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Citation: Jankowski, Peter J., Steven J.
Sandage, and David C. Wang. 2023.
(Re)Framing Resilience: A
Trajectory-Based Study Involving
Emerging Religious/Spiritual
Leaders. Religions 14: 333. https://
doi.org/10.3390/rel14030333
Academic Editors: Heather Boynton
and Jo-Ann Vis
Received: 6 February 2023
Revised: 15 February 2023
Accepted: 27 February 2023
Published: 2 March 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
religions
Article
(Re)Framing Resilience: A Trajectory-Based Study Involving
Emerging Religious/Spiritual Leaders
Peter J. Jankowski 1, 2, * , Steven J. Sandage 2,3 and David C. Wang 4
1Marriage and Family Therapy, Bethel Seminary, St. Paul, MN 55112, USA
2Albert and Jessie Danielsen Institute, Boston University, Boston, MA 02215, USA
3Psychology of Religion, MF Norwegian School of Theology, Religion and Society, Gydas vei 4,
0363 Oslo, Norway
4
School of Psychology & Marriage and Family Therapy, Fuller Theological Seminary, Pasadena, CA 02215, USA
*Correspondence: pjankows@bethel.edu
Abstract:
The COVID-19 pandemic has provided a unique circumstance for the study of resilience,
and clergy resilience has garnered increased research attention due to greater recognition that
religious/spiritual leaders are at risk for elevated levels of anxiety and burnout. We examined
longitudinal patterns of change during the pandemic in a sample of emerging leaders (N= 751;
M
age
= 32.82; SD 11.37; 49.9% female; 59.8% White). In doing so, we offered a conceptual and
methodological approach based on historical and critical evaluations of the study of resilience.
Results revealed a subgroup that exhibited resilience over three waves of data. The labeling of this
trajectory was based on established criteria for determining resilience: (a) significant adversity in the
form of COVID-19 stress at time 1, which included the highest levels of the subjective appraisal
of stress; (b) risk in the form of low religiousness/spirituality and greater likelihood of reporting
marginalized identifications, relative to those who were flourishing; (c) a protective influence for
transformative experiences to promote positive adaptation; and (d) interruption to the trajectory in
the form of improvement in levels of symptoms and well-being. Practical implications center on the
potential for transformative experiences to clarify emotional experience and construct new meaning.
Keywords:
resilience; COVID-19; well-being; symptoms; latent trajectory analysis; religious/spiritual
leaders; flourishing
1. Introduction
Clergy resilience is garnering increased research attention (e.g., Clarke 2022;Clarke
et al. 2022), in large part due to greater recognition that religious/spiritual (R/S) leaders
are an understudied, high-risk population (e.g., Terry and Cunningham 2020). As helping
professionals, R/S leaders are susceptible to elevated levels of burnout (Clarke et al. 2022)
and mental-health symptoms (Proeschold-Bell et al. 2015), including clinical levels of
post-traumatic stress (Ruffing et al. 2021). R/S leaders face a variety of stressors, such
as direct and indirect exposure to trauma (Wang et al. 2014), navigating multiple and
potentially competing role demands, unrealistic expectations from some congregants,
isolation, and limited remuneration. Studies on resilience are emerging within the larger
literature on clergy well-being, and we agree that the study of resilience “may provide
valuable intelligence to mitigate” the potential for burnout and mental-health symptoms
(Clarke 2022, p. 2). A subset of clergy well-being research has focused on seminary students,
during a time when the development of mitigation strategies is particularly pertinent (e.g.,
Lowe et al. 2022). However, conceptual and methodological problems with the study of
resilience may limit the utility of findings to inform mitigation efforts.
Religions 2023,14, 333. https://doi.org/10.3390/rel14030333 https://www.mdpi.com/journal/religions
Religions 2023,14, 333 2 of 20
1.1. Conceptual and Methodological Concerns
Raetze et al. (2022) suggested that the literature on resilience has a construct validity
problem in the form of Kelley’s (1927)jingle fallacy, “where different meanings are attributed
to a single construct label” (p. 868), while Williams and Kemp (2019) noted at least eight
distinct definitions for resilience. Furthermore, the jingle fallacy calls attention to the
misconception that all operationalizations of a similarly named construct are assessing
the same construct (Hodson 2021;Lilienfeld and Strother 2020). Raetze et al. (2022) also
suggested that the proliferation of research across disciplines has resulted in a conceptual
“scattering in the wind” (p. 874). Possible conceptual drift can be seen in the move
away from the origins of resilience as a developmental construct (Masten 2021;O’Connor
et al. 2015;Zimmerman and Arunkumar 1994) to framing resilience as a self-perceived
personality trait, defined as a capacity for or likelihood of “showing positive adaptation in
the face of significant adversity” (Bonanno 2012;Britt et al. 2016, p. 380).
Infurna and Luthar (2018) challenged the conceptualization of resilience as a stable
high-functioning trajectory (e.g., Bonanno 2012) and suggested that evidence for the preva-
lence of this trajectory was a methodological artifact. Historically, resilience was defined as
“functional development against the odds” (Raetze et al. 2022, p. 884) and “doing better
than expected, given the adversity” (Infurna and Luthar 2018, p. 51). Resilience seemed to
be the exception rather than the norm. However, Masten (2001) also noted resilience to be
“a common phenomenon” (p. 227). Yet, her use of common seemed less about prevalence
and more about “ordinary human adaptive processes” (p. 234) and “ordinary, normative
human resources” (p. 235), and particularly so relative to the view that resilience consisted
of the “rare and special qualities” of individuals (p. 235). Masten contrasted a narrow
trait perspective of resilience with that of a process view that highlighted greater potential
for positive adaptation. In fact, Masten (2021) described multiple longitudinal pathways
that depict resilience. Infurna and Luthar (2018) similarly broadened the definition of
resilience to include other longitudinal patterns of change. Conceptualizations of resilience
contextualize the term common, and perhaps over decades of research, there have been
varying moves away from a view that resilience involves normative developmental and
systemic factors that contribute to multiple positive adaptive processes to that of resilience as
a single stable trajectory of healthy functioning.
From a construct validation perspective, evolution differs from drift. Research find-
ings can contribute to the evolution of a theoretical construct (Hoyt et al. 2006), an aspect
of which is clarification of its nomological network (Lilienfeld and Strother 2020). The
construct of resilience has evolved such that systemic and process elements have become
more pronounced (Masten 2021;Williams and Kemp 2019). Additionally, evolution is
distinct from the aforementioned problem of meaning proliferation (i.e., jingle fallacy) and
the problem of construct proliferation. Hodson (2021) applied Kelley’s (1927)jangle fallacy,
defined as assuming “differently-named constructs are distinct from each other” (Hodson
2021, p. 577), to caution against redundant constructs. Brown et al. (2020) suggested that the
constructs of resilience and growth may represent a jangle fallacy, and coping, flourishing,
well-being, and thriving can be added to this list of conflated constructs (Ettinger et al.
2022;Gupta and McCarthy 2022). The conceptual overlap among these well-being-related
constructs becomes even more challenging when R/S constructs are included in analyses,
given noted concerns about the redundancy between various operationalizations of reli-
giousness/spirituality and well-being (Jankowski et al. 2022c). Hodson (2021) called for
“researchers to prioritize construct validity in their work” (p. 577), which at a minimum
would seem to involve clarification of conceptual distinctions among constructs and an
assessment of the discriminant validity evidence supporting those distinctions.
1.2. (Re)Framing Resilience
The “target stressor event” (Bonanno 2012, p. 755) is a key to punctuating resilience
and has historically constituted “significant adversity,” typically delineated by events that
can be considered traumatic or chronic (Britt et al. 2016, p. 381), whereas contemporary
Religions 2023,14, 333 3 of 20
resilience scholarship also includes “acute life events” (Bonanno 2012, p. 754). Raetze
et al. (2022) called for preciseness when defining significant adversity, and Britt et al.
(2016) acknowledged that defining significant adversity involves the individual’s subjective
appraisal of the exposure. Although an exposure or target stressor event can constitute
significant adversity and therefore place individuals at risk for positive adaptation, it can be
useful to consider adverse events as distinct from risk factors (e.g., Raetze et al. 2022). Risk
factors include stressful life events (i.e., stressors) and other individual (e.g., experiential
avoidance) and contextual (e.g., social inequality) variables that tend to have a negative
influence on positive adaptation (Masten 2021).
Zimmerman and Arunkumar (1994) defined resilience as the “factors and processes
that interrupt” a maladaptive trajectory to “adaptive outcomes even in the presence of
adversity” (p. 4). They called for corresponding longitudinal data analytic strategies,
and Bonanno (2012) emphasized the need for “repeated longitudinal [designs]
. . .
, with
outcome measurements beginning as close as possible to the target stressor event” (p. 755).
Nevertheless, Zimmerman and Arunkumar (1994) acknowledged a place for cross-sectional
designs to provide “a snapshot” of resilience processes (p. 10), and yet, “the construct of
interest is longitudinal,” despite the prevalence of cross-sectional designs (Raetze et al. 2022,
p. 885). As such, designs are needed to map risk factors, protective factors, and outcomes
over time (Zimmerman and Arunkumar 1994), each of which highlights additional method-
ological considerations. At least one clearly identifiable target stressor event is needed
since resilience is a response, in the context of other individual and/or system-level risks or
exposures that would typically predict maladaptation. Second, at least one protective factor
is needed that serves to interrupt the predicted maladaptive trajectory (Zimmerman and
Arunkumar 1994). Third, an outcome for conceptualizing positive adaptation is needed.
O’Connor et al. (2015) defined resilience as “good outcomes in spite of serious threats to
adaptation” (p. 602), and Masten (2021) suggested “observable ‘good adaptation’ in the
context of adversity” (p. 117). The term observable connotes that resilience is inferred from
“observed pathways of manifested resilience” (p. 117), and ideally with multiple indicators
for determining good or positive adaptation.
In the current study, we examined self-reported COVID-19 stress as a risk factor, with
the pandemic as a contextual factor serving as the target stressor event. Time 1 data were
collected from seminary students during the 2021 spring semester, one year post-pandemic
declaration, following a post-holiday surge in infections, the 2020 death toll in the United
States (US) reaching 346,000 and 1,824,590 globally, public debates about vaccinations and
stay-at-home orders, and political upheaval following the 2020 US election of Joe Biden
as president (American Journal of Managed Care 2021). A January 2021 report by the
American Psychological Association (2021) indicated that US adults reported their highest
levels of stress since the pandemic began. Thus, the pandemic during the early months
of 2021 connotes significant adversity. We also examined demographic risk factors for
elevated mental-health symptoms during the pandemic (i.e., young adult, female gender,
liberal ideological commitment, sexual minority, racial/ethnic minority; e.g., Filindassi
et al. 2022;Fish et al. 2021;Na et al. 2022;Robillard et al. 2020;Thomeer et al. 2022).
We examined potential protective R/S factors, informed by the relational spirituality
model (RSM; Sandage et al. 2020). The RSM focuses on ways individuals relate to whatever
they consider sacred or ultimate, and the model has been widely utilized in research
examining the well-being of seminary students and R/S leaders (e.g., Jankowski et al.
2019;Jankowski et al. 2022b,2022d;Sandage et al. 2010,2011). We used secure attachment
to God as a protective R/S factor since it demonstrated a particular protective effect
against elevated symptom levels in response to the pandemic in a diverse national US
sample, which included various religious affiliations (Zhu and Upenieks 2022). Based
on attachment theory, secure attachment to God typically facilitates emotion regulation
in connection to a benevolent attachment figure (safe-haven function) and exploration of
new R/S meaning (secure-base function; Sandage et al. 2020). As a developmental model,
the RSM focuses on factors associated with change, and so we assessed transformative
Religions 2023,14, 333 4 of 20
experiences as a protective factor (e.g., Manning et al. 2019). There is a long history of
research on transformative experiences within the psychology of religion, and two prior
studies with seminary students used the measure employed in this study (Sandage et al.
2010,2011).
1.3. Transformative Experiences
Chirico et al. (2022) offered an integrative, interdisciplinary definition for transforma-
tive experiences as “brief experiences, perceived as extraordinary and unique,
. . .
involving
epistemic expansion
. . .
heightened emotional complexity” (p. 14). Chirico et al. high-
lighted Miller and C’de Baca’s (2001) contribution of quantum change, defined as sudden
rather than gradual change, although these experiences can be embedded in “a continuing
growth process” (C’de Baca and Wilbourne 2004, p. 531). The construct of post-traumatic
growth was also highlighted by Chirico et al. (2022), with emphasis on “stressful and
traumatic events as key elicitors” of transformative experiences (p. 3). Post-traumatic
growth (PTG) tends to emphasize change as “an ongoing process” (Tedeschi and Calhoun
2004, p. 1) and “not simply a return to baseline—it is an experience of improvement” (p. 4).
Although quantum change and PTG connote positive growth, they can involve negative
outcomes. C’de Baca and Wilbourne (2004), for example, observed that most descriptions
of quantum change consisted of improvements in functioning, yet negative outcomes were
also reported. Similarly, stressful events can result in negative outcomes (Tedeschi and
Calhoun 2004), labeled “posttraumatic depreciation” (Tedeschi et al. 2017, p. 11).
Prior research by Sandage et al. (2010,2011) found that a self-reported transformative
experience, framed as quantum change, moderated curvilinear associations between R/S
seeking (i.e., self-identity and meaning exploration) and R/S dwelling (i.e., felt security,
perceived closeness), and R/S seeking and generativity, promoting greater seeking and
dwelling, and protecting against a negative influence for seeking on generativity. From an
RSM perspective, their results supported a dialectical–developmental association between
secure R/S connections and the exploration of new R/S meaning, which might become
integrated or reconciled for some individuals during transformative experiences (Sandage
et al. 2020). Their results also highlight how transformative experiences often involve R/S
themes (Miller and C’de Baca 2001;Skalski and Hardy 2013).
The construct of PTG provides a bridge between the resilience and the transforma-
tive experiences literatures. Specifically, growth is distinguished from return to baseline
(Tedeschi and Calhoun 2004), and return to baseline is often used to further distinguish
recovery from resilience (Infurna and Jayawickreme 2019). Recovery, resilience, and growth
each focus on individuals’ responses to significant adversity; with longitudinal plots of
outcomes for recovery showing the return-to-baseline trajectory, resilience depicted by a
stable, plateau trajectory of positive adaptation, and a growth trajectory characterized by
gradual improvement beyond baseline (Bonanno 2012;Infurna and Jayawickreme 2019;
Masten 2021). However, interrupting the risk trajectory (Zimmerman and Arunkumar 1994)
is central to historical and developmental definitions of resilience. Masten (2021) also
depicted resilience as a plateau trajectory, with the qualifier that the trajectory differed
from an anticipated pattern of maladaptation given the risk factor(s). As such, recovery and
growth trajectories, which depict a change in the pattern of responses, may better represent
resilience than a stable plateau of high functioning (Infurna and Luthar 2018).
Tedeschi and Calhoun (2004) also differentiated growth from effective coping, stating
that growth “cannot easily be reduced to simply a coping mechanism” (p. 15). Elsewhere,
Tedeschi and Kilmer (2005) suggested that resilience was defined by effective coping, im-
plying that effective coping maintains stability rather than promotes growth. However,
Infurna and Jayawickreme (2019) suggested that reporting self-perceived growth may itself
be a coping strategy, rather than an indicator of improvement. In fact, self-perceived growth
may be an indicator of positive appraisal style, which Schäfer et al. (2022) described as
a convergence of protective factors that shape individuals’ perception of adversity. Self-
perceived growth would therefore be akin to optimism, hope, and finding meaning as
Religions 2023,14, 333 5 of 20
indicators of positive appraisal. We view effective coping strategies (e.g., positive ap-
praisal; Schäfer et al. 2022) as potential protective factors contributing to positive adaptive
responses to adversity and ineffective coping strategies (e.g., avoidance) as potential risk
factors (Masten 2021). As such, coping strategies could be associated with any number of
adversity-response trajectories.
1.4. The Current Study
In the current study, we used the RSM to conceptualize and model the R/S factors
of secure God attachment and transformative experiences as protective, distinct from the
outcomes of anxiety and burnout, using a sample of North American seminary students
representing a diverse range of Christian traditions. We expected to identify distinct
subgroups that differed by change processes on the outcomes of anxiety and work-related
burnout over three time points, at least one of which would constitute a resilience trajectory.
As such, our study is consistent with trajectory-based studies of resilience that examine
post-adversity responses (Galatzer-Levy et al. 2018), and especially those studies that have
examined responses to the pandemic (Schäfer et al. 2022). Furthermore, we expected that
a resilience trajectory would differ from other trajectories on levels of risk (i.e., COVID-
19 stress, demographic variables), protective factors (i.e., religiousness/spirituality), and
subjective (i.e., positive emotion) and eudaimonic (i.e., life purpose) well-being. Last,
we expected that a resilience trajectory could be further differentiated by transformative
experiences, with these experiences promoting an adaptative response to adversity.
2. Method
2.1. Participants
Data were collected from graduate students at 18 North American seminaries. We
used a sample of participants for whom we had three time points of data (N= 751;
M
age
= 32.82; SD 11.37; range = 21–72). Among those providing demographic data, 47.4%
identified as male (49.9% female) and a majority identified as heterosexual (89.1%). Partici-
pants identified as 9.5% Black, 14.5% Asian, 59.8% White, 4.7% Hispanic, 2.7% other, and
5.3% multiracial. A majority indicated their religious affiliation as evangelical Protestant
(46.9%), whereas others identified as 20.1% mainline Protestant, 15.6% Catholic, and 3.2%
historically Black Protestant, and 2.9% identified as unaffiliated/none and 10.4% as “other”
religion/affiliation. A majority (at wave 3; 75.3%) indicated vocational goals involving
professional leadership in a church/parish, parachurch organization, and/or missions.
2.2. Procedure
As part of a larger study (e.g., Jankowski et al. 2022b,2022c), seminary students
consented and completed a self-report online survey during the spring of 2021. The sur-
vey included items on religious/spiritual beliefs, practices and experiences, symptoms,
and well-being. The survey was constructed using select items from a variety of existing
instruments to address the need for efficient measurement strategies within these educa-
tional contexts. In exchange for participating, students received a USD 25 gift card. This
process was repeated at prior and subsequent time points. Data from waves 4–6 were
used in the current study because data for burnout and COVID-19 stress were collected
beginning at wave 4. Time 2 (wave 5) was approximately 6 months after time 1 (M= 6.78,
SD = 0.79), during fall 2021, and time 3 (wave 6) was approximately 6 months later (M= 5.52,
SD = 0.72), during spring 2022.
2.3. Measures
Anxiety. We used the 7-item General Anxiety Disorder-7 scale (
ω
= 0.91 at wave 4;
e.g., “Worrying too much about different things;” Spitzer et al. 2006), with higher scores
indicating greater levels of anxiety symptoms.
Burnout. We used the 7-item work-related burnout subscale from the Copenhagen
Burnout Inventory (
ω
= 0.90 at wave 4; e.g., “Do you feel worn out at the end of the
Religions 2023,14, 333 6 of 20
working day?” Kristensen et al. 2005), with higher scores indicating greater psychological
and physical exhaustion related to work.
COVID-19 stress. We used the 6-item traumatic stress subscale from the COVID Stress
Scales (
ω
= 0.92 at wave 4; e.g., “I had trouble sleeping because I worried about the virus;”
Taylor et al. 2020), with higher scores indicating greater levels of perceived stress related to
COVID-19.
Ideological commitment. We used a single item (Perry 2015) that assessed ideological
commitment on religious/spiritual matters ranging from 1 (very conservative) to 7 (very
liberal).
Attachment to God. We used the anxiety (
ω
= 0.89 at wave 4; five items, e.g., “I worry
a lot about my relationship with God”) and avoidance subscales (
ω
= 0.80 at wave 4; five
items, e.g., “I just don’t feel a deep need to be close to God;” Beck and McDonald 2004),
with higher scores on each representing greater perceived insecurity in relating to God.
Gratitude. We used six items (
ω
= 0.76 at wave 4; e.g., “I have so much in life to
be thankful for;” McCullough et al. 2002), with higher scores indicating greater levels of
positive emotion, consistent with the conceptualization of gratitude as a positive emotion
(e.g., Watkins et al. 2018). Prior factor analytic work found joy and gratitude to load on
the same factor, consistent with prior findings of a strong correlation between joy and
gratitude (Jankowski et al. 2022c). Joy and gratitude did show differential associations
with external correlates suggesting that joy and gratitude are somewhat distinct positive
emotions (Jankowski et al. 2022c).
Life purpose. We used the 4-item subscale from the Claremont Purpose Scale
(
ω
= 0.92 at wave 4; e.g., “How clearly do you understand what it is that makes your
life feel worthwhile?” Bronk et al. 2018), with higher scores indicating greater presence of
life purpose.
Transformative experience. We used a single item to assess transformative experience,
based on Miller and C’de Baca’s (2001) work. The item read: “Some people experience
a highly memorable period of minutes or hours, through which they find themselves
immediately, dramatically, and permanently changed (for better or for worse). These
experiences usually take them by surprise, rather than being something the person chose
or decided. Often this involves sudden significant shifts in spirituality, personality, self-
identity, perceptions of reality, and emotional life. Over the past 6 months, have you ever
had such an experience yourself?” Participants rated the item using “no/unsure/yes,” and
then responded to an open-ended item asking them to briefly describe their experience.
Participants’ open-ended responses were coded using thematic analysis (Braun and
Clarke 2006), with the aim of generating a multicategorical variable for the quantitative
analysis. Transforming qualitative data for quantitative analysis can be consistent with
thematic analysis (e.g., Robinson 2022), and it is also consistent with mixed-method research,
and specifically, a concurrent nested design in which the integration of qualitative and
quantitative data occurs during data analysis (Hanson et al. 2005). Thematic analysis
moved from inductive initial coding based on the semantic content of responses (i.e.,
summary descriptions using participants’ words), to grouping initial codes into broader
themes, and then to deductive, theory-informed coding to refine and name themes (e.g.,
insightful versus mystical transformative experiences; Miller and C’de Baca 2001;Skalski
and Hardy 2013). This process resulted in four themes: (1) stressful life events (n= 68; or
positive/negative life events; Skalski and Hardy 2013), including the subtheme of COVID-
19 as a stressor (e.g., “I got married and moved into the married students apartments with
my spouse;” “Realizing the toll the pandemic and isolation is taking on my family—my
marriage, my child’s development, and myself was depressing and heart wrenching”);
(2) self-identity development (n= 101; Skalski and Hardy 2013), including work-/vocation-
/call-related subthemes (e.g., “I’ve learned a lot about myself during seminary, and have
had several moments where I became aware of how I was impacting others (positively or
negatively), and how this did or didn’t align with scripture. Seminary is helping me become
more self-aware!” “Deep insights regarding my calling into Spiritual Direction, such that I
Religions 2023,14, 333 7 of 20
heard several deep validations I was hearing a valid call”); (3) social justice/compassion
(n= 19; C’de Baca and Wilbourne 2004; e.g., “Realization of how Christianity has been
used as a weapon of oppression over and over. Also, the deep and lasting impact of
colonialism, coming from a country that was colonized. How deeply entrenched is the
white supremacist culture;” “Awakening to need to be a voice calling for justice within
the church”); and (4) spiritual/mystical experiences (n= 62; Miller and C’de Baca 2001;
Skalski and Hardy 2013; e.g., “While practicing corporate centering prayer, I had a powerful
experience of God’s presence;” “During a time of private worship, I experienced a lift of
heaviness that had settled into my ‘gut.’ It didn’t come back. I feel like in that time of
worship I was set free of something dark, and assignment from the enemy”).
2.4. Data Analytic Plan
Data were analyzed using mixture modeling procedures in Mplus (version 8.4; Muthén
and Muthén and Muthén 1998–2019; i.e., type = mixture; estimation = maximum-likelihood
estimation with robust standard errors; missing data were handled using full-information
maximum-likelihood estimation (FIML). FIML estimation was appropriate given a non-
significant Little’s MCAR test (
χ2
(38) = 40.82, p= 0.35). Data exhibited multivariate
non-normality (Mardia’s kurtosis statistic = 67.29, p< 0.001). We used a parallel growth
mixture model to examine simultaneous changes in anxiety and burnout, with each process
sharing a single latent categorical variable (StatModel n.d.).
We used a two-step method to fix parameters of the latent growth mixture model
before introducing external variables (Bakk and Kuha 2018; see also Asparouhov and
Muthén 2021). We then examined the influence of covariates on subgroup membership
using multinomial logistic regression for continuous variables and modeling categorical
covariates as class indicators (Asparouhov and Muthén 2021; Muthén and Muthén and
Muthén 1998–2019). We used the Wald test of parameter constraints and the model con-
straint command to examine subgroup differences on the categorical covariates (Muthén
and Muthén and Muthén 1998–2019). For the multicategorical covariates we followed up a
significant Wald test with tests of the difference between probability parameters (Muthén
2014). We then examined subgroup differences on the levels of continuous distal outcomes
by modeling the variables as class indicators (Muthén 2013; Muthén and Muthén and
Muthén 1998–2019) and tested the difference within and between parameters at different
time points (Muthén 2013). The complete data, which included covariates and distal out-
comes, were missing completely at random based on a nonsignificant Little’s MCAR test
(
χ2
(209) = 242.85, p= 0.054) and exhibited multivariate non-normality (Mardia’s kurtosis
statistic = 664.19, p< 0.001).
Class enumeration was based on Bayesian and Akaike information criteria (BIC, AIC),
with smaller values indicating better fit (Berlin et al. 2014). We also considered entropy, a
measure of subgroup separation and classification accuracy, with values > 0.60 acceptable
and > 0.80 good (Berlin et al. 2014), along with the average posterior class probability
(AvePP), with values greater than 0.70 indicating that “the classes [are] well separated and
the latent class assignment accuracy adequate” (Masyn 2013, p. 570). Last, we considered
subgroup size, parsimony, and interpretability (Masyn 2013).
3. Results
We set the nonsignificant variance of the slope for anxiety to zero to aid convergence,
thereby approximating latent trajectory analysis rather than remaining a growth mixture
model (Jung and Wickrama 2008). A plot of information criterion values showed a “point
of ‘diminishing returns’ in model fit,” (Nylund-Gibson and Choi 2018, p. 443; see Figure 1)
at the three-class model. As such, we opted for interpreting the three-class model. Entropy
for the three-class model was 0.85, suggesting good subgroup separation and classification
accuracy, and the average posterior class probability (AvePP) values were adequate (
≥
0.69).
Class 1 reported the highest levels of anxiety and work-related burnout symptoms and
showed improvement over time (see Figure 2). Class 2 reported moderate levels of symp-
Religions 2023,14, 333 8 of 20
toms and showed deterioration over time. Class 3 reported the lowest levels of symptoms,
a small decline in anxiety, and no change in burnout.
Religions 2023, 14, x FOR PEER REVIEW 8 of 21
of ‘diminishing returns’ in model fit,” (Nylund-Gibson and Choi 2018, p. 443; see Figure
1) at the three-class model. As such, we opted for interpreting the three-class model. En-
tropy for the three-class model was 0.85, suggesting good subgroup separation and clas-
sification accuracy, and the average posterior class probability (AvePP) values were ade-
quate (≥0.69). Class 1 reported the highest levels of anxiety and work-related burnout
symptoms and showed improvement over time (see Figure 2). Class 2 reported moderate
levels of symptoms and showed deterioration over time. Class 3 reported the lowest levels
of symptoms, a small decline in anxiety, and no change in burnout.
Figure 1. Plot of information criteria values to determine number of profiles. Note: BIC = Bayesian
Information Criterion. AIC = Akaike Information Criterion.
Class membership was associated with demographic variables. As age increased par-
ticipants were less likely to belong to class 1 relative to class 3 (B = −0.07, SE = 0.02, p <
0.001), and identifying as more ideologically liberal was associated with a greater likeli-
hood of belonging to class 1 (B = 0.48, SE = 0.10, p < 0.001). Similarly, as age increased
participants were less likely to belong to class 2 relative to class 3 (B = −0.03, SE = 0.01, p =
0.002), and when participants identified as more ideologically liberal they were more
likely to belong to class 2 relative to class 3 (B = 0.33, SE = 0.07, p < 0.001). In addition,
participants who identified as heterosexual were less likely to belong to class 1 relative to
class 3 (χ2 = 4.52(1), p = 0.03) and class 2 relative to class 3 (χ2 = 3.87(1), p = 0.049). Further-
more, participants who identified as female were more likely to belong to class 2 relative
to class 3 (χ2 = 6.36(1), p = 0.01). Participants who identified their religious affiliation as
evangelical were more likely to belong to class 3 relative to classes 1 (χ2 = 6.26(1), p = 0.01)
and 2 (χ2 = 5.22(1), p = 0.02).
There was also a significant difference between subgroups on the multicategorical
covariate (i.e., “no,” “unsure,” “yes”) about a recent transformative experience (χ2 =
11.77(4), p = 0.02). Comparisons of the multicategorical variable by class indicated that
classes 2 and 3 differed (χ2 = 7.42(2), p = 0.02). Pairwise comparisons by response revealed
that class 3 was more likely to respond “no” relative to class 1 (ΔP = −0.16, SE = 0.09, per-
centile bootstrap (PC) 95% confidence interval (CI) [−0.36, −0.02]; 500 bootstrap samples)
and class 2 (ΔP = −0.15, SE = 0.06, PC95%CI [−0.25, −0.03]). Alternatively, results based on
dummy coding for the multicategorical variable revealed that class 3 was more likely to
respond “no” relative to “yes” (χ2 = 11.04(2), p = 0.004) relative to class 1 (ΔP = 0.16, SE =
0.09, PC95%CI [0.01, 0.36]) and class 2 (ΔP = 0.16, SE = 0.06, PC95%CI [0.03, 0.27]).
Figure 1.
Plot of information criteria values to determine number of profiles. Note: BIC = Bayesian
Information Criterion. AIC = Akaike Information Criterion.
Religions 2023, 14, x FOR PEER REVIEW 9 of 21
Figure 2. Plot of the estimated means for the parallel growth mixture model. Note: N = 747. First line
segment = plot of anxiety scores, second line segment = plot of work-related burnout. Scale range
for anxiety 0–3, and for burnout 1–5. Slope 1 variance set to zero to aid convergence. Below are the
intercept and slope means for each trajectory, SE = standard error: Class 1: anxiety intercept = 2.14,
SE = 0.14, p < 0.001; slope = −0.67, SE = 0.09, p < 0.001; burnout intercept = 3.47, SE = 0.12, p < 0.001;
slope = −0.21, SE = 0.09, p = 0.02. Class 2: anxiety intercept = 1.18, SE = 0.12, p < 0.001; slope = 0.39, SE
= 0.05, p < 0.001; burnout intercept = 3.02, SE = 0.09, p < .001; slope = 0.22, SE = 0.06, p < 0.001. Class
3: anxiety intercept = 0.47, SE = 0.02, p < 0.001; slope = −0.03, SE = 0.01, p = .03; burnout intercept =
2.55, SE = 0.03, p < 0.001; slope = −0.01, SE = 0.02, p = 0.73.
There was also a significant difference between subgroups on the multicategorical
covariate (i.e., “stressful life events,” “self-identity development,” “social justice/compas-
sion,” “spiritual/mystical experience”) involving participants’ descriptions of their trans-
formative experience (χ2 = 80.02(6), p < 0.001). Comparisons by class indicated that class 1
differed from classes 2 (χ2 = 14.26(3), p = 0.003) and 3 (χ2 = 59.10(3), p < 0.001). Pairwise
comparisons by response revealed that class 1 was less likely to report a theme of “social
justice/compassion” relative to class 2 (ΔP = −0.17, SE = 0.06, PC95%CI [−0.31, −0.06]) and
class 3 (ΔP = −0.06, SE = 0.09, PC95%CI [−0.10, −0.03]), and class 1 was less likely to describe
a theme of “spiritual/mystical experience” relative to class 3 (ΔP = −0.23, SE = 0.07,
PC95%CI [−0.33, −0.08]). Alternatively, for the comparison based on dummy coding for
the multicategorical variable, class 1 was more likely to report a theme of “stressful life
events” relative to “social justice/compassion” than class 2 (χ2 = 7.72(1), p = 0.005) and class
3 (χ2 = 9.48(1), p = 0.002). Furthermore, there was a difference for reporting a theme of
“stressful life events” relative to “spiritual/mystical experience” for class 1 relative to class
2 (χ2 = 5.71(1), p = 0.02) and class 3 (χ2 = 64.34(1), p < 0.001), with class 1 more likely to
report a theme of “stressful life events.”
Class 1 reported the highest levels of COVID-19 stress at time 4 (M = 2.01, SE = 0.14),
relative to both class 2 (M = 0.44, SE = 0.11; ΔM = 1.57, SE = 0.17, p < 0.001) and class 3 (M
= 0.18, SE = 0.03; ΔM = 1.82, SE = 0.15, p < 0.001), with class 2 reporting higher levels of
COVID-19 stress than class 3 (ΔM = 0.25, SE = 0.11, p = 0.02). Class 3 reported the highest
levels of life purpose (M = 3.99, SE = 0.04) relative to class 1 (ΔM = −0.46, SE = 0.15, p =
0.003) and class 2 (ΔM = −0.86, SE = 0.11, p < 0.001), the highest levels of gratitude (M =
6.33, SE = 0.03) relative to class 1 (ΔM = −0.76, SE = 0.16, p < 0.001) and class 2 (ΔM = −0.61,
SE = 0.12, p < 0.001), the lowest levels of anxious God attachment (M = 2.96, SE = 0.14)
relative to class 1 (ΔM = 0.79, SE = 0.26, p = 0.002) and class 2 (ΔM = 1.31, SE = 0.19, p <
0.001), and the lowest levels of avoidant God attachment (M = 1.06, SE = 0.08) relative to
class 1 (ΔM = 0.97, SE = 0.23, p < 0.001) and class 2 (ΔM = 0.96, SE = 0.18, p < 0.001). Classes
1 and 2 did not differ in levels of gratitude, anxious God attachment, or avoidant God
attachment, but did differ in life purpose (ΔM = 0.40, SE = 0.19, p = 0.03) with class 2 re-
porting the lowest levels of life purpose (M = 3.13, SE = 0.10).
The lower symptom levels and greater levels of subjective (e.g., positive emotion)
and eudaimonic (e.g., life purpose) well-being that characterized class 3 are consistent
with conceptualizations of flourishing (e.g., Jankowski et al. 2020; Keyes 2002). In contrast,
the moderate symptom levels and low levels of life purpose and gratitude that
Figure 2.
Plot of the estimated means for the parallel growth mixture model. Note: N = 747. First
line segment = plot of anxiety scores, second line segment = plot of work-related burnout. Scale
range for anxiety 0–3, and for burnout 1–5. Slope 1 variance set to zero to aid convergence. Below
are the intercept and slope means for each trajectory, SE = standard error: Class 1: anxiety intercept
= 2.14, SE = 0.14, p< 0.001; slope =
−
0.67, SE = 0.09, p< 0.001; burnout intercept = 3.47, SE = 0.12,
p< 0.001; slope =
−
0.21, SE = 0.09, p= 0.02. Class 2: anxiety intercept = 1.18, SE = 0.12, p< 0.001;
slope = 0.39, SE = 0.05, p< 0.001; burnout intercept = 3.02, SE = 0.09, p< .001; slope = 0.22, SE = 0.06,
p< 0.001. Class 3: anxiety intercept = 0.47, SE = 0.02, p< 0.001; slope =
−
0.03, SE = 0.01, p= 0.03;
burnout intercept = 2.55, SE = 0.03, p< 0.001; slope = −0.01, SE = 0.02, p= 0.73.
Class membership was associated with demographic variables. As age increased
participants were less likely to belong to class 1 relative to class 3 (B=
−
0.07, SE = 0.02,
p< 0.001), and identifying as more ideologically liberal was associated with a greater
likelihood of belonging to class 1 (B= 0.48, SE = 0.10, p< 0.001). Similarly, as age increased
participants were less likely to belong to class 2 relative to class 3 (B=
−
0.03, SE = 0.01,
p= 0.002), and when participants identified as more ideologically liberal they were more
likely to belong to class 2 relative to class 3 (B= 0.33, SE = 0.07, p< 0.001). In addi-
tion, participants who identified as heterosexual were less likely to belong to class 1
relative to class 3 (
χ2
= 4.52(1), p= 0.03) and class 2 relative to class 3 (
χ2
= 3.87(1),
p= 0.049). Furthermore, participants who identified as female were more likely to be-
long to class 2 relative to class 3 (
χ2
= 6.36(1), p= 0.01). Participants who identified their
religious affiliation as evangelical were more likely to belong to class 3 relative to classes 1
(χ2= 6.26(1), p= 0.01) and 2 (χ2= 5.22(1), p= 0.02).
Religions 2023,14, 333 9 of 20
There was also a significant difference between subgroups on the multicategorical
covariate (i.e., “no,” “unsure,” “yes”) about a recent transformative experience (
χ2
= 11.77(4),
p= 0.02). Comparisons of the multicategorical variable by class indicated that classes 2 and
3 differed (
χ2
= 7.42(2), p= 0.02). Pairwise comparisons by response revealed that class
3 was more likely to respond “no” relative to class 1 (
∆
P=
−
0.16, SE = 0.09, percentile
bootstrap (PC) 95% confidence interval (CI) [
−
0.36,
−
0.02]; 500 bootstrap samples) and
class 2 (
∆
P=
−
0.15, SE = 0.06, PC95%CI [
−
0.25,
−
0.03]). Alternatively, results based on
dummy coding for the multicategorical variable revealed that class 3 was more likely
to respond “no” relative to “yes” (
χ2
= 11.04(2), p= 0.004) relative to class 1 (
∆
P= 0.16,
SE = 0.09, PC95%CI [0.01, 0.36]) and class 2 (∆P= 0.16, SE = 0.06, PC95%CI [0.03, 0.27]).
There was also a significant difference between subgroups on the multicategorical co-
variate (i.e., “stressful life events,” “self-identity development,” “social justice/compassion,”
“spiritual/mystical experience”) involving participants’ descriptions of their transforma-
tive experience (
χ2
= 80.02(6), p< 0.001). Comparisons by class indicated that class 1
differed from classes 2 (
χ2
= 14.26(3), p= 0.003) and 3 (
χ2
= 59.10(3), p< 0.001). Pairwise
comparisons by response revealed that class 1 was less likely to report a theme of “social
justice/compassion” relative to class 2 (
∆
P=
−
0.17, SE = 0.06, PC95%CI [
−
0.31,
−
0.06])
and class 3 (
∆
P=
−
0.06, SE = 0.09, PC95%CI [
−
0.10,
−
0.03]), and class 1 was less likely
to describe a theme of “spiritual/mystical experience” relative to class 3 (
∆
P=
−
0.23,
SE = 0.07, PC95%CI [
−
0.33,
−
0.08]). Alternatively, for the comparison based on dummy
coding for the multicategorical variable, class 1 was more likely to report a theme of “stress-
ful life events” relative to “social justice/compassion” than class 2 (
χ2
= 7.72(1), p= 0.005)
and class 3 (
χ2
= 9.48(1), p= 0.002). Furthermore, there was a difference for reporting
a theme of “stressful life events” relative to “spiritual/mystical experience” for class 1
relative to class 2 (
χ2
= 5.71(1), p= 0.02) and class 3 (
χ2
= 64.34(1), p< 0.001), with class 1
more likely to report a theme of “stressful life events.”
Class 1 reported the highest levels of COVID-19 stress at time 4 (M= 2.01, SE = 0.14),
relative to both class 2 (M= 0.44, SE = 0.11;
∆
M= 1.57, SE = 0.17, p< 0.001) and class 3
(M= 0.18, SE = 0.03;
∆
M= 1.82, SE = 0.15, p< 0.001), with class 2 reporting higher levels
of COVID-19 stress than class 3 (
∆
M= 0.25, SE = 0.11, p= 0.02). Class 3 reported the
highest levels of life purpose (M = 3.99, SE = 0.04) relative to class 1 (
∆
M =
−
0.46, SE = 0.15,
p= 0.003) and class 2 (
∆
M=
−
0.86, SE = 0.11, p< 0.001), the highest levels of gratitude
(M= 6.33, SE = 0.03) relative to class 1 (
∆
M=
−
0.76, SE = 0.16, p< 0.001) and class 2
(
∆
M=
−
0.61, SE = 0.12, p< 0.001), the lowest levels of anxious God attachment (M= 2.96,
SE = 0.14) relative to class 1 (
∆
M= 0.79, SE = 0.26, p= 0.002) and class 2 (
∆
M= 1.31,
SE = 0.19, p< 0.001), and the lowest levels of avoidant God attachment (M= 1.06,
SE = 0.08) relative to class 1 (
∆
M= 0.97, SE = 0.23, p< 0.001) and class 2 (
∆
M= 0.96,
SE = 0.18, p< 0.001). Classes 1 and 2 did not differ in levels of gratitude, anxious
God attachment, or avoidant God attachment, but did differ in life purpose (
∆
M= 0.40,
SE = 0.19, p= 0.03) with class 2 reporting the lowest levels of life purpose (M= 3.13,
SE = 0.10).
The lower symptom levels and greater levels of subjective (e.g., positive emotion) and
eudaimonic (e.g., life purpose) well-being that characterized class 3 are consistent with
conceptualizations of flourishing (e.g., Jankowski et al. 2020;Keyes 2002). In contrast, the
moderate symptom levels and low levels of life purpose and gratitude that characterized
class 2, along with moderate levels of the stressor and higher insecure God attachment
and the deterioration over time, are consistent with formulations of languishing. Concep-
tualizations of languishing often emphasize cross-sectional assessments of low well-being,
although the term languishing has been applied to declines in well-being over time (e.g.,
O’Donnell et al. 2022). We extended the latter notion to include the deterioration of symp-
toms over time. Last, the high symptom levels and low levels of life purpose and gratitude
for class 1, along with highest levels of the stressor despite evidence of improvement on
symptom levels, are consistent with multidimensional, developmental-process depictions
of resilience (e.g., Masten 2021;Zimmerman and Arunkumar 1994).
Religions 2023,14, 333 10 of 20
In addition, the resilient class (i.e., growth trajectory) reported a significant decline in
COVID-19 stress from time 4 to time 5 (
∆
M= 0.76, SE = 0.37, p= 0.04; d= 1.77), whereas
the flourishing (i.e., stable-plateau trajectory) and languishing (i.e., deterioration trajectory)
classes reported no change in the level of COVID-19 stress from time 4 to time 5. Of note,
the growth displayed by the resilient seemed tied to the decrease in levels of COVID-19
stress over time. In fact, among the resilient, reporting a transformative experience theme of
“stressful life events” was associated with a greater rate of improvement in COVID-19 stress
(B=
−
0.50, SE = 0.20, p= 0.01), as the dummy-coded multicategorical variable predicted
the latent-change score for COVID-19 stress, which was modeled to be class specific in a
follow-up analysis. The flourishing reported an increase in life purpose from times 4 to 6
(d=
−
0.17, SE = 0.05, p= 0.001), and the resilient reported an increase in gratitude from
time 4 to time 6 (d=
−
0.42, SE = 0.20, p= 0.04). There were no changes in anxious and
avoidant God-attachment dimensions over time. Last, wave 1 (fall 2019) anxiety did not
differ from wave 4 anxiety levels among the resilient (
∆
M=
−
0.64, SE = 0.43, p= 0.14),
although the trend was toward increased anxiety. The flourishing subgroup reported an
increase in anxiety symptoms from wave 1 to wave 4 (
∆
M=
−
0.07, SE = 0.03, p= 0.02).
Nevertheless, their levels of anxiety remained lower than the resilient and the languishing.
The resilient (
∆
M= 1.14, SE = 0.44, p= 0.01) and the languishing (
∆
M= 1.00, SE = 0.32,
p= 0.002) reported higher levels of anxiety than the flourishing at wave 1, whereas the
resilient and the languishing did not differ.
Taken together, the mapping of “social justice/compassion” themes for the languishing
seems consistent with their more marginalized demographic profile relative to the flour-
ishing, with those in the languishing class more likely to identify as younger, more liberal,
non-heterosexual, female, and non-evangelical. The mapping of “stressful life events”
themes for the resilient seems consistent with their highest levels of anxiety, burnout, and
COVID-19 stress, and their reported improvement may be indicative of the alleviation
of stressors, including COVID-19-related stress and their transformative experience. For
the flourishing, the mapping of “spiritual/mystical experiences” seems consistent with
their predominantly evangelical identification and their highest levels of felt security and
perceived closeness in their attachment relating to God.
Sensitivity Analyses
We conducted sensitivity analyses by “running alternative, justifiable analyses to
see whether a reported result would still hold up” (Nuijten 2022, p. 392). For the first
check, we examined a piecewise mixture model that included data for anxiety from waves
1–4 as a first slope segment and then the data for waves 4–6 as a second slope segment
(Muthén and Muthén and Muthén 1998–2019). We set the second slope segment for anxiety
to zero to aid convergence. The results are depicted in Figure 3and support our earlier
contention and subsequent finding that a resilience trajectory need not be limited to a
high-functioning plateau. Wave 1 was collected pre-pandemic declaration during fall 2019,
with the declaration about a public-health emergency occurring on 31 January 2020 (U.S.
Department of Health & Human Services 2020), just as the measurement window for wave
2 data collection was opening. As Figure 3shows, the resilient and the languishing had
comparable levels of anxiety at wave 1 (
∆
i =
−
0.26, SE = 0.40, p= 0.53), and although the
languishing initially showed a plateau over times 1–4, their anxiety significantly increased
over waves 4–6. In contrast, after showing an increase in anxiety over waves 1–4, the
resilient reported a decline in anxiety during waves 4–6. The trajectory matches depictions
of recovery (Infurna and Jayawickreme 2019), except that wave 6 anxiety was significantly
lower than wave 1 anxiety (d= 0.48, SE = 0.10, p< 0.001), and therefore the trajectory is
more consistent with growth, although this depends on conceptualizations of “gradual
improvements to near-previous levels over time” (p. 156). However, as we describe below,
descriptions of change over time are but one aspect to discerning resilience, and as we noted
previously, resilient patterns of change can encompass growth and/or recovery.
Religions 2023,14, 333 11 of 20
Religions 2023, 14, x FOR PEER REVIEW 11 of 21
4–6. In contrast, after showing an increase in anxiety over waves 1–4, the resilient reported
a decline in anxiety during waves 4–6. The trajectory matches depictions of recovery (In-
furna and Jayawickreme 2019), except that wave 6 anxiety was significantly lower than
wave 1 anxiety (d = 0.48, SE = 0.10, p < 0.001), and therefore the trajectory is more consistent
with growth, although this depends on conceptualizations of “gradual improvements to
near-previous levels over time” (p. 156). However, as we describe below, descriptions of
change over time are but one aspect to discerning resilience, and as we noted previously,
resilient patterns of change can encompass growth and/or recovery.
Figure 3. Plot of the estimated means for the sensitivity analysis for the piecewise parallel growth
mixture model. Note: N = 751. First line segment = plot of anxiety scores for times 1–4, second line
segment = plot of anxiety scores for times 4–6, third line segment = plot of work-related burnout for
times 4–6. Scale range for anxiety 0–3, and for burnout 1–5. Slope 2 variance set to zero to aid con-
vergence. Below are the intercept and slope means for each trajectory, SE = standard error. Resilient:
anxiety intercept = 1.06, SE = 0.27, p < 0.001; slope1 = 0.37, SE = 0.09, p < 0.001; slope2 = −0.70, SE =
0.08, p < 0.001; burnout intercept = 3.51, SE = 0.12, p < 0.001; slope = −0.23, SE = 0.12, p = 0.049. Lan-
guishing: anxiety intercept = 1.32, SE = 0.24, p < 0.001; slope1 = −0.03, SE = 0.10, p = 0.78; slope2 = 0.39,
SE = 0.07, p < 0.001; burnout intercept = 3.01, SE = 0.09, p < 0.001; slope = 0.24, SE = 0.07, p < 0.001.
Flourishing: anxiety intercept = 0.49, SE = 0.04, p < 0.001; slope1 = < .001, SE = 0.01, p = 0.99; slope2 =
−0.04, SE = 0.01, p = 0.005; burnout intercept = 2.56, SE = 0.03, p < 0.001; slope = −0.01, SE = 0.02, p =
0.69.
Second, in another sensitivity analysis, we used gratitude and life purpose as addi-
tional indicators in the mixture model, along with anxiety and work-related burnout. Each
process shared a single latent categorical variable. We set the nonsignificant variances of
the slopes for anxiety and life purpose to zero to aid convergence. A plot of BIC values
indicated three- and four-class solutions as viable. We opted for the three-class solution
because two classes in the four-class solution seemed redundant. Three of the four inter-
cepts exhibited a low degree of class separation (i.e., ≤ 1 standard deviation (SD) difference
between classes, as indicated by standardized mean difference effect size; Grimm et al.
2021; for anxiety: Δi = −0.28, SE = 0.16, p = 0.07; d = 0.86; for burnout: Δi = −0.25, SE = 0.22,
p = 0.25; d = 0.48; for life purpose: Δi = 0.67, SE = 0.31, p = 0.03; d = 0.99), whereas gratitude
exhibited a high degree of separation (≥ 3 SD difference between classes; Grimm et al.
2021; Δi = 2.08, SE = 0.20, p < 0.001; d = 4.33). The slopes were in the same direction, except
for gratitude, with one trajectory showing significant growth and the other a nonsignifi-
cant slope or plateau. Like Figure 2, Figure 4 shows the resilient trajectory with the highest
levels of symptoms, and this time, also with the lowest levels of subjective and eudaimonic
well-being, each of which showed improvement.
Figure 3.
Plot of the estimated means for the sensitivity analysis for the piecewise parallel growth
mixture model. Note: N = 751. First line segment = plot of anxiety scores for times 1–4, second
line segment = plot of anxiety scores for times 4–6, third line segment = plot of work-related burnout
for times 4–6. Scale range for anxiety 0–3, and for burnout 1–5. Slope 2 variance set to zero to aid
convergence. Below are the intercept and slope means for each trajectory, SE = standard error. Resilient:
anxiety intercept = 1.06, SE = 0.27, p< 0.001; slope1 = 0.37, SE = 0.09, p< 0.001; slope2 =
−
0.70, SE = 0.08,
p< 0.001; burnout intercept = 3.51, SE = 0.12, p< 0.001; slope =
−
0.23, SE = 0.12, p= 0.049. Languishing:
anxiety intercept = 1.32, SE = 0.24, p< 0.001; slope1 =
−
0.03, SE = 0.10, p= 0.78; slope2 = 0.39, SE = 0.07,
p< 0.001; burnout intercept = 3.01, SE = 0.09, p< 0.001; slope = 0.24, SE = 0.07, p< 0.001. Flourishing:
anxiety intercept = 0.49, SE = 0.04, p< 0.001; slope1 = < 0.001, SE = 0.01, p= 0.99; slope2 =
−
0.04,
SE = 0.01, p= 0.005; burnout intercept = 2.56, SE = 0.03, p< 0.001; slope = −0.01, SE = 0.02, p= 0.69.
Second, in another sensitivity analysis, we used gratitude and life purpose as addi-
tional indicators in the mixture model, along with anxiety and work-related burnout. Each
process shared a single latent categorical variable. We set the nonsignificant variances of
the slopes for anxiety and life purpose to zero to aid convergence. A plot of BIC values
indicated three- and four-class solutions as viable. We opted for the three-class solution
because two classes in the four-class solution seemed redundant. Three of the four inter-
cepts exhibited a low degree of class separation (i.e.,
≤
1 standard deviation (SD) difference
between classes, as indicated by standardized mean difference effect size; Grimm et al.
2021; for anxiety:
∆
i =
−
0.28, SE = 0.16, p= 0.07; d= 0.86; for burnout:
∆
i =
−
0.25, SE = 0.22,
p= 0.25; d= 0.48; for life purpose:
∆
i = 0.67, SE = 0.31, p= 0.03; d= 0.99), whereas gratitude
exhibited a high degree of separation (
≥
3 SD difference between classes; Grimm et al. 2021;
∆
i = 2.08, SE = 0.20, p< 0.001; d= 4.33). The slopes were in the same direction, except for
gratitude, with one trajectory showing significant growth and the other a nonsignificant
slope or plateau. Like Figure 2, Figure 4shows the resilient trajectory with the highest
levels of symptoms, and this time, also with the lowest levels of subjective and eudaimonic
well-being, each of which showed improvement.
Next, we used the automated three-step procedures in Mplus to model auxiliary
variables (Asparouhov and Muthén 2021). First, we compared subgroups on the dummy-
coded multicategorical transformative-experiences variables using DCAT. Second, we
examined demographic, COVID-19 stress, and attachment to God as risk and protective
covariates predicting class membership using R3STEP. The flourishing subgroup was more
likely than the languishing to respond “no” to a recent transformative experience relative
to responding “yes” (
χ2
= 4.36(1), p= 0.037). The resilient subgroup was more likely to
report a theme about “stressful life events” relative to the flourishing (
χ2
= 4.70(1), p= 0.03)
and less likely to report a theme about “spiritual/mystical experience” than the flourishing
(
χ2
= 121.47(1), p< 0.001) and the languishing (
χ2
= 9.55(1), p= 0.002). Relative to the
flourishing, participants who identified as heterosexual were less likely to belong to the
resilient subgroup (B=
−
1.13, SE = 0.57, p= 0.048). Relative to the flourishing, the languishing
subgroup were more likely to identify as non-White (B=
−
0.87, SE = 0.42, p= 0.04), female
Religions 2023,14, 333 12 of 20
(B= 0.89, SE = 0.40, p= 0.02), and younger (B=
−
0.05, SE = 0.02, p= 0.01), and less likely
to identify as evangelical (B=
−
1.04, SE = 0.45, p= 0.02). The resilient were more likely to
report higher levels of COVID-19 stress. Specifically, relative to the resilient, as levels of
COVID-19 stress increased participants were less likely to belong to the flourishing (B=
−
1.55,
SE = 0.27, p< 0.001) and the languishing (B=
−
1.11, SE = 0.62, p= 0.004). Last, as God-attachment
anxiety increased participants were more likely to belong to the resilient (B= 0.57, SE = 0.18,
p= 0.002), relative to the flourishing, and similarly, as God-attachment avoidance increased
participants were more likely to belong to the resilient (B= 0.49, SE = 0.17, p= 0.004) relative to the
flourishing.
Religions 2023, 14, x FOR PEER REVIEW 12 of 21
Figure 4. Plot of the estimated means from the sensitivity analysis for the parallel growth mixture
model. Note: N = 751. First line segment = plot of anxiety scores, second line segment = plot of work-
related burnout, third line segment = plot of life purpose, fourth line segment = plot of gratitude.
Resilient class: anxiety intercept = 2.12, SE = 0.14, p < 0.001; slope = −0.69, SE = 0.08, p < 0.001; burnout
intercept = 3.46, SE = 0.11, p < 0.001; slope = −0.22, SE = 0.08, p = 0.01; life purpose intercept = 3.45, SE
= 0.13, p < 0.001; slope = 0.17, SE = 0.06, p =0.005; gratitude intercept = 5.50, SE = 0.20, p < 0.001; slope
= 0.18, SE = 0.09, p = 0.04.
Next, we used the automated three-step procedures in Mplus to model auxiliary var-
iables (Asparouhov and Muthén 2021). First, we compared subgroups on the dummy-
coded multicategorical transformative-experiences variables using DCAT. Second, we ex-
amined demographic, COVID-19 stress, and attachment to God as risk and protective co-
variates predicting class membership using R3STEP. The flourishing subgroup was more
likely than the languishing to respond “no” to a recent transformative experience relative
to responding “yes” (χ2 = 4.36(1), p = 0.037). The resilient subgroup was more likely to
report a theme about “stressful life events” relative to the flourishing (χ2 = 4.70(1), p = 0.03)
and less likely to report a theme about “spiritual/mystical experience” than the flourishing
(χ2 = 121.47(1), p < 0.001) and the languishing (χ2 = 9.55(1), p = 0.002). Relative to the flour-
ishing, participants who identified as heterosexual were less likely to belong to the resilient
subgroup (B = −1.13, SE = 0.57, p = 0.048). Relative to the flourishing, the languishing sub-
group were more likely to identify as non-White (B = −0.87, SE = 0.42, p = 0.04), female (B
= 0.89, SE = 0.40, p = 0.02), and younger (B = −0.05, SE = 0.02, p = 0.01), and less likely to
identify as evangelical (B = −1.04, SE = 0.45, p = 0.02). The resilient were more likely to
report higher levels of COVID-19 stress. Specifically, relative to the resilient, as levels of
COVID-19 stress increased participants were less likely to belong to the flourishing (B =
−1.55, SE = 0.27, p < 0.001) and the languishing (B = −1.11, SE = 0.62, p = 0.004). Last, as God-
attachment anxiety increased participants were more likely to belong to the resilient (B =
0.57, SE = 0.18, p = 0.002), relative to the flourishing, and similarly, as God-attachment
avoidance increased participants were more likely to belong to the resilient (B = 0.49, SE =
0.17, p = 0.004) relative to the flourishing.
Results supported the main analyses. The resilient reported transformative experi-
ence themes of “stressful life events” and the highest levels of anxiety, burnout, and
COVID-19 stress, and their trajectory showed significant improvement over time on anx-
iety, burnout, life purpose, and gratitude.
4. Discussion
We found evidence of a subgroup of participants who exhibited resilience over three
waves of data collection, approximately 12 months in elapsed time. In fact, we found evi-
dence for each of the established criteria for determining resilience. First, we identified
significant adversity in the form of self-reported levels of COVID-19 stress at time 1 (i.e.,
Figure 4.
Plot of the estimated means from the sensitivity analysis for the parallel growth mixture
model. Note: N = 751. First line segment = plot of anxiety scores, second line segment = plot of
work-related burnout, third line segment = plot of life purpose, fourth line segment = plot of gratitude.
Resilient class: anxiety intercept = 2.12, SE = 0.14, p< 0.001; slope =
−
0.69, SE = 0.08, p< 0.001; burnout
intercept = 3.46, SE = 0.11, p< 0.001; slope =
−
0.22, SE = 0.08, p= 0.01; life purpose intercept = 3.45,
SE = 0.13, p< 0.001; slope = 0.17, SE = 0.06, p=0.005; gratitude intercept = 5.50, SE = 0.20, p< 0.001;
slope = 0.18, SE = 0.09, p= 0.04.
Results supported the main analyses. The resilient reported transformative experience
themes of “stressful life events” and the highest levels of anxiety, burnout, and COVID-19
stress, and their trajectory showed significant improvement over time on anxiety, burnout,
life purpose, and gratitude.
4. Discussion
We found evidence of a subgroup of participants who exhibited resilience over three
waves of data collection, approximately 12 months in elapsed time. In fact, we found
evidence for each of the established criteria for determining resilience. First, we identified
significant adversity in the form of self-reported levels of COVID-19 stress at time 1 (i.e.,
wave 4), one year post-pandemic declaration and during a time of rising infection rates
and societal upheaval in North America, and in the US in particular. Second, we found
additional evidence of risk, as the resilient reported lower religiousness/spirituality in
terms of both secure attachment to God and spiritual/mystical transformative experiences.
However, lower religiousness/spirituality need not necessarily be a risk factor, as this
depends on socio-cultural context, the operationalization of religiousness/spirituality, and
the outcome used to assess positive adaptation (Jankowski et al. 2022b). Nevertheless,
low religiousness/spirituality can be a source of dissonance and stress for students in the
context of seminary training. Members of the resilient subgroup were also more likely
to identify as a sexual minority and younger relative to the flourishing. Members in the
languishing subgroup also reported marginalized identities, with even more demographic
identifications relative to the flourishing than the resilient, and yet there were no differences
Religions 2023,14, 333 13 of 20
for covariate influence on membership between the resilient and the languishing, except that
the resilient reported the highest levels of COVID-19 stress. Third, we found evidence of
growth in the trajectory for the resilient in levels of symptoms and well-being, and fourth, we
found evidence for transformative experiences to exert a protective influence and promote
positive adaptation. Thus, not only did we find evidence of “positive outcomes in otherwise
risky situations” (Zimmerman and Arunkumar 1994, p. 13), but we also found evidence for
“stressful and traumatic events as key elicitors” of growth (Chirico et al. 2022, p. 3). As such,
we subsumed growth under the broader construct of resilience given the interruption to a
maladaptive trajectory (Zimmerman and Arunkumar 1994). Additionally, resilience seemed
to correspond to a decline in COVID-19 stress among the resilient,and their transformative
experiences related to “stressful life events” were elicitors of growth.
We did not conceptualize resilience as a healthy plateau (e.g., Bonanno 2012). In
fact, we see a fuller depiction of the plateau trajectory we found as consistent with a
conceptualization of flourishing, and specifically, low levels of reported COVID-19 stress,
high levels of religiousness/spirituality, low symptom levels, and high well-being. Rather
than resilience, Norris et al. (2009) suggested the term resistance to describe a stable low-
symptom trajectory. They argued that resistance involves stability, whereas resilience involves
adaptability. Masten (2021) also used the term resistance for a stable plateau trajectory;
however, her depiction featured (a) midrange functioning, and (b) resistance as one type
of resilience. Masten’s labeling thus differs from depictions of a single stable trajectory of
high functioning as resistance (Norris et al. 2009) or resilience (Bonanno 2012). Norris et al.
(2009) also suggested that resistance connotes the availability of resources to mitigate the
effects of the stressor. As Willen (2022) suggested, “structural factors, ideological contexts,
and relations of power
. . .
predispose some people to languish, and others to flourish”
(p. 1). In our study, the flourishing were least likely to identify as marginalized on each of
the demographic indicators, suggesting the availability of resources.
Furthermore, as Schäfer et al. (2022) stated, “the concept of resilience as a positive
outcome despite stressor exposure (i.e., adversity) implies that it can only be assessed if
individuals are exposed to stressors” (p. 1181). Schäfer et al. reviewed trajectory-based
studies of resilience based on “the pandemic as a societal stressor” and noted that at the
individual level, exposure “might have varied substantially” (p. 1184). Hence, there is a
need to assess individual-level experience of the stressor, distinct from the outcome. Schäfer
et al. also indicated that resilience as a high-functioning plateau trajectory may simply
“reflect low levels of stress rather than better psychological adaptation” (p. 1184). The
plateau trajectory we observed was associated with low levels of felt COVID-19 stress,
lower likelihood of reporting a transformative experience, and lower likelihood of reporting
themes related to “stressful life events,” suggesting that this was a low-exposure group.
In fact, Lai et al. (2015) differentiated a low-symptom trajectory from a resilient trajectory
on the basis that the low-symptom trajectory reported “the lowest exposure to disaster
related stressors” (p. 519). Similarly, our flourishing subgroup represented low exposure
to the COVID-19 stressor, consistent with Tedeschi and Calhoun’s (2004) description of
flourishing as involving low perceived threat and/or distress related to adversity.
In a prior cross-sectional study involving a smaller subsample from the current study
sample, Jankowski et al. (2022b) found that a flourishing subgroup was more likely to have
an overly positive, exaggerated view of their level of religiousness/spirituality relative to
other classes. An overly positive view may be an indicator of the general coping process of
positive appraisal (Schäfer et al. 2022) or R/S coping in which greater perceived closeness
with God shelters against felt adversity by regulating negative emotions, including perhaps
through experiential avoidant processes that minimize threat and/or distress (Jankowski
et al. 2022a). Flourishing as coping seems consistent with both Tedeschi and Kilmer’s (2005)
suggestion that effective coping maintains stability, and Keyes’ (2002) notion that flourishing
can function “as a stress buffer” (p. 219). Taken together, flourishing as a stress buffering
coping process seems most consistent with Norris et al.’s (2009) depiction of resistance in
which “coping resources have effectively blocked the stressor” (p. 2191). The flourishing
Religions 2023,14, 333 14 of 20
subgroup in our study could therefore depict a low-exposure subgroup simply because the
impact of the pandemic was minimal, or because coping resources effectively defended
against negative impact from the pandemic.
The interruption of a maladaptive trajectory seems to be an oft-neglected yet essential
aspect of the definition of resilience. Historically, resilience as a developmental construct
had the connotation of an unexpected or contradictory outcome to that which would be
predicted by the risk factor(s), including the target stressor event. As Masten (2001) stated,
“risks are actuarially based predictors of undesirable outcomes drawn from evidence that
this status or condition is statistically associated with higher probability of a ‘bad’ outcome”
(p. 228). Our inclusion of waves 1–3 in the first sensitivity analysis permitted us to discern
an interruption to the trajectory of the resilient that included pre-exposure (wave 1), one
year post-declaration of the pandemic (waves 2 and 3) and heightened societal stress (wave
4), and finally one year post-peak societal stress levels (waves 5 and 6). In contrast, we
did not see evidence that the flourishing experienced significant adversity, nor was there
an interruption to their trajectory over time. Rather, the flourishing reported greater R/S
protective factors in terms of secure God attachment and transformative-experience themes
of “spiritual/mystical experiences,” and identity indicators of resource privilege that may
have mitigated felt threat and/or distress related to the pandemic.
Last, the languishing trajectory we observed was consistent with a commonly re-
ported trajectory in longitudinal studies of responses to adversity, that of delayed onset
(Galatzer-Levy et al. 2018;Schäfer et al. 2022). As Schäfer et al. (2022) noted, “individuals
with moderate-stable trajectories might be at particular risk for delayed responses to the
pandemic” (p. 1181). Consistent with Schäfer et al.’s assertion, our sensitivity analysis
involving waves 1–6 showed a stable plateau of moderate symptom levels over waves
1–4 and then an increase in symptoms over waves 4–6 for the languishing. Schäfer et al.
further distinguished the delayed onset trajectory from a moderate-stable trajectory of mid-
level functioning and a chronic trajectory with stable low levels of positive adaptation.
Rather than delayed onset, we opted for the term languishing based on the results from the
main analysis, which depicted deterioration over three time points, and because the term
languishing has been used to describe a deterioration trajectory (O’Donnell et al. 2022),
albeit the fullest descriptor is probably delayed-onset languishing. Finally, Keyes’ (2002) dual-
continuum classification referred to types of languishers, which included a non-symptomatic
and low-well-being group (i.e., pure languishers), however, those classifications were based
on the operationalization of well-being and symptoms as categorical, using arbitrary cut
points (Zhao and Tay 2022). Zhao and Tay (2022) pointed out that empirically, “an absolute
zero point is often scarce” (p. 8), and as such, the notion that no symptoms should dis-
tinguish types of languishing seems questionable. In fact, Zhao and Tay, using continuous
indicators to empirically generate latent classes, did not even find a languishing subgroup
characterized by low symptoms and low well-being in one of their samples.
When symptoms and well-being are measured on a continuous scale, it seems better
to conceptualize languishing as higher symptoms and lower well-being relative to the other
classes. Furthermore, empirically generated latent classes should be interpreted within the
research context, including data limitations (e.g., time-unstructured data, number of time
points), indicator selection, psychometric evidence, analytic specifications of a particular
model, and class separation (Masyn 2013;van der Nest et al. 2020). As such, it is important
that latent classes not be reified (Masyn 2013). As Ram and Grimm (2009) cautioned,
mixture modeling is an “exploratory
. . .
post-hoc analysis technique that seeks out the
story the data are trying to tell—a story that is limited by the specific bounds imposed
during model specification” (p. 572). Practically then, the meaning of any particular
class is always relative to the others within a particular research context. It would seem,
however, that the labels for dual-continuum classifications and post-adversity responses
have become somewhat reified over time. Blaug (2015) defined reification as a cognitive
bias that involves “the human tendency to invent knowledge, to forget that authorship and
come to believe it real; to confront the socially constructed as natural; to make a ‘thing’ of
Religions 2023,14, 333 15 of 20
an idea” (p. 3). Willen (2022) drew attention to the potential for research on flourishing
and languishing to risk “reification of analytic categories” (p. 3), and Williams and Kemp
(2019) noted the potential for descriptions of resilience to be reified. It seems perhaps that
notions about languishing as an absence of symptoms despite low well-being and resilience
as a single stable trajectory of healthy functioning have come to be seen as essential fixed
properties of “literal entities” and the trajectories “literal depictions of reality” (Nagin and
Tremblay 2005, p. 882). Rather, latent classes are but “useful statistical fiction” and “an
approximation of a more complex
. . .
population distribution” (Nagin and Tremblay 2005,
pp. 873, 888). Languishing and resilience are therefore descriptors, which appear to have
use as signifiers for a variety of longitudinal processes. However, as important as it is to
guard against reification, researchers must also be mindful to avoid jingle-jangle fallacies,
or meaning and construct proliferation. Descriptors must be consistent with the existing
nomological network for a phenomenon, with empirical evidence for convergent and
discriminant conceptualizations. We chose our labels for the trajectories based on historical
and critical evaluations of the research on patterns of change in response to adversity, and
the empirical evidence supporting their use as distinct descriptors.
5. Conclusions
We suggest that promoting resilience among seminary students centers on the two core
aspects of transformative experience: clarifying emotional experience and generating new
meaning (Chirico et al. 2022). The notion that attending to novel and/or moving emotional
experience can propel growth has a long history in dialectical constructivist theories of
change (e.g., Greenberg and Pascual-Leone 2001;Mahoney 2002). Emotions indicate and
facilitate the “disordering processes
. . .
of a complex system’s attempts to reorganize its life.
New life patterns emerge out of the chaos and dysfunction that ensue when old patterns are
no longer viable” (Mahoney 2002, p. 748). Mahoney (2002) highlighted the contrasts between
self and other, and self and larger system, that can generate disruption, whereas Greenberg
and Pascual-Leone (2001) highlighted the internal processes whereby an individual resolves
the contrasts between bodily-felt experience and the narrative construction of meaning.
They stated that the “emergence of new meaning is facilitated by vivid evocation
. . .
of
emotionally laden experience, which brings emotions into contact with reflective processes”
(p. 179). These dialectics of transformative experience fit the RSM framework in which
stressful experiences can potentially challenge prior R/S understandings and necessitate
emotional processing and new R/S meaning (Sandage et al. 2020).
Those who are training helping professionals within the seminary context could
consider ways to provide opportunities for students to gain awareness of and reflection
on felt, lived experience within safe, secure, and supportive relationships. As Mahoney
(2002) stated, “the experience of who one is, what one is capable of, and personal worth—
develop within human relationships. Changes
. . .
also develop within such relationships”
(p. 748). In addition, one of the themes that emerged in Skalski and Hardy’s (2013)
qualitative study of transformative experiences was the positive influence of a trusted,
supportive other, which for some included “an intimate personal experience of God,
however conceptualized” (p. 174). Transformative experiences typically emerge out of
normative developmental processes as the evolving individual adapts to changes within
the environment, in response to often unexpected stressful life events. However, there is
also a sense in which transformative experiences can be intentionally elicited, for example,
through R/S practices such as meditation, prayer, ritual, as well as aesthetic experiences
and “social events hinging on connection with others” (Chirico et al. 2022, p. 9). In our
experience, seminaries often offer resources for (a) cognitive theological reflection and
(b) R/S practices, whereas it is more uncommon for students to find resources to help
process and regulate complex emotions with the discovery of new meaning. Yet, the
RSM suggests that positive development necessitates the integration of healthy relational
holding environments, emotion regulation, and holistic reflection on meaning (Sandage
et al. 2020).
Religions 2023,14, 333 16 of 20
Last, seminary leadership may want to consider ways to identify students who are
languishing. The presence of a languishing subgroup in our findings draws attention to the
influence of risk factors, including larger contextual factors. Willen’s (2022) critique about
the scholarship on responses to adversity highlighted
troubling blind spots—including, above all, a worrying inattention to the ways in
which structure, power, and inequity affect who gets to flourish, who is likely to
languish, and who our social structures and institutions, as currently designed,
are—and are not—designed to help recover from hardship. (p. 6)
Willen’s critique is another reminder that resilience,languishing, and flourishing pro-
cesses are embedded within a larger social context, the latter of which may not be conducive
to positive adaptation for all. Willen’s critique also invites questions about ways seminar-
ies can offer hospitable and supportive environments for the formation needs of diverse
students, including those with marginalized identifications. Considering structure, power,
and inequity led McCormick (2009) to recast resilience based on an Aboriginal “view of
community and the philosophy of ‘all my relations’” (p. 4). Instead of resilience, McCormick
offered the construct of response-able, that is, “the ability to respond to challenges” (p. 5).
Response-ability is a relational construct, “combining spirituality, family strength, elders,
ceremonial ritual, oral traditions, identity and support” (p. 4). Skalski and Hardy (2013)
also referenced response-ability, and did so in the context of the potential for processing
emotional experience within a safe, secure relationship to promote positive transformative
experiences. Masten (2021) suggested that the social context may be such that “functioning
deteriorates or remains poor until more favorable conditions occur, either naturally or
through intervention” (p. 121). Thus, conditions may inhibit individuals from being able to
respond in ways that promote positive adaptation. More specifically, in the context of our
study, seminaries may not be meeting the unique developmental needs of those belonging
to the languishing subgroup.
We examined longitudinal patterns of change in a sample of seminary students to
offer a conceptual and methodological approach for studying resilience, based on historical
and critical evaluations about the study of resilience. We found one trajectory that met
select criteria for determining resilience. Nevertheless, we acknowledge limitations to
our study. First, we began using the COVID-19 stress and burnout measures at wave 4
because the pandemic had not yet been declared at wave 1, the COVID-19 stress measure
was developed after the start of the study, and burnout seemed of greater importance
over the course of the pandemic (e.g., Filindassi et al. 2022;Lowe et al. 2022). Pre- and
post-adversity data permit a fuller evaluation of interruption and change in the trajectories
over time (Schäfer et al. 2022), although post-adversity data are frequently used to study
adversity-response trajectories (Galatzer-Levy et al. 2018). We did conduct a sensitivity
analysis to model pre- and post-adversity change for anxiety. Nevertheless, it would have
been ideal to also have pre-adversity data for burnout. Relatedly, the pandemic is ongoing,
as formal declaration about the end of the public-health emergency in the US has not yet
been declared. Other change patterns may be identified were additional waves of data
collection to occur. In addition, more frequent assessments closer together in time may
capture greater heterogeneity in responses to adversity (Infurna and Luthar 2018). As
Schäfer et al. (2022) commented, “the lessons learned from the pandemic indicate the need
to frequently measure both multidimensional functioning and stressor exposure” (p. 1185).
In addition, our sample consisted of students at Christian-affiliated seminaries, which may
limit the generalizability of our findings. However, prior research using a subset of the
current study sample at wave 1 found that the demographic profile by R/S classifications
were comparable to those from a nationally representative US sample of religious- and
non-religious-identifying persons (Jankowski et al. 2022b). Nevertheless, research is needed
with students training for leadership in other religious traditions and cultural contexts, and
with diverse R/S identifications. We also recognize that this research may not generalize to
more experienced R/S leaders. The literature on burnout and well-being among practicing
R/S leaders is weighted toward cross-sectional findings, and more research is needed
Religions 2023,14, 333 17 of 20
using longitudinal designs. Last, future research should examine mechanisms of resilience
(Infurna and Luthar 2018), and specifically, emotion-regulatory flexibility and positive
appraisal, which seem to be emerging as particularly relevant (Schäfer et al. 2022).
Author Contributions:
Conceptualization, P.J.J., S.J.S. and D.C.W.; methodology, P.J.J.; formal anal-
ysis, P.J.J.; data curation, D.C.W.; writing—original draft preparation, P.J.J.; writing—review and
editing, P.J.J., S.J.S. and D.C.W.; resources, S.J.S.; project administration, D.C.W.; funding acquisition,
S.J.S. and D.C.W. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by John Templeton Foundation grant number 61515 and The
Peale Foundation grant title “Positive Psychology and Formation-Based Flourishing among Spiritual
Leaders and Therapists”.
Institutional Review Board Statement:
The study was conducted in accordance with the Declaration
of Helsinki and approved by the Institutional Review Board of Biola University (Protocol #SS19-
014_SE) on 27 August 2019.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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