology, 11*, 616169. https://doi.org/10.3389/fpsyg.2020.616169

Means, A. J., & Slater, G. B. (2021). Collective disorientation in the pandemic conjuncture. *Cultural Studies*, **35**(2–3), 514–522. https://doi.org/10.1080/09502386.2021.1898030

Micillo, L., Rioux, P.-A., Durán, E. M., Kuebel, S., Cellini, N., van Wassenhove, V., Grondin, S., & Mioni, G. (2021). Time perspective predicts levels of anxiety and depression during the COVID-19 outbreak: A cross-cultural study. [preprint] *PsyArXiv*. https://doi.org/10.31234/osf.io/8tqap

Nelson, J. L. (2020). Doubt, disorientation, and death in the plague time. *Hastings Center Report*, **50**(3), 4. https://doi.org/10.1002/hast.11116

Neter, J., Wassermann, W., Kutner, M. H., & Wasserman, W. (1989). *Applied linear regression models* (2nd ed.). Irwin.

Ogden, R. (2020). The passage of time during the UK Covid-19 lockdown. *PLoS One*, **15**(7), e0235871. https://doi.org/10.1371/journal.pone.0235871

Ogden, R. (2021). Distortions to the passage of time during England's second national lockdown: A role for depression. *PLoS One*, **16**(4), e0250412. https://doi.org/10.1371/journal.pone.0250412

Ogden, R., & Piovesan, A. (2022). How long was it for you? Memories of the duration of the UK covid-19 lockdown. *PLoS One*, **17**(7), e0271609. https://doi.org/10.1371/journal.pone.0271609

Oinas, E. (2021). Disorientation, disbelief, distance. In I. M. Heath, A. K. Darkwah, J. Beoku-Betts, & B. Purkayastha (Eds.), *Global feminist autoethnographies during COVID-19* (1st ed., pp. 263–274). Routledge. https://doi.org/10.4324/9781003223832-27

Perroy, B., Velasco, P. F., Gurchani, M. U., & Casati, R. (2022). Temporal disorientation and the Covid-19 crisis: present episodic confusion, past-oriented sustained disbelief, and future-oriented anxiety are the three main ways in which people were temporarily disoriented during the pandemic. *PsyArXiv*. https://doi.org/10.31234/osf.io/k8p5c

Pierce, M., Hope, H., Ford, T., Hatch, S., Hotopf, M., John, A., Kontopantelis, E., Webb, R., Wessely, S., McManus, S., & Abel, K. M. (2020). Mental health before and during the COVID-19 pandemic: A longitudinal probability sample survey of the UK population. *The Lancet Psychiatry*, **7**(10), 883–892. https://doi.org/10.1016/S2215-0366(20)30308-4

Pisano, F., Torromino, G., Brachi, D., Quadrini, A., Incoccia, C., & Marangolo, P. (2021). A standardized prospective memory evaluation of the effects of COVID-19 confinement on young students. *Journal of Clinical Medicine*, **10**(17), 3919. https://doi.org/10.3390/jcm10173919

Ratcliffe, M. (2005). The feeling of being. *Journal of Consciousness Studies*, **12**(8–10), 43–60.

Ratcliffe, M. (2021). Disorientation, distrust and the pandemic. *Global Discourse*, **11**(3), 463–466. https://doi.org/10.1332/204378921X16158114033083

Santomauro, D. F., Mantilla Herrera, A. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., Abbafati, C., Adolph, C., Amlag, J. O., Aravkin, A. Y., Bang-Jensen, B. L., Bertolacci, G. J., Bloom, S. S., Castellano, R., Castro, E., Chakrabarti, S., Chattopadhyay, J., Cogen, R. M., Collins, J. K., ... Ferrari, A. J. (2021). Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. *The Lancet*, **398**(10312), 1700–1712. https://doi.org/10.1016/S0140-6736(21)02143-7

Sobol, M., Blachnio, A., & Przepiórka, A. (2020). Time of pandemic: Temporal perspectives related to compliance with public health regulations concerning the COVID-19 pandemic. *Social Science & Medicine*, **265**, 113408. https://doi.org/10.1016/j.socscimed.2020.113408

Solomon, E. A., Lega, B. C., Sperling, M. R., & Kahana, M. J. (2019). Hippocampal theta codes for distances in semantic and temporal spaces. *Proceedings of the National Academy of Sciences*, **116**(48), 24343–24352. https://doi.org/10.1073/pnas.1906729116

Stephan, A. (2012). Emotions, existential feelings, and their regulation. *Emotion Review*, **4**(2), 157–162. https://doi.org/10.1177/1754073911430138

Stock, J. H., & Watson, M. W. (2011). *Introduction to econometrics* (3rd ed.). Addison-Wesley.

Tavares, R. M., Mendelsohn, A., Grossman, Y., Williams, C. H., Shapiro, M., Trope, Y., & Schiller, D. (2015). A map for social navigation in the human brain. *Neuron*, **87**(1), 231–243. https://doi.org/10.1016/j.neuron.2015.06.011
Tutzer, F., Frajo-Apor, B., Pardeller, S., Plattner, B., Chernova, A., Haring, C., Holzner, B., Kemmler, G., Marksteiner, J., Miller, C., Schmidt, M., Sperner-Unterweger, B., & Hofer, A. (2021). Psychological distress, loneliness, and boredom among the general population of Tyrol, Austria during the COVID-19 pandemic. *Frontiers in Psychiatry, 12*, 691896. https://doi.org/10.3389/fpsyt.2021.691896

van Gils-Schmidt, H. J., Verdonschot, C. P., & Schaubroeck, K. (2020). Editorial ‘the value of disorientation’. *Ethical Theory and Moral Practice*, 23(3–4), 495–499. https://doi.org/10.1007/s10677-020-10113-z

van Wassenhove, V. (2022). Temporal disorientations and distortions during isolation. *Neuroscience & Biobehavioral Reviews*, 137, 104644. https://doi.org/10.1016/j.neubiorev.2022.104644

Vandekar, S., Tao, R., & Blume, J. (2020). Erratum to: A robust effect size index. *Psychometrika*, 85(4), 946. https://doi.org/10.1007/s11336-020-09732-3

Velasco, P. F., Perroy, B., Gurchani, U., & Casati, R. (2023). Lost in pandemic time: A phenomenological analysis of temporal disorientation during the Covid-19 crisis. *Phenomenology and the Cognitive Sciences*, 22(5), 1121–1144.

Velasco, P. F., Perroy, B., & Casati, R. (2021). The collective disorientation of the COVID-19 crisis. *Global Discourse*, 11(3), 441–462.

Viganò, S., & Piazza, M. (2020). Distance and direction codes underlie navigation of a novel semantic space in the human brain. *The Journal of Neuroscience*, 40(13), 2727–2736. https://doi.org/10.1523/JNEUROSCI.1849-19.2020

Wearden, J. H. (2015). Passage of time judgments. *Consciousness and Cognition*, 38, 165–171. https://doi.org/10.1016/j.concog.2015.06.005

Wessels, M., Utegaliyev, N., Bernhard, C., Welsch, R., Oberfeld, D., Thönes, S., & von Castell, C. (2022). Adapting to the pandemic: Longitudinal effects of social restrictions on time perception and boredom during the Covid-19 pandemic in Germany. *Scientific Reports*, 12(1), 1863. https://doi.org/10.1038/s41598-022-05495-2

Wooldridge, J. M. (2013). *Introductory econometrics: A modern approach* (5th ed.). Cengage Learning.

Zauberman, G., Levav, J., Diehl, K., & Bhargave, R. (2010). 1995 feels so close yet so far: The effect of event markers on subjective feelings of elapsed time. *Psychological Science*, 21(1), 133–139. https://doi.org/10.1177/0956797609356420

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**How to cite this article:** Fernandez Velasco, P., Perroy, B., Gurchani, U., & Casati, R. (2024). Social and temporal disorientation during the Covid-19 pandemic: An analysis of 3306 responses to a quantitative questionnaire. *British Journal of Psychology, 115*, 577–598. https://doi.org/10.1111/bjop.12704