Question:
You are given the full text of a scientific research paper.

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    https://doi.org/10.1177/0022146520970190
Journal of Health and Social Behavior
2020, Vol. 61(4) 398–417
© American Sociological Association 2020
DOI: 10.1177/0022146520970190
jhsb.sagepub.com
Original Article
The global COVID-19 pandemic presented a threat
to rival the Spanish influenza pandemic, more than
a hundred years before (Sly 2020). In many nations,
public health measures intended to prevent the
spread of the virus and “flatten the curve” in terms
of the rate of transmission resulted in extreme
changes to norms of social contact (Lai 2020;
Morgan 2020). In Canada, public gatherings were
banned and citizens were urged to stay at home as
much as possible (Government of Alberta 2020;
Loewen 2020; Public Health Agency of Canada
2020). The purpose of the current study is to apply a
synthesis of Durkheimian and life course perspec-
tives to examine whether the social estrangement
created by these public health measures resulted in
an increase in psychological distress in the Canadian
public, as well as whether social estrangement and
consequent psychological distress were more pre-
dominant in older respondents.
To address these questions, we compare two
national probability samples of working Canadians
from the Canadian Quality of Work and Economic
Life Study. The first was gathered in late September
2019; the second was gathered in 2020, from March
17 to March 23, when social isolation measures were
enacted in Canada. Comparison of these two sam-
ples in measures of feelings of isolation, community
distrust, and symptoms of psychological distress
allow us to examine not only whether the average
level of psychological distress increased in the
970190 HSBXXX10.1177/0022146520970190Journal of Health and Social BehaviorBierman and Schieman
research-article2020
1University of Calgary, Calgary, AB, Canada
2University of Toronto, Toronto, ON, Canada
Corresponding Author:
Alex Bierman, Department of Sociology, University of
Calgary, 2500 University Drive NW, Calgary, AB T2N
1N4, Canada.
Email: aebierma@ucalgary.ca
Social Estrangement and
Psychological Distress before
and during the COVID-19
Pandemic: Patterns of Change
in Canadian Workers
Alex Bierman1
 and Scott Schieman2
Abstract
This article argues that the COVID-19 pandemic and associated social distancing measures intended to
slow the rate of transmission of the virus resulted in greater subjective isolation and community distrust,
in turn adversely impacting psychological distress. To support this argument, we examine data from the
Canadian Quality of Work and Economic Life Study, two national surveys of Canadian workers—one from
late September 2019 (N = 2,477) and the second from mid-March 2020 (N = 2,446). Analyses show that
subjective isolation and community distrust increased between the two surveys, which led to a substantial
rise in psychological distress. Increases in subjective isolation were stronger in older respondents, resulting
in a greater escalation in psychological distress. These findings support a Durkheimian perspective on the
harm to social integration and mental health caused by periods of rapid social change but also illustrate
how a life course context can differentiate individual vulnerability to disintegrative social forces.
Keywords
COVID-19, life course perspective, mental health, psychological distress, social integration

Bierman and Schieman
399
population but also whether population differences
in subjective social isolation and distrust explain the
evolution in levels of distress. We therefore contrib-
ute to the research in the sociology of mental health
by examining whether public health measures
intended to stop the spread of the COVID-19 virus
may also have had substantial adverse consequences
for public mental health in North America.
Background
Social Integration and Mental Health
during the Pandemic
The primary basis of our study is in the fundamental
Durkheimian insight that social integration provides
a binding influence on suicide (Durkheim [1897]
1951). Subsequent theorizing has clarified this core
insight, emphasizing that assimilation into a larger
whole through a web of social attachments acts a
bulwark against vulnerabilities that can provoke
anxiety while also assuaging hopelessness and
“metaphysical exigencies” that can occur in states
of individual atomization (Abrutyn and Mueller
2014:334). These arguments cohere with a social-
psychological perspective in which social discon-
nectedness develops into perceptions of social
isolation that stimulate negative cognitive processes
by acting as aversive figural signals of social vul-
nerability (Cacioppo and Cacioppo 2014; Dahlberg,
Andersson, and Lennartsson 2018). Disintegratory
conditions will therefore be reflected in a subjective
state of social isolation that arouses feelings of anxi-
ety and dread. Empirical research supports this gen-
eral
perspective,
demonstrating
that
social
detachment can spur perceptions of social isolation
that are substantially associated with psychological
distress (Cacioppo et al. 2011; Swader 2019).
A Durkheimian perspective further builds on
these insights to explicate the negative conse-
quences of rapid social change for mental health
(Lester 2001). From this perspective, times of
social turbulence weaken the social bonds of soci-
ety (Berkman et al. 2000), thereby creating condi-
tions that deplete societal integration (Zhao and
Cao
2010).
Similarly,
expanding
from
a
Durkheimian perspective, Abrutyn and Mueller
(2016) argue that periods of social disruption can
threaten or sever meaningful social ties, in turn cre-
ating subsequent negative emotions. Rapid social
change that interferes with established patterns of
social interactions therefore acts as a destructive
influence on social integration, in turn enhancing
feelings of social isolation in the population that
give rise to psychological discomfort. From this
perspective, then, even if policies of social distanc-
ing were necessary to slow the spread of the
COVID-19 virus, the degree to which these mea-
sures interrupted established patterns of social
interaction was likely to create a disintegratory state
that heightened subjective social isolation, thereby
incurring mental health damages through increased
psychological distress.
Despite empirical support linking individual
levels of social integration to mental health out-
comes (Turner and Turner 2013), as well as research
demonstrating how contextual levels of social inte-
gration can influence individual outcomes such as
suicidal ideation (e.g., Maimon and Kuhl 2008;
Winfree and Jiang 2010), there is much less direct
evidence for the consequences of societal change
for integration and subsequent effects on mental
health. One of the primary areas of empirical evi-
dence comes from periods of economic turmoil
(Cockerham 2017), as increasing rates of foreclo-
sure and unemployment were associated with
spikes in suicide rates following the Great
Recession (Houle and Light 2014; Phillips and
Nugent 2014). Closer to individual outcomes,
meso-level changes in foreclosure rates following
the recession were also inversely associated with
individual mental health (Houle 2014; Settels
2020). An additional line of research linking social
change to integration has argued that increases in
birth cohort size and births to unwed mothers are
causative agents in declining levels of social inte-
gration that affect suicide rates (O’Brien and
Stockard 2006; Stockard and O’Brien 2002). It is
notable, however, that much of this research is con-
ducted purely at a contextual level and does not
clearly tie rapid social change to individual experi-
ences of social isolation. The current study there-
fore builds on this body of evidence to demonstrate
whether increases in perceptions of social isolation
contributed to a rise in psychological distress fol-
lowing the onset of the pandemic.
Consequences of the Pandemic for
Community Distrust
The disintegratory conditions of the pandemic may
have had additional consequences for social estrange-
ment by resulting in decreasing social trust. A focus
on social trust is directed by Abrutyn’s (2019) recent
proposition that disintegrative forces that work
against social solidarity can lead individuals to feel
an increasing sense of threat. This argument reso-
nates with a social-psychological perspective on trust

400
Journal of Health and Social Behavior 61(4)
that positions a willingness to make one’s self vulner-
able in uncertain situations as fundamental to a high
degree of social trust (Baumert et al. 2017). Societal
atomization that enhances a sense of threat will deter
individual openness to vulnerability that is elemental
in building trust. These general processes are highly
relevant in the context of the COVID-19 pandemic
because the novel requirements to maintain social
distance signaled that the threat posed by members of
the community inherently could not be contained,
thereby fomenting distrust of others.
Evidence from prior pandemics supports these
arguments. In particular, evidence from the Spanish
influenza pandemic suggests that pandemics can
lower levels of social trust (Aassve et al. 2020). For
example, Barry (2005:329) describes how fear
eroded social trust in Philadelphia during the
Spanish influenza pandemic: “Fear began to break
down the community of the city. Trust broke down.
Signs began to surface of not just edginess but
anger, not just finger-pointing or protecting one’s
own interests but active selfishness in the face of
general calamity.” Similarly, suspicion increased
following the H1N1 outbreak in the late 2000s, as
individuals infected with the virus were seen as put-
ting others at risk (Gilman 2010). Consequently,
because public leaders addressing the COVID-19
pandemic began to call for social distancing, and in
particular warn people to guard against interactions
with others outside of their homes and in the com-
munity, we expect distrust of others in the commu-
nity to have increased as well.
Decreasing trust in members of the community
is in turn likely to have substantial consequences for
psychological distress. Trust in members of one’s
own neighborhood is associated with better mental
health (Murayama et  al. 2015; Tomita and Burns
2013; Wu et al. 2018) even when additional aspects
of social trust are taken into account (Carpiano and
Fitterer 2014). Trust in members of the community
can be important for mental health by increasing a
sense of being accepted and facilitating social sup-
port, as well as by reinforcing informal social con-
trol that serves to prevent harmful health behaviors
(Fujiwara and Kawachi 2008; Glanville and Story
2018). Conversely, feeling suspicion and needing to
be on guard of the people we come into contact with
outside of our homes acts as a stressor that increases
psychological distress (Ross 2011). In the context of
increased isolation associated with the COVID-19
pandemic, neighbors may become a focus of social
acceptance and sources of support, with the result
that declining trust in the member of one’s commu-
nity will act as a further stressor that elevates
psychological distress. Within this research, we
therefore examine whether increases in psychologi-
cal distress following the onset of the COVID-19
pandemic were not only attributable to increases in
subjective social isolation, but a rise in community
distrust as well.
Integrating a Life Course Perspective
Durkheimian perspectives on the consequences of
societal change for social integration tend to frame
these processes expansively, focusing on broad
dimensions of social change and their summative
integratory consequences. In the current research,
though, we integrate insights from a life course per-
spective into the study of societal change and social
integration. We suggest a concept of “integratory
vulnerability,” in which a life course context—and
particularly cohort membership and the timing of
societal events in the life course—critically differ-
entiates individual vulnerability to disintegratory
societal events.
We are guided to the pivotal role of integratory
vulnerability by the strong emphasis of research in
the life course perspective on the differentiated
ramifications of large-scale economic events for
individual lives (Elder 1999). A key paradigmatic
principle of a life course perspective is that histori-
cal events can affect people differently depending
on the timing of these events in the life course
(Elder, Johnson, and Crosnoe 2003). Recent
research exemplifies these patterns, demonstrating
that the timing of the Great Recession as individuals
entered the labor market had subsequent implica-
tions for individual earning capabilities (Atherwood
and Sparks 2019).
The question of timing is especially relevant to
the threat of the COVID-19 pandemic. The threat of
serious adverse health consequences due to con-
traction of the virus are greater in older individuals
(Heymann and Shindo 2020). Consequently, age
may have been critical in determining integratory
vulnerability: Older individuals may have isolated
to a greater degree as a result of the pandemic and
also may have experienced a greater distrust of
members of their community due to their height-
ened vulnerability and subsequent fear of contract-
ing the virus. Greater increases in feelings of
isolation and distrust would in turn lead to more
substantial gains in psychological distress.
Yet, differentiation in the consequences of his-
torical events can also occur in part because the
times in which individuals are born into and develop
alter the resources and deficits that different birth

Bierman and Schieman
401
cohorts bring to bear in times of crisis (Elder 1994;
Keyes et al. 2010). Birth cohorts may be differently
equipped to meet the challenges of historical turbu-
lence, in turn altering the degree to which historical
events create negative repercussions across age
cohorts. We suggest that we will observe differ-
ences in integratory vulnerability across cohorts
due to a “digital divide” between cohorts in the
comfort and use of internet and other electronic
means of communications (Friemel 2016). A com-
mon characterization of the divide between cohorts
is that younger cohorts, particularly those born
before 1980, are “digital immigrants” and those
born after the 1980s are “digital natives” (Nevin
and Schieman 2020; Prensky 2001). In support of
this characterization, research thoroughly docu-
ments that members of younger age cohorts have
greater comfort and facility in internet use (Büchi,
Just, and Latzer 2016; Hargittai and Dobransky
2017). Concomitantly, there tends to be greater reti-
cence toward the use of social networking technol-
ogy with age (Yu et al. 2016). Older users may find
less social utility and fulfillment from social net-
working technology (Lüders and Brandtzæg 2017)
and instead prefer face-to-face interactions (Yuan
et al. 2016).
Evidence of cohort differences in comfort
with and utility in online social interactions sug-
gests that we will observe that patterns of change
in subjective social isolation and community
distrust differ across age cohorts. Members of
younger cohorts may have been more able to gain
social sustenance through online interactions and
as a result felt less isolation in the wake of social
distancing measures. Similarly, younger cohorts
may have been more able to use social media and
other electronic resources to gain information on
local spread of the infection and means of mini-
mizing risk of transmission, which may in turn
serve to lessen generalized suspicion of one’s
neighbors.
It is critical to underscore that the sum of integra-
tory vulnerability due to both age and cohort effects
is that older individuals are likely to experience more
substantial social estrangement. A lack of ability to
differentiate between age and birth cohort in the cur-
rent analyses is therefore not a substantial weakness
because we expect moderation by both factors to be
in the same direction. Within this research, we there-
fore examine whether increases in subjective social
isolation and community distrust are observed more
prominently in older individuals, leading to stronger
increases in psychological distress.
Summary of Expectations
Figure 1 summarizes the primary expectations of
this article. First, the figure indicates that we expect
to observe increased feelings of social isolation and
community distrust in 2020, following the outbreak
of the COVID-19 pandemic. Second, we expect
that subjective social isolation and community dis-
trust will be associated with greater psychological
distress. Consequently, increases in subjective
social isolation and community distrust will lead to
a rise in psychological distrust following the out-
break of the pandemic. However, Figure 1 also
shows positive paths between age and the paths
between wave of survey and the measures of social
estrangement, which illustrates that we expect
amplified increases in subjective social isolation
and community distrust among older members of
our study, in turn leading to greater increases in
psychological distress.
Figure 1.  Model of Social Estrangement and Psychological Distress Following the Outbreak of
COVID-19.

402
Journal of Health and Social Behavior 61(4)
Data and Methods
Data
Data were derived from two waves of the Canadian
Quality of Work and Economic Life Study
(C-QWELS), national surveys intended to examine
social conditions and well-being among Canadians
who were currently employed. Data were gathered
by the study authors in cooperation with the Angus
Reid Forum, a Canadian national survey research
firm that maintains an ongoing national panel of
Canadian respondents. The C-QWELS I was gath-
ered from September 19 to September 24, 2019, and
was an online survey conducted among a represen-
tative sample of 2,524 working Canadians. The
response rate was 42%, but results were statistically
weighted according to the most current education,
age, gender, and region census data to ensure a sam-
ple representative of working Canadians. The
C-QWELS II was conducted from March 17 to
March 23, 2020 with another nationally representa-
tive sample of 2,528 working Canadians. The
response rate was 43%, and responses were simi-
larly weighted. Of the 5,052 total respondents,
4,923 were retained in the analytic sample (2019
sample = 2,477; 2020 sample = 2,446), a retention
rate of over 97%, suggesting little bias due to list-
wise deletion.
Focal Measures
Psychological distress.  Psychological distress was
measured using five common symptoms of nonspe-
cific psychological distress (Kessler et  al. 2002):
feel anxious or tense, feel nervous, feel restless or
fidgety, feel sad or depressed, feel hopeless.
Respondents indicated the frequency they experi-
enced each symptom in the previous month, with
response scales of all of the time, most of the time,
some of the time, a little of the time, and none of the
time. All responses were coded so that higher values
indicated more frequent symptoms. Psychological
distress was measured as the mean of responses to
these five questions (Cronbach’s α = .877).
Community distrust.  Similar to other studies (e.g.,
Carpiano and Fitterer 2014; Fujiwara and Kawachi
2008), community distrust was measured using a sin-
gle item that asked, “Thinking about the people in your
neighbourhood—that is, the local area in which you
live,” how much do you agree or disagree with the
statement, “My neighbours can be trusted.” Response
choices were strongly agree, somewhat agree, some-
what disagree, strongly disagree. Responses were
coded so that higher values indicated greater disagree-
ment, thereby creating a measure of community dis-
trust. However, a small proportion of respondents
indicated strong disagreement, and ancillary analyses
showed that standard errors were substantially inflated
due to the small number of these cases. We therefore
combined responses of strongly disagree and disagree
into an overall “disagree” category.
Subjective social isolation.  Subjective social isola-
tion was measured using one item that asked respon-
dents how often they felt “isolated from other people”
in the previous month, with the same response cate-
gories as the distress items.
Age.  Age was measured in years of age, with a
top value of 74 to avoid the undue influence of
sparse values of high age in tests of moderation. Age
was centered over a value of 40, the approximate
median age in the sample, to provide clearer inter-
pretations of the interactions.
Wave of survey.  Membership in the surveys was
indicated by a dichotomous variable in which a
value of 0 indicated the respondent participated in
the September 2019 survey and a value of 1 indi-
cated the respondent participated in the March 2020
sample. In the results, these were referred to as the
2019 and 2020 samples, respectively.
Control Measures
Generalized trust.  To take broader social trust into
account, respondents were asked a common survey
question on social trust: “Generally speaking, would
you say that most people can be trusted, or that you
can’t be too careful in dealing with people? Please
tell me what you think, where 1 means you can’t be
too careful and 5 means most people can be trusted.”
In the analyses, an indicator of the lowest level of
trust was contrasted with a set of dichotomous indi-
cators for each of the other categories of trust.
Ancillary analyses showed that community and
generalized trust were not substantially correlated,
suggesting that each were distinct indicators of
trust.
Employment conditions.  Because analyses were
based on two samples of working Canadians, employ-
ment conditions were taken into account to address
the degree to which occupational experiences contrib-
uted to psychological distress as well as the extent to
which individuals may have experienced changes in
work conditions and scheduling due to working at

Bierman and Schieman
403
home. Occupational class was measured using a five-
category classification—professional, administrative,
sales, clerical, and laborer—with professional as ref-
erence. Number of work hours in main job were con-
trolled using a set of dichotomous indicators, in which
part-time (≤ 29 hours or less) was contrasted with full-
time (30 – 49) and extended hours (≥ 50). Working
more than one job was controlled by a dichotomous
indicator in which the higher value indicated that the
respondent worked more than one job. Degree of
working at home was taken into account using a mea-
sure in which individuals who never worked at home
were contrasted to categories of a few times a year,
about once a month, about once a week, more than
once a week, and every day/mainly work at home.
The degree of control over work scheduling was taken
into account by asking respondents, “How much con-
trol do you have in scheduling your work hours?”
with responses of none contrasted to very little, some,
a lot, and complete control.
Familial statuses.  Familial statuses that may pro-
vide support and ward off social estrangement were
taken into account with a dichotomous indicator in
which the higher value indicated that the respondent
lived with a romantic partner and a dichotomous
variable in which the higher value indicated that the
respondent lived with at least one child under the
age of 18.
Social and economic statuses.  Social and eco-
nomic statuses that may contribute to both social
estrangement and psychological distress were also
controlled, including education, income, economic
hardship, gender, and minority status. Education
was operationalized as a set of categories in which
individuals with a university degree were compared
to a category of high school, some university or col-
lege/trade school, and graduated from college or
trade school; because less than 2% of the weighted
sample at each wave had less than a high school
degree, these respondents were grouped with those
with a high school degree. Income was measured as
a set of categories in which $150,000 or more in
household income was compared to less than
$25,000, $25,000 to less than $50,000, $50,000 to
less than $100,000, and $100,000 to less than
$150,000. Because individuals who do not provide
income often reside in high-income categories and
taking nonresponse into account would help to con-
trol for biases in self-reports, missing income was
considered as an additional analytic category. Fur-
thermore, because the measure of income did not
address more proximal experiences of economic
deprivation that may have been associated with the
pandemic, we also included a commonly employed
measure of economic hardship that has been shown
to be a valid indicator of physical and mental health
(e.g., Kahn and Pearlin 2006). Respondents were
asked, “How do your finances usually work out by
the end of the month?,” with responses of a lot of
money left over used as a comparison group to not
enough to make ends meet, barely enough to get by,
just enough to make ends meet, and a little money
left over. Gender was coded as 0 = men, 1 = women.
Racial and ethnic minority status is typically mea-
sured in Canada using the designation of “visible
minority” (Little 2016), and in keeping with this
convention, visible minority status was measured
by asking respondents, “Would you say you are a
member of a visible minority here in Canada (in
terms of your ethnicity/race)?” Responses were
indicated by a dichotomous variable in which the
higher value indicated visible minority.
Plan of Analysis
All primary analyses were conducted in Stata 16.1.
Analyses were conducted in three stages. In the first
stage, we examined bivariate differences between the
focal study measures in the two waves of the surveys.
In the second stage, we examined predictors of com-
munity distrust and subjective social isolation in mul-
tiple regression models. Each outcome was examined
using two models. First, we examined between-wave
differences in the outcome independent of the control
variables. Second, we tested whether between-wave
differences in each measure differed by respondent’s
age by testing an interaction between wave of survey
and age. Because both community distrust and sub-
jective social isolation were based on an ordinal
response scale, we utilized ordinal logistic regression
in the multiple regression analyses (Hoffmann 2016).
Ordinal logistic regression models depend on an
assumption that the change in risk based on a predic-
tor is the same between each category of the depen-
dent variable (Williams 2006), and preliminary
analyses that applied a Brant test (Brant 1990) sup-
ported this assumption for the association between
wave of survey and both outcomes as well as for the
interaction term.
In a third stage of analyses, we used ordinary
least squares (OLS) regression to examine the asso-
ciation between wave of survey and psychological
distress. We first examined between-wave differ-
ences in psychological distress while holding con-
stant all background controls. In additional models,
we sequentially controlled for community distrust

404
Journal of Health and Social Behavior 61(4)
and subjective social isolation, which demonstrated
the extent to which each explained between-wave
differences in psychological distress (MacKinnon
2008). To account for the noncontinuous nature of
the measures of community distrust and subjective
social isolation, each measure was entered into the
regression model as a set of categorical indicators,
with strong trust or no sense of isolation as the ref-
erence group, respectively. We then repeated this
process by removing the measures of community
distrust and subjective social isolation and testing
an interaction between wave of survey and age,
which demonstrated the extent to which between-
wave differences in psychological distress differed
by age. A reintroduction of the measures of com-
munity distrust and subjective social isolation into
the model examined the extent to which these fac-
tors explained age-based contingencies in between-
wave changes in psychological distress.
Results
Table 1 displays the distribution of measures for
each survey wave and for the combined sample.
Table 1 shows a shift in both community distrust
and subjective social isolation toward greater dis-
trust and isolation. Generalized trust appeared rela-
tively stable, however, which reflects the importance
of considering trust in specific targets with whom an
individual may interact rather than more diffuse per-
ceptions of trust. Table 1 also shows that mean lev-
els of psychological distress increased between the
two waves. Although the difference in distress is not
statistically significant, ancillary analyses showed
that the lack of statistical significance was largely
due to small compositional differences between the
two surveys. For example, simply controlling for
compositional differences in age and presence of
children led to an estimation of significant greater
distress in March compared to September. We there-
fore turn to the multivariate analyses that examine
differences between the two waves of surveys when
taking these compositional factors into account.
Multiple Regression Analyses
Table 2 displays the results of the ordered logistic
regression analyses of community distrust and sub-
jective social isolation. Model 1 shows that, inde-
pendent of the controls, respondents in 2020
evidenced a significantly increased risk of commu-
nity distrust. Being a respondent in the 2020 survey
was associated with almost 50% greater odds of
reporting a higher level of distrust than being a
respondent in the 2019 sample. However, these
between-wave differences did not vary by age; the
interaction between wave of survey and age in
Model 2 is not significant.
Turning to subjective social isolation, Model 3
shows that respondents in 2020 also had increased
risk of subjective social isolation. Being a respon-
dent in the 2020 sample was associated with
36% greater odds of reporting a higher level of iso-
lation than being a respondent in the 2019 sample.
Furthermore, between-wave differences in subjec-
tive social isolation differ by age; Model 4 shows
that the interaction between wave of survey and age
is statistically significant.
To explicate this interaction, Figure 2 presents
the estimated odds ratios for between-wave differ-
ences in subjective social isolation across the range
of ages in the survey. This figure shows that, for
respondents in their 20s and 30s, the odds ratios for
between-wave differences in subjective social iso-
lation are relatively small and are not statistically
significant for those in their 20s. By age 40, how-
ever, respondents in 2020 had 33% greater odds of
reporting increased feelings of isolation, and this
difference was significant. The between-wave odds
of subjective social isolation increased further in
strength in later age cohorts. Respondents at age 50
had almost 50% greater odds of increased feelings
of isolation in 2020, and by age 60, the odds were
63% greater in 2020. In accordance with our expec-
tations, then, the increased risk of subjective social
isolation following the COVID-19 outbreak was
greater among older respondents.
Table 3 presents the results of the OLS regres-
sion analyses of psychological distress. Model 1
shows that between-wave increases in distress are
significant, independent of controls. To demon-
strate the strength of this difference, we examined
the semistandardized difference, in which the met-
ric difference is divided by the standard deviation
of distress (McClendon 1994), thereby expressing
this difference in units of standard deviations of dis-
tress. When semistandardized, this difference was
.069. It should be emphasized that this difference
was observed in the population of working adults in
a relatively short six-month period, and subsequent
analyses will demonstrate that this increase is a
combination of much stronger and much weaker
age-variegated changes in distress.
Model 2 controls for categories of community
distrust. When compared to respondents who
reported strong agreement with trust in neighbors,
respondents in the combined disagreement category
reported significantly higher levels of psychological

Bierman and Schieman
405
Table 1.  Sample Descriptives.
2019 Survey
2020 Survey
Merged Surveys
p
Distress
2.357
2.404
2.380

Community distrust
  Strongly agree with trust in neighbors
.348
.265
.307

  Somewhat agree with trust in neighbors
.502
.550
.526

  Somewhat disagree/strongly disagree with
trust in neighbors
.150
.185
.167
***
Subjective isolation
  None of the time
.401
.345
.373

  A little of the time
.263
.245
.254

  Some of the time
.209
.249
.229

  Most of the time
.104
.126
.115

  All of the time
.023
.036
.029
***
Age
41.967
41.914
41.940

Generalized trust
  You can’t be too careful
.122
.101
.112

  2
.167
.178
.172

  3
.400
.390
.395

  4
.243
.265
.254

  Most people can be trusted
.068
.067
.067

Occupational class
  Professional
.398
.404
.401

  Administrative
.156
.124
.140

  Sales
.188
.195
.192

  Clerical
.181
.170
.175

  Laborer
.077
.107
.092
***
Work hours
  Part-time
.183
.218
.200

  Full-time
.682
.657
.670

  Extended hours
.135
.125
.130
*
Working multiple jobs
  One job
.775
.776
.776

  More than one job
.225
.224
.225

Work at home
  Never
.353
.334
.343

  A few times a year
.108
.112
.110

  About once a month
.068
.081
.074

  About once a week
.113
.113
.113

  More than once a week
.163
.171
.167

  Every day/mainly work at home
.195
.190
.193

Control over work scheduling
  None
.158
.141
.149

  Very little
.197
.200
.198

  Some
.273
.267
.270

  A lot
.233
.245
.239

  Complete control
.140
.147
.144

Living with romantic partner
  Partner
.648
.648
.648

  No partner
.352
.352
.352

(continued)

406
Journal of Health and Social Behavior 61(4)
2019 Survey
2020 Survey
Merged Surveys
p
Any children in household
  No children
.678
.618
.648

  Children
.322
.382
.352
***
Education
  High school
.096
.121
.108

  Some university or college/trade school
.206
.217
.211

  College/trade school
.230
.231
.230

  University degree
.469
.432
.450
*
Income
  < $25,000
.061
.068
.065

  $25,000 to < $50,000
.146
.136
.141

  $50,000 to < $100,000
.302
.304
.303

  $100,000 to < $150,000
.222
.229
.226

  ≥ $150,000
.172
.163
.168

  Missing income
.096
.100
.098

Finances at end of month
  A lot of money left over
.113
.109
.111

  A little money left over
.406
.425
.415

  Just enough to make ends meet
.256
.249
.253

  Barely enough to get by
.172
.162
.167

  Not enough to make ends meet
.053
.056
.054

Gender
  Men
.513
.514
.513

  Women
.487
.486
.487

Visible minority
  Not a visible minority
.873
.863
.868

  Visible minority
.127
.138
.132

Note: N = 4,923 (2019 sample = 2,477; 2020 sample = 2,446). Descriptives are weighted. Means are presented for
continuous measures, proportions for categorical measures. Data are from the Canadian Quality of Work and
Economic Life Study.
*p < .05, ***p < .001, two tailed.
Table 1.  (continued)
distress. The difference in distress for individuals in
the somewhat agree category is weaker, however, and
not statistically significant. These results showed that
it is marked distrust in neighbors—as indicated by
disagreement with a statement of trust in neighbors—
that is the clear distressing aspect of community
distrust. The difference in distress for individuals
who distrusted neighbors was also relatively strong,
with a semistandardized value of .178. Furthermore,
the between-wave difference in distress declined
almost 15% from the previous model, from .060
to .052, and reduced in significance from p < .01 to
p < .05, indicating that increased community
distrust contributed to explaining between-wave
differences in psychological distress (MacKinnon,
2008). There was also a commensurate decline in the
semistandardized between-wave difference in distress,
from .069 to .060.
Model 3 introduces controls for categories of
responses to subjective social isolation, with no
feelings of isolation as the reference group. All cate-
gories of feelings of isolation are significantly asso-
ciated with greater distress. Furthermore, these
differences are quite substantial. Ancillary analyses
showed that the semistandardized coefficient feeling
isolated some of the time was .835, whereas the
semistandardized coefficient for feeling isolated
most or all of the time was 1.33 and was 2.024 for all
of the time. There is also a substantial decrease in the
between-wave difference in psychological distress
when feelings of isolation are taken into account, as
the between-wave increase in distress is entirely

407
Table 2.  Ordinal Logistic Regression Analyses of Community Distrust and Subjective Social Isolation.
Community Distrust
Subjective Social Isolation

Model 1
Model 2
Model 3
Model 4

b
SE
exp(b)
p
b
SE
exp(b)
p
b
SE
exp(b)
p
b
SE
exp(b)
p
Focal predictors
  Change from 2019 to 2020
.391
.060
1.479
***
.379
.062
1.461
***
.311
.057
1.365
***
.299
.058
1.349
***
  Age
−.011
.003
.989
***
−.013
.004
.987
***
−.032
.002
.968
***
−.037
.003
.964
***
  Survey × Age
.006
.005
1.006
.009
.005
1.009
*
Control measures

  Generalized trusta

    2
−.138
.130
.871
−.142
.130
.867
−.286
.126
.751
*
−.291
.127
.748
*
    3
−.495
.116
.609
***
−.501
.117
.606
***
−.505
.115
.603
***
−.511
.116
.600
***
    4
−1.119
.124
.326
***
−1.123
.124
.325
***
−.616
.121
.540
***
−.618
.121
.539
***
    Most people can be trusted
−1.612
.164
.199
***
−1.617
.164
.198
***
−.995
.158
.370
***
−.999
.158
.368
***
  Occupational classb

    Administrative
−.080
.096
.923
−.081
.096
.922
.130
.090
1.139
.129
.090
1.137

    Sales
.073
.091
1.076
.076
.091
1.079
.136
.087
1.146
.139
.087
1.149

    Clerical
.072
.102
1.075
.072
.103
1.075
−.083
.098
.920
−.084
.098
.920

    Laborer
.162
.108
1.176
.163
.108
1.177
−.030
.100
.971
−.025
.100
.975

  Work hoursc

    Full-time
.093
.085
1.097
.090
.085
1.094
−.120
.084
.887
−.125
.084
.883

    Extended hours
−.148
.121
.862
−.154
.121
.857
−.132
.114
.876
−.139
.115
.870

    Working multiple jobs
−.100
.074
.905
−.102
.074
.903
.130
.069
1.139
.128
.069
1.137

  Work at homed

    A few times a year
.019
.104
1.019
.021
.105
1.021
.095
.099
1.099
.097
.099
1.102

    About once a month
.064
.115
1.066
.069
.115
1.071
−.065
.117
.937
−.057
.117
.944

    About once a week
.007
.109
1.007
.007
.109
1.007
.233
.100
1.262
*
.232
.100
1.262
*
    More than once a week
−.016
.095
.984
−.013
.095
.987
.280
.090
1.323
**
.286
.090
1.331
**
    Every day/mainly work at home
−.138
.095
.871
−.133
.095
.875
.290
.092
1.336
**
.297
.092
1.346
**
  Control over work schedulinge

    Very little
.081
.102
1.084
.079
.102
1.083
−.074
.094
.929
−.078
.094
.925

    Some
.049
.099
1.050
.049
.099
1.050
−.209
.093
.811
*
−.211
.093
.810
*
    A lot
−.069
.104
.933
−.071
.104
.932
−.337
.097
.714
**
−.342
.097
.711
***
    Complete control
−.123
.120
.885
−.127
.119
.881
−.565
.115
.568
***
−.576
.115
.562
***
  Not living with romantic partner
.114
.073
1.121
.119
.073
1.126
.335
.071
1.398
***
.343
.071
1.409
***
(continued)

408
Community Distrust
Subjective Social Isolation

Model 1
Model 2
Model 3
Model 4

b
SE
exp(b)
p
b
SE
exp(b)
p
b
SE
exp(b)
p
b
SE
exp(b)
p
  Any children in household
−.176
.067
.839
**
−.171
.068
.843
*
−.029
.063
.971
−.020
.064
.981

  Educationf

    High school
.138
.117
1.148
.146
.116
1.157
.064
.113
1.066
.079
.113
1.082

    Some university or college/trade school
−.033
.088
.968
−.033
.088
.968
−.202
.084
.817
*
−.202
.085
.817
*
    College/trade school
−.012
.081
.988
−.009
.081
.991
−.210
.077
.810
**
−.206
.077
.814
**
  Incomeg

    < $25,000
.272
.187
1.312
.271
.187
1.311
.375
.161
1.454
*
.375
.161
1.454
*
    $25,000 to < $50,000
.304
.127
1.355
*
.299
.127
1.348
*
.154
.120
1.167
.147
.120
1.158

    $50,000 to < $100,000
.281
.096
1.324
**
.276
.096
1.317
**
.104
.088
1.109
.096
.089
1.101

    $100,000 < $150,000
.052
.095
1.054
.050
.095
1.051
−.014
.089
.986
−.015
.089
.985

    Missing income
.207
.127
1.230
.202
.127
1.224
.091
.118
1.095
.083
.119
1.086

  Finances at end of monthh

    A little money left over
.103
.111
1.109
.107
.111
1.113
.176
.104
1.193
.183
.104
1.201

    Just enough to make ends meet
.157
.120
1.171
.160
.120
1.174
.336
.112
1.399
**
.340
.112
1.406
**
    Barely enough to get by
.343
.131
1.409
**
.344
.130
1.411
**
.710
.123
2.035
***
.714
.123
2.043
***
    Not enough to make ends meet
.206
.167
1.229
.204
.167
1.226
.848
.155
2.336
***
.845
.156
2.328
***
  Women
−.045
.064
.956
−.045
.064
.956
−.011
.061
.990
−.012
.061
.989

  Visible minority
.230
.089
1.259
*
.229
.089
1.258
*
.274
.085
1.315
**
.272
.085
1.312
**
Cut 1
−.985
.213
−.994
.214
−.648
.199
−.654
.199

Cut 2
1.642
.214
1.634
.215
.518
.198
.513
.198

Cut 3
—
—
—
—
1.902
.201
1.897
.201

Cut 4
—
—
—
—
3.696
.217
3.689
.217

Note: N = 4,923. Data are from the Canadian Quality of Work and Economic Life Study.
aYou can’t be too careful is reference.
bProfessional is reference.
cPart-time is reference.
dNever is reference.
eNone is reference.
fUniversity degree is reference.
g≥$150,000 is reference.
hA lot of money left over is reference.
*p < .05, **p < .01, ***p < .001, two-tailed.
Table 2.  (continued)

Bierman and Schieman
409
negated and the difference is no longer statistically
significant. Increasing levels of subjective social iso-
lation from September 2019 to March 2020 therefore
substantially explain increases in psychological
distress
Model 4 removes the indicators of community
distrust and subjective social isolation but intro-
duces an interaction between wave of survey and
age. This interaction is significant, demonstrating
that between-wave differences in psychological
distress varied significantly by age cohort. Figure 3
clarifies the meaning of this interaction by depict-
ing the semistandardized coefficients for differ-
ences in psychological distress across values of age.
Figure 3 shows that at younger ages, between-wave
differences in psychological distress are not signifi-
cant. By age 40, however, respondents in 2020
reported significantly higher mean levels of psy-
chological distress. These differences increased in
strength at older ages. For respondents at age 50,
there was an increase of over a tenth of a standard
between 2019 and 2020, and for respondents at age
60, this increase was almost a sixth of a standard
deviation, indicating a change in population mental
health that is relatively substantial, especially in the
short amount of time between waves.
Model 5 introduces controls for categories of
community distrust. The interaction between wave
of survey and age in Model 5 was almost entirely
unchanged compared to the coefficient for the same
interaction in Model 4. Community distrust there-
fore does not explain age cohort contingencies in
between-wave differences in psychological distress,
but this is to be expected because age did not moder-
ate between-wave differences in risk of community
distrust. However, the ordered logistic regression
analyses did show significant age contingencies in
between-wave differences in risk of subjective
social isolation. Model 6 shows that controlling for
categories of subjective social isolation reduces the
size of the interaction by approximately a third, and
this interaction is no longer significant. Moreover,
ancillary analyses showed that even at age 60,
between-wave differences in psychological distress
were no longer significant once subjective social
isolation was taken into account. That older respon-
dents were more vulnerable to an increased risk in
subjective social isolation between 2019 and 2020
therefore explains why older respondents were more
at risk for an increase in psychological distress
between waves of the survey.
Discussion
A central basis of Durkheimian theory is in the con-
sequences of social integration for population
health (Berkman et al. 2000). Working from this
perspective, theorists have hypothesized that soci-
etal instability can lead to a loss of social integra-
tion, with subsequent emotional ill effects (Abrutyn
and Mueller 2016). The outbreak of the COVID-19
pandemic presents a rare opportunity for a natural
experiment that permits comparison of the popula-
tion both shortly prior to and after the initiation of
social distancing measures. Our study therefore
permits the examination of how the wide-scale
alteration of established patterns of social interac-
tions were associated with changes in psychologi-
cal distress, as well as two likely mechanisms for
these effects.
Our comparisons of two surveys of the Canadian
working population—one prepandemic in September
2019 and another in mid-March 2020 as the pan-
demic accelerated—demonstrates that when the two
samples were adjusted to be compositionally equal,
there was evidence of an increase in population levels
of psychological distress during the pandemic. Two
factors indicative of a loss of social integration con-
tributed to explaining the rise in psychological dis-
tress. Both subjective social isolation and community
distrust increased substantially in the intervening six
months, with the growth in subjective social isolation
especially explaining changes in the population level
of psychological distress. However, subsequent anal-
yses showed important life course contingencies in
these effects, with increases in subjective social isola-
tion and consequent psychological distress far more
pervasive among middle-aged and older individuals.
Figure 2.  Odds Ratios of Increased Risk in
Subjective Isolation across Ages.
Note: Dark bars indicate statistically significant odds
ratios, and light bars indicate nonsignificant odd ratios.
Difference at age 30 is significant at p < .05; for later
ages, it is significant at p < .001.

410
Table 3.  Ordinary Least Squares Regression Analyses of Psychological Distress.
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

b
SE
p
b
SE
p
b
SE
p
b
SE
p
b
SE
p
b
SE
p
Focal predictors
  Change from 2019 to 2020
.060
.023
**
.052
.023
*
−.015
.019
.052
.024
*
.044
.024
−.020
.020

  Age
−.021
.001
***
−.020
.001
***
−.014
.001
***
−.023
.001
***
−.022
.001
***
−.015
.001
***
  Survey × Age
.004
.002
*
.004
.002
*
.002
.001

  Community distrusta

    Somewhat agree with trust in neighbors
.047
.026
.011
.022
.046
.026
.011
.022

    Somewhat disagree/strongly disagree with
trust in neighbors
.153
.039
***
.069
.032
*
.152
.039
***
.068
.032
*
  Subjective isolationb
    A little of the time
.366
.024
***
.364
.024
***
    Some of the time
.721
.026
***
.720
.026
***
    Most of the time
1.149
.036
***
1.147
.037
***
    All of the time
1.746
.078
***
1.747
.078
***
Control measures
  Generalized trustc
    2
−.245
.050
***
−.241
.050
***
−.156
.040
***
−.247
.050
***
−.242
.050
***
−.156
.040
***
    3
−.376
.046
***
−.361
.046
***
−.232
.036
***
−.378
.046
***
−.363
.046
***
−.233
.036
***
    4
−.472
.048
***
−.444
.048
***
−.299
.038
***
−.473
.048
***
−.445
.048
***
−.299
.038
***
    Most people can be trusted
−.630
.059
***
−.594
.060
***
−.395
.050
***
−.630
.059
***
−.595
.059
***
−.395
.050
***
  Occupational classd
    Administrative
.057
.038
.059
.037
.039
.031
.056
.037
.058
.037
.038
.031

    Sales
.060
.034
.057
.034
.028
.027
.061
.034
.059
.034
.029
.027

    Clerical
−.069
.040
−.071
.039
−.049
.032
−.069
.040
−.071
.040
−.049
.032

    Laborer
.035
.043
.032
.043
.050
.037
.036
.043
.033
.042
.051
.037

    Work hourse
    Full-time
−.025
.033
−.027
.033
.002
.027
−.027
.033
−.029
.033
.001
.027

    Extended hours
−.098
.044
*
−.097
.044
*
−.080
.037
*
−.102
.045
*
−.100
.045
*
−.083
.037
*
    Working multiple jobs
.034
.029
.035
.029
.003
.023
.033
.029
.034
.029
.002
.023

  Work at homef
    A few times a year
.051
.039
.050
.038
.034
.031
.051
.039
.050
.038
.034
.031

    About once a month
.081
.047
.081
.047
.092
.039
*
.084
.047
.084
.047
.093
.039
*
    About once a week
.102
.042
*
.102
.042
*
.065
.032
*
.102
.041
*
.102
.041
*
.065
.032
*
    More than once a week
.152
.038
***
.153
.038
***
.101
.031
**
.154
.038
***
.155
.038
***
.102
.031
**
    Every day/mainly work at home
.118
.037
**
.122
.037
**
.068
.030
*
.122
.037
**
.125
.037
**
.070
.030
*
  Control over work schedulingg
    Very little
.005
.039
.004
.039
.028
.033
.003
.039
.003
.039
.027
.033

    Some
−.003
.038
−.003
.038
.046
.032
−.003
.038
−.003
.038
.046
.032

(continued)

411
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6

b
SE
p
b
SE
p
b
SE
p
b
SE
p
b
SE
p
b
SE
p
    A lot
−.068
.039
−.066
.038
.001
.033
−.069
.038
−.068
.038
.000
.033

    Complete control
−.164
.047
***
−.162
.047
**
−.057
.039
−.168
.047
***
−.166
.047
***
−.060
.039

  Not living with romantic partner
.050
.028
.048
.028
−.031
.023
.053
.028
.050
.028
−.030
.023

  Any children in household
−.104
.026
***
−.099
.026
***
−.092
.022
***
−.100
.026
***
−.096
.026
***
−.090
.022
***
  Educationh
    High school
.098
.046
*
.093
.046
*
.063
.036
.104
.046
*
.099
.046
*
.066
.036

    Some university or college/trade school
.023
.034
.023
.034
.060
.028
*
.023
.034
.023
.034
.060
.028
*
    College/trade school
−.030
.030
−.030
.030
.007
.025
−.028
.030
−.028
.030
.008
.025

  Incomei
    < $25,000
.074
.067
.066
.066
−.028
.055
.074
.067
.066
.066
−.028
.055

    $25,000 to < $50,000
.129
.046
**
.122
.046
**
.084
.038
*
.126
.046
**
.119
.046
**
.082
.038
*
    $50,000 to < $100,000
.022
.035
.017
.035
−.012
.030
.019
.035
.014
.035
−.013
.030

    $100,000 to < $150,000
.044
.034
.043
.034
.034
.029
.043
.034
.042
.034
.033
.029

    Missing income
.005
.047
.001
.047
−.025
.040
.002
.047
−.002
.047
−.027
.040

  Finances at end of monthj
    A little money left over
.113
.037
**
.113
.038
**
.093
.030
**
.116
.037
**
.116
.038
**
.095
.030
**
    Just enough to make ends meet
.289
.042
***
.287
.042
***
.233
.033
***
.291
.042
***
.289
.042
***
.234
.033
***
    Barely enough to get by
.467
.048
***
.462
.048
***
.317
.039
***
.468
.048
***
.463
.048
***
.318
.039
***
    Not enough to make ends meet
.614
.065
***
.611
.066
***
.429
.053
***
.613
.065
***
.610
.066
***
.428
.053
***
  Women
.123
.025
***
.124
.025
***
.129
.020
***
.123
.025
***
.124
.025
***
.129
.020
***
  Visible minority
.052
.036
.047
.036
−.023
.028
.052
.036
.046
.036
−.023
.028

Constant
2.395
.075
***
2.338
.077
***
1.898
.064
***
2.400
.075
***
2.343
.077
***
1.902
.064

R2
.249
.252
.480
.250
.253
.481

Note: N = 4,923. Data are from The Canadian Quality of Work and Economic Life Study.
aStrongly agree with trust in neighbors is reference.
bNone of the time is reference.
cYou can’t be too careful is reference.
dProfessional is reference.
ePart-time is reference.
fNever is reference.
gNone is reference.
hUniversity degree is reference.
i≥ $150,000 is reference.
jA lot of money left over is reference.
*p < .05, **p < .01, ***p < .001, two-tailed.
Table 3.  (continued)

412
Journal of Health and Social Behavior 61(4)
Figure 3.  Standardized Difference in
Psychological Distress across Ages.
Note: Dark bars indicate statistically significant
differences, and light bars indicate nonsignificant
differences. Difference at age 40 is significant at p < .05
and is significant at p < .001 for later ages.
These findings are especially notable because
they provide an important qualification to the
intended public health protections intended by the
COVID-19 pandemic social distancing measures.
Although evidence suggests that social distancing
measures are critical for helping limit the spread of
infection and strains on the health care system
(Delen, Eryarsoy, and Davazdahemami 2020;
Lewnard et al. 2020), it has also been suggested the
social consequences of stay-at-home orders may
have had psychological costs (Douglas et al. 2020;
Tull et al. 2020). Our analyses provide support for
these concerns, suggesting that the necessity of
social distancing was concomitant with a rise in
psychological distress. The mental health costs of
social distancing are especially important to take
into account because these measures also curtailed
individuals’ abilities to seek out medical or thera-
peutic assistance for increased distress. Advocates
have suggested that a critical response to social dis-
tancing policies is the fortification of programs and
mechanisms that will help to address a surge in
mental health problems during the pandemic
(Galea, Merchant, and Lurie 2020), and the results
of the current research support these proposals.
Some have also suggested that psychological
vulnerability to the adverse effects of social dis-
tancing may be differentially distributed in the pop-
ulation, with older adults especially at risk of social
isolation and subsequent adverse consequences for
mental health (Armitage and Nellums 2020). The
results of the current research support these con-
cerns as well because respondents at older ages
experienced greater risk of increases in a sense of
isolation, with subsequent heightened increases in
psychological distress. Furthermore, the survey
data analyzed in this study are intended to be repre-
sentative of Canadian workers, which underrepre-
sents the larger population of older adults, many of
whom are retired. Because the working population
will tend to have at least some social contact
through interwork relations, it is likely that this
study minimizes the consequences of social dis-
tancing measures for a sense of isolation among the
larger population of older adults. The risks to the
psychological well-being of older adults as a result
of COVID-19 social distancing measures are there-
fore likely even stronger than those presented here.
These findings support a more nuanced perspec-
tive on Durkheimian expectations regarding the
consequences of social change for social integra-
tion. Fundamental to a Durkheimian perspective is
that “the social fabric is eroded by rapid social
change” (Turner 2003:9), thereby emphasizing the
dissolution of social integration in times of social
change. From a life course perspective, however,
birth cohorts are born with and acquire different
resources and vulnerabilities as a consequence of
their placement in historical time and place (Elder
1994; Keyes et al. 2010). An emphasis on cohort
membership is particularly relevant to the study of
disintegratory social change because some have
characterized those born before 1980 as digital
immigrants and those born in 1980 and after as dig-
ital natives (Nevin and Schieman 2020; Prensky
2001), with the result that older cohorts were likely
to gain less social fulfillment from digital forms of
social interactions that substituted for more conven-
tional forms of interactions. In fact, our results fol-
lowed this categorization. Respondents younger
than 40 (and therefore born after 1980) were much
less at risk for increased feelings of social isolation
than those born after 1980. The current research
therefore suggests that cohorts may possess differ-
ent capabilities in conserving and maintaining
social integration based not only on facilities with
technology but also on ingrained patterns of social
practices and expectations. For cohorts in which
patterns of face-to-face contact and in-person meet-
ings are less common, there may be weaker suscep-
tibility to reductions in social integration as a result
of social change.
A life course perspective invites further theoreti-
cal refinement through its emphasis on timing
(Elder 1999). A key tenet of a life course perspec-
tive is that similar events and experiences can influ-
ence individuals differently depending on the
timing of these events in the life course (Elder et al.
2003), but Durkheimian perspectives have less

Bierman and Schieman
413
clearly articulated the degree to which social
change may vary in its influence on social integra-
tion due to timing in the life course. It is likely that
the age-differentiated patterns we observed in the
current research are not only due to birth cohort dif-
ferences, but also to the degree to which older indi-
viduals had more established patterns and bases of
social interaction that could be disrupted by large-
scale social change. Timing of the pandemic in the
life course as well as historical forces of cohort
change also likely shaped vulnerabilities to disinte-
gratory influences of social distancing measures.
A central theoretical contribution of the current
research is therefore in the concept of integratory
vulnerability—in which a life course context differ-
entiates vulnerability to disintegratory societal
events. The current research in fact underscores the
importance of considering life course context as a
key dimension of integratory vulnerability: Critical
contingencies in changes in subjective social isola-
tion and psychological distress would have been
overlooked in the absence of the insights provided
by a life course perspective. The results of this
research therefore suggest that attention to the life
course context of integratory vulnerability will help
both theorists and empirical researchers to specify
why and for whom the consequences of social
change on social integration and well-being are
likely to be especially pertinent. We especially wish
to emphasize that both birth cohort as well as age
are likely to contribute to integratory vulnerability.
The sociological study of life course contexts has a
poor history of overemphasizing age to the neglect
of birth cohort influences, and an appropriate level
of theorizing and empirical study of life course con-
texts should take both of these dual influences into
account.
An emphasis on the contribution of birth cohort
membership to integratory vulnerability also extends
previous work framing cohort change as a disinte-
gratory agent (O’Brien and Stockard 2006; Stockard
and O’Brien 2002). This work essentially positions
variations in intercohort characteristics as proxies for
social change, but a Durkheimian perspective often
views social change in broader terms, such as those
of wide-scale economic collapse (Cockerham 2017).
The concept of integratory vulnerability extends
these ideas to propose that an understanding of the
role of birth cohort as an integratory influence is bet-
ter served through an intersectional emphasis: Social
change occurs at both a societal and cohort level,
with the result that the two intersect to influence indi-
vidual outcomes. Thus, an important clarification
and extension to a Durkheimian perspective in future
theorizing is to specify social change simultaneously
at a societal and cohort level, with particular care to
the way the two forms of change may intersect to
shape consequences for social integration.
Although declining levels of social integration
can lead to individual feelings of isolation, more
recent theorizing has also linked loss of social inte-
gration to an increased sense threat (Abrutyn 2019).
Rises in threat can in turn harm community trust
because individuals become reticent to allow them-
selves to be vulnerable in the context of trusting rela-
tionships. Evidence from previous pandemics
supports these assertions, showing how fear can
undermine levels of public trust and enhance selfish
motives (Barry 2005). We observed some evidence
of this increase as well. Even in the short time
between surveys, the odds of greater distrust in
neighbors increased precipitously. Furthermore, we
observed an increasing risk of community distrust
even though overall trust remained relatively consis-
tent between waves of the survey. The increase spe-
cifically in distrust of others in local surroundings
suggests that individuals began to look at one another
more suspiciously even if they did not perceive peo-
ple more generally as less trustworthy. Essentially,
the loss of bonds of integration and increasing threat
inherent in social distancing measures led to less
trust in people with whom individuals were likely to
come into contact in the community.
There may be a hesitancy to attribute substantial
meaning to the loss of community trust because
community distrust only minorly explained between-
wave differences in psychological distress. Yet,
health is only one area of social life which is influ-
enced by trust, as trust is a core dimension of human
relations. Without trust, individuals cannot engage in
fundamental processes of reciprocity that serve to
build equity in human relationships (Cialdini and
Goldstein 2004). An erosion of trust could therefore
lead to a loss of social order, as was observed in
Barry’s (2005:330) chronicle of Philadelphia during
Spanish influenza pandemic, in which the city
“turned into itself. There was no trust, no trust, and
without trust all human relations were breaking
down.” Thus, increasing levels of distrust during the
COVID-19 pandemic could have even more sub-
stantial consequences for the disturbance of social
order as the pandemic continued beyond the early
stages observed in the current study, especially if a
greater number of people shifted to more extreme
levels of distrust. These findings therefore also
underscore an important additional area for future
theoretical refinement. Although a primary emphasis
in Durkheimian research is on the ramifications of

414
Journal of Health and Social Behavior 61(4)
social integration for health, an important additional
direction of theoretical refinement is to consider how
contexts of decreasing social integration could also
create more widespread harms to social order
through a loss of social trust.
Several limitations to this study should be noted.
First, both community distrust and subjective social
isolation were assessed with single-item measures.
Although community distrust has previously been
measured using single questions (e.g., Carpiano and
Fitterer 2014; Fujiwara and Kawachi 2008), single-
item measures typically have lower levels of reli-
ability than multiple-item scales. However, that we
see similar increases in both measures, as well as a
reliable scale of distress, suggests that the changes
observed in these analyses are not simply due to
random fluctuations caused by unreliability. In
addition, it should be emphasized that although the
changes observed here are likely attributable to the
COVID-19 pandemic and associated social distanc-
ing measures, we cannot directly link any observed
changes to the COVID-19 pandemic. This does not
weaken the underlying findings of this article that
the Canadian working population experienced a
dramatic increase in social estrangement that led to
greater levels of psychological distress.
One additional theoretically motivated issue is
that we do not control for changes in religious
involvement. Durkheim ([1897] 1951) emphasized
the importance of religious involvement as a source
of social integration, and social distancing in
response to COVID-19 necessitated the cessation of
religious attendance, thereby contributing to a loss
of social integration. At the same time, additional
research suggests that a key conduit for these effects
is likely to be through an increase in subjective
social isolation (Rote, Hill, and Ellison 2013), with
the implication that our focus on subjective social
isolation absorbs the primary consequences of
declines in religious attendance. Stress research has
also emphasized the importance of anticipatory
stressors during times of social crisis (Pearlin and
Bierman 2013). During the COVID-19 pandemic,
concerns in regards to contracting the virus or future
economic hardship also likely exacted a toll on men-
tal health as the pandemic continued and the econ-
omy contracted beyond the time frame in the current
study. Finally, and similar to the question of retired
older adults, these analyses focused on employed
individuals, and it is possible that unemployed indi-
viduals may have also experienced an even greater
increase in subjective social isolation in the absence
of social interactions with coworkers as well as a
greater burden due to economic stressors.
Conclusion
The COVID-19 pandemic represents a once-in-a-
lifetime shock to social life across societies. From
a Durkheimian perspective, it is unsurprising that
we observe consequences of such rapid and all-
encompassing social change for integration and,
ultimately, population health. In times of great
turbulence and social disruption, it is critical to
maintain meaningful social ties, but sustaining
those bonds becomes increasingly more difficult,
resulting in challenges to mental health. As the
pandemic accelerated, we began to observe the
expected fallout for social life and mental health.
However, the patterns are not equivalent across age.
Subjective social isolation increased more dramati-
cally during this period among older respondents,
leading to a more substantial rise in psychological
distress during this period. This research therefore
presents an important qualification to a Durkheimian
perspective by demonstrating that a life course con-
text plays a crucial role in differentiating individual
vulnerability to disintegrative large-scale social
forces and their consequences for mental health.
Acknowledgments
We thank Paul Glavin for his suggestions as this article
was being developed.
Declaration of Conflicting
Interests
The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of
this article.
Funding
The authors disclosed receipt of the following financial
support for the research, authorship, and/or publication of
this article: Funding from the University of Toronto
COVID-19 Action Initiative 2020 and Tri-Council Bridge
funding supported this research (Scott Schieman, PI).
ORCID iDs
Alex Bierman
 https://orcid.org/0000-0003-2573-0969
Scott Schieman
 https://orcid.org/0000-0003-2882-0417
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Author Biographies
Alex Bierman is associate professor in the Department of
Sociology at the University of Calgary. His research
broadly focuses on the degree to which social inequality
and social change condition stress exposure and the conse-
quences of stress exposure for well-being, particularly in
the context of aging.
Scott Schieman, professor of sociology at the University
of Toronto, studies stress, work, and health in large
population-based studies. He is particularly interested in
the stress of higher status and its impact on broader social
inequalities in health and well-being. He holds a Canada
Research Chair in the Social Contexts of Health.

    Policy Problem:

Answer: The first wave of COVID-19 containment measures in Canada—especially stay-at-home orders and bans on public gatherings—unintentionally generated a parallel public-health crisis: a sharp, measurable rise in psychological distress among workers, driven by escalating feelings of social isolation and declining trust in neighbours. National survey data collected just six months apart show that the odds of reporting higher isolation grew by 36 percent overall—and by more than 60 percent for workers aged 50 plus—while pronounced distrust in local communities also surged. Together, these disintegrative forces fully explain the contemporaneous uptick in anxiety, sadness and hopelessness documented across the labour force. The mental-health fallout is therefore not a diffuse consequence of the pandemic itself but a predictable by-product of the social-distancing policies used to curb viral transmission, with older workers bearing the brunt. Unless governments address this hidden cost—by embedding targeted, stigma-free mental-health supports and socially reconnective strategies into public-health planning—efforts to protect Canadians from COVID-19 will continue to erode social cohesion and strain health-care systems with preventable psychological morbidity.