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Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC)


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Resilience may be viewed as a measure of stress coping ability and, as such, could be an important target of treatment in anxiety, depression, and stress reactions. We describe a new rating scale to assess resilience. The Connor-Davidson Resilience scale (CD-RISC) comprises of 25 items, each rated on a 5-point scale (0-4), with higher scores reflecting greater resilience. The scale was administered to subjects in the following groups: community sample, primary care outpatients, general psychiatric outpatients, clinical trial of generalized anxiety disorder, and two clinical trials of PTSD. The reliability, validity, and factor analytic structure of the scale were evaluated, and reference scores for study samples were calculated. Sensitivity to treatment effects was examined in subjects from the PTSD clinical trials. The scale demonstrated good psychometric properties and factor analysis yielded five factors. A repeated measures ANOVA showed that an increase in CD-RISC score was associated with greater improvement during treatment. Improvement in CD-RISC score was noted in proportion to overall clinical global improvement, with greatest increase noted in subjects with the highest global improvement and deterioration in CD-RISC score in those with minimal or no global improvement. The CD-RISC has sound psychometric properties and distinguishes between those with greater and lesser resilience. The scale demonstrates that resilience is modifiable and can improve with treatment, with greater improvement corresponding to higher levels of global improvement.
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Research Article
Kathryn M. Connor, M.D.,
and Jonathan R.T. Davidson, M.D.
Resilience may be viewed as a measure of stress coping ability and, as such, could
be an important target of treatment in anxiety, depression, and stress reactions.
We describe a new rating scale to assess resilience. The Connor-Davidson
Resilience scale (CD-RISC) comprises of 25 items, each rated on a 5-point scale
(0–4), with higher scores reflecting greater resilience. The scale was
administered to subjects in the following groups: community sample, primary
care outpatients, general psychiatric outpatients, clinical trial of generalized
anxiety disorder, and two clinical trials of PTSD. The reliability, validity, and
factor analytic structure of the scale were evaluated, and reference scores for
study samples were calculated. Sensitivity to treatment ef fects was examined
in subjects from the PTSD clinical trials. The scale demonstrated good
psychometric properties and factor analysis yielded five factors. A repeated
measures ANOVA showed that an increase in CD-RISC score was associated
with greater improvement during treatment. Improvement in CD-RISC score
was noted in proportion to overall clinical global improvement, with greatest
increase noted in subjects with the highest global improvement and deterioration
in CD-RISC score in those with minimal or no global improvement. The CD-
RISC has sound psychometric properties and distinguishes between those with
greater and lesser resilience. The scale demonstrates that resilience is modifiable
and can improve with treatment, with greater improvement corresponding to
higher levels of global improvement. Depression and Anxiety 18:76–82, 2003.
&2003 Wiley-Liss, Inc.
Key words: resilience; stress coping; wellbeing; posttraumatic stress disorder;
anxiety; depression
Resilience embodies the personal qualities that enable
one to thrive in the face of adversity. Research over the
last 20 years has demonstrated that resilience is a
multidimensional characteristic that varies with con-
text, time, age, gender, and cultural origin, as well as
within an individual subjected to dif ferent life circum-
stances [e.g., Garmezy, 1985; Garmezy and Rutter,
1985; Rutter et al., 1985; Seligman and Csikszentmi-
halyi, 2000; Werner and Smith, 1992]. One theory for
this variability was developed by Richardson and
colleagues, who proposed the following resiliency
model [Richardson et al., 1990; Richardson, 2002].
Beginning at a point of biopsychospiritual balance
(‘‘homeostasis’’), one adapts body, mind, and spirit to
current life circumstances. Internal and external
stressors are ever-present and one’s ability to cope
with these events is influenced by both successful and
unsuccessful adaptations to previous disruptions. In
some situations, such adaptations, or protective
Department of Psychiatry and Behavioral Sciences, Duke
University Medical Center, Durham, North Carolina
Contract grant sponsor: Smith Kline Beecham; Contract grant
sponsor: Pfizer Pharmaceuticals; Contract grant sponsor: Pure
World Botanicals, Inc.; Contract grant sponsor: Organon; Con-
tract grant sponsor: NIH; Contract grant number: R01 MH56656-
Correspondence to: Dr. Connor, Box 3812, DUMC, Durham, NC
27710. E-mail:
Received for publication 15 September 2002; Accepted 1 April 2003
DOI: 10.1002/da.10113
Published online in Wiley InterScience (
&& 2003 WILEY-LISS, INC.
factors, are ineffective, resulting in disruption of the
biopsychospiritual homeostasis. In time, response to this
disruption is a reintegrative process, leading to one of
four outcomes: (1) the disruption represents an oppor-
tunity for growth and increased resilience, whereby
adaptation to the disruption leads to a new, higher level
of homeostasis; (2) a return to baseline homeostasis, in
an effort to just get past or beyond the disruption; (3)
recovery with loss, establishing a lower level of home-
ostasis; or 4) a dysfunctional state in which maladaptive
strategies (e.g., self-destructive behaviors) are used to
cope with stressors. Resilience may thus also be viewed
as measure of successful stress-coping ability.
The clinical relevance of resilience and related
constructs has been noted previously. Maddi and
Khoshaba theorized that hardiness was an index of
mental health [Maddi and Khoshaba, 1994] and recent
data has supported this hypothesis [Ramanaiah et al.,
1999]. Tsuang [2000] has emphasized the substantial
clinical implications that follow a better understanding
of the forces that mould resilience. With regard to
trauma and posttraumatic stress disorder (PTSD), it
has been shown that hardiness contributes to protec-
tion against developing chronic PTSD after combat
[King et al., 1998; Waysman et al., 2001].
The growing focus on health promotion and well-
being, shifting emphasis away from pathology and
problem-orientation, provides an opportunity to revisit
the role of resilience in health. Yet there is relatively
little awareness about resilience or its importance in
clinical therapeutics. Conventionally, therapeutic trials
have focused more heavily on measuring morbidity,
although quality of life elements are now included in
many trials. A number of scales have been developed to
measure resilience [Bartone et al., 1989; Wagnild and
Young, 1993] or aspects of resilience [e.g., hardiness:
Hull et al., 1987, Kobasa, 1979; perceived stress, Cohen
et al., 1983]. However, these measures have neither
been widely used nor applied to specific populations
[Carlson, 2001; Mosack, 2002] and thereby lack
generalizability. Of striking note, a textbook of psy-
chiatric measures recently published by the American
Psychiatric Association contains not a single resilience
measure [American Psychiatric Association, 2000].
The need for well-validated measures of resilience
that are simple to use is thus evident. While several
scales have been developed, they have not gained wide
acceptance and no one scale has established primacy.
With these considerations in mind, the Connor-
Davidson Resilience Scale (CD-RISC) was developed
as a brief self-rated assessment to help quantify resilience
and as a clinical measure to assess treatment response.
We recently became interested in the concept of
resilience as being relevant to treatment outcome in
anxiety, depression, and stress reactions. This interest
arose in part from a finding that fluoxetine produced
greater therapeutic benefit on stress coping than
placebo in PTSD [Connor et al., 1999]. Furthermore,
in reviewing the account of Sir Edward Shackleton’s
heroic expedition in the Antarctic in 1912 [Alexander,
1998], it was noted that the expedition’s leader
possessed many personal characteristics compatible
with resilience and that this may perhaps have
contributed to the successful survival of each member
of the expedition in the face of overwhelming odds.
Together, these observations prompted the authors to
undertake the development of a short self-rated
resilience measure.
The content of the scale was drawn from a number
of sources. From Kobasa’s work with the construct of
hardiness [Kobasa, 1979], items reflecting control,
commitment, and change viewed as challenge were
included. The following features were drawn from
Rutter’s work [Rutter, 1985]: developing strategy with a
clear goal or aim, action orientation, strong self-
esteem/confidence, adaptability when coping with
change, social problem solving skills, humor in the
face of stress, strengthening effect of stress, taking on
responsibilities for dealing with stress, secure/stable
affectional bonds, and previous experiences of success
and achievement (these last two features may reflect the
underpinnings of resilience). From Lyons [1991], items
assessing patience and the ability to endure stress or
pain were included. Lastly, from Shackleton’s experi-
ences, it was noted that the role of faith and a belief in
benevolent intervention (‘‘good luck’’) were likely
important factors in the survival of the expedition,
suggesting a spiritual component to resilience. Table 1
summarizes the salient features of resilience.
With the above considerations, the CD-RISC was
constructed, with the following goals in mind: to
develop a valid and reliable measure to quantify
TABLE 1: Characteristics of resilient people
Reference Characteristic
Kobasa, 1979 View change or stress as a challenge/opportunity
Kobasa, 1979 Commitment
Kobasa, 1979 Recognition of limits to control
Rutter, 1985 Engaging the support of others
Rutter, 1985 Close, secure attachment to others
Rutter, 1985 Personal or collective goals
Rutter, 1985 Self-eff icacy
Rutter, 1985 Strengthening effect of stress
Rutter, 1985 Past successes
Rutter, 1985 Realistic sense of control/having choices
Rutter, 1985 Sense of humor
Rutter, 1985 Action oriented approach
Lyons, 1991 Patience
Lyons, 1991 Tolerance of negative affect
Rutter, 1985 Adaptability to change
Current Optimism
Current Faith
Research Article: Resilience Scale: (CD-RISC) 77
resilience, to establish reference values for resilience in
the general population and in clinical samples, and to
assess the modifiability of resilience in response to
pharmacologic treatment in a clinical population.
The CD-RISC contains 25 items, all of which carry
a 5-point range of responses, as follows: not true at all
(0), rarely true (1), sometimes true (2), often true (3),
and true nearly all of the time (4). The scale is rated
based on how the subject has felt over the past month.
The total score ranges from 0–100, with higher scores
reflecting greater resilience. The individual items
comprising the scale are listed in Table 2.
Subjects were drawn from the following study
samples: a random-digit dial based general population
sample [i.e., non help-seeking (Group 1, n¼577;
included subjects with complete data only); primary
care outpatients (Group 2, n¼139); psychiatric out-
patients in private practice (Group 3, n¼43); subjects
in a study of generalized anxiety disorder (GAD;
Group 4, n¼25); and subjects in two clinical trials of
PTSD (Group 5, n¼22; Group 6, n¼22)]. Of note,
subjects in Group 6 are included only for between-
group diagnostic comparisons and in the assessment of
pre- to post-treatment change. Each study protocol
was approved by the Duke University Medical Center
Institutional Review Board and all subjects provided
informed consent.
Demographic characteristics of Groups 1–5 (n¼
806) were as follows: female 65% (n¼510), male
35% (n¼274); white 77% (n¼588), non-white 23%
(n¼181); and mean (sd) age 43.8 (15.3) years (n¼763).
Some missing data occurred for all of these compar-
isons, which explains why the figures do not total 806
in the various comparisons (e.g., data were not always
available for gender, ethnic status, etc.).
The data were analyzed with the following objec-
tives: (1) to establish reference scores for the CD-RISC
and to assess whether scores were af fected by clinical
category or demographic factors, (2) to assess the
reliability and validity of the scale, (3) to assess the
factor composition of the CD-RISC in the general
population, and (4) to assess the extent to which CD-
RISC scores can change with clinical improvement
with treatment and over time.
Given that several of the samples were not normally
distributed, median CD-RISC scores were calculated
for each group and pairwise comparisons were per-
formed using the Wilcoxon Rank Sum test, with
Po.05 being regarded as significant. A Bonferroni
correction was used for multiple comparisons to derive
the zscore. Of note, mean CD-RISC scores are also
presented for clinical reference. A Kruskal-Wallis test
was used for multiple group comparisons, with the
expectation that degrees of resilience would be lower in
psychiatric outpatients than in the general population
or primary care patients.
Descriptive statistics were used to characterize
CD-RISC scores in the full sample by gender,
ethnicity, and age. Analysis of variance was used
to analyze categorical variables (e.g., gender and
ethnicity) and correlation with the continuous measure
of age.
The reliability and validity of the scale were assessed
as follows. Test retest reliability was examined in
subjects from Groups 4 and 5 in whom no clinical
change was noted between two consecutive visits.
Internal consistency was evaluated by using Cronbach’s
alpha for the total and item-total scores in subjects
from Group 1. Convergent validity was assessed in
various groups by correlating the CD-RISC with
measures of hardiness [Kobasa Hardiness Scale; Koba-
sa et al., 1979], perceived stress [Perceived Stress Scale
(PSS-10); Cohen et al., 1983], and stress vulnerability
[Stress Vulnerability Scale (SVS); Sheehan et al., 1990],
as well as measures of disability [Sheehan Disability
Scale(SDS); Sheehan et al., 1983] and social support
[Sheehan Social Support Scale (SSSS); Sheehan, 1990].
Divergent validity was assessed by correlating CD-
RISC scores with the Arizona Sexual Experience
Scale [ASEX; McGahuey et al., 2000] in subjects from
Group 4.
TABLE 2: Content of the Connor-Davidson Resilience
Item no. Description
1 Able to adapt to change
2 Close and secure relationships
3 Sometimes fate or God can help
4 Can deal with whatever comes
5 Past success gives confidence for new challenge
6 See the humorous side of things
7 Coping with stress strengthens
8 Tend to bounce back after illness or hardship
9 Things happen for a reason
10 Best effort no matter what
11 You can achieve your goals
12 When things look hopeless, I don’t give up
13 Know where to turn for help
14 Under pressure, focus and think clearly
15 Prefer to take the lead in problem solving
16 Not easily discouraged by failure
17 Think of self as strong person
18 Make unpopular or diff icult decisions
19 Can handle unpleasant feelings
20 Have to act on a hunch
21 Strong sense of purpose
22 In control of your life
23 I like challenges
24 You work to attain your goals
25 Pride in your achievements
Connor and Davidson78
An exploratory factor analysis using an ORTHO-
MAX rotation was conducted by using data from the
general population sample (Group 1).
The effects of time and treatment on resilience were
assessed by comparing pre- and post-treatment CD-
RISC scores in treatment responders and non-respon-
ders in the clinical trial samples (Groups 4, 5, and 6) by
using a repeated measures analysis of variance (ANO-
VA), with response as the grouping variable and time as
the repeated measure. Response was defined by a
Clinical Global Improvement (CGI-I; Guy; 1976)
score of 1 (very much improved) or 2 (much improved).
Mean (sd) and median (1st, 4th quartile) CD-RISC
scores were calculated for the full sample (Groups 1–5)
and for the individual study groups (Table 3). Results of
pairwise comparisons are listed in Table 4 and
significant dif ferences were found for the following
groups: general population (Group 1) vs. each of
the other groups, primary care (Group 2) vs. GAD
(Group 4), and primary care vs. PTSD (Groups 5
and 6). Statistical significance was obtained in the
overall multiple comparison model (w
¼142.80, df ¼5,
Mean (sd) scores were also calculated by demo-
graphic grouping, and no dif ferences were observed in
the characteristics evaluated. A gender comparison
revealed a mean score of 77.1 (16.3) for women and
77.2 (14.2) for men (P¼.63). Mean CD-RISC scores by
racial group were as follows: white subjects, 77.4 (14.8)
and non-white subjects, 76.7 (18.1) (P¼.83). The
mean (sd) age of the full sample was 43.8 (15.4) years,
and no correlation was found between age and CD-
RISC score (Pearson r¼.06, n.s.).
Internal consistency. Cronbach’s afor the full
scale was 0.89 for Group 1 (n¼577) and item-total
correlations ranged from 0.30 to 0.70 (Table 5).
Test–retest reliability. Test–retest reliability was
assessed in 24 subjects from the clinical trials of GAD
(Group 4) and PTSD (Group 5) in whom little or no
clinical change was observed from time 1 to time 2.
The mean (sd) CD-RISC scores at time 1 [52.7 (17.9)]
and time 2 [52.8 (19.9)] demonstrated a high level
of agreement, with an intraclass correlation coef ficient
of 0.87.
Convergent validity. CD-RISC scores were posi-
tively correlated with the Kobasa hardiness measure in
psychiatric outpatients (Group 3, n¼30; Pearson
r¼0.83, Po.0001). Compared to the Perceived Stress
Scale (PSS-10), the CD-RISC showed a significant
negative correlation (Group 3, n¼24; Pearson
r¼0.76, Po.001), indicating that higher levels
of resilience corresponded with less perceived
stress. The Sheehan Stress Vulnerability Scale (SVS)
was similarly negatively correlated with the CD-RISC
(Spearman r¼0.32, Po.0001) in 591 subjects
from the combined sample. This result also indicates
that higher levels of resilience correspond to
lower levels of perceived stress vulnerability. As a
measure of disability, the CD-RISC demonstrated a
TABLE 3: Connor-Davidson Resilience Scale scores by
study group
Study group
no. NMean (sd)
(1st, 4th Q)
General population 1 577 80.4 (12.8) 82 (73, 90)
Primary care 2 139 71.8 (18.4) 75 (60, 86)
Psychiatric outpatients 3 43 68.0 (15.3) 69 (57, 79)
GAD patients 4 24 62.4 (10.7) 64.5 (53, 71)
PTSD patients 5 22 47.8 (19.5) 47 (31, 61)
6 22 52.8 (20.4) 56 (39, 61)
GAD ¼generalized anxiety disorder; PTSD ¼posttraumatic stress
TABLE 4: Pairwise comparisons of Connor-Davidson
Resilience Scale scores
Mean rank
Critical rank
Group1 vs.
Group2 114.70 66.36 Yes
Group3 193.80 111.02 Yes
Group4 290.50 146.31 Yes
Group5 362.80 152.56 Yes
Group6 329.70 152.56 Yes
Group2 vs.
Group3 79.10 122.55 No
Group4 175.80 155.24 Yes
Group5 248.10 161.15 Yes
Group6 215.00 161.15 Yes
Group3 vs.
Group4 96.70 178.95 No
Group5 169.00 184.09 No
Group6 135.90 184.09 No
Group4 vs.
Group5 72.30 207.29 No
Group6 39.20 207.29 No
Group5 vs .
Group6 33.10 211.75 No
Group 1¼general population; Group 2¼primary care; Group
3¼psychiatric outpatients; Group 4¼GAD clinical trial subjects;
Groups 5 and 6¼PTSD clinical trial subjects.
ao.05; Bonferonni correction used to derive zscore; z¼2.94
GAD ¼generalized anxiety disorder; PTSD ¼posttraumatic stress
Research Article: Resilience Scale: (CD-RISC) 79
significant negative correlation with the Sheehan
Disability Scale (SDS) (Pearson r¼0.62,
Po.0001) in psychiatric patients (Groups 3 and 4,
n¼40). Lastly, the Sheehan Social Support Scale
(SSS) correlated significantly with the CD-RISC in
589 subjects (Spearman r¼; 0.36, Po.0001). Thus,
greater resilience, as expected, is associated with less
disability and greater social support.
Discriminant validity. The CD-RISC was not
significantly correlated with the ASEX at baseline
(Group 4, n¼23; r¼0.34, P¼.11) or at endpoint
(n¼19; r¼0.30, P¼.21).
Analysis of data from subjects in the general
population sample yielded five factors whose eigenva-
lues were, respectively, 7.47, 1.56, 1.38, 1.13, and
1.07. These factors could be broadly interpreted in
the following manner. Factor 1 reflects the notion
of personal competence, high standards, and tenacity.
Factor 2 corresponds to trust in one’s instincts,
tolerance of negative af fect, and strengthening effects
of stress. Factor 3 relates to the positive accept-
ance of change, and secure relationships. Factor 4
was related to control and Factor 5 to spiritual
influences. The factor pattern for the scale is presented
in Table 5.
In subjects with PTSD (Groups 5 and 6), non-
responders (n¼30) had mean (sd) pre and post
treatment scores of 54.0 (16.5) and 54.9 (18.8),
respectively. Among responders (n¼19), mean
pre- and post-treatment scores were 56.8 (18.4) and
68.9 (19.8), respectively. Significant effects were
observed for time (F¼17.36; df 1, 47; Po.0001)
and for time response category (F¼12.87; df 2, 47;
Po001), indicating that CD-RISC scores increased
significantly with overall clinical improvement.
Greater improvement was noted in CD-RISC
score in proportion to the degree of global clinical
improvement. For example, in subjects with a CGI-I
score of 1 (n¼7), there was a mean increase of 19.9
(26.6%) in the CD-RISC score, compared to an
increase of 7.9 (16.2%) for those with a CGI-I score
of 2 (n¼7), and a deterioration of 0.8 (1.3%) in those
with a CGI-I of 3 or more (minimal or no improve-
ment; n¼18) (F¼3.42, df 2, Po.05). Significant
effects for time (F¼14.82; df 2, 29; P¼.006) and for
TABLE 5: Item-total correlations and rotated factor pattern for the Connor-Davidson Resilience Scale
Factor (Eigenvalue)
Item Item-total correlation
1 (7.436) 2 (1.563) 3 (1.376) 4 (1.128) 5 (1.073)
24 0.61 0.70870 0.14250 0.04339 0.19253 0.01779
12 0.62 0.63998 0.22255 0.20851 0.05018 0.11083
11 0.62 0.62497 0.11656 0.13206 0.21732 0.06408
25 0.56 0.60385 0.04385 0.14600 0.22531 0.11798
10 0.52 0.59601 0.17001 0.16642 0.03336 0.10776
23 0.59 0.55800 0.32628 0.00758 0.12202 0.04681
17 0.70 0.40381 0.35512 0.12714 0.35236 0.00409
16 0.62 0.39651 0.37804 0.26274 0.18958 0.03547
20 0.40 0.08774 0.67393 0.05234 0.06238 0.23265
18 0.58 0.29395 0.57585 0.01006 0.19034 0.08147
15 0.57 0.29967 0.53047 0.04440 0.23134 0.01552
6 0.58 0.11507 0.52564 0.40443 0.12267 0.03711
7 0.55 0.14586 0.46703 0.30584 0.01699 0.27429
19 0.64 0.17227 0.43428 0.27115 0.39728 0.01199
14 0.64 0.25215 0.42942 0.26572 0.36228 0.10734
1 0.55 0.07334 0.08512 0.75885 0.10762 0.03223
4 0.64 0.07074 0.19156 0.61921 0.40002 0.02811
5 0.69 0.26961 0.37932 0.55332 0.09561 0.08239
2 0.36 0.23482 0.08203 0.53775 0.14060 0.31552
8 0.67 0.34423 0.34073 0.43996 0.16462 0.04038
22 0.63 0.21396 0.12493 0.09219 0.77469 0.02935
13 0.62 0.15177 0.03725 0.20513 0.54772 0.40077
21 0.64 0.36495 0.15438 0.02278 0.53186 0.32889
3 0.30 0.01386 0.01460 0.15972 0.15786 0.77820
9 0.40 0.12061 0.24612 0.00029 0.05145 0.73662
Calculated from standardized variables; Chronbach’s a¼0.93.
Connor and Davidson80
time CGI group effect (F¼7.70; df 2, 29; P¼.002)
were noted.
The CD-RISC has been tested in the general
population, as well as in clinical samples, and
demonstrates sound psychometric properties, with
good internal consistency and test–retest reliability.
The scale exhibits validity relative to other measures of
stress and hardiness, and reflects different levels of
resilience in populations that are thought to be
differentiated, among other ways, by their degree of
resilience (e.g., general population vs. patients with
anxiety disorders). Clinical improvement with even
short-term pharmacotherapy in patients with PTSD, a
condition with a propensity toward heightened vulner-
ability to the effects of stress, is accompanied by up to
25% or greater increase in resilience, depending upon
level of global improvement. Furthermore, subjects
with PTSD who showed very much improvement
attained CD-RISC scores close to the mean of the
general population. To the authors’ knowledge, this
is the first demonstration that increased resilience,
as operationally defined, can be associated with a
pharmacologic intervention.
Three areas can be identified where the CD-RISC
might be usefully applied. A number of investigators
have considered possible biologic aspects of resilience.
For example, resilience is characterized by a response
profile to major stress in which low baseline catecho-
laminergic activity is transformed into high catechola-
mine production, along with increased tissue-specific
response (e.g., glucose levels) and an attenuated
cortisol response [Dienstbier, 1991]. Gormley [2000]
has opined that SSRI drugs may facilitate this
process in depressive, obsessive-compulsive, and panic
disorders but provides no actual evidence in support of
his assertion. The authors have shown previously that
fluoxetine has such an effect in PTSD [Connor et al.,
1999]. It is also possible that relationships exist
between resilience and central serotonergic function
[Andrews et al., 1988; Healey and Healey, 1996]. Thus,
the CD-RISC might prove useful in studies of the
biology of resilience.
A second application of the scale could be in clinical
practice with contemporary resiliency interventions.
Such interventions explore resilience qualities with
individuals, identify them, and nurture them [Rak,
2002]. In focusing on strengths and positive attributes,
an individual tends to become engaged in more
adaptive pursuits, and their problems tend to diminish.
The CD-RISC is compatible with such interventions,
as an aid to identifying resilient characteristics but also
in assessing response to the intervention.
A third potential application of the scale might be in
studies designed to investigate adaptive and maladap-
tive strategies for coping with stress, and as a tool to
assist in screening individuals for high-risk, high-stress
activities or occupations. For example, resilience
(hardiness) was identified as a strong predictor of
protection from PTSD in a cohort of Vietnam veterans
[King et al., 2000]. Lyons [1991] noted a strengthening
effect of extreme trauma in many trauma survivors and
a scale such as the CD-RISC might be useful in
studying such individuals.
The authors note several limitations of this
report. The CD-RISC is a wave two resilience
measure, using the scheme outlined by Richardson
[Richardson, 2002], assessing characteristics of resi-
lience, and does not assess the resiliency process or
provide information about the theory of resilience.
While divergent validity was demonstrated, the mea-
sure used to assess divergence (ASEX, a measure of
sexual functioning) was weakly, albeit nonsignificantly,
correlated with CD-RISC and this finding most likely
reflects the heterogeneity of the resilience construct.
The CD-RISC has not been validated against an
objective (i.e., behavioral or third party) measure, or
against biological measures of resilience, such as
neuropeptide Y responses to extreme stress [Morgan
et al., 1999]. The authors also recognize that it is
possible to perform well in one area in the face of
adversity (e.g., work) but to function poorly in another
(i.e., interpersonal relationships). Would such a person
be considered resilient? Furthermore, resilience may
either be a determinant of response or an effect of
exposure to stress. Assessment of such directional
factors was not undertaken in this report. A prospective
study would be able to inform whether resilience pre-
dated exposure to trauma, protected against post-
trauma problems, or, if through circumstances, some
survivors developed further resilience post-trauma.
The CD-RISC is a brief, self-rated measure of
resilience that has sound psychometric properties. By
using the CD-RISC, the findings of this study
demonstrate the following: resilience is quantifiable
and influenced by health status (i.e., individuals with
mental illness have lower levels of resilience than the
general population); resilience is modifiable and can
improve with treatment; and greater improvement in
resilience corresponds to higher levels of global
improvement. The CD-RISC could have potential
utility in both clinical practice and research.
Acknowledgements We thank Larry Tupler and
Erik Churchill for their statistical support and Dr. George
Parkerson for facilitating access to primary care subjects.
Alexander C. 1998. The Endurance: Shackleton’s legendary antarctic
expedition. New York: Alfred A. Knopf.
American Psychiatric Association. 2000. Handbook of psychiatric
measures. Washington, DC: American Psychiatric Association.
Research Article: Resilience Scale: (CD-RISC) 81
Andrews W, Parker G, Barrett E. 1998. The SSRI antidepressants:
exploring their ‘‘other’’ possible properties. J Af fect Disorder
Bartone PT, Ursano R, Wright K, Ingraham L. 1989. The impact of
military air disaster on the health of assistance workers. J Nerv
Mental Dis 177:317–328.
Carlson DJ. 2001. Development and validation of a College
Resilience Questionnaire. Dissertation Abstracts International, A
(Humanities and Social Sciences). vol 62, Jan 2001, 20025.
Cohen S, Kamarck T, Mermelstein R. 1983. A global measure of
perceived stress. J Health Soc Behav 24:386–396.
Connor KM, Sutherland SM, Tupler LA, Churchill LE,
Malik ML, Davidson JRT. 1999. Fluoxetine in posttraumatic
stress disorder: a randomized, placebo-controlled trial. Br J Psych
Dienstbier RA. 1991. Behavioral correlates of sympathoadrenal
reactivity: the toughness model. Med Sci Sports Med 23:846–852.
Garmezy N. 1985. Stress resistant children: the search for protective
factors. In: Recent research in developmental psychopathology,
book suppl number 4 to J Child Psychol Psych. Oxford: Pergamon
Garmezy N, Rutter M. 1985. Acute stress reactions. In: M Rutter, L
Hersob, editors. Child and adolescent psych: modern approaches.
Oxford: Blackwell.
Gormley N. 2000. Is neuroticism a modifiable risk factor for
depression? Ir J Psych Med 17:41–42.
Healey D, Healey H. 1998. The clinical pharmacologic profile of
reboxetine: does it involve the putative neurobiological substrates
of wellbeing? J Affect Disord 51:313–322.
Hull JG, Van Treuren RR, Virnelli S. 1987. Hardiness and health: a
critique and alternative approach. J Personality Soc Psychol
King LA, King DW, Fairbank JA, Keane TM, Adams GA. 1998.
Resilience-recovery factors in post-traumatic stress disorder among
female and male Vietnam veterans: hardiness, postwar social
support, and additional stressful life events. J Personality Soc
Psychol 74:420–434.
Kobasa SC. 1979. Stressful life events, personality, and health: an
inquiry into hardiness. J Personality Soc Psychol 37:1–11.
Lyons J. 1991. Strategies for assessing the potential for positive
adjustment following trauma. J Traumatic Stress 4:93–111.
Maddi SR, Khoshaba DM. 1994. Hardiness and mental health. J Pers
Assess 63:265–274.
McGahuey CA, Gelenberg AJ, Laukes CA, Moreno FA, Delgado PL.
2000. The Arizona Sexual Experience Scale (ASEX): reliability and
validity. J Sex Marital Therapy 26:25–40.
Morgan CA III, Wang S, Southwick SM, Rasmusson A, Hazlett G,
Hauger RL, Charney DS. 2000. Plasma neuropeptide-Y concen-
trations in humans exposed to military survival training. Biol
Psychiatry 47:902–909.
Mosack KE. 2002. The development and validation of the R-PLA: a
resiliency measure for people living with HIV/AIDS (immune
deficiency). Dissertation Abstracts International: Section B: the
Sciences and Engineering. vol 62, Mar 2002, 3844.
Rak CF. 2002. Heroes in the nursery: three case studies in resilience.
J Clin Psychol 58:247–260.
Ramanaiah NV, Sharpe JP, Byravan A. 1999. Hardiness and major
personality factors. Psychol Rep 84:497–500.
Richardson GE. 2002. The metatheory of resilience and resiliency.
J Clin Psychol 58:307–321.
Richardson GE, Neiger B, Jensen S, Kumpfer K. 1990. The
resiliency model. Health Education 21:33–39.
Rutter M. 1985. Resilience in the face of adversity: protective factors
and resistance to psychiatric disorders. Br J Psych 147:598–611.
Seligman MEP, Csikszentmihalyi M. 2000. Positive psychology. Am
Psychologist 55:5–14.
Sheehan DV. 1983. The Anxiety Disease. New York: Bantam Books.
Sheehan DV, Raj AB, Harnett Sheehan K. 1990. Is buspirone
effective for panic disorder? J Clin Psychopharmacol 10:3–11.
Tsuang MT. 2000. Genes, environment and mental health wellness.
Am J Psychiatry 157:489–491.
Wagnild GM, Young HM. 1993. Development and psychometric
validation of the Resilience Scale. J Nurs Meas 1:165–178.
Waysman M, Schwarzwald J, Solomon Z. 2001. Hardiness: an
examination of its relationship with positive and negative long-
term changes following trauma. J Traumatic Stress 14:531–548.
Werner E, Smith R. 1992. Overcoming odds: high risk children from
birth to adulthood. Ithaca, NY: Cornell University Press.
Connor and Davidson82
... Resilience helps people cope effectively with stressful situations and reduce the psychological consequences of these stressful events (Havnen et al., 2020). Connor & Davidson (2003) consider resilience as their perception of traits that enable people to cope with life events and adversities. The results of recent research indicate the important role of resilience in effectively coping with the psychological consequences of the coronavirus 2019 pandemic (Ebrahimi et al., 2020& McCleskey & Gruda, 2021. ...
... The range of scores obtained is between 29 and 116, and high scores mean more spirituality in the individual. Parsian & Dunning (2009) 3) Resilience Scale: Connor & Davidson (2003) designed the Resilience Questionnaire to measure resilience to stress. The Resilience Scale consists of 25 items that are answered and scored based on a Likert scale of 5 options (0 to 4). ...
... The range of scores on this scale is between 0 and 100, with higher scores on this scale indicating higher levels of resilience. In Connor and Davidson (2003) study, the mean and standard deviation of the scale for the normal group were 80.4 and 12.8. Connor and Davidson (2003) report appropriate statistical characteristics of the scale. ...
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Coronavirus 2019 (COVID-19) as the biggest threat to public health in 2020, is a two-year period that has caused many problems to people around the world. The aim of this study was to compare spirituality, resilience and self-compassion in students with high and low levels of COVID-19 anxiety. The method of the present study is descriptive causal-comparative. The statistical population of the study consisted of male and female students of the Faculty of Literature and Humanities of the University of Guilan in the academic year 2020-2021. Participants were selected by available methods from each group of 135 students with high levels of COVID-19 anxiety and 135 students with low levels of COVID-19 anxiety. Students were assessed using COVID-19 Anxiety Scale Wheaton et al. (2012), spirituality Parsian & Dunning (2009), resilience Connor & Davidson (2003), and Self-Compassion Questionnaire Neff (2003). Data analysis was performed using multivariate analysis of variance by SPSS software version 24. Findings from multivariate analysis of variance showed that there was a significant difference between students with and without COVID-19 anxiety in terms of spirituality, resilience and self-compassion; So that the average scores of spirituality, resilience and self-compassion in students with COVID-19 anxiety are lower compared to students without COVID-19 anxiety (P<0.001). According to the findings of the present study, students with high levels of COVID-19 anxiety have less spirituality and resilience, which in turn aggravates the symptoms in individuals. On the other hand, it was found that self-judgment, feelings of isolation and over-assimilation are high in people with COVID-19 anxiety; For this reason, psychologists and counselors need to pay more attention to these areas in order to reduce the unreasonable severity of anxiety in students.
... The BRS, a 6-item instrument developed to measure resilience as the ability to "bounce back" from stress, has demonstrated excellent evidence of convergent, divergent, and construct validity (principal component analysis = 1-factor solution) (43). The CD-RISC 10 is a 10-item version of the 25-item CD-RISC, which was developed to measure resilience as a stress coping ability (44). The CD-RISC 10 has demonstrated convergent and divergent validity, and good to excellent agreement with the original CD-RISC scale (45). ...
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Introduction Stroke, a life-threatening stressor, often negatively impacts stroke-survivor (SS) quality of life (QoL). Annual age-adjusted incidence and death rates for stroke are significantly higher among Black Americans than among White Americans. Racism, a significant stressor, occurs at structural, cultural, and interpersonal levels and contributes to health disparities for Black SS. Resilience, a dynamic process of positive adaptation to significant stress, is impacted by factors or resources both internal and external to the individual. This study aims to examine the effects of experiences of racism and resilience on Black SS QoL during early stroke recovery. This article presents the study protocol. Methods and analyses This will be a prospective observational mixed-methods study. Black community-dwelling adults who are within 4 weeks of a stroke will be eligible for inclusion. Baseline measures will include the exposure variables of experiences of racism and resilience. Covariates measured at baseline include sociodemographic variables (age, sex, marital status, education, income, health insurance, employment status, number of people in household, residential address), clinical variables (date and type of stroke, inferred Modified Rankin Scale, anxiety and depression screening), and psychosocial variables (COVID-19 stress, perceived stress, mindfulness). The outcome variable (QoL) will be assessed 6-months post-stroke. Multiple-level linear regression models will be used to test the direct effects of experiences of racism, and the direct and indirect effects of resilience, on QoL. Qualitative data will be collected via focus groups and analyzed for themes of racism, resilience, and QoL. Discussion Racism can compound the stress exerted by stroke on Black SS. This study will occur during the COVID-19 pandemic and in the aftermath of calls for social justice for Black Americans. Experiences of racism will be measured with instruments for both “everyday” discrimination and vigilance. Sociodemographic variables will be operationalized to assess specific social determinants of health that intersect with structural racism. Because of the long-standing history of racism in the United States of America (USA), cultural influences and access to resources are central to the consideration of individual-level resilience in Black SS. Study results may inform the development of interventions to support Black SS QoL through enhanced resilience.
... In the current study, the reliability was found to be α = 0.89. (Connor & Davidson, 2003). This is a 25-item questionnaire developed as a clinical measure to assess the positive effects of treatment for stress reactions, anxiety, and depression. ...
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The COVID-19 pandemic and lockdowns potentially severely impact adolescents’ mental well-being. This research aims to study students’ subjective well-being during the covid-19 pandemic in Iran and investigate the role of loneliness, resilience, and parental involvement. For this study, 629 students (female = 345) were recruited by purposive sampling. Students were assessed on the Student’s Subjective Well-Being, Loneliness Scale, Resilience Scale, and Parental Involvement. The results confirm our hypothesis that the relationship between parental involvement and students’ subjective well-being is mediated by loneliness. Furthermore, the results indicated a partial mediation of resilience in the relationship between parental involvement and students’ subjective well-being. This study theoretically contributes to a better understanding of the factors determining the impact of traumatic events such as a pandemic on adolescents’ mental health. The implications of this study indicate interventions that can be carried out to minimize the negative psychological consequences of the pandemic.
... Hence in present study state resilience was measured by using Connor-Davidson Resilience Scale (CD-RISC) (8). So, the higher score indicated the higher state resilience in participants. ...
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The present study intended to explore the moderating role of social skills in State Resilience and the rate of recovery among drug addicts. The sample size was 100 recovering addicts from different drug rehabilitation centers were recruited from twin cities (Islamabad and Rawalpindi) of Pakistan. The Social Skills Inventory (SSI) was used to assess social skills, while Connor-Davidson Resilience Scale (CD-RISC) and Substance Use Recovery Evaluator (SURE) was used to assess state resilience and recovery among study participants. Results suggest a significant positive correlation between social skills and recovery ( r = 0.27; p < 0.01). Similarly, state resilience was found to be significantly correlated with social skills ( r = 0.35; p < 0.01), while state resilience was not significantly correlated with recovery. The moderation analysis for the interaction of social skills on state resilience was non-significant [β = 0.002, 95% CI (−0.002, 0.00), t = 1.01, p = 0.316]. Results also indicate the significant relationship of social skills in the prevention of relapse against drug use.
The COVID-19 pandemic has taken a significant toll on people worldwide for more than 2 years. Previous studies have highlighted the negative effects of COVID-19 on the mental health of healthcare workers (HCWs) more than the positive changes, such as post-traumatic growth (PTG). Furthermore, most previous studies were cross-sectional surveys without follow-ups. This study draws on PTG follow-up during the COVID-19 outbreak at 12-month intervals for 2 years since 2020. The trajectories and baseline predictors were described. A convenience sampling method was used to recruit frontline nurses or doctors at the COVID-19-designated hospital who were eligible for this study. A total of 565 HCWs completed the 2 years follow-up and were used for final data analysis. The latent growth mixture models (GMM) was used to identify subgroups of participants with different PTG trajectories. Multinomial logistic regression model was used to find predictors among sociodemographic characteristics and resilience at baseline. Four trajectory PTG types among HCWs were identified: ‘Persistent, “Steady increase”, “High with drop”, and “Fluctuated rise.” Comparing the “Persistent low” type, the other three categories were all associated with older age, higher education. Furthermore, “Persistent low” was also negatively associated with resilience at baseline. The PTG of HCWs with different characteristics showed different trends over time. It is necessary to increase the measure frequency to understand the PTG status in different times. Improving HCW’s resilience could help improve staff PTG.
Studies have shown that hope is an important protective factor. At present, few of the available studies on hope have been conducted on people undergoing compulsory rehabilitation. This study explores the mediating role of resilience between family support and hope, and whether relapse plays a moderating role between family support, resilience, and hope. A total of 647 people with substance use disorder completed surveys on Perceived Social Support from Family Scale, Connor-Davidson Resilience Scale, and Herth Hope Index. Structural equation modeling was used to examine the moderated mediation analysis. Family support not only has a direct effect on hope, but also has a significant indirect effect on hope through resilience. The indirect effect of family support on hope via resilience was significant among both the non-relapse group and relapse group; in addition, both the association between family support and resilience and the relation between resilience and hope were moderated by relapse experience. The results indicate that interventions targeting resilience might be an effective approach to improving hope among people with substance use disorder in China.
Background Accumulative evidence indicates a role for adiponectin, a polypeptide secreted by adipose tissue, in the pathophysiology of posttraumatic disorder (PTSD) via metabolic and inflammatory pathways. This study examined adiponectin as a potential predictive biomarker for PTSD among female rape survivors. Methods We evaluated the relationship of baseline serum adiponectin levels to the development of probable PTSD at 3- and 6-months post rape-exposure and compared adiponectin levels between 542 rape-exposed (RE) and 593 rape-unexposed women (RUE). Probable PTSD were defined as Davidson Trauma Scale score ≥40. Data were analysed using multivariate regression models and a generalized estimating equation (GEE) model. We adjusted for clinically relevant covariates associated with PTSD, as well as adiposity indices. Results Participants who were in the mid-and high adiponectin tertile groups versus the lowest tertilegroup had a significantly reduced risk of probable PTSD among at 6 months follow-up, independent of adiposity(aOR = 0.45[0.22–1.05], p = 0.035; aOR = 0.44[0.22–0.90], p = 0.024). However, there was no effect of group (RE vs. RUE). Limitations Adiponectin assays were conducted on non-fasting blood samples and information on chronic medication, dietary factors and levels of physical activity were not collected. There was a high attrition rate among rape exposed participants. Conclusions Our results show that higher serum adiponectin levels are associated with reduced risk of probable PTSD over a 6-month period. This finding supports the hypothesis that serum adiponectin is a potential candidate risk biomarker for PTSD.
With the growing importance of the healthcare sector, resilience has become a fundamental personal quality that healthcare professionals need to cultivate to cope with adverse events in daily work. Distress in the workplace cannot only impact the well-being of healthcare professionals but also negatively affect the capability to care effectively for others. This study was conducted to determine the score and level of resilience among private primary healthcare professionals and their relationships with independent variables. Sets of questionnaires on resilience based on the Connor–Davidson resilience scale-10 (CD-RISC-10) were completed by 164 general practitioners (GPs) and 87 community pharmacists (CPs). Inferential analysis was used to assess the difference, correlation, association, and predictor among dependent and independent variables. The validity and reliability of the study instrument were assessed using Modern Test Theory (MTT) and Classical Test Theory (CTT). The majority of GPs and CPs possessed the lowest resilience level. There were significant differences between CD-RISC-10 with gender, age, and years of experience in GPs as well as overall. Significant associations were found between CD-RISC-10 with all independent variables, except for the highest education level in GPs and overall. This study revealed significant correlations between independent variables with CD-RISC-10 in GPs and overall. However, there were nonsignificant differences, associations, and correlations among CPs between all independent variables and CD-RISC-10. Gender was the predictor of CD-RISC-10 in GPs, while age and years of experience were the predictors of CD-RISC-10 in GPs and overall. There was no predictor of independent variables for CPs. In multinomial logistics regression, years of experience and gender were the significant predictors of CD-RISC-10 among GPs. The CD-RISC-10 instrument had good validity and reliability. Overall, healthcare professionals showed a low level of resilience. This emphasized the need to cultivate and build resilience, as it is a desirable, important element when working in harsh and unprecedented healthcare settings.
Studies have been conducted within the domain of internal corporate social responsibility, yet less attention has been given to how sustainable internal corporate social responsibility can be employed to accelerate performance sustainability in medium-sized manufacturing companies. Additionally, the culture of internal corporate social responsibility practice in SMEs has been largely ignored by most of the existing studies. This research, therefore, identified the potential influence of work-life balance, wellbeing at workplace, resilience, and job stress on the performance and sustainability of the SME sector. This is achieved through the conceptualization of a research model that empirically tested the influence of four exogenous variables on performance sustainability using data from 270 respondents from Malaysia, having employed Partial Least Square Structural Equation Modeling (PLS-SEM) as a technique of analysis. The results of the study reveal that wellbeing at workplace and job stress as dimensions of sustainable internal corporate social responsibility have a strong influence on performance sustainability. Practitioners can gain valuable insights into how to effectively use workplace wellbeing and job stress to achieve performance sustainability, which is especially important now that SMEs rely heavily on sustainable competitive advantage to stay in business and create value for organizations.
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Two models positing direct versus moderating effects of hardiness were examined in relation to long term positive and negative changes following exposure to traumatic stress. Participating in the study were 164 Israeli POWs and a matched group of 184 veterans of the 1973 Yom Kippur War. Participants completed a battery of questionnaires that included the Personal Views Survey (hardiness); the Trait, Attitude, and Behavior Change questionnaire; and questions related to their captivity/war experiences. Findings were consistent with a model that posits moderating effects of hardiness on both long term negative and positive changes. The discussion addresses the possible role of hardiness in relation to negative and positive outcomes of traumatic events.
Studied personality as a conditioner of the effects of stressful life events on illness onset. Two groups of middle- and upper-level 40-49 yr old executives had comparably high degrees of stressful life events in the previous 3 yrs, as measured by the Schedule of Recent Events. One group of 86 Ss suffered high stress without falling ill, whereas the other group of 75 Ss reported becoming sick after their encounter with stressful life events. Illness was measured by the Seriousness of Illness Survey (A. R. Wyler et al 1970). Discriminant function analysis, run on half of the Ss in each group and cross-validated on the remaining cases, supported the prediction that high stress/low illness executives show, by comparison with high stress/high illness executives, more hardiness, that is, have a stronger commitment to self, an attitude of vigorousness toward the environment, a sense of meaningfulness, and an internal locus of control. (43 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).
The health education and prevention professions are in the midst of a philosophical revolution attempting to build upon negative directioned risk reduction programs, which are driven by the medical model, to competency models. Noted psychologists and psychiatrists have suggested that competency and resiliency characteristics are strengths that are more protective than risk reduction efforts. Many suggested traits have been proposed for the resilient individual. This article reviews those characteristics but more importantly, describes the traits within the resiliency model as a process applicable to health education. This expanded view of prevention includes perspectives on the value of personal disruption and adversity as avenues to promote growth and increased protective factors. The process of psychological reintegration is the ability to learn new skills from the disruptive experience and put life’s perspective back in a way that will increase abilities to negotiate life events. The model also identifies four points during the disruptive process, where health educators/prevention specialists can intervene to protect, enhance, support, and facilitate reintegration.