Copyright Warning

Use of this thesis/dissertation/project is for the purpose of private study or scholarly research only. Users must comply with the Copyright Ordinance. Anyone who consults this thesis/dissertation/project is understood to recognise that its copyright rests with its author and that no part of it may be reproduced without the author’s prior written consent.

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

1

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)

6

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

7

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).

61

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

63

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.

68

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

References

Abraído-Lanza, A.F., Guier, C., & Colón, R.M. (1998). Psychological thriving among Latinas with chronic illness. Journal of Social Issues, 54(2), 405-424.

Affleck, G., Tennen, H., Croog, S., & Levine, S. (1987). Causal attribution, perceived benefits, and morbidity following a heart attack: An eight-year study. Journal of Consulting and Clinical Psychology, 55(1), 29-35.

Affleck, G., Tennen, H., Pfeiffer, C., & Fitfield, J. (1987). Appraisals of control and predictability in adapting to a chronic disease. Journal of Personality and Social Psychology, 53, 273-279.

Aldwin, C.M., & Levenson, M.R. (2004). Posttraumatic growth: A developmental perspective. Psychological Inquiry, 15(1), 19-21.

Antoni, M., Lehman, J., Kilbourn, K., Boyers, A., Culver, J. Alferi, S., Yount, S., McGregor, B., Arena, P., Harris, S., Price, A., & Carver, C. (2001). Cognitive-behavioral stress management intervention decreases the prevalence of depression and enhances benefit finding among women under treatment for early-stage breast cancer. Health Psychology, 20(1), 20-32.

Armeli, S., Gunthert, K.C., & Cohen, L.H. (2001). Stressor appraisals, coping, and post-event outcomes: The dimensionality and antecedents of stress-related growth. Journal of Social and Clinical Psychology, 20(3), 366-395.

Aspinwall, L.G., & Taylor, S.E. (1992). Modeling cognitive adaptation: A longitudinal investigation of the impact of individual differences and coping on college adjustment and performance. Journal of Personality and Social Psychology, 63(6), 989-1003.

Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.

Berg, B.L. (2004). Qualitative research methods for the social sciences (5th ed). Boston: Pearson.

Boey, K.W. (1999). Distressed and stress resistant nurses. Issues in Mental Health Nursing, 19, 33-54.

Bower, J.E., Kemeny, M.E., Taylor, S.E., & Fahey, J.L. (1998). Cognitive processing, discouvery of meaning, CD 4 decline, and AIDS-related mortality among bereaved HIV-seropositive men. Journal of Consulting and Clinical Psychology, 66, 979-986.

Burlew, K., Telfair, J., Colangelo, L. & Wright, E.C. (2000). Factors that influence adolescent adaptation to sickle cell disease. Journal of Pediatric Psychology, 25(5), 287-299.

79

Cadell, S. Regehr, C., & Hemsworth, D. (2003). Factors contributing to posttraumatic growth: A proposed structural equation model. American Journal of Orthopsychiatry, 73(3), 281-289.

Calhoun, L.G., & Tedeschi, R.G. (2001). Posttraumatic growth: The positive lessons of loss. In Neimeyer, R.A. (Ed). Meaning reconstruction and the experience of loss. Washington: APA, 157-172.

Campbell, W.K., Brunell, A.B., & Foster, J.D. (2004). Sitting here in limbo: Ego shock and posttraumatic growth. Psychological Inquiry, 15(1), 22-25.

Carver, C.S. (1998). Resilience and thriving: Issues, models, and linkages. Journal of Social Issues, 54(2), 245-266.

Chan, C. (2003). The social impact of SARS: Sustainable action for rejuvenation of society. Retrieved October 2, 2003 from http://web.hku.hk/%7Ebhealth/Documents/articles/YPSY006/Social_Impacts_of_SARS.pdf

Chan, S. (2003). Nurses fighting against severe acute respiratory syndrome (SARS) in Hong Kong. Journal of Nursing and Scholarship, 35(3), 209.

Chan, D.W., & Hui, E.K.P. (1998). Stress, support and psychological symptoms among guidance and non-guidance secondary school teachers in Hong Kong. School Psychology International, 19(2), 169-178.

Chan, I.W.S., Lai, J.C.L., & Wong, K.W.N. (2006). Resilience is associated with better recovery in Chinese people diagnosed with coronary heart disease. Psychology & Health, 335-349.

Chang, E.C. (1998). Does dispositional optimism moderate the relation between perceived stress and psychological well-being? A preliminary investigation. Personality and Individual Differences, 25, 233-240.

Chang, E.C. & Sanna, L.J. (2003). Experience of life hassles and psychological adjustment among adolescents: does it make a difference if one is optimistic or pessimisitic? Personality and Individual Difference, 34, 867-879.

Cheng, C. (1997). Role of perceived social support on depression in Chinese adolescents: A prospective study examining the buffering model. Journal of Applied Social Psychology, 27(9), 800-820.

Cheng, C., & Tang, C.S.K. (2004). The psychology behind the masks: Psychological responses to the severe acute respiratory syndrome outbreak in different regions. Asian Journal of Social Psychology, 7, 3-7.

Cheng, C., Wong, W.M., & Tsang, K. (in press). Perception of benefits and costs during SARS outbreak: An 18-month prospective study. Journal of Consulting and Clinical Psychology.

Cheng, S.T., & Hamid, P.N. (1995). An error in the use of translated scales: The

80

Rosenberg Self-Esteem Scale for Chinese. Perceptual and Motor Skills, 81, 431-434.

Cheng, S., & Lam, D. (1997). Relationships among life stress, problem solving, self-esteem, and dysphoria in Hong Kong adolescents: Test of a model. Journal of Social and Clinical Psychology, 16(3), 343-355.

Cheng, S., & Wong, C.W. (2005). Psychological intervention with sufferers from severe acute respiratory syndrome (SARS): Lessons learnt from empirical findings. Clinical Psychology and Psychotherapy, 12, 80-86.

Chou, K.L. (2000). Assessing Chinese adolescents’ social support: the multidimensional scale of perceived social support. Personality and Individual Differences, 29, 299-307.

Chua, S., Cheung, V., McAlonan, G.., Cheung, C., Wong, J., Cheung, E., Chan, M., Wong, T., Choy, K., Chu, C., Lee, P., & Tsang, K. (2004). Stress and psychological impact on SARS patients during the outbreak. Canadian Journal of Psychiatry, 49, 385-390.

Clinical Trials Centre (2004). SARS statistics for Hong Kong: General updates. Retrieved March 1, 2004, from http://www.hku.hk.ctc.index.htm

Cohen, S. (1988). Psychological models of the role of social support in the etiology of physical disease. Health Psychology, 7(3), 269-297.

Cohen, S., & Wills, T.A. (1985). Stress, social, support, and the buffering hypothesis. Psychological Bulletin, 98, 310-357.

Cordova, M., Cunningham, L., Carlson, C., & Andrykowski, M. (2001). Posttraumatic growth following breast cancer: A controlled comparison study. Health Psychology, 20, 176-185.

Cowan, P.A., Cowan, C.P., Schulz, M. (1996). Thinking about risk and resilience in families. In Hetherinto, E.M. & Blenchman, E.A. (Eds.), Stress, coping and resilience in children and families. Mahwah, NJ: Lawrence Erlbaum Associates.

D’Imperio, R.L., Dubow, E.F., & Ippolito, M.F. (2000). Resilient and stress-affected adolescents in an urban setting. Journal of Clinical Child Psychology, 29(1), 129-142.

Danoff-Burg, S. & Revenson, T. (2005). Benefit-finding among patients with rheumatoid arthritis: Positive effects on interpersonal relationships. Journal of Behavioral Medicine, 28(1), 91-104.

Dean, A., Kolody, B., & Wood, P. (1990). Effects of social support from various sources on depressive in elderly persons. Journal of Health and Social Behavior, 31, 148-161.

Dumont, M. & Provost, M.A. (1999). Resilience in adolescents: Protective role of

81

social support, coping strategies, self-esteem and social activities on experience of stress and depression. Journal of Youth and Adolescence, 28(3), 343-363.

Epel, E.S., McEwen, B.S., & Ickovics, J.R. (1998). Embodying psychological thriving: Physical thriving in response to stress. Journal of Social Issues, 54, 301-302.

Field, A. (2003). Discovering statistics using SPSS for windows. Thousand Oaks, CA: Sage.

Frantz, T., Farrell, M., & Trolley, B. (2001). Positive outcomes of losing a loved one. In Neimeyer, R.A. (Ed). Meaning reconstruction and the experience of loss. Washington: APA.

Garmezy, N., Masten, A.S., & Tellegen, A. (1984). The study of stress and competence in children: A building block of developmental psychopathology. Child Development, 55, 97-111.

Helgeson, V.S. (1999). Applicability of cognitive adaptation theory to predicting adjustment to heart disease after coronary angioplasty. Health Psychology, 18(6), 561-569.

Helgeson, V.S. (2003). Cognitive adaptation, psychological adjustment, and disease progression among angioplasty patients: 4 years later. Health Psychology, 22(1), 30-38.

Helgeson, V.S., & Cohen, S. (1996). Social support and adjustment to cancer: Reconciling descriptive, correlational, and intervention research. Health Psychology, 15, 135-148.

Helgeson, V.S., & Fritz, H.I. (1999). Cognitive adaptation as a predictor of new coronary events after percutaneous transluminal coronary angioplasty. Psychosomatic Medicine, 61, 488-495.

Ho, S., Chan, C., & Ho. R. (2004). Posttraumatic growth in Chinese cancer survivors. Psycho-Oncology, 13(6), 377-389.

Ho, S., Kwong, R., Mak, C., & Wong, K. (2004). Positive changes after SARS. Paper presented at Hospital Authority Convention, Hong Kong.

Ho, S., Kwong-Lo, R., Mak, C., & Wong, J. (2005). Fear of severe acute respiratory syndrome (SARS) among health care workers. Journal of Consulting and Clinical Psychology, 73, 344-349.

Hospital Authority (2004). HA Statistical Report 2003/2004. Table 7.1 Staff in Hospital Authority as at 31 March 2004. Retrieved December 8, 2004, from http://ww.ha.org.hk/hesd/nsapi/drivernsapi20.so/?MIval=ha_visitor_index&intro=ha%5fview%5ftemplate%26group%3dIFN%26Area%3dPBL

Jex, S.M., Cvetanovski, J., & Allen, S.J. (1994). Self-esteem as a moderator of the impact of unemployment. Journal of Social Behavior and Personality, 9(1),

82

69-80. Ji, L., Zhang, Z., Usborne, E., & Guan, Y. (2004). Optimism across cultures: In

response to the severe acute respiratory syndrome outbreak. Asian Journal of Social Psychology, 7, 25-34.

Jung, J. (1997). Balance and source of social support in relation to well-being. Journal of General Psychology, 124, 77-90.

Kaplan, H.B. (1999). Toward an understanding of resilience: A critical review of definitions and models. In Glantz, M.D. & Johnson, J.L. (Eds.), Resilience & development: Positive life adaptations (pp. 17-83). New York: Kluwer Academic.

Kobasa, S.C. (1982). Commitment and coping in stress resistance among lawyers. Journal of Personality and Social Psychology, 42(4), 707-717.

Lai, J.C.L. (1995). The moderating effect of optimism on the relation between hassles and somatic complaints. Psychological Reports, 76, 883-894.

Lai, J.C.L. (2003). Psychological impact of economic restructuring in Hong Kong. Paper presented at the 5th Biennial Conference of the Asian Association of Social Psychology, Manila, Philippines.

Lai, J.C.L., Cheung, H., Lee, W.M., & Yu, H. (1998). The utility of the revised life orientation test to measure optimism among Hong Kong Chinese. International Journal of Psychology, 33, 45-56.

Lai, C.L., Hamid, P.N., & Cheng, S.T. (2000). Health belief and optimism as predictors of preventive health decisions in Hong Kong Chinese. Psychological Reports, 86, 10-59-1070.

Lai, J.C.L. & Wong, W.S. (1998). Optimism and coping with unemployment among Hong Kong Chinese women. Journal of Research in Personality, 32, 454-479.

Lai, C.L., & Yue, X.D. (2000). Measuring optimism in Hong Kong and mainland Chiense with the revised Life Orientation Test. Personality & Individual Differences, 28, 781-796.

Lau, J.T.F., Yang, X., Tsui, H.Y., & Kim, J.H. (2003). Monitoring community responses to the SARS epidemic in Hong Kong – from day 10 to day 62. Journal of Epidemiology and Community Health, 57, 864-870.

Lazarus, R.S., & Folkman, S. (1984). Stress, appraisal and coping. New York: Springer.

Lechner, S.C.,& Antoni, M.H. (2004). Posttraumatic growth and group-based interventions for persons dealing with cancer: What have we learned so far? Psychological Inquiry, 15(1), 35-40.

Lechner, S.C., Zakowski, S.G., Antoni, M.H., Greenhawt, M., Block, K., & Block, P. (2003). Do sociodemographic and disease-related factors influence

83

benefit-finding in cancer patients? Psycho-Oncology, 12, 491-499. Leung, C.M., Ho, S.M.Y., Kan, S., Hung, C.H., & Chen, C.N. (1993). Evaluation of

the Chinese version of the hospital anxiety and depression scale. A cross-cultural perspective. International Journal of Psychosomatics, 40, 29-34.

Leung, C.M., Wing, Y.K., Kwong, P.K., Lo, A., & Shum, K. (1999). Validation of the Chinese-Cantonese version of the Hospital Anxiety and Depression Scale and comparison with the Hamilton Rating Scale of Depression. Acta Psychiatrica Scandinavica, 100, 456-461.

Leung, G.M., Lam, T.H., Ho, L.M., Ho, S.Y., Chan, B.H.Y., Wong, I.O.L., & Hedley, A.J. (2003). The impact of community psychological responses on outbreak control for severe acute respiratory syndrome (SARS) in Hong Kong. Journal of Epidemiology and Community Health, 57, 857-863.

Ma, J.L.C. (1997). Factors influencing adjustment of patients suffering from nasopharynx carcinoma: Implications for oncology social work. Social Work in Health Care, 25(4), 83-103.

Maercker, A., & Zoellner, T. (2004). The Janus face of self-perceived growth: Toward a two-component model of posttraumatic growth. Psychological Inquiry, 15(1), 41-47.

Major, B., Richards, C., Cooper, M.L., Cozzarelli, C., & Zubek, J. (1998). Personal resilience, cognitive appraisals, and coping: An integrative model of adjustment to abortion. Journal of Personality and Social Psychology, 74, 735-752.

McFarland, C., & Alvaro, C. (2000). The impact of motivation on temporal comparisons: Coping with traumatic events by perceiving personal growth. Journal of Personality and Social Psychology, 79, 327-343.

McMillen (2004). Posttraumatic growth: What’s it all about? Psychological Inquiry, 15(1), 48-52.

McMillen, J.C., Smith, E.M., & Fisher, R.H. (1997). Perceived benefit and mental health after three types of disaster. Journal of Consulting and Clinical Psychology, 65, 733-739.

Milam, J.E. (2004). Posttraumatic growth among HIV/AIDS patients. Journal of Applied Social Psychology, 34(11), 2353-2376.

Nolen-Hoeksema, S., & Davis, C.G. (2004). Theoretical and methodological issues in the assessment and interpretation of posttraumatic growth. Psychological Inquiry, 15(1), 60-64.

Park, C.L. (1998). Stress-related growth and thriving through coping: The roles of personality and cognitive processes. Journal of Social Issues, 54(2), 267-277.

Park, C.L. (2004). The notion of growth following stressful life experiences: Problems and prospects. Psychological Inquiry, 15(1), 69-75.

84

Park, C.L., Cohen, L.H., & Murch, R.L. (1996). Assessment and prediction of stress-related growth. Journal of Personality, 64(1), 71-105.

Pearlin, L.I., Menaghan, E.G., Lieberman, M.A., & Mullan, J.T. (1981). The stress process. Journal of Health and Social Behavior, 22, 337-356.

Rutter, M. (1985). Resilience in the face of adversity: Protective factors and resistance to psychiatric disorder. British Journal of Psychiatry, 147, 589-611.

Ryff, C.D. & Singer, B.H. (2000). Biopsychosocial challenges of the new millennium. Psychotherapy and Psychosomatics, 69(4), 170-177.

Saller, E. & Stallard, P. (2004). Posttraumatic growth in child survivors of a road traffic accident. Journal of Traumatic Stress, 17(4), 335-340.

Scheier, M.F., & Carver, C.S. (1985). Optimism, coping and health: Assessment and implications of generalized outcome expectancies. Health Psychology, 4, 219-247.

Scheier, M.F., Carver, C.S., & Bridges, M.W. (1994). Distinguishing optimism from neuroticism and (trait anxiety, self-master, and self-esteem): A reevaluation of the life orientation test. Journal of Personality, 55, 169-210.

Scheier, M.F., Matthews, K.A., Owens, J.F., George, J.S., Lefebvre, R.C., Abbott, R.A., & Carver, C.S. (1989). Dispositional optimism and recovery from coronary artery bypass surgery: The beneficial effects on physical and psychological well-being. Journal of Personality and Social Psychology, 57(6), 1024-1040.

Scott, M.T. & Snyder, C.R. (2005). Getting unstuck: The roles of hope, finding meaning, and rumination in the adjustment to bereavement among college students. Death Studies, 29(5), 435-459.

Scully, J.A., Tosi, H., & Banning, K. (2000). Life event checklists: Revisiting the Social Readjustment Rating Scale after 30 years. Educational and Psychological Measurement, 60(6), 864-876.

Sears, S.R., Stanton, A.L., & Danoff-Burg, S. (2003). The yellow brick road and the emerald city: Benefit finding, positive reappraisal coping and posttraumatic growth in women with early-stage breast cancer. Health Psychology, 22, 487-497.

Siegel, K. & Schrimshaw, E.W. (2000). Perceiving benefits in adversity: stress-related growth in women living with HIV/AIDS. Social Science and Medicine, 51, 1543-1554.

Skinner, E. & Edge, K. (1998). Reflections on coping and development across the lifespan. International Journal of Behavioral Development, 22, 357-366.

Smith, S.G., & Cook, S.L. (2004). Are reports of posttraumatic growth positively biased? Journal of Traumatic Stress, 17(4), 353-358.

85

Stanton, A.L., & Low, C.A. (2004). Toward understanding posttraumatic growth: Commentary on Tedeschi and Calhoun. Psychological Inquiry, 15(1), 76-80.

Sumi, K. (1997). Optimism, social support, stress, and physical and psychological well-being in Japanese women. Psychological Reports, 81, 299-306.

Sun, L.N.N., & Stewart, S.M. (2000). Psychological adjustment to cancer in a collective culture. International Journal of Psychology, 35(5), 177-185.

Taylor, S.E., & Brown, J.D. (1988). Illusion and well-being: A social psychological perspective on mental health. Psychological Bulletin, 103,193-210.

Taylor, S.E., Kemeny, M.E., Bower, J.E., Gruenewald, T.L., & Reed, G..M. (2000). Psychological resources, positive illusions, and health. American Psychologist, 55(1), 99-109.

Tedeschi, R. G.., & Calhoun, L. G. (1995). Trauma and transformation. Thousand Oaks, CA: Sage.

Tedeschi, R. G.., & Calhoun, L. G. (1998). Posttraumatic growth: future directions. In R.G.. Tedeschi, C.L. Park, & L.G.. Calhoun, Posttraumatic growth: Positive changes in the aftermath of crisis (pp. 215-238). Mahwah, NJ: Lawrence Erlbaum Associates.

Tedeschi, R.G., & Calhoun, L.G. (2004). Posttraumatic growth: Conceptual foundations and empirical evidence. Psychological Inquiry, 15(1), 1-18.

Tomich, P.L., & Helegeson, V.S. (2004). Is finding something good in the bad always good? Benefit finding among women with breast cancer. Health Psychology, 23, 16-23.

Urcuyo, K. R., Boyers, A.E., Carver, C.S., & Antoni, M.H. (2005). Finding benefit in breast cancer: Relations with personality, coping, and concurrent well-being. Psychology and Health, 20(2), 175-192.

Wanberg, C.R. (1997). Antecedents and outcomes of coping behaviors among unemployed and reemployed individuals. Journal of Applied Psychology, 82(5), 731-744.

Wanberg, C.R., & Banas, J.T. (2000). Predictors and outcomes of openness to changes in a reorganizing workplace. Journal of Applied Psychology, 85(1), 132-142.

Weiss, T. (2002). Posttraumatic growth in women with breast cancer and their husbands: An intersubjective validation study. Journal of Psychosocial Oncology, 20(2), 65-80.

Weiss, T. (2004). Correlates of posttraumatic growth in married breast cancer survivors. Journal of Social and Clinical Psychology, 23(5), 733-746.

West, S.G., Aiken, L.S., & Krull, J.L. (1996). Experimental personality designs: Analyzing categorical by continuous variable interactions. Journal of

86

Personality, 64, 1-48. Widows, M.R., Jacobsen, P.B., Booth-Jones, M., & Fields, K.K. (2005). Predictors of

posttraumatic growth following bone marrow transplantation for cancer. Health Psychology, 24(3), 266-273.

Wong, K.W.N., Lau, P.M.Y., Ho, G.M.C., Mak, G.H.F., Chan, B.B., AuYeung, S., Choi, J.S.P. (2004) Effectiveness of Contemporary Physiotherapy Treatment in Retarding the Progression and its symptoms of Osteonecrosis in Patients with SARS. Paper presented at the Fourth Pan-Pacific Conference on Rehabilitation, Hong Kong.

Wortman, C.B. (2004). Posttraumatic growth: Progress and problems. Psychological Inquiry, 15(1), 81-89.

Wu, K.K., Chan, S.K., & Ma, T.M. (2004, May). A three-month follow-up study on psychological impact of SARS for adult patients. Paper presented at Hospital Authority Convention, Hong Kong.

Wu, K.K., & Lam, D.J. (1993). The relationship between daily stress and health: Replicating and extending previous findings. Psychology and Health, 8, 329-344.

Yu, D.S.F., Lee, D.T.F., & Woo, J. (2004). Psychometric testing of the Chinese version of the medical outcomes study social support survey (MOS-SSS-C). Research in Nursing and Health, 27, 135-143.

Zigmond, A.S., & Snaith R.D. (1983). The hospital anxiety and depression scale. Acta Psychiatrica Scandinavica, 67, 361-370.

Zimet, G.D., Dahlem, N.W., Zimet, S.G., & Farley, G.K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52, 30-41.

87

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