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POSTTRAUMATIC GROWTH
AMONG HEALTHCARE WORKERS
AFTER SARS
CHAN WING SUM
MASTER OF PHILOSOPHY
CITY UNIVERSITY OF HONG KONG
JUNE 2006
CITY UNIVERSITY OF HONG KONG
香港城市大學
Posttraumatic Growth among
Healthcare Workers after SARS
非典型肺炎疫潮後醫護人員的創傷後成長
Submitted to
Department of Applied Social Studies 應用社會科學系
in Partial Fulfillment of the Requirements for the Degree of Master of Philosophy
哲學碩士學位
by
Chan Wing Sum 陳詠心
June 2006 二零零六年六月
Abstract
This study examined severity of trauma, personal resilience (a composite
measure of optimism, self-esteem and perceived control), and perceived social
support in relation to posttraumatic growth experience in response to the 2003 SARS
outbreak among 426 nurses in Hong Kong. Severity of trauma was operationalized
by recruiting three groups of nurses including non-infected nurses without any
contact with SARS patients (Group 1), non-infected nurses who took care of SARS
patients during the outbreak (Group 2), and infected nurses (Group 3). The last group
was expected to experience the highest level of trauma. Results of multiple
regression analyses showed that higher level of trauma predicted more posttraumatic
growth (Group 3 > Group 2 > Group 1) and posttraumatic stress (Group 3 > Group
1). Besides, higher personal resilience and perceived social support predicted more
posttraumatic growth and less posttraumatic stress. These two variables, however,
did not moderate the link between trauma and posttraumatic growth or posttraumatic
stress in the nurses. Implications of these findings were discussed, with special
attention given to the uniqueness of the SARS experience and its psychosocial
impact.
Acknowledgements
I am very much indebted to the fantastic support and advice from my
supervisor, Dr Julian Lai, which enlightened my vision on the topic and whose
incredible patience eventually guided me through this study. I would like to thank
Associate Professor Samuel Ho, Department of Psychology and Assistant Professor
Chou Kee-lee, Department of Social Work and Social Administration, both of Hong
Kong University, for allowing me to use in this study the CPTGI and MSPPSS-C. I
am grateful to the Association of Hong Kong Nursing Staff for their help with data
collection. Last, but not least, I must extend my sincere appreciation to all the nurses
who took the time to participate in this study, especially to those who had worked so
selflessly and courageously throughout the SARS outbreak.
TABLE OF CONTENTS
1. Introduction……………………………………………………………. 2. Literature Review………………………………………………………
2.1 Concept of Resilience……………………………………………..2.1.1 Cognitive adaptation theory…………………………….........2.1.2 Social support…………. ……………………..……...….….
2.2 Posttraumatic growth………………………………………………2.2.1 Definitions of growth……………………………...…………2.2.2 Models of growth………………………………...………..…2.2.3 Is posttraumatic growth real or myth?……………………… 2.2.4 Assessment of posttraumatic growth…………………………2.2.5 Predictors of posttraumatic growth………………………….
2.2.5.1 Severity of trauma……………………………………… 2.2.5.2 Personal characteristics…………………………….……
2.2.6 Moderators……………………………………………………2.3 Implications for assessing psychological profiles of healthcare
workers……………………………………………………………2.4 Theoretical framework ………………………………. ………..…
3. Research Objectives and Hypotheses ………………………………….3.1 Research objectives………………………………………………. 3.2 Hypotheses……………………………………………..…………
4. Methodology……………………………………………………………4.1 Participants………………………………………………….….....4.2 Use of semi-structured interview for exploration ……………..….4.3 Quantitative study ……………………………………………….. 4.3.1 Predictors…………………………..…………………………
4.3.1.1 Optimism……………………………………………….. 4.3.1.2 Self-esteem………………………………..…………….. 4.3.1.3 Perceived control……..………………………………….4.3.1.4 Perceived social support…………………………………
4.3.2 Dependent variables …………………………………………4.3.2.1 Posttraumatic growth…………………………………….4.3.2.2 Posttraumatic stress……………………….……………..
4.3.3 Control variables……………………………………………..
1 5 5 6 10 12 14 15 18 21 24 24 27 30 31 33 36 36 36 38 38 39 40 43 43 44 45 46 47 47 47 48
5. Results…………………………………………………………………..
5.1 Exploratory factor analysis …………………………….…………5.2 Descriptive statistics ………….………………………….…….…5.3 Multiple regression analysis …….……………………………......
6. Discussion………………………………………………………………6.1 Posttraumatic growth and posttraumatic stress among
nurses ……………..........................................................................6.2 The relationships between severity of trauma with posttraumatic
growth and posttraumatic stress………………………………….. 6.3 Personal resilience and social support as predictors and
moderators of posttraumatic growth and posttraumatic stress ………………………………………………………………
6.4 Implications of the study……….……………….…………………6.5 Limitations of this study…………………………………………. 6.6 Future work…………………………………………………….… 6.7 Conclusions………………………………………………………..
50 50 52 53 61 61 65 68 73 75 75 76
References…………………………………………………………………… Appendix A Questionnaire…………………………………...…………… Appendix B Intercorrelations of variables…………………...……………
78 87 93
List of Tables and Figures
Table 1 Demographic data for three groups of nurses
….…………… 42
Table 2 Posttraumatic growth items, means, and standard deviations
...…………….. 51
Table 3 Means and standard deviations of perceived level of trauma, optimism, self-esteem, perceived control, personal resilience, social support, posttraumatic growth, depression and anxiety for three groups of nurses
….…………… 53
Table 4 Hierarchical regression analyses predicting posttraumatic growth from severity of trauma (groups), personal resilience and perceived social support
...…………….. 56
Table 5 Hierarchical regression analyses predicting
anxiety from severity of trauma (groups), personal resilience and perceived social support
...…………….. 57
Table 6 Hierarchical regression analyses predicting depression from severity of trauma (groups), personal resilience and perceived social support
………………. 59
Figure 1 Theoretical framework of the present study ……………… 35
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1. Introduction
Severe Acute Respiratory Syndrome (SARS) is a highly infectious and deadly
disease that was first reported in Guangdong, China. It subsequently caused
worldwide panic as it rapidly spread to 29 regions (Cheng & Tang, 2004) and
produced 8,422 probable cases with 916 fatalities (Chua, Cheung, McAlonan,
Cheung, Wong, Cheung, Chan, Wong, Choy, Chu, Lee, & Tsang, 2004).
At the outset, SARS was an unknown disease with the medical world having
very limited knowledge about its origins, transmission path and medical treatment.
Moreover, the symptoms of SARS were very similar to other viral infections of the
respiratory tract, so it was extremely difficult to identify SARS sufferers by normal
diagnosis. Such difficulties in managing the outbreak led to epidemic fear and panic
in the Hong Kong community (Lau, Yang, Tsui, & Kim, 2003; Leung, Lam, Ho, Ho,
Chan, Wong, & Hedley, 2003).
Due to Hong Kong’s proximity to the mainland and increasing cases reported, it
became the focal point of the disease internationally. Officially, 1,755 cases were
reported, resulting in 299 deaths - a mortality rate of 17.1% (Clinical Trials Centre,
2004), which accounted for 21% of all SARS cases and 33% of deaths reported
worldwide. Healthcare workers in hospitals became the group at highest risk given
their close contacts with SARS patients. Amongst the 386 healthcare workers
2
infected, six eventually lost their lives (Clinical Trials Centre, 2004). It was therefore
to be expected that high levels of fear and distress were experienced among the
healthcare workers during the outbreak of SARS in Hong Kong (Cheng & Wong,
2005; Ho, Kwong-Lo, Mak, & Wong, 2005).
No doubt contracting SARS was a traumatic experience as the SARS disaster is
claimed to be comparable to the September 11 tragedy (Einhorn, 2003, cited in
Cheng & Tang, 2004). For those infected, apart from loss of physical health,
prominent psychological distress and psychiatric complications were frequently
reported (Chua et al, 2004; Cheng & Wong, 2005). Some SARS patients even have
suffered from different complications such as impaired cardiovascular function,
generalized weakness, different joints pain, and osteo-necrosis (Wong, Lau, Ho, Mak,
Chan, AuYeung, & Choi, 2004). It is difficult to imagine the trail of social,
psychological and physical devastation brought on those families misfortune enough
to have experienced SARS’s deadly effects.
Despite the aforementioned immeasurable pains, interestingly, positive reactions
such as more focus on caring for the sick, enhanced family relationship, greater
appreciation of life, and increased awareness of personal and public hygiene were
reported (C. Chan, 2003; Chua et al, 2004; Ji, Zhang, Usborne, & Guan, 2004). This
indicates that traumatic experiences may produce positive changes that have not been
3
adequately studied in previous research.
Yet, research findings on resilience have demonstrated that some people are able
to adapt well in face of adversity (e.g. Cowan, Cowan, & Suhulz, 1996). A composite
measure of optimism, self-esteem and perceived control, termed as “personal
resilience” by Wanberg (1997), and “social support” (e.g. Helgeson & Cohen, 1996)
are important protective resources which have been shown to reduce the probability
of maladjustment during stressful encounters. Furthermore, recent literatures on
trauma and stress have shown that some people exhibit positive changes in certain
life domains after struggling with a trauma and those positive changes are referred to
as “posttraumatic growth” (Tedeschi & Calhoun, 1995, 1998, 2004). The
phenomenon of posttraumatic growth is highly meaningful as it demonstrates the
virtues and positive strengths of human beings. Although the construct of
posttraumatic growth facilitates our appreciation of the multitude of the
psychological impact of SARS, further understanding is hindered by the scarcity of
data regarding this phenomenon in the Chinese population.
As mentioned in the above, SARS adversely affected Hong Kong, in particular
exposing healthcare workers to the disease. It is therefore worthwhile to assess the
psychological profile and posttraumatic growth of healthcare workers in order to
provide more information to health authorities, which will enable them to better
4
prepare intervention program to cope with similar outbreaks in future. There are
three main objectives of this paper. The first objective is to assess the posttraumatic
growth and posttraumatic stress among healthcare workers after the outbreak of
SARS in Hong Kong. Secondly, it seeks to examine the extent to which severity of
trauma, personal resilience, and social support may predict posttraumatic growth and
posttraumatic stress. The final objective is to explore the moderating effects of
personal resilience and social support on the links between severity of trauma with
posttraumatic growth and posttraumatic stress.
Six chapters are included in this thesis. Chapter two reviews past studies in the
areas of resilience and posttraumatic growth. Chapter three presents the hypotheses
of this thesis. Chapter four presents in detail the methods used in this research study.
Chapter five presents the empirical results of this study. Based on the statistical
analyses, the final chapter discusses the aspects of posttraumatic growth reported, the
relationships between the predictors of posttraumatic growth and posttraumatic stress,
clinical applications of this study, and its limitations.
5
2. Literature Review
2.1 Concept of resilience
One of the most impressive qualities of human beings is the ability to withstand
different kinds of life adversities. As such, researchers have long been interested in
answering the question as to why some people can cope successfully during stressful
encounters while others seemingly cannot. The concept of resilience first emerged
from studies on the role of psychosocial factors in stress resistance. There are two
major conceptions of resilience. The first stems from the observation of children who
are able to adapt well without showing any negative outcome in face of adversity
(Cowan, Cowan & Schulz, 1996; Garmezy, Maten, & Tellegen, 1984; Rutter, 1985).
Thus two components: the levels of risk/stress and competence/well-being are
critical criteria for the identification of resilience. For instance, D’Imperio, Dubow,
and Ippolito (2000) classified stress-affected students and resilient students according
to their stress level (operationalized by experience of life events) and competence
level (antisocial behavior, academic performance and school archival records). Those
who were high on both stress and competence levels were regarded as resilient.
Dumont and Provost (1999) conducted similar classification by matching daily
hassles score and depression score to differentiate resilient and vulnerable
adolescents. Resilient people were those high on daily hassles (high level of stress)
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and low on depression (psychological healthy). Apart from assessing the level of
stress and well-being, group differences on self-esteem, social support, coping styles
were also examined in the study conducted by Dumont and Provost (1999).
The other concept of resilience focuses on personal resources, both internal and
external, that an individual possesses, when he/she encounters stress. Kaplan (1999)
conceptualized resilience as a constellation of personal attributes or dispositions,
upon which one could draw to cope with difficult life situations, and eventually
moderated the relationship between risk factors and outcomes. Research studies have
tried to identify protective factors that can predict better adjustment and buffer the
effects of stress on physical and psychological health during stressful encounters
(Aspinwall & Taylor, 1992; Burlew, Telfair, Colangelo, & Wright, 2000; Major,
Richards, Cooper, Cozzarelli, & Zubek, 1998; Ryff & Singer, 2000; Taylor, Kemeny,
Bower, Gruenewald, & Reed, 2000). Viewed from this perspective, resilient people
are those who have those protective resources.
2.1.1 Cognitive adaptation theory
From the study among cancer patients, Taylor and Brown (1988) formulated
the cognitive adaptation theory. They observed that patients would activate a set of
cognitions to help themselves to readjust to the life-threatening disease. This set of
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cognitions activated during the readjustment process is mainly associated with three
themes. The first theme is optimism, which denotes a generalized tendency to expect
positive outcomes, or the belief that good things, rather than bad things, will
generally occur in one’s life (Scheier & Carver, 1985). The second theme is
perceived control, which refers to the feeling of gaining control or mastery over a
threatening event so as to manage it or keep it from recurring. Both beliefs regarding
1) one’s own control on the disease and 2) the physician or treatments can control the
disease are essential to their positive adjustment. The last theme is self-esteem, which
means to feel good about oneself despite personal tragedy.
According to Taylor and Brown (1988), this set of adaptive cognitions is
basically a reaction or a response to adversity, and protects the individual from
experiencing unbearable psychological distress. In other words, the cognitive
adaptation theory postulates that people are adaptable in nature, in face of a
life-threatening disease such as cancer, if they can respond by raising their optimism,
self-esteem, and perceived control, they are more able to adapt to the adversity and
remain psychologically healthy.
Other researchers (Major et al 1998; Wanberg 1997) studied and supported the
cognitive adaptation theory. But instead of viewing them as a reaction to external
conditions, they viewed the three variables as highly correlated and relatively stable
8
personality traits denoting “personal resilience”.
Empirical findings show that personal resilience facilitates coping and
adjustment in various contextual stressors such as abortion (Major et al, 1998),
unemployment (Wanberg, 1997), organizational change (Wanberg & Banas, 2000),
transition to college (Aspinwall & Taylor, 1992), AIDS (Taylor et al, 2000), and
coronary heart disease (Helgeson, 1999, 2003).
Ryff and Singer (2000) emphasized the linkage between psychological factors
and vulnerability to diseases and suggested application of psychosocial therapies for
prevention, treatment and rehabilitation in future. Empirical support to the positive
effect of personal resilience was given by Helgeson and Fritz (1999) who tested
whether personal resilience would predict new coronary events or not after the first
percutaneous transluminal coronary angioplasty (PTCA). The results indicated that
personal resilience was a significant predictor of fewer new coronary events, even
when the effects of demographic variables and medical variables were statistically
controlled. Helgeson and Fritz (1999) concluded that persons who were high in
optimism, self-esteem and perceived control were at lower risk for a new cardiac
event after the first PTCA.
Apart from predicting better adjustment, the moderating roles of optimism,
self-esteem, and perceived control concerning their stress-buffering effects on
9
psychological well-being during stressful encounters have also been tested (Affleck,
Tennen, Pfeiffer, & Fitfield, 1987; Chang, 1998; Chang & Sanna, 2003; Cheng &
Lam, 1997; Jex, Cvetanovski, & Allen, 1994; Lai, 1995; Lai & Wong, 1998). Lai
(1995) found that the health of optimistic undergraduates was less negatively
affected by an increase in daily hassles in comparison to their pessimistic peers and
Lai and Wong (1998) found that optimistic women were less psychologically
impaired in face of unemployment. Similarly, Jex, Cvetanovski and Allen (1994)
investigated self-esteem as a moderator of the impact of unemployment and their
empirical results indicated that unemployment was associated with high levels of
anxiety and depression only among female respondents with low self-esteem.
Moreover, Affleck et al’s (1987) study on rheumatoid arthritis patients proved that
perceived control was related to better mood for those with moderate or severe daily
symptoms but not for those with mild symptoms. Moderator represents the
situational relationship. In other words, the stress level will predict an individual’s
psychological well-being or psychological strain but its predictability is also
dependent on one’s personality. Helgeson (1999) used personal resilience, a
composite score of optimism, self-esteem, and perceived control to predict
adjustments to coronary heart disease because the three constructs were strongly
related to each other and loaded on a single factor. She found that personal resilience
10
not only predicted good adjustments but personal resilience was also associated with
better life satisfaction for patients who sustained a new cardiac event which
symbolized an enhanced threat or additional problem. In other words, Helgeson’s
(1999) findings imply that personal resilience may interact with threat severity to
predict psychological adjustments during personal setbacks. It is thus worthwhile to
speculate upon the moderating role of personal resilience on the link between stress
and psychological well-being.
2.1.2 Social support
Apart from studying the internal resources such as personal resilience,
extensive efforts have also been directed towards investigating the role of an external
resource - social support in resilience research (Boey, 1999; Chan & Eadaoin, 1998;
Cheng, 1997; Cohen, 1988; Cohen & Wills, 1985; Dean, Kolody & Wood, 1990;
Dumont & Provost, 1999; Helgeson & Cohens, 1996; Jung, 1997; Ma, 1997; Sun &
Stewart, 2000; Wu & Lam, 1993).
Helgeson and Cohens (1996) described three different aspects of social support.
First, emotional support refers to the availability of a person with whom an
individual can discuss problems, share feelings and disclose worries. It includes both
verbal and nonverbal communication of caring, empathizing, reassuring, and
11
comforting. Expression of feelings may reduce distress. Second, instrumental support
means having someone who can offer tangible assistance such as provision of
financial aids, material resources, and/or other needed services. Third, informational
support is the availability of advice, guidance and information that are useful to the
individual. Emotional and informational support have beneficial effects on a broad
range of stressful events but instrumental support is assumed to be effective only
when the resources provided are relevant to the specific problem (Cohen, 1988;
Cohen & Wills, 1985).
As to social support, there are three main sources: family, friends, and
significant others (Zimet, Dahlem, Zimet & Farley, 1988). Perceived social support
refers to an “individual’s confidence or expectation of the availability of adequate
support when needed” (Cheng, 1997, p.813). Perceptions of adequate social support
or satisfaction with the amount and quality of social support are associated with less
depressive symptoms among different populations such as cancer patients (Ma, 1997;
Sun & Stewart, 2000), school teachers (Chan & Eadaoin, 1998), and the elderly
(Dean, Kolody & Wood, 1990) regardless of the stress level. This direct beneficial
effect of social support represents the main effect model (Cohen, 1988; Cohen &
Will, 1985). Furthermore, Cheng (1997) found that perceived social support
moderated the relationship between stressful life events and depression among
12
Chinese adolescents even after statistically controlling the baseline measure of
depression. Under high stress level, respondents with lower perceived social support
from family and peer had significantly higher depression scores than those with
higher perceived social support. Cheng’s (1997) findings support the buffering model
of social support put forward by Cohen and Wills (1985). Cohen and Wills (1985)
postulated that adequate social support may prevent stress appraisal reactions on the
potentially stressful event and inhibit maladaptative responses during the experience
of stress, and so “buffer” a person from the pathogenic influence. Skinner and Edge
(1998) suggested that social support yields stress-buffering effect by improving
recipient’s ability to cope with the adversity through suggestions and modeling.
Social integration which measures an individual’s structural social ties tends to yield
main effect model and perceived availability of support tends to yield
stress-buffering effects (Cohen, 1988; Cohen & Wills, 1985).
Taken together, the research findings on personal resilience and social support
have proven that these two factors are crucial to one’s resistance to the negative
impacts arising from personal setbacks.
2.2 Posttraumatic growth
For decades, mainstream literature on stress and coping has focused exclusively
13
on the negative consequences (e.g. anxiety or depression) of stressful encounters and
how to heal damages and repair weaknesses. This may portray an incomplete picture
of adjustment to an adversity (Cordova, Cunningham, Carlson, & Andrykowski,
2001). Given the same stressful event, some people may not be impaired but they are
able to withstand the stressful encounter successfully and even obtain personal gains
in various aspects. The study of posttraumatic growth can be viewed as an extension
of resilience research. Recently, researchers have advocated a “widening of the focus
of the lens” so that both positive and negative outcomes are investigated (Tedeschi &
Calhoun, 1998, p.235). Yet, the literature relating to the positive sequelae of
traumatic events is growing.
Empirical research has shown that many people experience positive changes or
perceived benefits in certain life domains in various ways following extremely
traumatic situations. Stressors such as cancer (Cordova et al, 2001; Ho, Chan, & Ho,
2004; Tomich & Helgeson, 2004; Weiss, 2004), HIV/AIDS (Siegel & Schrimshaw,
2000; Milam, 2004), chronic illness of arthritis (Abraido-Lanza, Guier, & Colon,
1998; Danoff-Burg & Revenson, 2005), heart attack (Affleck, Tennen, Croog, &
Levine, 1987); traffic accident (Saller & Stallard, 2004); and bereavement (Frantz,
Farrell, & Trolley, 2001; Scott & Snyder, 2005) would result in positive outcomes
including enhanced appreciation of life, positive health behavioral changes, increased
14
personal strength and competencies, improved social relationships, better coping
skills, new opportunities in life, and spiritual growth etc.
2.2.1 Definitions of growth
At present, there is no uniformity in terminology regarding positive outcomes in
stress research. Different researchers use different terms to refer to this concept. Park,
Cohen, and Murch (1996) studied positive changes or outcomes that a person reports
experiencing following stressful encounters and they called it stress-related growth.
Carver (1998) used the term thriving to describe the situation that the person not only
returns to the previous level of functioning but has higher level of functioning after
an adversity. Some other researchers adopted the term benefit finding, referring to the
personal growth resulted from a challenging life experience (e.g. Antoni, Lehman,
Kilbourn, Boyers, Culver, Alferi, Yount, McGregor, Arena, Harris, Price, & Carver,
2001; Lechner, Zakowski, Antoni, Greenhawt, Block, & Block, 2003; Tomich &
Helgeson). Tedeschi and Calhoun (1995) termed the experience of positive changes
that occur as a result of the struggle with highly challenging life crises as
posttraumatic growth. In spite of the different terminologies used to describe the
phenomenon, all the definitions focus on the possible positive impacts of a traumatic
event. The term, “posttraumatic growth” has been adopted in the current study as
15
contracting SARS is undoubtedly a traumatic experience (Cheng & Wong, 2005).
2.2.2 Models of growth
Tedeschi and Calhoun (1998, 2004) have proposed a model for understanding
the process of posttraumatic growth. The model highly emphasizes the role of
cognitive processes in bringing out posttraumatic growth. Specifically, it has been
pointed out that the occurrence of a traumatic event serves to disrupt or shatter one’s
stable beliefs about the world and his/her place in it. The event itself must be
challenging enough to activate this cognitive process. In the process of schematic
reconstruction, the person may disengage or give up certain goals and basic
assumptions to accommodate the changed reality of life after trauma. Through
constructive rumination, new schemas, revised life goals or new meanings will be
built up to incorporate information about successful coping with the trauma and
possible future events, thus resulting in positive changes. Tedeschi and Calhoun
(2004) use the analogy of physical rebuilding after an earthquake to describe the
cognitive rebuilding that transforms people who have experienced trauma to be more
resistant to future stressful encounters. In addition, certain individual characteristics
such as optimism, openness to experience, and extraversion may increase the
possibility of posttraumatic growth. Supportive of others and their empathetic
16
acceptance of disclosures about survivors’ distress also contribute to the development
of posttraumatic growth by providing emotional comfort and new cognitive schemas
or perspectives related to posttraumatic growth.
Park (1998) advocated using the transactional model of stress and coping
(Lazarus & Folkman, 1984) which emphasizes the interactions of characteristics of
the person, characteristics of the stressor, and one’s cognitive appraisals, in order to
study posttraumatic growth. According to the transactional theory, there are two
levels of appraisals. Primary appraisal involves judgments about whether an event is
stressful or not. If the event is appraised as being stressful, the event is then classified
as associated either with a harm/loss (an injury or damage that has already taken
place), a threat (something that can produce harm or loss), or a challenge (the
potential for growth, mastery, or some form of gain). The secondary appraisal
involves the evaluation of one’s available resources, options and possibilities to cope
with the situation. To sum up, transactional theory takes into account individual
differences, especially internal cognitive appraisals intervening between the stressor
and the reaction, which finally determines the outcome of a stressful encounter. In
other words, it emphasizes the meaning that an event has, for the individual, made
stress the consequence of appraisal and not the antecedent. So, transactional theory
defines stress as “a particular relationship between the person and the environment
17
that is appraised by the person as taxing or exceeding his or her resources and
endangering his or her well-being” (Lazarus & Folkman, 1984, p.19). Implied in the
definition, stress is not only the product of imbalance between objective demands
and response capacity, but of the “appraisal” of these factors.
Based on the transactional theoretical framework, Park (1998) has pinpointed
factors such as optimism, hope, religiousness, social support, the perceived
controllability of the event, the perceived stressfulness of the event, the perceived
coping efficacy, and the types of coping strategies one uses to manage the stressful
event are all predictors of posttraumatic growth. All these personal factors may exert
direct or indirect effects on the degree of growth following stressful experiences.
Carver (1998) postulated that higher level of functioning occurs after a
traumatic event because of the desensitization to subsequent stressors, enhanced
recovery potential, consistently higher level of functioning, gains in newly developed
skills and knowledge, greater self-confidence (i.e. psychological sense of mastery),
and strengthened personal relationships. In his catastrophe model, two predictors –
“self-confidence in coping” and “the importance of the event” interact to determine
whether one will engage in coping or give up during adversity. When the event is
important, people with high self-confidence tend to keep trying by putting greater
effort but those with low self-confidence tend to give up. Since people are subjective
18
and so lead to differences in “self-confidence” and evaluation of “importance of
event”. This may explain why some thrive whilst others are impaired facing with the
same event. Therefore Carver (1998) also highlighted the personality variables such
as optimism, perceived control, hope, hardiness, self-efficacy, and coping styles that
may be related to posttraumatic growth. Besides, situational factors such as
satisfactory social support may promote posttraumatic growth.
Although different researchers have adopted different definitions and models to
study posttraumatic growth, they uniformly put equal emphasis on both the
characteristics of the person and the characteristics of the trauma/stressful event as
critical factors in determining the occurrence of posttraumatic growth. Within all of
the individual characteristics or personal resources studied, optimism and social
support are consistently documented in all three models highlighted as predictors of
posttraumatic growth.
2.2.3 Is posttraumatic growth real or myth?
Since the majority of studies on posttraumatic growth were retrospective and
self-report in nature, without including any control group for comparison, the
question as to whether posttraumatic growth represents real change or positive
response bias is subject to debate (Aldwin & Levenson, 2004; Campbell, Brunell, &
19
Foster, 2004; Lechner & Antoni, 2004; Park, 2004; Stanton & Low, 2004; Tedeschi
& Calhoun, 2004; Wortman, 2004). Respondents may inflate the degree of personal
growth they have experienced, through the self-enhancing strategy to help alleviate
their distress in the face of threat. Therefore, the reported positive changes may not
be genuine growth and may involve some form of defensive functioning or cognitive
coping effort. (Maercker & Zoellner, 2004; McFarland & Alvaro, 2000;
Nolen-Hoeksema & Davis, 2004; Wortman, 2004). Cheng, Wong, and Tsang (in
press) went further to investigate the defensiveness of posttraumatic growth by
classifying participants into mixed group (reporting both benefits and costs of a
traumatic experience), benefit group (only reporting benefits), and cost group (only
reporting costs). They found that participants giving a mixed account of benefits and
costs were less defensive than those only reporting benefits or cost.
Some researchers have put much effort to address the aforementioned
methodological problems so as to demonstrate the validity of posttraumatic growth.
For instance, a recent study conducted by Cordova et al (2001), compared breast
cancer survivors with those age- and education-matched healthy women, found that
the breast cancer group showed a higher level of posttraumatic growth, than the
matched-healthy control group, in aspects of “relating to others”, “appreciation of
life”, and “spiritual change”. This finding shows that posttraumatic growth exists to
20
some degree. In addition, some studies found no relationship between social
desirability and reports of posttraumatic growth (Park et al, 1996; Tedeschi &
Calhoun, 1996) and positive response bias did not account for reports of
posttraumatic growth (Cordova et al, 2001). Furthermore, Smith and Cook (2004)
warned that asking the respondents to respond to the Posttraumatic Growth Inventory
(Tedeschi & Calhoun 1995) that was linked to a specific stressor actually
underestimated growth as the participants would be cautious about attributing their
growth experiences to a traumatic event. This empirical evidence further supports the
use of self-report method to measure posttraumatic growth would not create a
positivity bias. Moreover, two studies have attempted to demonstrate the validity of
posttraumatic growth by not only collecting the self-report positive changes from the
participants but also asking the participants’ significant others such as family
members or friends to rate the level of positive changes experienced by the
participants (Park et al 1996; Weiss, 2002). Both studies found moderate correlations
between participants’ self-report posttraumatic growth scores and those provided by
informants. These research findings have provided empirical support about the
validity of posttraumatic growth to certain extent.
21
2.2.4 Assessment of posttraumatic growth
The methodology of studying posttraumatic growth is quite different from that
in mainstream psychological research, which is dominated by quantitative methods.
Qualitative methods, mainly semi-structured interview, are commonly used to
investigate post-traumatic growth (e.g. Frantz, Farrell, & Trolley, 2001; McMillen,
Smith, & Fisher, 1997; Sigel & Schrimsahw, 2000). Frantz, Farrell, and Trolley
(2001) used four open-ended questions to investigate any positive experiences gained
by people who had lost a loved one. One third of the respondents reported
strengthened relationship with family and friends and 32% said that they became
more mature, self-confident, independent and stronger. Sigel and Schrimshaw (2000)
used semi-structured interview to examine posttraumatic growth in women with
HIV/AIDS and different aspects of positive changes were identified. These include
health behaviors, spirituality, interpersonal relationships, life value, and career goals.
McMillen, Smith, and Fisher (1997) interviewed survivors of tornado, mass killing,
and plane crash. They identified perceived benefits from the disasters such as
personal growth, increased family closeness, spiritual growth, increase community
closeness etc. These qualitative studies have revealed how diverse posttraumatic
growth can be experienced. Qualitative method is well suited for portraying
respondents’ own personal experience and allows the researcher to ask questions
22
about the reasons ascribed for, and the ways in which individuals put together their
own accounts of what has happened to them (Berg, 2004). However, the results are
difficult to quantify and interpret without using a standardized measure (Cordova et
al, 2001).
To overcome the limitations of the qualitative study, some measures have been
developed to assess positive life changes among people who have coped with any
type of trauma. For instance, the Posttraumatic Growth Inventory (PTGI) developed
by Tedeschi and Calhoun (1995) and the Stress-Related Growth Scale (SRGS)
developed by Park, Cohen, and Murch (1996). Since both scales were validated
among college students in Western societies, their applicability to clinical contexts
and other cultures is not guaranteed and modifications may be needed for these
scales to apply to different situations. For instance, Ho, Chan, and Ho (2004) adapted
the PTGI to study posttraumatic growth among Chinese cancer survivors. They
found that posttraumatic growth could be simply reduced to two dimensions, namely:
Interpersonal and Intrapersonal, instead of five dimensions, New Possibilities,
Relating to Others, Personal Strength, Spiritual Change, and Appreciation of Life
respectively, which were identified from the original PTGI.
Researchers may also adopt the scale for one context to another clinical setting.
For instance, Antoni et al (2001) and Tomich and Helgeson (2004) adapted and
23
screened out irrelevant items from the scale used to assess perceived benefits among
parents of children with special needs (Behr, Murphy, & Summers, 1991, cited in
Tomich & Helgeson, 2004). They then used the modified scales to assess
posttraumatic growth among breast cancer patients, though the number of items they
ultimately included in the measurements was different - 17 and 20 respectively.
It is not uncommon for researchers to employ both qualitative and quantitative
methodologies to develop appropriate measures of posttraumatic growth in specific
clinical contexts. For instance, Abraido-Lanza et al (1998) utilized a three-year
longitudinal design to explore posttraumatic growth among Latinos facing multiple
adversities: poverty and chronic illness (i.e. arthritis). Qualitative data were collected
through a structured face-to-face interview with open-ended questions at the initial
assessment to understand respondents’ own experiences of posttraumatic growth and
the information was subsequently used to develop a quantitative measure of
posttraumatic growth. Eight growth domains: family appreciation, life appreciation,
appreciation of friendship, gained positive attitude, personal strength, enhanced
spirituality, empathy, and patience were eventually identified.
24
2.2.5 Predictors of posttraumatic growth
2.2.5.1 Severity of trauma
Since posttraumatic growth is a developing literature, some aspects of this
phenomenon have not yet been completely understood (Siegel & Schrimshaw, 2000).
One of the unanswered questions is the relationship between the magnitude of
trauma and the level of growth experienced. A major disruption or loss has been
documented to be necessary to bring forth posttraumatic growth (Tedeschi &
Calhoun, 1995). That means an event must be sufficiently traumatic to disrupt
existing schemas to bring about posttraumatic growth. Calhoun and Tedeschi (2001)
proposed a nonlinear but positive relationship between the traumatic degree of the
event and the level of growth. When the losses become so extreme to the extent they
overwhelm one’s ability to adapt and cope, the possibility of growth may diminish.
Yet, there is no convincing empirical evidence regarding the precise shape of the
posttraumatic growth curve in relation to the magnitude of trauma as findings from
previous research remain inconclusive (Armeli, Gunthert, & Cohen, 2001; Cordova
et al, 2001; McMillen, Smith, & Fisher, 1997; Lechner et al, 2003; Park et al, 1996;
Tomich & Helgeson, 2004; Widows, Jacobsen, Booth-Jones, & Fields, 2005).
McMillen, Smith, and Fisher (1997) attempted to assess the severity of trauma
by asking respondents about the characteristics of disasters. They found that greater
25
posttraumatic growth was reported in those respondents who thought they were
going to die. Cordova et al (2001) operationalised the level of trauma by a
two-question proxy measure which assesses the subjective perception of breast
cancer survivors about whether their experience constituted a traumatic stressor. The
results showed that greater posttraumatic growth was reported among those
respondents who perceived breast cancer a threat to life and the experience elicited
responses such as fear, helplessness or horror. Park et al (1996) also found that the
initial stressfulness of a negative event was a significant predictor of posttraumatic
growth and concluded that an event that caused more initial distress enlarged the
opportunities of positive changes to emerge due to greater disruption of one’s
schemas. In addition, Armeli, Gunthert, and Cohen (2001) used 23 items to measure
participants’ appraisal of the stressful event in terms of loss, threat, challenge,
severity, and predictability on seven-point scales. They found that highly stressful
event characterized by high threat and loss was a critical factor in predicting
posttraumatic growth. Even with similar levels of coping ability and resources,
participants who perceived the event as severe, high in threat or loss reported more
growth than those who perceived the event as otherwise. Widows et al (2005) again
found that greater posttraumatic growth was related to more stressful appraisal of the
bone marrow transplantation experience among cancer patients. Whilst all these
26
studies subjectively measured the severity of a trauma and adopted different ways to
assess posttraumatic growth, they revealed that more severe trauma offers greater
opportunities for posttraumatic growth.
Some researchers used stages of disease (I to IV) among cancer patients to
operationalize the severity of trauma (e.g. Lechner et al, 2003; Tomich & Helgeson,
2004). In Lechner et al’s (2003) study, Stage II cancer patients had significantly
higher posttraumatic growth scores than patients of Stage I and IV yet they were not
different from the posttraumatic growth scores of Stage III patients. Not only they
found a significant quadratic estimation regression analysis in the data collected but
also their work provided empirical evidence to the inverted U-shaped, quadratic,
rather than a positive linear relationship between the severity of trauma and
posttraumatic growth.
Tomich and Helgeson’s (2004) study yielded slightly different results. Stage II
patients’ posttraumatic growth mean score was significantly higher than that of Stage
I patients. However, Stage III patients’ posttraumatic growth mean score was
insignificantly different from those of Stage I or II although Stage III patients’
posttraumatic growth mean score was the highest among the three groups, most
probably due to the small numbers of patients recruited at Stage III (n = 22). This
study only demonstrated that severity of disease was associated with posttraumatic
27
growth but failed to conclude the precise shape of the relationship between severity
of trauma and posttraumatic growth level. Since both Lechner et al’s (2003) and
Tomich and Helgeson’s (2004) studies were on cancer patients, studies on other
clinical settings which use objective measurement of the severity of trauma would
definitely provide further empirical support as to whether there is an upper limit on
the positive relationship between the two variables.
2.2.5.2 Personal characteristics
One of the most challenging aspects for studying posttraumatic growth is to
determine what kinds of personal characteristics and other psychosocial resources are
closest related to posttraumatic growth; that is, to predict who will “grow” after a
traumatic event.
Certain demographic variables are found to be significant correlates of
posttraumatic growth. Younger participants (Lechner et al, 2003; Milam, 2004;
Widows et al, 2005), female respondents (Calhoun & Tedeschi, 2001; Milam, 2004;
Park et al, 1996), and less educated samples (Urcuyo, Boyers, Carver, & Antoni,
2005; Weiss, 2004; Widows et al, 2005) have been found to report more
posttraumatic growth, but not all studies on posttraumatic growth produced similar
results. For instance, Lechner et al (2003) failed to find any significant association
28
between demographic variables, including gender, education or income level, and
posttraumatic growth. On the other hand, Sears, Stanton and Danoff-Burg (2003)
found greater posttraumatic growth among more educated participants. Moreover,
evidence on socio-economic status seem to be conceptually contradictory because
posttraumatic growth has been found to be positively associated with higher income
in Cordova et al’s (2001) study and with lower socio-economic groups by Tomich
and Helgeson’s (2004). Therefore, while demographic variable significantly
correlates with posttraumatic growth in one sample may not necessarily correlate in
another sample.
Personality qualities such as optimism, self-esteem, and perceived control have
been consistently documented as predictors of posttraumatic growth (Abraido-Lanza
et al, 1998; Affleck & Tennen, 1996; Antoni et al, 2001; Carver, 1998; Lechner &
Antoni, 2004; Milam, 2004; Park et al, 1996; Park 1998; Tedeschi & Calhoun, 1998;
Urcuyo et al, 2005). There is a high degree of overlap among the predictors in the
studies of resilience and posttraumatic growth. As such, some researchers believe
that these variables are capable of predicting good adjustments during a stressful
encounter that are also be relevant to posttraumatic growth (e.g. Carver, 1998; Ho,
Chan, & Ho, 2004). Personal resilience (a composite measure of optimism, perceived
control and self-esteem) has been extensively studied in the field of stress and coping,
29
yet rarely adopted to predict posttraumatic growth. Chan, Lai, and Wong (2006)
examined the predictive power of personal resilience on posttraumatic growth among
67 Chinese coronary heart disease patients. Personal resilience was found to be a
significant predictor of posttraumatic growth although an eight-week rehabilitation
program exerted a weak mediating effect on the link between personal resilience and
posttraumatic growth. Currently, studies on the predictors of posttraumatic growth
are quite diverse and upon which it is difficult to draw a consistent conclusion. It
may be useful to identify composite measures such as personal resilience as one
predictor to condense the number of predictors. This may facilitate cross-study
comparisons, thus advancing research on posttraumatic growth.
Social support is another variable commonly documented to be positively
associated with posttraumatic growth (Carver, 1998; Cadell, Regehr, & Hemsworth,
2003; Lechner & Antoni 2004; McMillen, 2004; Park et al, 1996; Tedeschi &
Calhoun, 1998, 2004). However, it has been proved to be unrelated to posttraumatic
growth in the context of breast cancer (Cordova et al, 2001) and arthritis
(Abraido-Lanza et al, 1998). Drawing from the empirical evidence, social support is
an essential correlate of posttraumatic growth but it may be situation-specific and
may not be as robust as personal resilience in predicting posttraumatic growth.
30
2.2.6 Moderators
Though quite a number of studies have examined the severity of trauma or
personal variables in predicting posttraumatic growth (Abraido-Lanza et al, 1998;
Cordova et al, 2001; McMillen et al, 1997; Lechner et al, 2003; Milam, 2004; Park et
al., 1996; Widows et al, 2005; Urcuyo et al, 2005; Weiss, 2004; Tomich & Helgeson,
2004), very few studies investigate the extent to which personal resources may
moderate the link between severity of trauma and posttraumatic growth. Amerli et al
(2001) used cluster analysis to identify five different event profiles according to
respondents’ coping abilities and social resources and then compared their
posttraumatic growth patterns. Their findings indicated that the highest posttraumatic
growth was reported by participants with a higher threat appraisal of the event as
well as higher levels of coping ability and social support. Thus, this empirical study
implies that posttraumatic growth may not be only contingent upon the perceived
severity of trauma but may also be dependent on the level of personal resources one
possesses. In other words, for posttraumatic growth to occur, one must experience an
event which is traumatic enough, but at the same time, he/she must has adequate
personal resources to protect himself or herself from being overwhelmed. However,
it is worth pointing out that the study by Amerli et al (2001) has certain limitations.
For example, they assessed severity of the event subjectively. Though they surveyed
31
two large samples of university alumni and college students about their most
stressful event in the past two years, the analyses focused on daily hassles and not
one single traumatic event, so its applicability to a clinical context is questionable.
The importance of studying moderators in the research on posttraumatic growth has
already been embodied in a remark made by Park (2004). In light of Amerli et al’s
(2001) empirical findings, it is worthwhile to explore the potential moderating roles
of personal resilience and social support on the relationship between severity of
trauma and posttraumatic growth.
2.3 Implications for assessing psychological profiles of healthcare workers
The psychological impact of SARS infection was comparable to that of other
life-threatening traumas such as severe traffic accidents (Wu, Chan, & Ma, 2004).
Subsequent psychological distress, anxiety, depression, other associated negative
psychological impacts such as worry about health, fatigue, fear, poor sleep, poor
concentration, depressed mood, and impaired judgment, were observed among SARS
patients (Chua et al, 2004; Cheng & Wong, 2005; Ho et al, 2005; Wu et al, 2004).
Furthermore, Chua et al (2004) found that positive psychological effects such as
awareness of hygiene, caring for others, willing to help, and civic-mindedness, were
also reported among SARS patients. In fact, both costs (personal feebleness, social
32
estrangement, and financial problems) and benefits (personal growth, interpersonal
appreciation, healthy lifestyle, and societal solidarity) brought about by the SARS
episode were identified in Cheng et al’s (in press) study. Interestingly, infected
healthcare workers had significantly more negative as well as positive psychological
impacts than SARS patients. Similarly, healthcare workers were a group more likely
to develop higher psychological distress than non-healthcare worker survivors
(Cheng & Wong, 2004).
Ho, Kwong, Mak and Wong (2004) investigated the posttraumatic growth
among Hospital Authority staff after SARS outbreak and found no significant
difference in posttraumatic growth between infected hospital staff and non-infected
staff. Does this imply that SARS experience was so stressful to hospital staff that
even without contracting SARS itself, it had triggered posttraumatic growth amongst
non-infected staff? Ho et al (2004) also found that posttraumatic growth was
unrelated to posttraumatic stress symptoms. The findings seem consistent with those
by Cordova et al’s (2001) in which it was reported that there is no significant
association between posttraumatic growth with depression and well-being. This may
well suggest that positive and negative impacts from a trauma may not be mutually
exclusive and may indeed co-exist (Tedeschi & Calhoun, 2004).
On the other hand, drawing on their findings that posttraumatic growth were
33
found to significantly account for outcome measures including anxiety, depression
and perceived health, Cheng and Wong (2004) recognized posttraumatic growth was
one of the important parameters for improving SARS survivors’ psychological health
as well as an element to be included in clinical intervention. By encouraging
survivors to review potential personal gains, Cheng and Wong (2004) believed that
positive reframing and meanings might subsequently be developed and thus reduce
the level of distress.
The SARS episode was a remarkable crisis in Hong Kong’s history of infectious
diseases, in particular the havoc on the healthcare system, with over 300 healthcare
workers infected. Healthcare workers had to face tremendous fear and stress when
they were carrying out their duties during the outbreak of SARS. Since infected
healthcare workers have been repeatedly shown to have higher psychological distress
than SARS patients, it is important to gain a more complete understanding of
healthcare workers’ psychological adjustment to the SARS episode. Useful
information can be collated for use in intervention services or facilitating recovery to
meet future challenges.
2.4 Theoretical Framework
To sum up, the aforementioned literature supports the notion that a trauma may
34
not only bring psychological damage to an individual but also yield positive
outcomes. Over focus on one aspect may limit our understanding of all the outcomes.
Higher level of stress is related to more depressive symptoms (Chang 1998; Chang &
Sanna, 2003). Personal resilience (Helgeson, 1999) and social support (Ma, 1997;
Sun & Stewart, 2000) are predictors of better psychological health outcomes in face
of a life-threatening disease. Moreover, variables such as severity of trauma, personal
resilience, and perceived social support are critical predictors of posttraumatic
growth (Cadell, Regehr, & Hemsworth, 2003; Chan et al, 2006; Lechner et al, 2003).
However, the number of empirical studies that examines the role of personal
resources in moderating the link between severity of trauma and posttraumatic
growth is extremely limited to fill this knowledge gap. On the other hand, the
moderating effects of personal resilience and perceived social support appear to be
much more established for stress-related symptoms like anxiety and depression
(Chang, 1998; Chang & Sanna, 2003; Cheng, 1997; Jex, Cvetanovski, & Allen,
1994). A theoretical framework to study posttraumatic growth and posttraumatic
stress among healthcare workers after the SARS outbreak has been attempted for the
current study (Figure 1).
35
Severity of trauma
Posttraumatic growth&
Posttraumatic stress
Personal Factors: 1. Personal resilience 2. Perceived social support
Figure 1. Theoretical framework of the present study.
36
3. Research Objectives and Hypotheses
3.1 Research objectives
The objectives of my study are essentially three-fold: Firstly, to assess the
posttraumatic growth and the posttraumatic stress of healthcare workers after SARS
outbreak; Secondly, to examine the degree to which the severity of SARS experience,
personal resilience, and perceived social support are related to posttraumatic growth
and posttraumatic stress; Lastly, to explore as to how far personal resilience and
perceived social support may moderate the influence of severity of trauma on
posttraumatic growth as well as posttraumatic stress. Drawing on the studies
discussed earlier, I formulated the following hypotheses:
3.2 Hypotheses
1. Severity of trauma is a significant predictor of posttraumatic growth and more
severe trauma will be associated with report of more posttraumatic growth.
2. Personal resilience and perceived social support are significant predictors of
posttraumatic growth.
3. Personal resilience and perceived social support significantly moderates the
relationship between the severity of trauma and posttraumatic growth.
4. Severity of trauma is a significant predictor of posttraumatic stress and more
37
severe trauma will be associated with report of more posttraumatic stress
(anxiety and depression).
5. Personal resilience and perceived social support are significant predictors of
posttraumatic stress (anxiety and depression).
6. Personal resilience and perceived social support significantly moderates the
relationship between the severity of trauma and posttraumatic stress (anxiety
and depression).
38
4. Methodology
4.1 Participants
Nursing staff was chosen as participants of this study because of their relative
large population size compared with other public healthcare professionals. As at 31
March 2004, nursing staff took up 37% of the total population of Hospital Authority
staff with doctors, allied health professionals, and health care/ward assistants only
accounted for 9%, 9%, and 13% respectively (Hospital Authority, 2004). Moreover,
55% of the healthcare workers contracted SARS in Hong Kong were nurse (S. Chan,
2003). A further reason to select only nursing staff was to minimize differences
associated with different job specifications of various healthcare professionals.
Doctors, other allied health professionals, and support staff carry out different duties
in hospitals. Different medical professions might expose to different threat levels
during the outbreak. The nursing staff was classified into three groups representing
different levels of traumatic experience as a result of SARS outbreak:
1) Never taking care of SARS patients and not being infected (none or low level of
trauma)
2) Taking care of SARS patients but not being infected (moderate level of trauma)
3) Taking care of SARS patients and being infected (severe level of trauma)
39
4.2 Use of semi-structured interview for exploration
As mentioned earlier, SARS is a new infectious disease to the extent that
interviewing both non-infected and infected nursing staff might reveal new
dimensions of posttraumatic growth specific to SARS experience. Nine
semi-structured interviews were conducted (three nurses in each category) during the
period from March to July 2004. Interviewees were recruited by convenient sampling
through referral. Three of those sampled were males and six were females, ages
ranged from 24 to 54 years. The interviewees were asked to provide information
mainly about three areas: the perceived stressfulness of SARS outbreak; the impacts
(both positive and negative changes) of SARS on them as individuals; and the
psychosocial variables that they thought were essential for bringing about the
positive changes. The descriptions they provided of the SARS outbreak were: sudden,
shock, traumatic, stressful, fearful, natural disaster, akin to a fatal illness such as
terminal cancer. One infected nurse went as far as suggesting that no particular
illness could be comparable to SARS. In addition to the high level of stress
experienced, negative impacts to one infected nurses were cited as low controllability
of life such as getting sick and fear of being sick. Another infected nurse revealed
that she had never thought of losing her life merely working as a nurse. Among the
non-infected nurses, one mentioned increasing workload after SARS outbreak. Two
40
female non-infected nurses claimed that they had considered early retirement after
SARS outbreak. Positive impacts including life appreciation, religious growth,
changed life priorities, cherishing and understanding family members and friends,
more patience, together with better nursing of the sick were commonly reported by
most of the interviewees. Optimism, self-esteem, social support, religious beliefs
were believed to be the most valuable factors in helping them to eradicate negative
impacts and bring forth the positive changes.
4.3 Quantitative study
Approval was obtained from the ethics committee of Association of Hong Kong
Nursing staff in December 2005 to conduct a mail survey. An invitation letter briefly
describing the aims of the study and a return envelope were enclosed with the
questionnaire (see Appendix A). Participation of the present study was completely
voluntary. 2,000 questionnaires were randomly mailed to the members of Association
of Hong Kong Nursing Staff working in six public hospitals: namely, Queen
Elizabeth Hospital, Prince of Wales Hospital, Tuen Mun Hospital, Princess Margaret
Hospital, United Christian Hospital and Wong Tai Sin Hospital in January 2005. Of
the 2,000 questionnaires mailed, 428 were returned by the end of May 2005,
resulting in a response rate of 21.4%. Two returned questionnaire were not completed
41
(completion less than 60%), resulting in a final sample of 426. For the final sample,
172 nursing staff (19 male, 153 female) reported having no contact with SARS
patients (group one); 216 of them (22 male, 194 female) were involved in taking care
of SARS patients but not infected (group two); and 38 of them (5 male, 33 female)
were infected healthcare workers (group three). 4% of the respondents were aged 24
or below, 54% in age group 25-34, 25% in age group 35-44, 13% in age group 45-54,
and 4% above 55. 52% of the respondents were married and 48% were single,
divorced or widowed. Religious backgrounds were 59% without religion beliefs,
32% Christian, 5% Catholic, 4% Buddhist. Education background of the sample was
31% diploma level, 63% bachelor degree, 6% master or doctoral level. 5% were
enrolled nurse, 82% were registered nurse, 11% were nursing officer or ward
manager, and 2% were departmental operation manager. 20% worked below 5 years,
37% worked 6-10 years, 24% worked 11-15 years, 6% worked 16-20 years, and 13%
worked above 21 years. 19% served in intensive care unit, 43% served in medical
unit, 10% served in surgical unit, 28% served in miscellaneous departments such as
oncology, orthopaedic, peadiatric, obstetrics & gynaecology, accident & emergency,
and central sterile supplies unit etc. The demographic characteristics for group one,
two and three are shown in Table 1.
42
Table 1
Demographic data for three groups of nurses 1 2 3
Group χ2
No contact with SARS
patients
Taking care of SARS patients
but being infected
Infected health care worker
N 172 216 38 Gender 0.28
Male 19
Female 153
Male 22
Female 194
Male 5
Female 33
Age % % % 58.08** 24 or below 7 2 0 25-34 39 62 76 35-44 25 24 24 45-54 19 12 0 55 or above 10 0 0
Martial status 8.44* Married 56 43 55
Single/Divorce/ Widow
44 57 45
Religion 6.47 No religion 52 57 68 Christian 34 36 27 Catholic 9 4 0 Baddish 4 2 5 Others 1 1 0
Education 19.25** Diploma 33 17 42 Degree 60 72 58 Master / PhD 7 11 0
Rank 26.82** EN 8 2 5 RN 66 84 95 NO 21 13 0 DOM 5 1 0
43
Table 1 (continued)
Demographic data for three groups of nurses 1 2 3
Group χ2
No contact with SARS
patients
Taking care of SARS patients
but being infected
Infected health care worker
Years of service % % % 42.60** 0 to 5 23 23 16 6 to 10 18 40 52 11 to 15 22 18 32 16 to 20 9 9 0 21 or above 28 10 0
Department 99.52** ICU 3 33 0 Medical 38 46 92 Surgical 12 8 8 Misc. 47 13 0
* p<0.05, **p<0.01
4.3.1 Predictors
4.3.1.1 Optimism
Optimism was assessed by the Chinese Revised Life Orientation Test (CRLOT),
which contained six items and adapted by Lai, Cheung, Lee, and Yu (1998) from the
original English version of revised Life Orientation Test (Scheier, Carver, & Bridges,
1994). However, studies using the CRLOT showed that one of the items (“If
something can go wrong for me, it will”) exhibited low and unstable corrected
item-total correlation, which explained the relative low level of internal consistency
44
of the scale (e.g. Cronbach α = .54 in Lai, Hamid & Cheng, 2000; Cronbach α = .61
in Lai & Yue, 2000). The CRLOT was further revised in a study by Lai (2003) in
which the problematic item was replaced by a new item “Looking into the future, I
do not see any positive scenarios”, which resulted in higher internal consistency
(Cronbach α = .74, N = 109). Participants were asked to respond to each item on a
five-point scale, ranging from “strongly disagree” (1) to “strongly agree” (5). In the
present sample, the mean optimism score was 17.89 (SD = 3.74) (Cronbach α =.77).
The mean optimism score of a group of Chinese patients with coronary heart disease
was 21.34 (SD = 3.72, Cronbach α =.73, N = 67) (Chan et al, 2006), which is higher
than that of the present sample.
4.3.1.2 Self-esteem
A nine-item revised Chinese version of the Rosenberg Self-esteem Scale was
used to measure self-esteem. Cheng and Hamid (1995) had shown that one of the
negatively phrased items in Rosenberg Self-esteem Scale, “I wish I could have more
respect for myself”, was syntactically problematic in Chinese and the actual meaning
was lost due to differences in syntax after the item was back-translated. This item
had a close-to-zero average correlation with the rest of the items. Moreover, the
problematic item was the only one that, when omitted from the calculation, raised the
45
alpha co-efficient. This specific item was thus excluded in the present measurement.
Participants answered each item on a five-point scale, ranging from “strongly not
resemble me” (1) to “strongly resemble me” (5). The mean score on self-esteem for
the present sample was 31.85 (SD = 5.13) and the internal consistency was high
(Cronbach α = .85). A group of Chinese coronary heart disease patients studied
recently by Chan et al (2006) tended to have a higher self-esteem mean score of
34.24 (SD = 4.87, Cronbach α =.82, N = 67).
4.3.1.3 Perceived control
Perceived control was assessed by the seven-item Mastery Scale (Pearlin,
Menaghan, Lieberman, & Mullen, 1981), which tapped a person’s general feeling of
personal control over life events. A back-translation was adopted to develop the
Chinese Mastery Scale from the original English version. Using a five-point scale,
ranging from “strongly disagree” (1) to “strongly agree” (5), participants were asked
how strongly they agreed with general statements such as “What happens to me in
the future mostly depends on me” and “I can do just about anything I really set my
mind to”. The mean score on perceived control for the present sample was 22.03 (SD
= 4.28) (Cronbach α = .78). A group of Chinese coronary heart disease patients
recently studied by Chan et al (2006) had a higher perceived control mean score of
46
25.36 (SD = 4.5, Cronbach α =.71, N = 67).
Optimism, self-esteem and perceived control were moderately correlated (r
ranged from .49 to .55, p<.01) and they were combined into a composite measure of
“personal resilience”. The composite was formed by adding standardized (z) scores
of the three scales.
4.3.1.4 Perceived social support
Social support was assessed by the Chinese version of the Multidimensional
Scale of Perceived Social Support (MSPSS-C), which has been back-translated and
validated by Chou (2000) from the English version of MSPSS (Zimet et al, 1988).
The MSPSS-C is a twelve-item scale on a seven-point rating with sound
psychometric properties similar to the original English version, measuring perceived
support from three sources, namely: Family, Friends, and Significant Other. The
mean score on perceived social support for the present sample was 63.30 (SD =
14.72) (Cronbach α = .96). The composite mean score for the MSPSS-C of the
present sample (M = 5.27, SD = 1.20, Cronbach α = .96) is higher than that from a
sample of 110 Chinese patients with heart failure (M = 4.14, SD = 1.51) (Yu, Lee, &
Woo, 2004).
47
4.3.2 Dependent variables
4.3.2.1 Posttraumatic growth
Posttraumatic growth was assessed by the Chinese Posttraumatic Growth
Inventory (CPGI), which was back-translated by Ho, Chan, and Ho (2004) from the
original English version developed by Tedeschi and Calhoun (1995). The original
PTGI comprises twenty one items with five different dimensions: (a) relating to
others (seven items); (b) new possibilities (five items); (c) personal strengths (four
items); (d) spiritual change (two items); and (e) appreciation of life (three items).
Based on the qualitative data collected by the nine semi-structured interviews
regarding the positive changes in response to the SARS outbreak, four reported items
that were not covered by the CPGI were added to the scale (“I learned to show
understanding for my friends and family members”; “Provide more careful nursing to
the patients”; “I learned to be more patient”; and “I cherish the relationship with my
family more”). The mean score was 68.22 (S.D. = 20.17) (Cronbach’s α = .95).
4.3.2.2 Posttraumatic stress
Posttraumatic stress was assessed by the fourteen-item Chinese Hospital
Anxiety and Depression Scale (CHADS), which was adapted by Leung, Ho, Kan,
Hung, and Chen (1993) from the original English version designed to be used in
48
hospital settings (Zigmond & Snaith, 1983). Leung et al (1993) showed that the
CHADS had good internal consistency similar to the English version. Seven items
measure anxiety and another seven items measure depression. Higher scores
represent higher levels of anxiety or depression. The mean scores and Cronbach α
values for the anxiety and depression subscales were M = 5.76 (S.D. = 3.68)
(Cronbach α = .87) and M = 3.78, (S.D. = 2.91) (Cronbach α = .75), respectively, in
the present sample. The anxiety and depression mean scores of the present sample
are lower than those reported in a prior study with 93 Chinese hospitalized patients
(anxiety: M = 6.94, SD = 4.37; depression: M = 8.34, SD = 5.16) (Leung, Wing,
Kwong, & Shum, 1999).
4.3.3 Control Variables
Demographic information such as sex, age, marital status, religious beliefs,
education, department, rank, and years of service were obtained from participants.
Those nurses who provided nursing to SARS patients were asked to give information
about the length of the period and numbers of SARS patients. Participants were also
asked to report any experience of other critical life events after the SARS outbreak to
the time participating in the present study. The top ten life events were selected from
the new rank order of Social Readjustment Rating Scale (SSRS) developed by Scully,
49
Tosi, and Banning (2000). 97 respondents reported experiencing other critical life
events. Two claimed experiencing divorce; 31 had serious injury or illness; 16 got
married; 25 claimed his or her family member having serious injury or illness; 27
experienced death of close family member; 12 had financial difficulties and 6
reported sex difficulties. They were also asked to report whether their family
members were infected with SARS and none indicated that his or her family member
had contracted SARS.
50
5. Results
5.1 Exploratory factor analysis
A Principal Component Analysis (PCA) with varimax rotation was conducted
to identify the factor structure of responses to the twenty five posttraumatic growth
items. Although PCA extracted four factors, after considering other information
(Urcuyo et al, 2005), it was more reasonable to treat the scale as unidimensional in
the present study. First, the screen plot clearly showed a sharp descent from factor
one (eigenvalue = 12.07) to factor two (eigenvalue = 1.24), following by a tailing off
of the curve. With a sample of more than 200 subjects, the screen plot provides a
reliable criterion for factor selection (Stevens, 1992, cited in Field, 2003). Second,
reviewing the content of the four factors, only the content of the first factor mainly
focused on relationship with others. No clear themes could be identified from the
other three factors. Third, all items had very high factor loadings (.50 to .79) on the
first un-rotated factor, except item sixteen which loaded more strongly on the second
factor (.66) than it did on the first factor (.40).
In Table 2, item sixteen, which tapped the religious growth of the respondents,
had the lowest mean rating and largest standard deviation (M = 1.76, S.D. = 1.73).
Similar result was found in another posttraumatic growth study on Chinese people
with coronary heart disease (Chan et al, 2006). This might be due to the fact that,
51
unlike participants from Western cultures, the majority of the Chinese samples did
not have any religious beliefs. Therefore, item sixteen was not applicable to most of
them. Moreover, item sixteen was the only one that, when omitted from the
calculation, raised the alpha coefficient. This specific problematic item was thus
excluded in the measurement of posttraumatic growth for the present study. Similarly,
the item on religious growth was also omitted in the analyses of Cheng et al’s (in
press) study because only one item was loaded on a factor.
Table 2
Posttraumatic growth items, means, and standard deviations (N = 426)
Item M SD 1. My priorities about what is important in life. 2.66 1.15 2. I’m more likely to try to change things which need changing. 2.59 1.06 3. An appreciation for the value of my own life. 3.09 1.04 4. A feeling of self-reliance. 2.53 1.20 5. A better understanding of spiritual matters. 2.78 1.11 6. Knowing that I can count on people in times of trouble. 2.44 1.16 7. A sense of closeness with others. 2.62 1.19 8. Knowing I can handle difficulties. 2.88 1.02 9. A willingness to express my emotions. 2.77 1.12 10. Being able to accept the way things work out. 2.95 1.13 11. Appreciating each day. 3.17 1.19 12. Having compassion for others. 3.32 1.06 13. I’m able to do better things with my life. 3.19 1.01 14. New opportunities are available which wouldn’t have been
otherwise. 2.19 1.31
15. Putting effort into my relationships. 2.40 1.15 16. I have a stronger religious faith. 1.76 1.73 17. I discovered that I’m stronger than I thought I was. 2.45 1.32 18. I learned a great deal about how wonderful people are. 2.75 1.16
52
Table 2 (continued) Posttraumatic growth items, means, and standard deviations (N = 426) Item M SD 19. I developed new interest. 2.06 1.38 20. I accepted needing others. 2.88 1.12 21. I established a new path for my life. 2.21 1.30 22. I learned to show understanding for my friends and family
members. 3.13 1.08
23. I provide more careful nursing to the patients. 3.03 1.19 24. I learned to be more patient. 2.79 1.23 25. I cherish the relationship with my family more. 3.59 1.18
5.2 Descriptive statistics
In order to ensure that the three different groups of nurses selected represent
three different levels of trauma triggered from SARS, the respondents were requested
to rate the perceived traumatic severity of SARS experience on a seven-point scale.
One-way ANOVA was conducted to examine the difference among the three groups
in their perceived level of trauma. In Table 3, results illustrated that there was a
significant difference in the groups’ level of trauma, F(2, 423) = 23.2, p < .05.
Bonferroni post hoc test showed that group one (M = 3.22) had significantly lower
level of trauma than group two (M = 3.87) and group three’s level of trauma (M =
5.05) was significantly higher than that of group two. Moreover, there was no
difference among the three groups in optimism, self-esteem, perceived control,
personal resilience or social support, but significant group differences were found in
53
posttraumatic growth, F(2, 423) = 6.95, p < .01, depression, F(2, 423) = 3.13, p < .05,
and anxiety, F(2, 423) = 3.08, p < .05.
Table 3 Means and standard deviations of personal resilience, social support, posttraumatic growth, depression and anxiety for three groups of nurses
Group 1 2 3 N = 172 216 38 F value
M (SD) M (SD) M (SD) Perceived level
of trauma 3.22 (1.52) 3.87 (1.54) 5.05 (1.52) 23.20**
Optimism 18.16 (3.97) 17.67 (3.57) 18.55 (3.10) 1.41 Self-esteem 32.09 (5.31) 31.80 (4.19) 31.03 (4.36) 0.72
Perceived control 22.27 (4.33) 21.97 (4.28) 20.74 (3.46) 2.05 Personal resilience (composite score)
0.17 (2.61) -0.01 (2.43) -0.29 (1.98) 0.83
Social support 63.81 (15.75) 62.68 (13.99) 63.00 (9.78) 0.35 Posttraumatic growth 64.51 ( 20.40) 67.41 (19.11) 77.21(10.62) 6.95**
Anxiety 5.30 (3.68) 5.94 (3.62) 6.76 (3.86) 3.08* Depression 3.57 (2.80) 3.73 ( 2.96) 4.88 (3.05) 3.13*
*p < 0.05, **p < 0.01
5.3 Multiple regression analysis
The relations of all the demographic and control variables and dependent
variables were examined and summarized (see Appendix B). Demographic variables:
age, marital status (single or married), religious beliefs (with or without religious
beliefs), and years of service were weakly related (rs ranged from .10 to .15, p < .05)
to posttraumatic growth. Experience of any life event other than SARS (with any or
54
without) was related to anxiety and depression. In addition, posttraumatic growth
was not associated with anxiety (r = .07, p > .05) or depression (r = -.05, p > .05).
Thus, the following analyses statistically controlled for relevant demographic and
control variables.
Hierarchical multiple regression analysis was used to examine the contribution
of severity of trauma, personal resilience, perceived social support, and their
interaction in predicting posttraumatic growth, anxiety and depression. Predictors
were centered to reduce the impact of multicollinearity (West, Aiken, & Krull, 1996).
Demographic and control variables that were related to the relevant dependent
measures were entered into the predictive equation on the first step. Severity of
trauma, represented by three groups of nurses, was entered on the second step.
Personal resilience and perceived social support were entered on the third step.
Scores reflecting the interaction between severity of trauma (groups) and Z scores for
personal resilience and perceived social support were entered on the fourth step.
According to Baron and Kenny (1986), significant interaction term between the
predictor and moderator indicates the presence of a moderator effect. So, if personal
resilience or perceived social support moderates the effect of severity of trauma on
the outcomes, the relationship between severity of trauma and the outcomes will
change according to the level of personal resilience or perceived social support and
55
the interaction term should predict the outcomes significantly. The significance of
variables was evaluated by whether their entry into the equation produced a
significant increase in the amount of explained variance in the dependent measure
(R2 change). The results of the multiple regression analyses for predicting
posttraumatic growth, anxiety and depression from severity of trauma (groups),
personal resilience and perceived social support were presented in Table 4, Table 5
and Table 6 respectively.
As Table 4 showed, for posttraumatic growth, severity of trauma (groups)
accounted for a significant 5% of additional variance in step two. In the third step,
personal resilience and perceived social support were found to account for a
significant 5% of additional variance in posttraumatic growth. In the fourth step, the
interaction terms group x personal resilience and group x perceived social support
were found to account for an insignificant 1% of additional variance.
The Betas representing group two and group three were .13, p < .05 and .23, p
< .01, respectively. This not only indicated that severity of trauma significantly
predicting posttraumatic growth even when the effects of demographic variables
were statistically controlled, but group two and group three also had significant
higher levels of posttraumatic growth than group one.
56
Table 4 Hierarchical regression analyses predicting posttraumatic growth from severity of trauma (groups), personal resilience and perceived social support Step Predictor R2 R2 change Beta t
1 0.03 0.05 * Marital status 0.15 2.71 ** Religion 0.11 2.17 * Age level 2 0.09 0.62 Age level 3 0.10 0.67 Age level 4 0.19 1.51 Age level 5 0.10 1.22 Year of service level 2 0.01 0.12 Year of service level 3 -0.06 -0.83 Year of service level 4 0.05 0.68 Year of service level 5 -0.13 -1.21 2 0.07 0.05 ** Group 2 0.13 2.49 * Group 3 0.23 4.47 ** 3 0.12 0.05 ** Personal resilience 0.15 2.85 ** Social support 0.14 2.81 ** 4 0.12 0.01 Group 2 x Personal resilience 0.08 1.10 Group 3 x Personal resilience 0.05 0.90 Group 2 x Social Support 0.09 1.25 Group 3 x Social Support 0.07 1.43
Adjusted R2 & standardized Beta were used, N = 426, *p < 0.05, **p < 0.01
To verify the mean difference between group two and three, the dummy codes
representing the three groups were recoded with group two as the baseline and then
re-run the regression. Results showed that posttraumatic growth of group three (M =
77.21) was significantly higher than that of group two (M = 67.41), β= .16, p < .01.
Thus, hypothesis 1 was supported. In step three, personal resilience and perceived
57
social support accounted for a significant 5% additional variance. The Betas of the
two variables were .15, p<.01 and .14, p<.01, respectively. This implied that both
personal resilience and perceived social support were significant predictors of the
posttraumatic growth. Hypothesis 2 was also supported. However, no significant
additional variance was found in step four, so personal resilience and perceived
social support did not moderate the link between severity of trauma (groups) and
posttraumatic growth. Hypothesis 3 was not supported.
Table 5 Hierarchical regression analyses predicting anxiety from severity of trauma (groups), personal resilience and perceived social support Step Predictor R2 R2 change Beta t
1 0.01 0.01 * Life events 0.11 2.22 * 2 0.03 0.02 *
Group 2 0.09 1.86 Group 3 0.13 2.46 * 3 0.30 0.28 **
Personal resilience -0.50 -11.60 ** Social support -0.14 -3.05 **4 0.30 0.01
Group 2 x Personal resilience 0.02 0.34 Group 3 x Personal resilience -0.08 -1.64 Group 2 x Social Support 0.04 0.67 Group 3 x Social Support 0.02 0.40
Adjusted R2 & standardized Beta were used, N = 426, *p < 0.05, **p < 0.01
As Table 5 showed, regarding anxiety, severity of trauma (groups) accounted for
58
a significant 2% of additional variance in step two but only the Beta representing
group three was significant, (β= .13, p < .05). After re-running the regression by the
new set of dummy codes with group two as the baseline, results showed that group
two was not different from group three in anxiety level (β= .07, p = .15). This
implied that group three (M = 6.76) had higher level of anxiety than group one (M =
5.30) but group two was not different from the other two groups in anxiety level. In
the third step, personal resilience (β= -.50, p < .01) and perceived social support (β
= -.14, p < .01) were found to account for a significant 28% of additional variance in
anxiety. In the fourth step, both the interaction terms group x personal resilience and
group x perceived social support did not account for any significant additional
variance.
For depression, as Table 6 showed, severity of trauma (groups) accounted for a
significant 2% of the variance in step two but again only the Beta representing group
three was significant, (β= .14, p < .05). The regression analyses with group two as
the baseline similarly showed that group two was not different from group three in
depression level (β= .08, p = .13). Thus, group three had higher level of depression
than group one but group two was not different from the other two groups in
depression. In the third step, personal resilience (β= -.49, p < .01) and perceived
social support (β= -.13, p < .01) were found to account for a significant 29% of
59
additional variance in depression. In the fourth step, both the interaction terms group
x personal resilience and group x perceived social support did not account for any
significant additional variance.
Table 6 Hierarchical regression analyses predicting depression from severity of trauma (groups), personal resilience and perceived social support Step Predictor R2 R2 change Beta t
1 0.01 0.01 * Life events 0.12 2.43 * 2 0.03 0.02 * Group 2 0.03 0.62 Group 3 0.14 2.56 * 3 0.32 0.29 ** Personal resilience -0.49 -11.59 ** Social support -0.13 -2.98 ** 4 0.32 0.00 Group 2 x Personal resilience 0.02 0.31 Group 3 x Personal resilience -0.05 -1.03 Group 2 x Social Support 0.02 0.32 Group 3 x Social Support 0.03 0.60
Adjusted R2 & standardized Beta were used, N = 426, *p < 0.05, **p < 0.01
In sum, the hierarchical regression analyses on anxiety and depression indicated
that severity of trauma (groups) was a significant predictor of posttraumatic stress
but only group three had higher level of distress than group one. Thus, hypothesis 4
was partially supported. Though the personal resilience and perceived social support
were significant predictors of posttraumatic stress, the two variables did not
60
moderate the link between the severity of trauma with anxiety and depression
respectively. Therefore, results were consistent with hypothesis 5 but were not
supporting hypothesis 6.
For the above models, multicollinearity diagnostic analyses showed that the VIF
values were all below 10 and the tolerance figures were larger than 0.2, so there was
no potential problem regarding multicollinearity (Field, 2003).
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6. Discussion
6.1 Posttraumatic growth and posttraumatic stress among nurses
The first objective of the present study is to assess the posttraumatic growth
and posttraumatic stress of healthcare workers involved in SARS. As we all know,
SARS is an infectious disease, virtually unknown to the medical profession. To avoid
missing any interesting posttraumatic growth domains that are specific to the SARS
episode, several semi-structured interviews were conducted and four items were
added to the 21-item CPTGI translated by Ho, Chan, and Ho (2004). The present
study fails to replicate in nurses the five domains of the original PTGI but indicates a
unidimensional pattern of posttraumatic growth. Ho, Chan, and Ho (2004) also found
a different factor structure when applying an adapted version of PTGI among
Chinese cancer survivors. Among the twenty-one items, only fifteen were retained on
the basis of very stringent selection criteria: factor loadings of all items exceeded 0.5
but not above 0.4 on another factor, and the difference between an item’s loading on
two factors should be larger than 0.3. The domains of growth were reduced to two
dimensions, namely interpersonal and intrapersonal. In this study, as four items were
added to the original CPTGI, it may change the factor structure of the scale. In
addition, the difference in factor structure may also be attributed to difference in the
target sample (cancer patients vs. nurses), and the traumatic experience under
62
investigation (cancer vs. SARS).
Secondly, in Table 2, three additional items that were derived from the
qualitative interviews: “I learned to show understanding for my friends and family
members”; “Provide more careful nursing to the patients”; and “I cherish the
relationship with my family more” yielded relative high mean ratings (above 3.0).
The mean value of “I cherish the relationship with my family more” had the highest
rating score of 3.59. This may imply that the items derived from the qualitative
interviews are more relevant to the respondents than those from the original PTGI
(e.g. item fourteen and twenty one about new possibilities). Ho et al’s (2005) study
on fear of SARS among healthcare workers also found that healthcare workers not
only feared that they would be infected by SARS but were equally or even more
worried about infecting family members. Both findings may imply that in Chinese
culture, family members or relationships with them form a very important dimension
of life.
As mentioned in the previous chapter, item sixteen on religious growth was
problematic and thus excluded in the statistical analyses of the present study. This
may reflect the problem of simply applying the original PTGI to Chinese culture as
many Chinese respondents do not have religious beliefs and consequently to whom
religious growth is not applicable. In addition, there is an open-ended question for
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respondents to complete on any positive changes not being mentioned among the
twenty five items. A few respondents indicated that they perceived being a nurse as a
meaningful job and their professionalism had been appreciated by the public after
SARS outbreak. Since the original PTGI was developed among college students,
McMillen (2004) questioned its effectiveness in capturing the full range of potential
post-adversity growth resulting from different traumatic events and positive changes
such as increased ability to help others, increased cooperation among neighbors,
increase community closeness, and increased faith in other people – all of which
seem to have been neglected in PTGI. The clinical application of PTGI needs to be
further tested in other contexts of adversity.
At the present moment, there is no particular scale that is capable of capturing
all of the domains of posttraumatic growth and there is little agreement on the
dimensions of posttraumatic growth. Abraido-Lanza et al (1998) identified eight
domains (family appreciation, life appreciation, appreciation of friendship, gained
positive attitude, personal strength, enhanced spirituality, empathy, and patience)
among patients with arthritis. Similarly, Armeli et al (2001) found seven subscales
from Stress-Related Growth Scale (treatment of others, religiousness, personal
strength, belongingness, affect-regulation, self-understanding, and optimism)
whereas some studies on breast cancer patients (e.g. Antoni et al, 2001; Tomich &
64
Helegeson, 2004; Urcuyo, 2005) suggested only one underlying factor for the
measurement of posttraumatic growth.
The posttraumatic growth the respondents reported not only differs according
to the various measuring instruments adopted but it also appears that the dimensions
of posttraumatic growth may vary across different types of traumatic events and
populations (Park, 2004). Much more effort will be needed to develop a more
comprehensive measurement of posttraumatic growth. In short, researchers should
be cautious in importing scales developed in different cultures and keep in mind
that modifications may be required to cater for different types of traumatic events.
Regarding posttraumatic stress, group three, the infected nursing staff, had the
highest anxiety and depression means scores, 6.76 and 4.88 respectively, which
were slightly above the anxiety (6.07) and depression levels (4.36) reported by a
group of 188 high functioning Chinese cancer survivors who had passed the
five-year disease-free period (Ho, Chan, and Ho, 2004). Since the present study was
conducted almost two years after the SARS outbreak and Wu, Chan, and Ma (2004)
found that some SARS patients showed recovery after three months, it is not
surprising that a relatively low level of posttraumatic stress has been found in the
present sample.
65
6.2 The relationships between severity of trauma with posttraumatic growth and
posttraumatic stress
The second objective of the present study is to examine the relationship between
severity of trauma with posttraumatic growth and posttraumatic stress. To achieve
this, three groups of nurses working in a similar environment but exposed to different
degrees of trauma during the SARS outbreak are selected. Group one, who did not
take care of any SARS patients, representing the control group, low or no level of
trauma. Groups two and three were nurses who had taken care of SARS patients, but
only group three were infected with SARS. As expected, group two represented
moderate level of trauma and group three represented severe traumatic experience.
Posttraumatic growth and posttraumatic stress levels among the three groups were
then compared. Results indicated that nurses experienced severe level of trauma had
higher level of psychological strain than those who experienced low level of trauma.
Similarly, supporting the predictions, results indicated that more severe trauma was
associated with report of more posttraumatic growth as group three had higher
posttraumatic growth than group two and group two had higher posttraumatic growth
than group one. These results seem to support Tedeschi and Calhourn’s (1995, 1998,
2004) theoretical contention that an event must be sufficiently traumatic to bring
forth posttraumatic growth. This finding is also consistent with other reports
66
(Cordova et al, 2001; McMillen et al, 1997; Park et al, 1996) but is contrary to the
curvilinear relation found in Lechner et al’s (2003) study on cancer patients. Stages
of cancer (I to IV) were adopted as an objective measure of the severity of life threat
in Lechner et al’s (2003) study and all the participants were cancer patients. However,
in the present study, only group three (of the nurses selected) were infected with
SARS, and the other two groups were SARS-free, thus who had not received any
medical treatment or experienced a life-threatening disease. As such, using three
groups of nurse to operationalize severity of trauma may not be identical to adopting
stages of cancer as in Lechner et al’s study (2003). Since the severity of trauma is
operationalized differently, it may produce different findings. This may also explain
why significant differences are only observed between group one and group three for
posttraumatic stress. Lechner et al (2003) also measured their participants’ perceived
threat level by one single item. Different from findings of the present study, stage IV
patients had a lower level of perceived threat than stage III patients. In other words,
the objective measure of the severity of cancer (i.e. using the four different stages of
cancer) was not consistent with the subjective measure of threat perception because
the subjective measure of threat was related to posttraumatic growth positively in a
linear fashion while the stages of disease related to the same outcome measure
non-linearly.
67
Furthermore, in contrast to the present findings, Ho et al (2004) failed to find
significant differences in posttraumatic growth between the infected and non-infected
Hospital Authority staff. This can be explained by the different methodologies used.
Instead of recruiting one type of professionals, Ho et al (2004) recruited respondents
from various disciplines such as doctors, nurses, anesthetists, physiotherapists, and
support staff without further classification of non-infected staff into those having
contact with SARS patients and those not having such contact. Another plausible
reason may be the difference in the timing of assessing posttraumatic growth. Ho et
al (2004) recruited hospital staff during the acute phase of the SARS outbreak. At
that time, the severity of threat among non-infected staff might be quite similar to
those infected healthcare professionals, so they reported similar level of
posttraumatic growth. However, the data of present study was collected at a later
time, in January 2005. Milam’s (2004) longitudinal study of persons with HIV found
that respondents’ level of posttraumatic growth would change over time and
identified different groups where posttraumatic growth was stable, increasing, or
decreasing over time. Thus, the timing of assessing growth may be a critical factor to
explain different results across studies.
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6.3 Personal resilience and social support as predictors and moderators of
posttraumatic growth and posttraumatic stress
Though personal resilience and perceived social support were found to be
significant predictors of posttraumatic growth and posttraumatic stress, even after
statistically controlling for demographic variables, the present study failed to support
the hypothesized moderating effects of these two variables on the links between
trauma with posttraumatic growth and posttraumatic stress. In fact, past research
findings have demonstrated the main effects of personal resilience and social support
on posttraumatic growth and psychological health outcomes (Chan et al, 2006;
Cadell et al, 2003; Helgeson, 1999; Chan & Eadaoin, 1998; Wu & Lam, 1993; Park
et al, 1996; Warnberg, 1997). Individuals with high level of optimism, self-esteem,
and perceived control are more likely to adopt active or problem-focused coping
styles (Dumont & Provost, 1999; Scheier, Matthews, Owens, George, Lefebvre,
Abbott, & Carver, 1989). Greater use of adaptive or problem-focused coping
strategies are not only related to better adjustment during stressful encounters
(Aspinwall & Taylor, 1992; Major et al, 1998) but are also associated with higher
posttraumatic growth (Armeli et al, 2001; Widows et al 2005). Social support, which
serves as sources of care, comfort, reassurance, encouragement, advice, and tangible
aids, is positively associated with psychological and physical well-being (Cohen,
69
1988; Cohen & Wills, 1985). Similar to the present study, some studies on perceived
social support only reveal the main effect on psychological distress but fail to lend
support to the buffering model of social support (e.g. Chan & Eadaoin, 1998; Wu &
Lam, 1993). In Calhoun and Tedeschi’s (2004) model of posttraumatic growth, social
support is a crucial factor that fosters posttraumatic growth as it allows narratives of
trauma and offers new perspectives to the survivors in rebuilding schemas.
Besides, a very interesting finding was observed when the regression models of
posttraumatic growth and posttraumatic stress were compared. Personal resilience
and social support explained almost 30% of additional variance in posttraumatic
stress regression model but they only accounted for 5% of additional variance in the
posttraumatic growth regression model. In other words, personal resilience and
perceived social support are stronger predictors of posttraumatic stress than
posttraumatic growth. This revelation may imply that the construct of posttraumatic
growth may not be subsumed under the concept of resilience – the ability to remain
psychologically healthy despite adversity. Put forward by Tedeschi and Calhoun
(2004), highly resilient people may be less affected by trauma, and thus may not
report very high level of posttraumatic growth. As such, posttraumatic growth should
be treated as a construct different from adaptation of or adjustment to stressful
encounters.
70
However, contrary to the predictions, personal resilience and perceived social
support does not moderate the link between severity of trauma with posttraumatic
stress and posttraumatic growth in the present study. The reasons for this observation
are not immediately apparent but could be speculatively related to the following
factors. Firstly, it is quite difficult to detect significant interactions statistically
(Chang & Sanna, 2003). Similar to the findings of present study, Cheng and Lam
(1997) found that self-esteem significantly predicted depressive symptoms among
Chinese adolescents but the moderating role of self-esteem in the association
between life stress and depressive symptoms was not supported. Sumi (1997) also
failed to prove that optimism or perceived social support interacted with perceived
stress in predicting somatic complaint, depression and anxiety, but significant
three-way interactions (optimism x social support x stress) were observed in the
hierarchical regression analyses. As such, Sumi (1997) suggested that investigating
interactions among stress, personality (e.g. optimism), and external resources (e.g.
social support) is necessary. In fact, Helgeson (1999) failed to find significant
interactions between personal resilience and new cardiac event (yes or no) on all the
four outcomes assessed. Personal resilience only interacted with new cardiac event in
predicting life satisfaction but not in well-being, physical health or mental health.
Helgeson (1999) recognized the low generalization of her findings to other stressors
71
which are less controllable than heart disease. It seems that personal resilience and
social support may not be moderators on all outcome measures or in all settings. One
plausible reason for the non-significant interaction effects may be attributed to the
unexpectedly high sensitivity of the two moderators to the context of the trauma.
SARS is a new and unique disease to confront the world. Patients with terminal
cancer are different from SARS survivors because for the former death may be
impending. Though adopting perceived social support as the measurement is one of
the necessary conditions to exhibit stress-buffering effect, other conditions such as a
sample with broad ranges of stress and the type of received support matching the
specific needs elicited by the stressor are also essential (Cohen, 1988; Cohen & Wills,
1985). The present study recruited a homogenous sample with only three levels of
trauma. The sample size of the infected nurses representing severe level of trauma is
relative small. In addition, SARS is a highly infectious disease and there may be an
intense fear of infecting other people like family members and friends who are the
major sources of social support. All these may reduce the chances of finding a
buffering effect for social support. Future research should look into the conditions
that when the two moderators work and why.
Since very few studies have examined moderators of posttraumatic growth,
investigation of the moderating roles of personal resilience and social support on the
72
trauma–posttraumatic growth link is highly exploratory in nature. Armeli et al (2001)
found that participants with higher threat appraisals would report higher levels of
posttraumatic growth when they also had much more social support and adequate
coping resources. However, the present study fails to replicate the results of Armeli
et al (2001) and this may be due to the differences in methodology. Firstly, Armeli et
al (2001) adopted cluster analysis to assess three-way interactions (threat appraisal x
social support x coping ability) in relation to posttraumatic growth, but in this study,
coping ability was not investigated and only two-way interactions (severity of trauma
x personal resilience and severity of trauma x perceived social support) were
examined in predicting posttraumatic growth. Secondly, Armeli et al (2001) used
43-item revised version of Park et al’s (1996) SRGS to assess posttraumatic growth.
Thirdly, severity of trauma was operationalized by three groups of nurses in the
present study but Armeli et al (2001) defined the stressfulness of the event by
referring to participants’ appraisal on the degree of loss, threat, control, severity etc.,
which was relatively subjective. Moreover, instead of using a homogeneous sample
with regard to a single traumatic event, participants in Armeli et al (2001) study were
two large groups of university alumni and college students. A wide variety of
stressful events such as relationship problems, personal illness or accident, academic
problems, family events, work-related problems etc. were reported as the most
73
stressful event experienced in the past two years. This kind of recall may be more
prone to error than assessing posttraumatic growth specific to one major life event.
All these differences may lead to the aforementioned discrepancy in findings.
Lastly, different from the past studies, participants of the present study are not
merely patients (Helgeson, 1999) or college students (Armeli et al, 2001) but
medical professionals and the traumatic experience came from their work
environment. Factors other than personal resources such as professionalism, or
commitment (Kobasa, 1982), which have not been examined in present study, may
play a role in counteracting huge stress or threat at work. The uniqueness of the
present sample may be another plausible source generating different results.
In sum, further studies are needed to verify the moderating effects of personal
resilience and social support on the link between the severity of trauma and
posttraumatic growth by larger samples with equal representation across all levels of
trauma defined. At this point, it is premature to conclude that personal resilience and
social support do not perform moderating roles in the phenomenon of posttraumatic
growth.
6.4 Implications of the study
The study of posttraumatic growth has important implications for a more
74
complete understanding of human functioning as it not only demonstrates that human
beings are very flexible and adaptable to traumatic life experiences, but it also shows
that human beings have extraordinary power to turn minus into plus, to covert loss to
gain. This implies that specific intervention programs can be set up to promote
posttraumatic growth (Antoni et al, 2001). The results of this study have undeniably
important values for the design of delivery of clinical intervention services for those
healthcare workers who suffered psychological distress from the SARS experience.
First, since personal resilience and perceived social support are inversely correlated
with posttraumatic stress but positively associated with posttraumatic growth,
psychosocial intervention focusing on esteem building, positive life orientation,
increasing sense of perceived control, and encouraging the seeking of social support
may not only help to alleviate stress symptoms, but also help to foster positive
changes. From another angle, psychosocial interventions may be more appropriate to
target those who are low in personal resilience and do not have a supportive social
network. Second, with respect to posttraumatic growth, family appreciation seems to
be the most commonly reported positive changes among the respondents. As such,
intervention program involving family members to provide care, understanding,
support, and encouragement may be effective in helping infected healthcare workers
to recover psychologically.
75
6.5 Limitations of this study
Although the present study attempts to make use of the strengths of both the
qualitative and quantitative methods, limitations nevertheless, exist. First, due to the
disapproval of Nursing Association of the request of a longitudinal study, data can
only be collected in the present study using cross-sectional and retrospective design.
Absence of any pre-illness measures limits the interpretability of results and
precludes the drawing of conclusions about the causal relationship between personal
resources and posttraumatic growth. Posttraumatic growth has been conceptualized
as an outcome in the literature. However, it is also possible that posttraumatic growth
increases one’s personal resources such as social support, optimism, self-esteem etc.
Secondly, a relative small sample of infected nurses has been recruited, which
created an unequal representation across the three different levels of trauma. Finally,
the present sample is a group of highly educated professionals and predominantly
females, so the results may not be readily extended to other SARS survivors.
6.6 Future work
Tomich and Helgeson (2004) found that posttraumatic growth was associated
with greater negative affect over time for breast cancer patients in more severe
disease stage, which calls into question the clinical value of posttraumatic growth.
76
Apart from using longitudinal or prospective design as many researchers have
suggested (Cordova et al, 2001; Ho, Chan, and Ho, 2004; Tedeschi & Calhoun, 2004;
Wortman, 2004), to avoid accepting posttraumatic growth at face value, future work
should be done to demonstrate its significance by investigating the beneficial effects
of posttraumatic growth on the survivors, especially the physical outcomes. For
instance, Affleck et al (1987) found that those patients who perceived benefits were
actually at decreased risk of suffering a subsequent heart attack. Two studies found
that reports of posttraumatic growth were associated with positive outcomes on
physical parameters such as cortisol (Epel, McEwen, & Ickovics, 1998) and
CD4T-cell (Bower, Kemeny, Taylor, & Fahey, 1998).
In addition, to enhance the validity of posttraumatic growth, Park (2004)
advised using behavioral measures rather than self-report in assessing the positive
changes. Wortman (2004) went further to suggest validating any reported growth by
peer-rating.
6.7 Conclusions
To conclude, the present study examined the relationship of severity of trauma
and posttraumatic growth. A linear relationship was found, the more severe the
trauma, the higher the posttraumatic growth. Personal resilience and perceived social
77
support were significant predictors of posttraumatic growth and posttraumatic stress
but they had stronger predictive powers on the negative impacts of a traumatic
experience. Personal resilience and social support failed to moderate the links
between severity of trauma and posttraumatic growth or posttraumatic stress. Future
research should focus on examining the important health benefits associated with
posttraumatic growth and in doing so, enhancing its validity.
78
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Appendix A 有關非典型肺炎疫潮的研究調查問卷
個人資料 (請在適合的英文字母上打圈 ○)
1. 性別: a. 男 b. 女
2. 年齡: a. 24或以下 b. 25 - 34 c. 35 - 44
d. 45 - 54 e. 55或以上
3. 婚姻狀況:
a. 已婚
b. 單身/分居/離婚/喪偶
如是喪偶,配偶是否因為患上SARS而過身? a. 是 b. 否
4. 宗教信仰: a. 沒有信仰 b. 基督教 c. 天主教
d. 佛教 e. 其他 _________
5. 教育程度:
a. 護士文憑
b. 學位
c. 碩士或博士
6. 所屬部門:__________________________
7. 護士職級: a. 登記護士 b. 註冊護士
c. 護士長或病房經理 d. 部門運作經理
8. 服務年期: a. 0 - 5 年 b. 6 - 10年 c. 11 - 15年
d. 16 - 20年 e. 21年或以上
9. 在2003年SARS疫潮期間,你曾否照顧SARS病人? a. 有 b. 沒有
如曾照顧SARS病人,
i. 期間約為 a. 1個月或以下 b. 1 - 3個月 c. 3個月或以上
ii. 照顧過SARS病人 a. 1 – 5 b. 6 – 10 c. 11 – 15
的人數 d. 16 – 20 e. 21 – 25 f. 26或以上
88
10. 你自己有否因照顧SARS病人而感染SARS? a. 有 b. 沒有
如有感染SARS,有沒有遇到以下的情況?
i. 治療SARS期間曾需插喉 a. 有 b. 沒有
ii. 曾需進入深切治療部接受治療 a. 有 b. 沒有
iii. 曾接受類固醇藥物治療 a. 有 b. 沒有
iv. 肺功能完全康復 a. 是 b. 不是
v. 關節痛或其他身體疼痛 a. 有 b. 沒有
vi. 骨枯 a. 有 b. 沒有
vii. 因為骨枯而需要接受手術 a. 有 b. 沒有
viii. 因為SARS的後遺症而現在仍接受藥物治療 a. 是 b. 不是
11. 由2003年SARS疫潮爆發至今,你有沒有經歷其他的事情? 如有,可圈出多個一項。
a. 喪偶
b. 離婚
c. 受傷/患重病
d. 分居
e. 入獄
f. 結婚
g. 家人受傷/患重病
h. 家人離世
i. 財務上有困難(如破產或欠債)
j. 性方面有困難
或
k. 我沒有經歷以上的事情
12. 你的家人有否感染SARS? a. 有 b. 沒有
13. 你覺得非典型肺炎疫潮對你所造成的創傷有多大?
1 2 3 4 5 6 7
沒有創傷 極大創傷
89
以下是一些描述你和家人及朋友的句子。請閱讀每一項,在各題右邊圈上適當的數字以表示你
對這些句子的同意程度。
1. 當我有需要的時候,總有一個好朋友在我身邊
2. 我有一個好朋友,無論開心或者不開心,我都
以同他/她分享。
3. 我的家人真的十分願意幫助我。
4. 我的家人可以給我情緒上需要的支持。
5. 我有一個真的可以安慰我的朋友。
6. 我的朋友真的願意嘗試幫助我。
7. 如果有甚麽事發生,我可以倚靠我的朋友。
8. 我可以和家人訴說我自己的問題。
9. 我有一些朋友,無論開心或者不開心,我都可
同他們分享。
10. 我生命中有個好朋友,他/她會關心我
11. 我的家人願意和我一起做決定。
12. 我可以同我的朋友訴說我自己的問題。
下列是描述你的句子。請閱讀每一項,在各題右邊
同意的程度。
1. 當前途未定的時候,我通常會預想最好的結果
2. 展望將來,我看不到有令我開懷的境況。
3. 我對前景常感樂觀。
4. 我很少想過事情會盡如我意。
5. 我極少預計好事會發生在我身上。
6. 總的來說,我預期發生在我身上的好事會多過
十分同意
十分不同意。 1 2 3 4 5 6 7
可1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
以1 2 3 4 5 6 7
的感受。 1 2 3 4 5 6 7
1 2 3 4 5 6 7
1 2 3 4 5 6 7
圈上適當的數字以表示你對該形容同意或不
非常不同意
不同意
沒有意見
同意
非常同意
。 1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
壞事。 1 2 3 4 5
90
下列是描述個人感受的句子。請閱讀每一項,在各題右邊圈上適當的數字以表示與你相似的程
度。
極之不像我
不像我
沒有意見
像我
極之像我
1. 我認為自己是個有價值的人,至少基本上是
與別人相等的。 1 2 3 4 5
2. 我覺得我有很多優點。 1 2 3 4 5
3. 總括來說,我覺得我是一個失敗者。 1 2 3 4 5
4. 我做事的能力和大部份人一樣好。 1 2 3 4 5
5. 我覺得自己沒有甚麼值得驕傲。 1 2 3 4 5
6. 我對於自己是抱著肯定的態度。 1 2 3 4 5
7. 總括而言,我對自己感到滿意。 1 2 3 4 5
8. 有些時候,我確實覺得自己很無用。 1 2 3 4 5
9. 有些時候,我認為自己是一無是處。 1 2 3 4 5
下列是描述你的句子。請閱讀每一項,在各題右邊圈上適當的數字以表示你對該形容同意或
不同意的程度。
非常不同意
不同意
沒有意見
同意
非常同意
1. 我實在沒有任何辦法去解決某些我所面對的問題。 1 2 3 4 5
2. 有時我覺得我的生命被任由擺佈。 1 2 3 4 5
3. 我難以控制發生在我身上的事。 1 2 3 4 5
4. 只要我下了決心做的事定能達到。 1 2 3 4 5
5. 當我處理生活上的困難時,常感到無助。 1 2 3 4 5
6. 將來發生在我身上的事,大多由我自己主宰。 1 2 3 4 5
7. 我絕少能改變生命中的要事。 1 2 3 4 5
91
下列句子描述經歷SARS這個疫潮後對您的生命可能帶來的轉變。
請仔細閱讀每一句子,然後根據以下的標準,選擇一個最接近您的感覺的答案。
完全
沒有 非常少 少 有些 多 非常多
1 我生命中重要事物的先後次序。 0 1 2 3 4 5
2 對於須要改變的事物,我更傾向於去改變它。 0 1 2 3 4 5
3 一種對自己生命價值的欣賞。 0 1 2 3 4 5
4 一種「依賴自己」的感覺。 0 1 2 3 4 5
5 對於心靈上的事物有更佳的瞭解。 0 1 2 3 4 5
6 知道當我有困難的時候,我可以依賴別人。 0 1 2 3 4 5
7 一種和別人很親近的感覺。 0 1 2 3 4 5
8 知道自己有能力處理困難。 0 1 2 3 4 5
9 願意表達自己的情緒。 0 1 2 3 4 5
10 能夠接受事情最後的結果。 0 1 2 3 4 5
11 欣賞每一天。 0 1 2 3 4 5
12 對別人有一種同情。 0 1 2 3 4 5
13 我能夠以我的生命做更好的事情。 0 1 2 3 4 5
14 因為這次事件而帶來新的機會。 0 1 2 3 4 5
15 花更多的精力於人際關係上。 0 1 2 3 4 5
16 我有一個更強的宗教信仰。 0 1 2 3 4 5
17 我發現我比想像中更強。 0 1 2 3 4 5
18 我體會到人是多美好。 0 1 2 3 4 5
19 我發展新的興趣。 0 1 2 3 4 5
20 我接受有需要幫助的人。 0 1 2 3 4 5
21 我建立了生命的新路向。 0 1 2 3 4 5
22 我懂得去體諒身邊的朋友及家人。 0 1 2 3 4 5
23 對病人的照顧更加細心。 0 1 2 3 4 5
24 我比以前更有耐性。 0 1 2 3 4 5
25 我更珍惜我和家人的關係。 0 1 2 3 4 5
除了以上所提及的改變外,還有什麼方面你覺得有正面的轉變? 請註明。
______________________________________________________________________________________
92
2003年SARS疫潮爆發至今,你有沒有以下的感受或想法嗎?
請閱讀下列每題, 並圈出最接近你因為經歷了SARS疫潮而導致的情緒狀況。
1. 我感到神經緊張: 8. 我感到缺乏衝勁, 整個人都慢下來:
A. 大部份時候感到 A. 差不多全部時候
B. 很多時候感到 B. 非常多時候
C. 有時候、間中感到 C. 有時候
D. 完全不感到 D. 完全沒有
2. 我依然享受我以前享受的事物: 9. 我有一種忐忑不安的驚恐(十五、十六的感覺):
A. 肯定和以前一樣 A. 完全沒有
B. 有點不及以前 B. 間中有
C. 只及以前小許 C. 相當多時候有
D. 和以前差得極遠 D. 很常有
3. 我有一種驚恐, 好像有些可怕的事情發生: 10. 我對自己儀容已失去興趣:
A. 很肯定有, 而且相當厲害 A. 肯定失去
B. 有, 但不太厲害 B. 比我應該關心的少
C. 有少許, 但不令我擔心 C. 可能比我以前關心的少
D. 完全沒有 D. 我像以前一樣關心
4. 我能看到事物有趣的一面並且會心微笑: 11. 我感到不能安靜, 像要不停地走動:
A. 和以前一樣 A. 很強烈
B. 有點不如以前 B. 相當強烈
C. 肯定不如以前 C. 不太強烈
D. 完全不能 D. 完全沒有
5. 煩惱念頭在我腦海中浮現: 12. 我對未來事抱有熱切的期望:
A. 絕大部份時候 A. 和以前一樣
B. 很多時候 B. 較為不如以前
C. 有時候 C. 肯定不如以前
D. 只是間中 D. 絕無僅有
6. 我感到高興: 13. 我突然感到驚惶失措:
A. 完全不感到 A. 非常多時候
B. 不時常感到 B. 相當多時候
C. 有時候感到 C. 不太多時候
D. 大部份時候感到 D. 完全沒有
7. 我能安坐並感到鬆弛: 14. 我能享受喜歡的書, 電台或電視節目:
A. 肯定能夠 A. 經常能夠
B. 通常能夠 B. 有時候能夠
C. 不時常能夠 C. 不常能夠
D. 完全不能 D. 絕少能夠
~ 完 ~
93
Appendix B Intercorrelations of variables
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1. Gender - 0.01 -0.08 0.02 -0.14** -0.04 -0.06 0.05 0.00 0.03 0.02 -0.03 0.08 0.01 0.06 0.02 0.02 0.02 0.05 -0.02
2. Age - 0.09 0.08 -0.10* 0.03 0.26** 0.31** -0.24** -0.16** -0.17** 0.02 -0.12* 0.11* 0.20** 0.17** 0.19** 0.11* -0.09 -0.07
3. Marital status - 0.05 -0.07 0.09 0.09 0.08 -0.09 -0.12* -0.12* 0.05 0.03 0.11* 0.17** 0.17** 0.15** 0.15** -0.09 -0.07
4. Religion - -0.04 0.08 0.06 0.05 -0.08 -0.09 -0.08 0.03 -0.03 -0.02 0.00 -0.06 -0.05 0.12* 0.07 0.09
5. Education - -0.05 0.14** 0.11* 0.07 0.06 0.04 -0.08 0.04 0.00 0.10* 0.03 0.05 -0.05 -0.03 0.00
6. Department - 0.02 0.04 -0.44** -0.52** -0.49** -0.09 0.00 0.08 0.06 0.09 0.07 0.03 -0.03 0.04
7. Rank - -0.45** -0.15** -0.08 -0.09 -0.09 -0.04 0.11* 0.25** 0.17** 0.20** 0.08 -0.07 -0.03
8. Year of service - -0.15** -0.14** -0.15** -0.07 -0.09 -0.09 0.08 0.07 0.08 0.10* -0.05 -0.01
9. Group - 0.77** 0.83** 0.07 -0.07 -0.03 -0.05 -0.09 -0.07 0.16** 0.12* 0.12*
10. Involvement (time) - 0.91** 0.01 -0.08 -0.08 -0.02 -0.03 -0.06 0.03 0.08 0.01
11. Involvement (no. of patient) - 0.06 -0.05 -0.04 -0.05 -0.02 -0.05 0.09 0.06 -0.02
12. Life event - -0.02 0.01 0.09 0.02 0.03 0.06 0.10* 0.11*
13. Social support - 0.31** 0.26** 0.23** 0.33** 0.17** -0.25** -0.29**
14. Optimism - 0.50** 0.49** 0.81** 0.20** -0.46** -0.49**
15. Self-esteem - 0.55** 0.83** 0.18** -0.41** -0.38**
16. Perceived control - 0.83** 0.13** -0.45** -0.40**
17. Personal resilience - 0.21** -0.54** -0.52**
18. Posttraumatic growth - 0.07 -0.05
19. Anxiety - 0.80**
20. Depression -
*p<0.05; **p<0.01