PSYCHOSIS AND PSYCHOLOGICAL STRESS
A dissertation submitted to the
Kent State University College of Arts and Sciences
in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
by
James P. Seghers
August 2011
i
Dissertation written by
James P. Seghers
B.A., St. John‘s College, 1998
M.S., California State University at Fullerton, 2005
Ph.D., Kent State University, 2011
Approved by
___________________________________ , Chair, Doctoral Dissertation Committee
Nancy M. Docherty
___________________________________ , Members, Doctoral Dissertation Committee
Stanford Gregory
___________________________________
John Gunstad
___________________________________
William E. Merriman
Accepted by
___________________________________ , Chair, Department of Psychology
Maria S. Zaragoza
___________________________________ , Dean, College of Arts and Sciences
Timothy S. Moerland
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ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to my advisor, Dr. Nancy M.
Docherty, for her invaluable guidance and support throughout the past six years. She
taught me how to do research, encouraged me to publish and travel, and inspired me with
her intellect, scholarship, and caring. I was honored to have Professors Stanford
Gregory, John Gunstad, and William Merriman on my dissertation committee. My
sincere thanks are due to all of them for their constructive comments and suggestions. I
extend my gratitude to the faculty at the Department of Psychology at Kent State
University for all their efforts and support which made my academic experience at Kent
State University fruitful and successful.
I owe this work to my wife, Helen, whom without her help, support, and humor I
would not have been able to finish this undertaking. I thank her for standing beside me
during the most difficult times and for her patience and understanding. I would also like
to offer my deepest thanks and appreciation to my parents, Jim Seghers and Ralda Singer,
and to their respective spouses, Michelle and Nick, for the innumerable contributions
they have made to my development. Finally, I would like to thank my brother Steven,
who has shared this life with me from almost the beginning; a better and more supportive
brother I could not ask for.
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS………………………..……………………………… iii
LIST OF TABLES……………………………………….…..……………………. vii
CHAPTER
I INTRODUCTION………………………………………..…………….……….. 1
Context of the Study…………………………………..………….…..…… 1
Aims of the Study………………………………….....................…..…… 3
General Hypotheses……………………………………………………..… 4
II REVIEW OF THE LITERATURE………………………….………...……..… 5
Stress, Life Events, and Psychosis……………………………………….... 5
Chronic and Daily Stressors……………………………………………….. 11
Mechanisms and Buffers of Stressful Experience…………………………. 16
Psychological Conceptualizations of Stress……………………………….. 22
The Transactional Model…………………………………………... 24
Conservation of Resources Theory………………………………… 26
III FORMAL HYPOTHESES…………………………………………..……….. 35
IV METHOD……………………………………………………………..………. 37
Participants…………………………………………………….………….. 37
Measures…………………………………………………..………………. 37
Diagnosis……………………………………………..…………… 37
iv
Symptom Severity……………………………..………………….. 38
Depression……………………………………………….………… 40
Anxiety………………………………………………….………… 41
Social Functioning………………………………………………... 41
Resource Losses and Gains……………………….………………. 42
Procedure……………………………………………………..…………… 44
Analyses…………………………………………………….…………….. 47
V RESULTS……………………………………………………….…………….. 49
Patient Descriptive Variables………………………………..…………….. 49
Reliability Analyses………………………………………………………. 49
Means and Standard Deviations………………………………………..…. 53
Resource Change and Current Emotional Functioning………………..….. 53
Resource Loss and Symptom Change…………………………………...… 56
Loss Spiral…………………………………………………………….…… 58
Change in Emotional Functioning as Mediator……………………….…... 59
Resource Gains and Symptom Change……………………………….…… 59
Supplementary Analyses…………………………………………….…….. 63
VI DISCUSSION…………………………………………………………….…… 66
Summary……………………………………………………………….….. 66
General Discussion…………………………………………………….….. 66
Limitations………………………………………………………………... 81
Future Research………………………………………………………...…. 82
v
APPENDICES…………………………………………………………….………. 84
Appendix A: Schedule for Affective Disorders and Schizophrenia
(SADS)………………………………………………………..…… 85
Appendix B: Positive and Negative Syndrome Scale (PANSS)……….… 103
Appendix C: Beck Depression Inventory-II (BDI-II)……………..….…. 104
Appendix D: State-Trait Anxiety Inventory (STAI)…………………..….. 106
Appendix E. Social Functioning Scale (SFS)………………………..….… 107
Appendix F: Conservation of Resources Evaluation (COR-E)……..……. 116
Appendix G: Subjective Resource Items From COR-E…………..………. 123
Appendix H: Correlation Matrix for All Study Variables…………….…... 125
REFERENCES……………………………………………………………..……… 127
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LIST OF TABLES
Table
Page
1.
Descriptive Information for Sample (n=77)………………..………………... 50
2.
Means and Standard Deviations for Dependent Variables…………………… 51
3.
Reliability Coefficients for Dependent Variables Measuring Unitary………. 52
Constructs.
4.
Means and Standard Deviations for Independent Variables…………………. 54
5.
Correlations of Predictor Variables with Time 2 Emotion Variables………... 55
6.
Correlations of Predictor Variables with Outcome Variables Computed
as Time 2 – Time 1 Change Scores (Δ)………………………………………. 57
7.
Mediation Analyses for Depression as Proposed Mediator Between
Resource Loss† and Psychosis Symptoms……………………………………. 60
8.
Mediation Analyses for State Anxiety as Proposed Mediator Between
Resource Loss† and Psychosis Symptoms……………………………………. 61
9.
Moderation Analyses for Resource Gain as a Proposed Buffer Between
Resource Loss and Outcome Variables: Resource Gain Calculated Using
Rated Independent Gains…………………………………………………….. 62
10. Moderation Analyses for Resource Gain as a Proposed Buffer Between
Resource Loss and Outcome Variables: Resource Gain Calculated Using
Self-Reported Independent Gains………………………………………….…. 64
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CHAPTER I
Introduction
Context of the Study
Within the domain of human psychopathology, psychosis stands out as a uniquely
difficult and puzzling phenomenon. It has a variety of subtypes, and is marked by a wide
range of symptoms that cluster together in diverse ways. Clouding matters further,
psychosis is etiologically and pathopysiologically complex. It encompasses, on the one
hand, genetic and biological, and on the other hand, psychological and social
determinants. Proposed almost forty years ago (Rosenthal, 1970; Zubin & Spring, 1977),
the dominant heuristic model for conceptualizing the etiology of psychotic – and more
specifically, schizophrenia-spectrum – disorders remains the diathesis-stress model. The
diathesis-stress model proposes, in its most basic form, that psychosis develops as a
function of genetic vulnerability to the disorder (the diathesis) interacting with
environmental stressors. According to the model, psychotic symptoms emerge whenever
the impact of stressors exceeds the individual's vulnerability level, the latter being a
within-person characteristic. Strong versions of the model posit that vulnerability to
psychosis is universal, though by no means equitably distributed; and that the experience
of stress is essential to the onset of acute psychosis. Thus, ―each of us is endowed with a
degree of vulnerability that, under suitable circumstances, will express itself in an episode
of schizophrenic illness‖ (Zubin & Spring, 1977, p. 109). Later conceptualizations
specify an important role for a range of mediating and moderating factors, such as social
1
2
resources, coping behavior, and cognitive capacity (e.g., Nuechterlein & Dawson, 1984).
Since the 1970s, the diathesis-stress model has been supported by a large body of
research demonstrating a causal role for both genetic and environmental factors (e.g.,
Harrison & Weinberger, 2005; Janssen et al., 2004), as well as research demonstrating
interaction effects between these causative domains (e.g., Tienari et al., 1994). At the
same time, the diathesis-stress model exerts a dominant heuristic influence on
conceptualizations of the origins of schizophrenia (e.g., Corcoran et al., 2003; Hooley &
Gotlib, 2000; Walker & Diforio, 1997), and provides a plausible explanation for some of
the disorder‘s more puzzling features, such as the episodic nature of acute psychotic
episodes. Psychosis sufferers themselves often cite a link between the experience of
stressors, level of distress, and psychotic symptoms when describing their own illness
(Phillips, Shona, Edwards, & McMurray, 2007). For example, when describing a
psychotic relapse, Phillips and colleagues cite a young woman who stated ―…a stressful
time in my life came up and the psychosis is now back‖ (Early Psychosis Prevention and
Intervention Centre [EPPIC], 2000, p. 30).
To date, within the broader framework of the diathesis-stress model, the
predominant method for assessing the impact of psychological stress on individuals
affected with psychosis has been through the study of life events. A substantial body of
research supports the occurrence of an increased number of stressful life events preceding
psychotic exacerbations (e.g., Bebbington et al., 1993; Day et al., 1987; Jacobs & Myers,
1976; Mazure, Quinlan, & Bowers, 1997). In addition to such retrospective study
designs, a number of major studies have prospectively examined the impact of stressful
3
life events on schizophrenia subjects, with results supporting a causal role for
psychological stress in psychotic relapse and symptom exacerbation (Hirsch et al., 1996;
Hultman et al., 1996; Ventura et al., 1989). Research also suggests an important role for
chronic and daily stressors. Findings have shown that daily hassles (e.g., financial
conflicts, lack of leisure time, job dissatisfaction) prospectively predict changes in levels
of psychotic symptoms (Malla & Norman, 1992; Norman & Malla, 1994). Similarly,
with respect to the home environment, there is strong evidence that ongoing negative
interactions with family members and loved ones can precipitate psychotic relapse (e.g.,
Butzlaff & Hooley, 1998). Moreover, research using the experience sampling
methodology (ESM) provides compelling support for the idea that psychotic symptoms
can exhibit continuous variation in intensity as a function of minor stressors (MyinGermeys, Krabbendam, Delespaul, & van Os, 2003; Myin-Germeys, Delespaul, & van
Os, 2005).
Aims of the Study
One area of weakness in the current research literature on environmental
determinants of psychosis is the fact that stressors are typically operationalized without
reference to a coherent theory of stress. Indeed, a recent search of the literature found
only two reports in which an established theory of psychological stress was referenced
explicitly in the study of stress and psychosis (Horan et al., 2005; Norman & Malla,
1993). This consideration provides a starting point for the present study. The study had
three related but distinguishable goals. First and foremost, the study retrospectively
examined the impact of stressful experience, operationalized based on conservation of
4
resources theory (COR; Hobfoll, 1989), on persons with schizophrenia-spectrum
disorders. COR theory is a general model of stress, and in past research has been applied
primarily to normal populations; this is the first study of its kind to apply COR theory to
a sample of seriously mentally ill participants. Second, the study attempted to replicate
earlier findings regarding the role of negative affect as a mediator of the impact of
stressful events on psychotic symptoms. Third, following COR theory, the study
investigated the putative buffering effect of resource gains (i.e., positive life events) on
the impact of resource losses.
General Hypotheses
It was hypothesized that psychological stress, operationalized in terms of resource
loss in accordance with COR theory, would be associated with worsening levels of
emotional functioning and with increases in the level of psychotic symptoms. Following
COR theory, an effect of loss spiral was hypothesized, in which interpersonal resources
interact with subsequent resource losses (i.e., stressors) to generate a deleterious effect on
psychotic symptoms. It was further predicted that resource gains (i.e., positive life
events) would buffer the impact of stressors to a significant extent. Failure to reject these
hypotheses would provide support for the importance of psychological events in the
pathophysiology of schizophrenia, and, at the same time, would provide support for the
applicability of COR theory to a seriously mentally ill population. It was also
hypothesized that increases in negative affect would partially mediate the anticipated
deleterious effect of resource loss on psychotic symptoms, which would support existing
findings on the relationship between emotional functioning and symptom exacerbations.
CHAPTER II
Review of the Literature
Stress, Life Events, and Psychosis
The general association between stress and illness is well established. In the field
of medicine, stress is associated with relapse or exacerbation of a number of illnesses,
such as asthma, ulcerative colitis, and multiple sclerosis (Corcoran, Gallitano, Leitman, &
Malaspina., 2001), as well as with outcome in myocardial infarction, melanoma, and
breast cancer (Leserman et al., 1999). In both medicine and psychology, the assessment
of life events has become the dominant method for operationalizing environmental
stressors. Life events refer to situations dictating significant life changes that are
experienced as stressful by individuals (Horan et al., 2005). For example, in the study of
HIV outcomes, cumulative stressful life events have been found to be associated with a
doubling of risk for the development of AIDS (Leserman et al., 2000). Numerous such
studies have shown that stress can worsen symptoms and precipitate relapse across a
wide range of psychiatric conditions, including postpartum psychosis, affective illness,
and alcohol dependence (reviewed in Corcoran et al., 2001). Although the role of stress
in psychosis onset has not been prospectively examined in prodromal patients, numerous
studies have found a relationship between stress and relapse of psychosis. Evidence
suggests that high levels of environmental stress in the form of discrete life events
interacts with vulnerability factors to increase the likelihood of psychotic exacerbation
(for a review, see Norman & Malla, 1993).
5
6
A critical point in the study of life events and stress occurred with Holmes and
Rahe (1967), who were among the first to suggest that experiencing stressful life events
could negatively influence health. Holmes and Rahe conducted an extensive review of
medical case histories, from which they developed a life events measure in which certain
life changing experiences (e.g., the death of a spouse) were weighted on a graded scale.
According to Dohrenwend (2006), most life events research over the last 40 years has
used the Holmes and Rahe scale, or similar checklist inventories. A radical extension of
this approach was endeavored eleven years later by Brown and Harris (1978), who
developed the Life Events and Difficulties Schedule (LEDS). The LEDS is an intensive
semistructured interview-based measure that allows for the assessment of individual
events in detail. With the LEDS, trained raters assess the ―contextual threat‖ of an event
by evaluating it with respect to the subject‘s unique circumstances (p. 90). The LEDS is
a narrative procedure, from which quantitative ratings of threat are derived based on
judgments as to how most people in the reference culture would be expected to react in
similar circumstances.
An important advantage of the narrative approach is that it enables researchers to
more readily distinguish between dependent and independent life events. Events that
occur outside one‘s control (such as being laid off because of company downsizing) are
classified as independent events, whereas events that are likely to be influenced by the
individual‘s own behavior (such as being fired for poor work performance) are classified
as dependent events. By separating the effects of independent stressors from the effects
of ongoing symptomatology, causal relationships are more readily discernible. Narrative-
7
rating approaches such as the LEDS have proved superior to checklist approaches in
terms of test-retest reliability, inter-rater agreement, and construct validity (Dohrenwend,
2006). The LEDS, or variants of it, was used in many of the major studies reported in
this review (e.g., Hirsch et al., 1996; Horan et al., 2005; Hultman, Wiselgren, & Oehman,
1997; Ventura, Nuechterlein, Lukoff, & Hardesty, 1989).
Research into the relationship between life events and schizophrenia dates to the
1960s, when Brown and Birley (1968; Birley & Brown, 1970) published a series of
groundbreaking studies on stress and psychosis. They reported that schizophrenic
patients, relative to community controls, described a significant excess of major stressful
events during the three week period preceding the onset of a psychotic episode. These
results held for clearly independent events, as well as for the aggregation of both
dependent and independent events. Subsequent research supported the finding of stress
exposure, specifically life events, increasing in the weeks to months leading up to relapse
(e.g., Bebbington et al., 1993; Day et al., 1987; Jacobs & Myers, 1976; Mazure, Quinlan,
& Bowers, 1997).When patients were their own controls (relapse vs. baseline), or when
―relapsing‖ patients were compared with ―nonrelapsing‖ patients, an association of life
events and relapse has been observed (Malla et al., 1990; Hultman et al., 1997; Ventura et
al., 1989). In a World Health Organization cross-national study, for example, Day and
colleagues replicated the findings of Brown and Birley (1968), reporting that
schizophrenia patients had significantly more stressful life events during the three weeks
preceding a relapse than they did during other time periods (Day et al., 1987).
Bebbington and colleagues (1993) assessed for life events preceding the onset of
8
psychoses in three diagnostic groups: schizophrenia, mania, and depressive psychoses.
Relative to a healthy control sample, they found a significant excess of life events for all
three groups during the six months preceding the onset of a psychotic episode.
Moreover, these results held even when analyses were limited only to independent
events. Mazure and colleagues (1997) assessed a number of biological markers in
conjunction with life event stressors. They found that pretreatment admission cortisol
levels were significantly and positively related to preadmission life event severity, thus
directly implicating biological mechanisms in terms of the impact of life stressors.
In a review of the literature on stress and psychosis, Phillips et al. (2007) note that
while many studies have found an increase in the number of life events experienced prior
to the onset of an acute psychotic episode (e.g., in addition to the above, Canton &
Fraccon, 1985; Chaven & Kulhara, 1988; Michaux, Gansereit, McCabe, & Kurland,
1967; Schwartz & Myers, 1977), others have not (Chung, Langeluddecke, & Tennant,
1986; Gruen & Baron, 1984). A limitation shared by all these studies is retrospective
design, in which patients or informants are asked to recall events occurring over a given
time interval (often a fixed interval preceding relapse or hospitalization). Even among
the healthy population, memory is a biased and sometimes unreliable function, and the
deleterious impact of psychosis on memory and cognitive functioning is well documented
(for a review, see Heinrichs & Zakzanis, 1998). To reduce the effects of such confounds,
Ventura and colleagues (1989) conducted a prospective study of relapse in 30
schizophrenia patients in which life events were regularly assessed over the course of a
year. For relapsing patients, they found that a significant increase in independent life
9
events occurred in the month preceding relapse, compared to both an analogous nonrelapse month for the same patients, and to the average events per month for nonrelapsing patients. Hultman and colleagues (1997) replicated these findings, showing an
increase in life-events three weeks preceding relapse. In another prospective study,
researchers failed to replicate the finding of increased life events during the weeks
preceding relapse, instead showing that life events made a cumulative contribution over
time to the risk of relapse (Hirsch et al., 1996). In this study, between 23% and 41% of
the relapse risk could be attributed to major life events, depending on the extent of
exposure.
Although most life event studies have targeted the impact of major events on
individuals already diagnosed with a psychotic disorder, there are a subset of studies that
speak to the question of whether or not stress can directly precipitate a psychotic break.
A number of quasi-experimental studies suggest that the experience of military combat
increases the probability of developing a psychotic disorder (Fowles, 1992; Phillips et al.,
1997). For example, Dohrenwend and Egri (1981) cite reports of battlefield psychoses
that are symptomatically indistinguishable from schizophrenia, occurring without
evidence of prior psychopathology. As argued by Fowles (1992), while such cases may
be dismissed as not evidencing real schizophrenia (they are typically quite brief, on the
order of a few days), they nevertheless show that an extreme stressor can produce a
schizophrenia-like psychosis. More distally, studies into the effects of childhood trauma
have found dose-response relationships between level of trauma and the likelihood of
developing psychosis (e.g., Janssen et al., 2004). Similarly, adopted-away children of
10
biological mothers with schizophrenia have been found to be at greater risk for psychotic
disorder if their adoptive families were rated as dysfunctional (Tienari et al., 1994).
While such evidence does not speak directly as to whether or not stress has an immediate
causal role in the psychotic break, such a role appears quite plausible, particularly given
the convergence of studies in which life events are directly implicated in the onset of
psychotic episodes following the initial break.
Taken as a whole, the life events literature provides evidence that stressful
experiences can precipitate psychotic symptoms. Because life events research often
excludes events that are not clearly causally independent of the subject‘s own behavior,
some scholars have argued that published life event studies are likely to underestimate
the contribution of psychosocial factors (Day et al., 1987; Dohrenwend & Egri, 1981).
While such exclusions are sound from the perspective of establishing causal
directionality, there can be little doubt that illness-related events also contribute to the
appearance and/or exacerbation of manifest symptoms. Indeed, many of the most
stressful events that individuals experience (relationship problems, legal problems, career
problems, and so on) are likely to be dependent in nature, to some extent or other. The
relevance of dependent life events is highlighted by the Jacobs and Myers (1976) study,
which found that subjects reported significantly more life events prior to hospital
admission than a healthy control group only when both independent and dependent
events were considered. Similarly, Lukoff and colleagues (Lukoff, Snyder, Ventura, &
Nuechterlein, 1984) found a significantly higher rate of dependent events for a
schizophrenia group, relative to controls. They concluded that schizophrenia patients are
11
likely to have a stress-prone lifestyle that produces an excess of unpleasant experiences.
While the evaluation of the specific effects of independent events is conceptually
important, research suggests an excess of dependent stressors for individuals with
schizophrenia, which quite plausibly plays a significant functional role in both
precipitating and maintaining psychotic symptoms. Conceptually, a framework of
complex causality emerges, such as, e.g., independent stressors exacerbating symptoms,
which produces increased dependent stressors, which further interacts with external
environmental determinants, further exacerbating core symptoms, and so on, culminating
in full relapse.
There are a number of notable methodological limitations in the life events
psychosis literature, such as the fact that potential moderating variables, such as
perceived control, desirability, appraisal, and predictability, are often unconsidered (for a
detailed review of methodological issues pertaining to life events research, see
Dohrenwend, 2006). Also, the impact of day-to-day experiences has not been taken into
account by most published life events studies, which focus on experiences that tend to
occur infrequently, by their very nature. Nevertheless, there is a growing body of
research overlapping the life events literature that directly incorporates issues of daily
living, as well as studies investigating mediators and moderators of stressful events.
Relevant research will be discussed in the following two subsections.
Chronic and Daily Stressors
In recognition of some of the limitations of the life events approach, the
experience of ‗minor‘ events has received increasing focus in recent years. In addition to
12
life events, it is now believed that there are more subtle everyday factors that are
associated with course of illness in psychosis-sufferers. Minor events and stressors that
are chronic in nature have been found to predict both psychotic symptom exacerbations
and subjective distress (e.g., Butzlaff & Hooley 1998; Norman & Malla, 1994, 2001).
Norman and Malla (1993) have gone so far as to suggest that chronic low-level stressors
(so-called daily hassles), experienced as part of everyday living, are more likely to exert a
more deleterious effect on individuals with schizophrenia than the more dramatic, but
also less frequent, stressors associated with life events research. Indeed, Norman and
Malla (1991) demonstrated in an earlier study that the level of distress reported by
individuals with schizophrenia was significantly correlated with the number of minor
stressors experienced, but not with the number of life events. Similarly, Beck and
Worthen (1972) reported that individuals with schizophrenia are likely to attribute
symptom exacerbation to stressors of ‗low severity.‘ Other studies have shown that socalled daily hassles, such as loneliness or relationship concerns, are associated with
exacerbations in comorbid symptoms, such as increased levels of reported anxiety and
depression (Malla & Norman, 1992; Norman & Malla, 1994), as well as with quality of
life (Caron, Lecomte, Stip, & Renaud, 2005). Minor daily events have been reported to
have an impact on psychological symptoms in general (Kanner et al., 1981; Monroe,
1983), subjective distress (Norman & Malla, 1991), relapse rates in schizophrenia (Malla
et al., 1990) and on changes in mood in both patients with psychosis and their first-degree
relatives (Myin-Germeys et al., 2001).
13
Do low level stressors directly impact positive psychotic symptoms? The weight
of available evidence suggests that they do. Malla and colleagues (Malla, Cortese, Shaw,
& Ginsberg, 1990) conducted a prospective study in which relapsing schizophrenia
patients were found to experience significantly more independent minor events during the
preceding year, relative to non-relapsing subjects. In a related study, Norman and Malla
(1994) assessed daily hassles monthly over a one-year period, and found that minor
events were independent predictors of changes in positive psychotic symptoms. More
recent research using the experience sampling method (ESM) provides further evidence.
ESM is a structured diary technique in which subjects are assessed within the context of
their daily lives, often at multiple time points throughout a given day. For life events
research, ESM offers researchers the advantage of being able to measure stressors and
symptoms naturalistically, rather than retrospectively within the context of a formal
interview. Based on evidence that psychotic symptoms can vary in intensity over weeks,
days, and even within the hours of a given day (Garety & Hemsley, 1994), MyinGermeys and colleagues (Myin-Germeys, Delespaul, & van Os, 2005) sought to assess
whether the intensity of psychotic symptoms changed in association with minor daily
events. They found that patients with a diagnosis of psychosis-in-remission exhibited
continuous variation in the intensity of psychotic symptoms as a function of minor
stressors. Another ESM study from this research group is noteworthy in that it directly
linked the effects of major life events and daily stressors, showing that life events were
associated with increased emotional reactivity to daily stressors (Myin-Germeys,
Krabbendam, Delespaul, & van Os, 2003). While this study did not directly assess
14
psychotic symptoms, the authors suggest that increased reactivity to daily life stress may
be a marker of increased vulnerability for psychosis (see also Myin-Germeys, van Os,
Schwartz, Stone, & Delespaul, 2001).
Further evidence of a relationship between chronic stress and positive psychotic
symptoms can be found in the expressed emotion (EE) literature. EE refers to family
members' negative emotional interactions with patients, as assessed via observed
criticism, hostility, and emotional overinvolvement. EE is typically interpreted as a form
of chronic social stress (Hooley & Gotlib, 2000). In a review of the EE construct, Hooley
and Gotlib (2000) note that high EE relatives tend to be controlling (Hooley & Campbell,
2000); to exhibit more reciprocal negativity and less reciprocal positivity (Simoneau,
Miklowitz, & Saleem, 1998); to talk more and listen less (Kuipers, Sturgeon, Berkowitz,
& Left, 1983); and to make more critical remarks directly to schizophrenic patients than
do low EE relatives (Miklowitz, Goldstein, Falloon, & Doane, 1984; Valone, Norton,
Goldstein, & Doane, 1983). Butzlaff and Hooley (1998) reported a meta-analysis of 27
studies in which it was found that living in a high EE home environment was associated
with more than a doubling of the rate of psychotic relapse for individuals diagnosed with
schizophrenia. Of particular note, this study included a file drawer analysis of the
number of null findings that would be required to reduce this result to statistical nonsignificance: that number was a notably high 1,246. According to Hooley and Gotlib
(2000), EE has been found to be predictive of relapse in other conditions, including major
depression (Hooley, Orley, & Teasdale, 1986; Vaughn & Leff, 1976) and bipolar disorder
15
(Miklowitz, Goldstein, Nuechterlein, Snyder, & Mintz, 1988). These results provide
strong support for the putative role of EE as a psychosocial stressor.
Direction of effect is in important issue when evaluating the results of EE studies,
as the positive relationship between high-EE and psychotic relapse can plausibly be
interpreted as an effect of schizophrenic symptomatology (e.g., patients engendering
criticism as a direct function of their disorder) rather than an independent cause of
symptom exacerbation. At least one literature review addressed this question directly,
and failed to support the hypothesis that high-EE is a function of patient characteristics
(Hooley & Gotlib, 2000). In another study, even when relevant patient variables were
controlled statistically, EE was found to make an independent contribution to psychotic
relapse (Nuechterlein, Snyder, & Mintz, 1992). The causal role of EE is perhaps most
persuasively supported by family intervention research, in which interventions targeting
reductions in EE have been found to be effective in preventing relapse (Leff, 1994; Leff,
Kuipers, Berkowitz, Eberlein-Fries, & Sturgeon, 1982). According to Hooley and Gotlib
(2000), such family-based interventions are associated with a 9- to 12-month relapse rate
of 10%, as compared to 50% for control subjects. There is also evidence that low EE can
protect against the effects of stressful life events (Nuechterlein et al., 1994).
Bidirectionality in patient-family interaction is now assumed (Corcoran et al., 2003), and
while the preponderance of evidence suggests that EE is at least partially independent of
patient characteristics, direction of effect is likely circular to some extent. Nevertheless,
the data on EE and psychotic symptomatology provides evidence that a chronic stressor
can precipitate psychotic symptoms.
16
Mechanisms and Buffers of Stressful Experiences
Given the documented influence of stressful experiences on the development and
course of schizophrenia, research efforts have focused on elucidating mechanisms
through which exposure to stressors results in symptom exacerbations. It has been
proposed that individuals with schizophrenia often have stress-prone lifestyles that
generate surplus life events as a result of factors such as limited social support networks
and coping abilities, socioeconomic factors, and stigmatization (Lukoff et al., 1984).
Notably, contemporary theories of stress have also emphasized the importance of
moderating factors. For example, according to the transactional model (Lazarus, 1999;
Lazarus & Folkman, 1984), the characteristics of life events themselves, such as their
frequency of occurrence and qualitative nature, are one determinant of how individuals
respond to potential stressors. Subjective appraisals, or evaluative judgments of the
individual encountering the event, are also central to this model, which conceptualizes
stress as a particular relationship with the environment in which a given stressor is
appraised as taking or exceeding personal resources or endangering one‘s well-being.
Appraisals are similarly emphasized in the conservation of resources theory of stress
(Hobfoll, 1989; 2001), which also emphasizes moderating influences of factors such as
sensitivity priming by prior stressors, current available resources, and resource gains (i.e.,
positive events).
One important variable that has received attention in the literature is stress
sensitivity (also referred to as stress or emotional reactivity), which has been studied
using both experimental and non-experimental methods. Docherty and colleagues have
17
conducted a series of experimental studies in which communication disturbance, believed
to vary as a function of subjective distress, was assessed using a natural speech protocol
in which subjects separately discuss good and bad memories. Healthy controls and
schizophrenia subjects have been found to exhibit an increased level of communication
disturbance when discussing bad memories (Burbridge, Larsen, & Barch, 2005; Cohen &
Docherty, 2004; Docherty, Hall, & Gordinier, 1998; Docherty & Hebert, 1997), a
phenomenon referred to as affective reactivity of speech, or simply speech reactivity.
Differences in levels of speech reactivity are believed to reflect underlying differences in
stress sensitivity (Dinzeo, Cohen, Nienow, & Docherty, 2004; Docherty, Rhinewine,
Nienow, & Cohen, 2001; Seghers & Docherty, 2009). Consistent with this interpretation,
patients who exhibit greater speech reactivity have been found to exhibit greater
physiological responsiveness on an acoustic startle task, as well as to report relatively
higher levels of subjective distress during the bad memory speech condition (Docherty et
al., 2001).
Suggestively, schizophrenia patients are significantly more speech reactive than
healthy controls (Docherty, 1996; Docherty & Hebert, 1997). It has been hypothesized
that increased stress sensitivity in schizophrenia patients manifests in vulnerability to
psychosis, rather than being merely a symptom of the disorder itself. Consistent with this
interpretation, speech reactivity has been found to be associated with greater severity of
the core positive syndrome of delusions and hallucinations (Docherty, Grosh, & Wexler,
1996). In the Seghers and Docherty study (2009), stress sensitivity was the strongest
predictor of speech reactivity, accounting for almost twice as much variance as other
18
hypothesized determinants, such as theoretically relevant neurocognitive impairments.
Because the stress sensitivity variable in this study was designed to control for the
magnitude of an index stressor, it was hypothesized to reflect the tendency to perceive
situations in a threatening manner, independent of situational determinants. Stress
sensitivity was thus interpreted in terms of cognitive appraisal, which has figured
prominently in contemporary models of both stress (e.g., Hobfoll, 1989; Lazarus &
Folkman, 1984) and psychosis (e.g., Garety, Kuipers, Fowler, Freeman, & Bebbington,
2001). This hypothesis is consistent with speculations that stress sensitivity is related to
neuroticism (Myin-Germeys & van Os, 2007), which has been found to be elevated in
schizophrenia patients (Horan, Subotnik, Reise, Ventura, & Nuechterlein, 2005), and to
be associated with both the severity of (Lysaker, Lancaster, Nees, & Davis, 2003) and the
risk for (Krabbendam et al., 2002; van Os & Jones, 2001) psychotic symptoms.
In addition to the experimental research noted above, stress sensitivity has been
studied using self-report measures, as well as naturalistically using ESM. Myin-Germeys
and colleagues (2001) conducted an ESM study in which patients, their relatives, and
healthy control subjects were assessed ten times per day for six days on measures of
stressful events, associated stress, and associated emotional reactivity. They reported that
patients and their relatives showed a significantly greater decrease in positive affect
following minor stressful events than did controls, as well as a significantly greater
increase in negative affect. This increase was greatest in the patient sample, suggesting a
dose-response relationship between familial risk and emotional reactivity that parallels
similar findings found for speech reactivity (Docherty, 1996; Docherty & Hebert, 1997).
19
In their review of stress reactivity and psychosis, Myin-Germeys and van Os (2007) cite a
submitted population study that provides further support for the hypothesized causal role
of stress sensitivity (Lataster et al., 2009). This study found significant associations
between sensitivity to stress and subclinical psychotic symptoms. Similar to the
association noted above between speech reactivity and positive symptoms, this finding
supports the hypothesis that stress-sensitivity is a vulnerability marker for psychosis.
Further, albeit indirect, evidence is found in research into stress sensitivity and childhood
trauma. Trauma has been found to increase the risk for psychosis in dose-response
fashion, and is positively associated with sensitivity to daily life stressors (Glaser et al.,
2006).
Another ESM study by Myin-Germeys and colleagues (Myin-Germeys,
Krabbendam, Delespaul, & van Os, 2003a) used the stress sensitivity construct to connect
findings on the effects of life events and daily hassles, respectively. Based on speculation
that life events may not directly trigger psychotic symptoms, but rather exert their effect
by cumulatively increasing subsequent vulnerability to stressors (e.g., see Bebbington et
al., 1996; Hirsch et al., 1996), they hypothesized that life events should increase
sensitivity to daily life stressors, as assessed naturalistically using ESM. In fact, they
found that while life events did not influence the subjective appraisal of stress, these
major events significantly increased emotional reactivity to daily stressors, measured in
terms of increased negative affect. Both a significant main effect of life events, and a
significant interaction between life events and daily stressors, was found. As the authors
note in their discussion, this interaction effect argues against the interpretation that life
20
events merely increased negative mood overall, rather than influencing reactivity to daily
life stressors. As will be discussed later in this review, these results fit particularly well
with Hobfoll's (1989; 2001) notion of loss cycles, in which the experience of stress, in
itself, drains personal resources and leaves the individual increasingly vulnerable to the
effects incipient stressors.
A report by Horan and colleagues (2005) suggests another possible moderator of
the effects of stressors, namely, self-efficacy. They conducted a 12-month longitudinal
study into the frequency, qualitative characteristics, and subjective appraisal of life events
in a mixed sample of recent-onset schizophrenia patients and healthy controls. The
schizophrenia subjects in this study did not rate negative events as more distressing than
did controls. However, they exhibited comparatively diminished self-efficacy in their
appraisal of life events, rating both positive and negative events as less controllable and
more poorly handled than did control subjects. Based on this result, the authors speculate
that self-efficacy may actually moderate the effect of life event stress. They note that low
self-efficacy and external locus of control have previously been reported in psychotic
disorders (e.g., Dohrenwend et al., 1998; MacDonald et al., 1998), but that theirs is the
first study to directly assess these characteristics with respect to naturally occurring life
events.
Social support and coping methods are additional variables that have been
investigated as moderators of life event stress. Patients with psychosis report smaller
social networks and lower levels of social support than healthy controls (Neeleman &
Power, 1994; Nettelbladt, Svensson, Serin, & Ojehagen, 1995). Poor social support has
21
in turn been associated with relevant outcome variables, including hospital admission
rates, dependency on psychiatric services, increased severity of negative symptoms, and
ventricular enlargement in the brain (Hultman et al., 1997). Conversely, one study
showed that greater social support resources were associated with improved prognosis for
first-episode schizophrenia patients (Erickson et al., 1989). This study also found that,
prior to their first episode, schizophrenia patients reported fewer and less satisfactory
social relationships than did a comparison sample of subjects with affective psychosis, as
well as a sample of healthy controls. This finding is consistent with the idea that social
support can buffer the effects of life events, such that vulnerable subjects with low social
support are especially susceptible to symptom outbreaks. Suggestively, given the
research reviewed above regarding stress sensitivity, social support has been shown to be
negatively correlated with emotional reactivity to daily stressors (Affleck, Tennen,
Urrows, & Higgins, 1994). Hultman and colleagues (1997) conducted a study which
examined relationships between social support, coping, and life events in schizophrenia
patients. They found a buffering effect for social factors, with the time between life
events and relapse being significantly extended for patients who reported a more
emotionally-close social network and more active support-seeking coping behaviors.
Unexpectedly, they also found that socially withdrawn subjects who reported being
contented with their isolation had higher relapse rates than similarly withdrawn patients
who were interested in expanding their social network. In their discussion, the authors
suggest that the socially isolated/contented profile may be a marker for high stress
sensitivity, thereby indicating increased vulnerability to relapse. While this consideration
22
is speculative, it suggests an alternative (or more complex) interpretation of findings that
social support buffers the effects of stress, namely, that a third variable – such as stress
sensitivity or severity of illness – leads to both decreased social integration and increased
vulnerability to relapse.
Psychological Conceptualization of Stress
The stress construct is undefined in the overwhelming bulk of the empirical and
theoretical work that has contributed to this review. Alternatively, this observation may
imply either the extreme simplicity (‗givenness‘) or complexity associated with this
concept. In fact, both simplicity and complexity may well apply. Leading figures in the
study of psychological stress, such as Richard Lazarus, have forcefully argued for the
conceptualization of stress as a subset of the emotions (Lazarus, 1993, 1999).
Along such lines, within the psychosis literature, the term stress generally appears to be
synonymous with distress, anxiety, anger, or any kind of emotional state that is
experienced as unpleasant or aversive. This is a simple, straightforward
conceptualization that is effectively indistinguishable from how the term is used by the
lay public. Phenomenologically, aversiveness is the central, defining characteristic of the
stress response; this is simple to understand. Nomothetically, however, the phenomenon
of psychological stress imposes difficulties, chief among which is the apparent
idiographic nature of stressful experience: that which is stressful for one person may very
well be experienced as affectively neutral or even pleasant by another. This difficulty –
that stressful conditions do not produce dependable effects – was noted at least as far
23
back as the 1950s (e.g., Lazarus & Eriksen, 1952), but it was not until the 1970s that this
variability achieved prominent recognition in academic psychology.
The challenges posed by variability in the stress response to equivalent stressors
bear directly on stress-related research, including psychosis research, because the stress
construct must necessarily be operationalized and measured in some way. Consider life
events research: if a given study makes use of a checklist of ‗stressful events‘ determined
a priori, and if the quality of a given event as ‗stressful‘ varies from person to person,
then the results of such research are likely to be attenuated or misleading to some extent.
Operationalizations of stressful life events do vary widely in psychosis research, as does
the extent to which potential moderating factors such as controllability, chronicity,
dependence/independence, prior history, and personal meaning are considered. The
model that appears to underlie the more traditional life events checklist approach to
assessing the magnitude of recent stressors, as exemplified in the Holmes and Rahe
(1967) scale, is essentially a static model in which stress is predicated upon change in life
circumstance. In the implicit, underlying framework used by Holmes and Rahe, for
example, relative uniformity of the stress response is assumed. This assumption is
evident in their Life Crisis Unit (LCU) measurement system, in which discrete events
such as divorce (LCU = 73) or home foreclosure (LCU = 30) are assessed based on an
invariant weighting scheme. In this model, not only is stress conceptualized as a result of
external events beyond one‘s control, but many seemingly positive life change events are
also included, such as marital reconciliation. The assumption is that positive events that
involve major life changes are also uniformly stressful.
24
The transactional model. In more contemporary psychosis research, to the extent
that there is an overarching psychological model of stress, very likely it is the
transactional model of Lazarus and Folkman (1984). Notably, of the studies that
contributed to this review, the transactional model is the only psychological theory of
stress to be specifically referenced by name (e.g., Horan et al., 2005). It is a dynamic
framework, conceptualizing stress as the result of complex, bidirectional interactions
between the person and the environment. The characteristics of events, such as their
frequency and quality, are emphasized as determinants of the stress response. At the
same time, evaluative appraisals of a given event are central to the model, which
conceptualizes stress as a particular relationship with the environment that is appraised as
draining or exceeding personal resources, or as endangering one‘s well-being.
In early experimental research, Lazarus and colleagues showed violent film
excerpts to participants, who were first read orienting passages designed to either
increase denial and distance, on the one hand, or perceived threat, on the other (Lazarus
& Alfert, 1964; Speisman, et al., 1964). The former group consistently showed reduced
psychophysiological reactions compared to controls, while the latter group showed
heightened reactions. Based on such evidence, Lazarus shifted to a general concept of
appraisal as the cognitive mediator of stress reactions, in which appraisal came to be
viewed as a universal process in which people evaluate the significance of events for their
personal well-being and react emotionally on that basis. In its mature form, the
transactional model of stress (also referred to as the cognitive-motivational-relational
theory of stress) emphasizes the (appraised) balance between psychological resources and
25
environmental demands (Lazarus, 1999). When there is a perceived imbalance between
demands and resources, the individual experiences stress. Conversely, if there is
misperceived balance between demands and resources (i.e., if one sees oneself as capable
of meeting a particular challenge, but is actually not capable), then the individual will not
experience stress, at least not until the imbalance comes to light.
The transactional model is appealing, because it reflects the complexity of real
human experience. According to Norman and Malla (1993), for example, ―the meaning
of some events to a patient, and therefore the level of stress engendered by them, is likely
to be lost on anyone who is not familiar with his/her circumstances‖ (p. 168). Within the
transactional model, the fact that stressfulness of a given experience might vary widely
from person to person is readily explained as a function of disparate appraisals between
individuals, reflecting differences in circumstances, life history, beliefs, and personality.
From a broad perspective, the transactional model can be viewed as an outgrowth
of the ‗cognitive revolution‘ of the 1960s and 1970s that reestablished meaning as a
central concept in psychology. Thus, it shares a kind of intellectual kinship with the
cognitive theories of Rotter (1954), Ellis (1957), Beck (1970), and others, as well as with
contemporary models of psychosis (e.g., Bentall et al., 1994; Bentall et al., 2001; Garety
et al., 2001), which specify a role for subjective appraisal., Nevertheless, despite the
apparent relevance of accounting for variability in the relative magnitude of stressors
from an idiographic perspective, along with the endorsement of established scholars in
the field such as Norman and Malla (1993), a recent review concluded that, to date, no
psychosis studies have been conducted that consider patient appraisals of presumed
26
stressful events (Phillips et al., 2007; a notable exception may be the recent ESM studies
of Myin-Germeys and colleagues [e.g., Myin-Germeys et al., 2003], in which the
appraised stressfulness of daily events was directly assessed). Such research is called for.
If it were found, for example, that highly idiosyncratic and/or unrealistic stress appraisals
mediate psychotic relapse or exacerbation, this would suggest a clear target for
psychosocial interventions.
While appraisal-based stress research may be relevant to understanding psychosis,
it also poses challenges. One potential confound discussed by Phillips and colleagues
(2007) concerns ―effort after meaning effects‖ (p. 310). This term refers to the potential
for subjects to unwittingly bias their responses in an attempt to make sense of their own
experiences. The assessment of event appraisals assumes that respondents are aware of,
understand, and can articulate their appraisals – by no means a given when studying
seriously mentally ill populations. Moreover, when the evaluation of the importance,
salience, or impact of events is relatively distal to the events themselves, it is quite
possible that responses will be variably contaminated by the effects of more current
events and situations. From this perspective, the use of ESM methodology, which would
allow subjects to input appraisal information within minutes or hours of a target event,
offers a compelling alternative to retrospective assessment.
Conservation of Resources theory. Resource-based theories of stress, which
have received increased attention in recent years (Antonovsky, 1979; Baltes, 1997;
Bandura, 1997; Holahan & Moos, 1987, 1991), have been presented as an alternative to
Lazarus' appraisal-based model. One resource-based theory that has gained considerable
27
attention in the stress literature, particularly in the study of trauma and post-traumatic
stress disorder, is Hobfoll‘s (1989) conservation of resources (COR) theory of stress.
COR theory posits as a basic tenet that ―people strive to retain, protect, and build
resources and that what is threatening to them is the potential or actual loss of these
valued resources‖ (p. 516). Those things people value, or that aid in obtaining that which
is valued, are termed resources. As presented by Hobfoll (1989), the view that people are
inherently motivated to build and protect valued resources is analogous to Freud‘s
(1900/1913) pleasure principle, Maslow‘s (1968) hierarchy of needs, and the emphasis in
social learning theory on obtaining positive reinforcement. There are four types of
resources recognized by COR theory: object resources (e.g., transportation, shelter,
material goods); condition resources (e.g., support system, employment, marriage);
personal resources (e.g., job and social skills, mastery, self-esteem); and energy resources
(e.g., credit, money, knowledge). This categorical system is presented as heuristic rather
than as absolute; it is acknowledged that there are other valid ways to categorize
resources, but it is further argued that the articulated four categories are useful and reflect
how resources have been discussed in the literature (Hobfoll, 1988).
In defining stress, Hobfoll (2001) borrows from the work of Kaplan (1983), who
defines stress as an internal state that:
…reflects the subject‘s inability to forestall or diminish perception,
recall, anticipation, or imagination of disvalued circumstances, those
that in reality or fantasy signify great and/or decreased distance from
28
desirable (valued) experiential states, and consequently, evoke a need
to approximate the valued states. (p. 196)
From the perspective of COR theory, the desirable experiential states referred to by
Kaplan are directly related to and based on valued resources. Hobfoll (2001) endorses
the inclusion of perception (read: appraisal) in Kaplan's definition, but maintains that
perceptions of resources in terms of associated value and gains/losses are primarily
reality-based within a given culture, reflecting shared social constructions to a greater
extent than person-specific variables. Thus, COR theory is opposed to a strictly cognitive
view of stress, although it acknowledges a subsidiary role for cognition within the context
of sociocultural determinants. This opposition is predicated, in part, on two criticisms of
appraisal theory. First, on a practical level, Hobfoll argues that predictive strength is
limited in appraisal theory because ―to obtain appraisals we must wait until the proximalmoment where stress occurs and constantly hark back to the individual for her or his
assessment at that state and time‖ (p. 340). Second, on more of a conceptual level,
Hobfoll argues that appraisal-based stress theory overemphasizes appraisals as being
peculiar to or characteristic of the individual (i.e., as idiographic), while offering little
information about why people make certain appraisals, the extent to which appraisals are
outgrowths of learned rules, and the extent to which they are shared and culturally
scripted. In COR theory, the idiographic aspects of appraisal are secondary to biological
and socioculturally-conditioned responses. Hobfoll cites various evidence to support this
view. In a study of victims of Hurricane Hugo, for example, self-reported loss of
resources was assessed based directly on COR theory. The authors report that resource
29
loss was more critical in determining psychological distress (accounting for 34% of the
variance) than were the coping responses (7.5% of variance) or demographic
characteristics (9.5% of variance) of the victims (Freedy, Shaw, Jarrel, & Masters, 1992).
Similarly, in a study of the effects of Hurricane Andrew, self-reported resource loss
accounted for 42% of the variance in post-traumatic stress symptoms, and was the only
hurricane experience variable that was significantly related to both of the immune
compromise variables studied, namely, decreased natural killer cell cytotoxicity and
increased white blood cell count (the latter indicating the body's response to attack;
Ironson et al., 1997).
Consistent with the COR model‘s basic premise, stress is believed to occur under
three circumstances of resource frustration. These are: (1) when resources are threatened
with loss; (2) when resources are actually lost; and (3) when there is failure to adequately
gain resources following significant resource investment. Acute stressful conditions are
believed to result in stress because they result in rapid resource loss, as in the case of
natural disaster. In contrast, chronic stressful conditions drain resources over time, with
the potency to eventually dry out strong resource reservoirs (Hobfoll & Lilly, 1993).
COR theory gives primacy to loss, which is considered disproportionately more salient
than gain (Hobfoll, 1988; Hobfoll, 1993). Thus, in contrast to the early work of Holmes
and Rahe, Hobfoll (2001) argues that changes and transitions are stressful only to the
extent that they encompass undesirable events, or losses (see also Dohrenwend, Link,
Dern, Shrout, & Markowitz, 1990; Thoits, 1983). This emphasis is reflected in COR
theory's first principle, which states that resource loss is more potent than resource gain.
30
COR theory further states that loss drains resources, and that resource gain can buffer
against the effects of resource loss.
COR theory derives four corollaries (Hobfoll, 2001), two of which will be
highlighted in terms of their relevance to psychosis research. The first derived corollary
states that those with fewer resources are more vulnerable to resource loss, and are less
capable of achieving resource gains. This corollary is consistent with research findings
regarding vulnerability to schizophrenia and other psychotic disorders, where the
condition of having few resources (e.g., low SES, insecure attachment, poor social
integration) is associated with increased risk for psychosis. The second derived corollary
presented by COR theory states that those who lack resources are relatively more
vulnerable to resource loss; and further that, because individuals rely on resources to
offset loss, and because stress results from loss, at each iteration of an ongoing stress
cycle there are fewer resources to rally in defense. Thus, ―loss begets future loss,‖ giving
rise to ―loss cycles‖ (Hobfoll, 2001, p. 354). For example, Lange and Hobfoll (1992)
found that initial loss due to chronic illness contributed to patients' anger reactions which,
in turn, alienated support.
In the psychosis literature, COR theory's second corollary is consistent with
research regarding the cumulative impact of major life events and chronic stressors in
terms of precipitating psychotic relapse and other symptom exacerbations, as well as with
findings regarding stress sensitivity. From the perspective of COR theory, as ongoing
stressful experiences drain resources from vulnerable individuals, resource loss will result
in increased inability to master everyday stressors (compare to the results of Myin-
31
Germeys et al., 2003, in which major stressors were associated with increased emotional
reactivity to daily events), such that vulnerable individuals may ultimately become so
depleted that they regress into a full psychotic state. As an extension of the second
corollary, COR theory predicts that childhood experience ―primes‖ sensitivity to loss
(Hobfoll, 2005, personal communication). This hypothesis is consistent with findings
regarding childhood risk factors for schizophrenia (e.g., Janssen et al., 2004); indeed, the
lives of many severely affected psychosis patients may be loosely conceptualized as an
enduring loss cycle from childhood through adulthood. The second corollary
corresponds particularly well with the findings of Glaser et al. (2006), who reported that
subjects reporting a history of childhood trauma were more stress reactive than those who
did not report such experiences.
In concluding this attenuated review of stress theory, which has focused on the
work of Lazarus and Folkman (1984) and Hobfoll (1989, 2001), it should be pointed out
that these theories may not be as far apart from each other as might first appear. Hobfoll
clearly acknowledges the importance of cognitive appraisal, going so far as to say that
―the best proximal indicator…of stress is personal appraisal‖ (Hobfoll, 2001, p. 340,
italics included). In this respect, as noted earlier, Hobfoll differentiates his theory from
the transactional model by maintaining that appraisal is primarily a shared, culturallyconditioned (i.e., ‗objective‘) phenomenon, rather than being centrally idiographic, as is
imputed to the transactional model. However, in a response to this characterization,
Lazarus (2001) states that his conceptualization of appraisal is not purely
phenomenological:
32
I take the position that, on the whole, people perceive and respond to
the realities of life more or less accurately – otherwise they could not
survive and flourish…the subjectivism you will see here, if this is what
it should be called, is really a compromise…between the objective
conditions of life and what people wish to fear. (p. 384)
As this quotation suggests, Lazarus, like Hobfoll, incorporates the concept of ‗objective
conditions‘ influencing cognitive appraisals. The difference is one of emphasis. As is
made clear in Lazarus (1991), actual resources such as wealth, social networks, and
competencies function in the transactional model as antecedents to the stress response
that exert an indirect effect, whereas subjective resources (resource appraisals) represent
direct precipitants of the stress process that are partially independent of the objective
circumstances. In contrast, as noted above, COR theory relegates appraisal to a
secondary role while elevating resources to a primary position. For Hobfoll, appraisal is
an indicator, rather than a cause, of stress. Underlying this change in emphasis, the
fundamental modification that lends COR theory its unique character is the emphasis on
objective resource status – and in particular resource change (loss/gain) – as the
fundamental determinant of psychological stress. From this perspective, it appears that
appraisal functions as an indicator of stress to the extent that it represents actual or
anticipated resource change. As part of this basic emphasis on objective determinants,
COR theory diverges from the transactional model by arguing that appraisals themselves,
in terms of the weighting of losses and gains, primarily reflect extra-personal cultural and
33
communal values over and above the effects of unique personal history, temperament,
and the like (Hobfoll, 2001).
COR theory‘s loss/gain dichotomy, and in particular the notion of resource-based
loss spirals and gain spirals (the latter refers to Corollary 3, not discussed above), extends
beyond the stipulations of the transactional model. In terms of gain, moreover, COR
theory‘s stipulation of failure to gain resources following resource investment as a source
of stress is an intuitively appealing feature missing from the transactional model. This
addition maps on well to some of the empirical research on burnout and ill health, such as
―effort-reward imbalance‖ (Siegrist, 1996), and may be applicable to frustrations
experienced by persons with psychotic illness in terms of attempting to improve their
circumstances under conditions of high likelihood of failure. Thus, in addition to the
modified emphasis on objective resources and appraisals, COR theory is noteworthy with
respect to the transactional model in that it represents a broadening of stress theory to
include positive strivings and frustrations thereof.
It should be emphasized that COR is not a rejection of the transactional model,
but rather a theory that builds on, modifies, and extends this earlier framework. On the
whole, COR theory advances the contributions of the transactional model by emphasizing
a broader range of phenomena (e.g., gain frustration; cultural scripting), and providing a
revised conceptualization of the role of resources and appraisals that more directly
grounds subjective processes in terms of objective circumstances. In terms of psychosis
research, moreover, COR theory‘s articulation of loss cycles and resultant psychological
priming provides a uniquely strong conceptual fit to empirical findings regarding the
34
effects of both childhood adversity and cumulative life events. While an in-depth
comparative analysis of the merits of the respective theories is beyond the scope of this
paper, it is clear that both the transactional model and COR theory have empirical
support, and present testable hypotheses that are relevant in terms of conceptualizing and
researching stress and psychosis. In the future, such research may help to further
understand and intervene in this important area.
CHAPTER III
Formal Hypotheses
The present study aimed to examine the impact of stressful experience,
operationalized based on COR theory, on persons with schizophrenia-spectrum disorders
or bipolar disorders with psychotic features. The following hypotheses were examined:
Hypothesis 1: Resources Loss and Current Emotional Functioning
Resource loss is positively associated with current levels of anxiety and
depression.
Hypothesis 2: Resource Gain and Current Emotional Functioning
Resource gains are negatively associated with current levels of anxiety and
depression.
Hypotheses 3A, 3B, and 3C: Resource Loss and Symptom Change
Hypothesis 3A. Self-reported resource losses are associated with worsening
levels of emotional functioning, assessed in terms of anxiety and depression; with
increases in the level of psychosis symptoms; and with global increases in
psychopathology.
Hypothesis 3B. Self-reported resource losses that are classified as independent of
the individual‘s behavior show the same patterns of associations described above
(Hypothesis 3A) for self-reported losses.
35
36
Hypothesis 3C. Rater-assessed resource losses that are classified as independent
of the participant‘s behavior show the same patterns of associations described above
(Hypothesis 3A) for self-reported resource losses.
Hypotheses 4: Loss Spiral
The interaction of low baseline social functioning with subsequent resource
losses is associated with worsening levels of emotional functioning, assessed in terms of
anxiety and depression; with increases in the level of psychosis symptoms; and with
global increases in psychopathology.
Hypothesis 5: Mediation
Changes in emotional functioning mediate the anticipated deleterious effect of
resource loss on psychosis symptoms.
Hypotheses 6A and 6B: Resource Gain and Symptom Change
Hypothesis 6A. Self-reported resource gains buffer the adverse effects of
resource loss on emotional functioning and psychosis symptoms.
Hypothesis 6B. When reported surplus gains are accounted for in a combined
―gain-minus-loss‖ variable (i.e., in which losses are subtracted from the corresponding
gains for each participant), the gains-minus-losses variable is associated with
improvement in emotional functioning, psychosis symptoms, and global
psychopathology.
CHAPTER IV
Method
Participants
Seventy-seven outpatients from a larger research project participated in the study.
Participants were recruited from a community mental health clinic. Participation was
limited to individuals diagnosed with schizophrenia, schizoaffective disorder, or bipolar
disorder with psychotic features, in accordance with guidelines established by the
Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric
Association, 2000). All diagnoses were validated based on the Schedule for Affective
Disorders and Schizophrenia – Lifetime Version (SADS-L; Endicott & Spitzer, 1978).
To be included in the study, participants had to be aged between 18 and 50 years, fluent
in English, and considered stabilized and capable of giving consent by their treating
therapist or psychiatrist. Participants were excluded from the study if they met DSM-IV
criteria for alcohol or drug abuse at the time of data collection, or if there was reliable
indication of organic brain damage (e.g., alcohol abuse resulting in detoxification, solvent
abuse, mental retardation, head injury, or seizure disorder). Assessment of possible
organic damage was based on observation, subject report, and clinical records.
Measures
A copy of all instruments used in the study can be found in the appendices.
Diagnosis. The Schedule for Affective Disorders and Schizophrenia (SADS;
Endicott & Spitzer, 1978; see Appendix A) is a structured diagnostic interview that was
37
38
adapted for use with the DSM-IV. The SADS is a widely used instrument for clinical
research. The interview includes over 200 items and takes from 1.5 to 2 hours to
administer. The SADS has been shown to have good inter-rater and test-retest reliability
(Endicott & Spitzer, 1978). Furthermore, validity studies have found that SADS scores
correlate with external measures of depression, anxiety, and psychosis (Coryell et al.,
1994; Johnson, Margo, & Stern, 1986).
The SADS was administered to participants by masters-level students in clinical
psychology. SADS interviews were audiotaped so that diagnostic information could be
reviewed later, helping to ensure accuracy and reliability of ratings. All diagnoses were
reviewed and approved by N.D., who has extensive diagnostic experience, and has
exhibited good diagnostic reliability in earlier studies (e.g. Kappa = .88 for
schizophrenia; Docherty, Serper, & Harvey, 1990).
Symptom Severity. The Positive and Negative Syndrome Scale (PANSS; Kay,
Fiszbein, & Opler, 1987; see Appendix B) is a widely used instrument in schizophrenia
research. The PANSS was used in the present study to measure the severity of
symptoms. The PANSS is a semi-structured interview, consisting of 31 items organized
based on three domains of functioning: positive, negative, and general symptoms. Each
item is given a score ranging from 1 (absent) to 7 (extreme). Scores on the PANSS can
be summed within each of the three domains, and can also be used to produce a global or
aggregate score as an overall indication of psychopathology.
The PANSS has been found to have good psychometric properties (Kay, Opler, &
Lindenmayer, 1989; Kay, Opler et al., 1987). In terms of reliability, studies of
39
schizophrenia patients have shown that the PANSS has adequate inter-rater reliability
(Bell, Milstein, Beam-Goulet, Lysaker, & Cicchetti, 1992; Kay et al., 1989; Kay, Opler et
al., 1987), good internal consistency (Bell, Lysaker, Milstein, & Beam-Goulet, 1994;
Kay, Fiszbein et al., 1987), and good to fair test-retest reliability (Bell et al., 1994; Kay,
Fiszbein et al., 1987). With regards to validity, studies have found that the positive and
negative items of the scale are negatively correlated with each other within a
schizophrenic population, as would be expected based on earlier research in this area.
Patients with primarily positive symptoms have been found to have different patterns of
scores than patients with primarily negative symptoms (Kay, Fiszbein et al., 1987).
Criterion validity of the PANSS has also been supported in studies of clinical and
cognitive functioning (Bell et al., 1994; Ehmann et al., 2004).
In the present study, PANSS ratings were based on symptom information for the
previous two weeks. PANSS ratings were made at both Time 1 and Time 2. The
diagnostic interviewers completed the PANSS scales at the conclusion of the SADS
diagnostic interview. Inter-rater reliability for the PANSS was good, r (ICC) = 0.81, p <
.001.
For analytical purposes, five scores were derived from the PANSS. These were
as follows: a positive symptoms scale score (PANSS-PS); a core positive symptoms score
based on delusions and hallucinations (PANSS-CPS); a negative symptoms scale score
(PANSS-NS); a core negative symptoms score based on flat affect and poverty of speech
(PANSS-CNS); and, as an index of global psychopathology, the PANSS total score
(PANSS-T).
40
The distinction between positive and negative symptoms, and the incorporation of
more focal operationalizations thereof (PANSS-CPS and PANSS-CNS), merits
elaboration. According to DSM-IV, positive symptoms ―reflect an excess or distortion of
normal functions‖ (p. 299). In contrast, negative symptoms are conceptualized as deficits
of normal functions. The additional distinction between core positive and core negative
symptoms corresponds to Crow's (1985) definition of the Type I (positive) and Type II
(negative) schizophrenia, in which a more narrow definition of the positive and negative
syndromes minimizes the association between them, while at the same time eliminating
from consideration symptoms that are more readily attributable to exogenous influences,
such as depression.
Depression. The Beck Depression Inventory – Second Edition (BDI-II; Beck,
Steer, & Brown, 1996; see Appendix C) was developed to assess self-reported depression
based on the DSM-IV criteria for major depression. The BDI-II consists of 21 items
assessing the severity of the affective, cognitive, motivational, psychomotor, and
vegetative components of depression, with higher scores indicating more severe
depression overall. In line with DSM-IV criteria, items refer to how the respondent has
been feeling for the previous two weeks. Items are scored on a four-point scale ranging
from 0 to 3, and are summed to produce an overall score.
The BDI-II is a well-validated and frequently used measure of depression. The
internal consistency of the BDI-II has been examined in various studies, with coefficient
alpha ranging from 0.89 (Steer et al., 2000) to 0.92 (Beck et al., 1996) in adult psychiatric
samples, and from 0.89 (Steer and Clark, 1997; Whisman et al., 2000) to 0.93 (Beck et
41
al., 1996) in college samples. Research showing that individuals with a diagnosis of
depression score significantly higher than those without depression on the BDI-II
provides evidence for criterion related validity (Osman et al., 2004). Convergent and
discriminant validity are evidenced by research reporting BDI-II scores to be more
strongly correlated with other measures of depression than assessments of constructs such
as anxiety among adults (Beck et al., 1996; Steer & Clark, 1997) and adolescents (Osman
et al., 2004).
Anxiety. The State-Trait Anxiety Inventory (STAI; Spielberger, Gorsuch,
Lushene, Vagg, & Jacobs, 1983; see Appendix D) is one of the most widely used scales
for the evaluation of anxiety (Kennedy, Schwab, Morris, & Beldia, 2001). Spielberger
(1983) defined state anxiety as a transitory emotional response to a stressful situation. In
contrast, trait anxiety was defined as an enduring personality characteristic that could
predispose persons to state anxiety during times of stress. In keeping with this
conceptualization, the STAI targets both in-the-moment anxiety as well as generalized
(presumptively trait-related) anxiety experiences. Strong psychometric support is
available for the STAI (Spielberger et al., 1983), and data from heterogeneous
community and psychiatric samples suggest good internal consistency and convergent
validity (Stanley et al., 2001; Kabacoff et al., 1997). The state form of the STAI, the
STAI-Y1, was used in this study.
Social functioning. The Social Functioning Scale (SFS; Birchwood et al., 1990;
see Appendix E) was developed to measure different areas of functioning that are crucial
to the community living of individuals with schizophrenia. The SFS is constructed to
42
measure social skills and performance among patients with schizophrenia. It is a selfadministered questionnaire, consisting of 76 items sorted into seven subscales as the sum
of all items within each area. Each subscale is standardized and normalized to a Scaled
Score (SS) with a mean of 100 and standard deviation of 15, based on a sample of 334
individuals with schizophrenia (Birchwood et al., 1990). The full scale score is the mean
of the seven subscale SSs. The SFS enumerates key skills and social behaviors which
informants record as present or absent. Items are rated on a four-point scale of frequency
or ability; with a higher score indicating a higher frequency or more competent behavior.
This scale has been shown to be a reliable, valid, and sensitive measure of social
functioning (Birchwood et al., 1990).
Resource losses and gains. The 74-item Conservation of Resources Evaluation
(COR-E; Hobfoll, Lilly, & Jackson, 1992; see Appendix F) was used to assess the
occurrence of recent stressful experiences. The COR-E reflects the resource-based
conceptualization of stress promulgated by COR theory. The COR-E was developed
through a series of group processes in which groups from the university and the
community nominated important resources in their lives (Hobfoll and Lilly, 1993). These
groups then evaluated the initial list, adding and deleting resources that were of little
importance, redundant, or too specific. Notably, in a subsequent group process
conducted in the Netherlands, students and faculty nominated 56 of the 74 resources that
were named originally, and did not nominate any resources that were not on the original
list (Lane & Hobfoll, 1992). The final list is seen as comprehensive and reflecting a
broad cultural base, but not as all-inclusive.
43
In its final form, the COR-E is a self-report measure that assess for losses, threatsof-loss, and gains in resources across the domains of work resources (e.g., stable
employment), personal resources (e.g., sense of pride), material resources (e.g., savings
or emergency money), energy resources (e.g., personal health), and interpersonal
resources (e.g., children's health). The magnitude of losses and gains is determined using
a 5-point Likert scale. The COR-E has been used as a measure in a numbers of studies
(e.g., Hobfoll & Lilly, 1993; Lane & Hobfoll, 1992; Luyster, Hughes, Waechter, &
Josephson, 2006), and has been found to be an efficacious predictor of psychological
stress reactions. Of note, factor analysis of the COR-E found expected developmental
differences, such that the first factor for students concentrated around issues of identity,
whereas finances and family health were more primary for a community sample (Hobfoll,
Lilly, & Jackson, 1991). For each sample, significant multifactor solutions were found.
These findings are important, as they run contrary to the view that negative affect simply
elicits a global sense of loss (e.g., ―I'm depressed, therefore I believe I am experiencing
all kinds of losses‖). Test-retest for COR-E loss and gain during the past year has ranged
from .64 to .67, consistent with findings for recent life event measures and suggesting the
report of loss and gain is not random or overly influenced by transient mood states
(Hobfoll & Lilly, 1993).
For the purposes of the proposed study, the COR-E was supplemented by a
follow-up interview in which participants were asked to provide narrative information for
any losses, threats-of-loss, or gains they indicated as having occurred during the
preceding six weeks. For any such changes, participants were first asked to discuss the
44
nature of the change in question. In the case of a reported loss, participants were asked to
describe how the situation became worse, or how it was threatened with getting worse.
Similarly, in the case of a reported gain, participants were asked to describe how the
situation became better with respect to the resource in question. Second, participants
were asked to explain, as best they could, what they believed to be the cause or causes
that gave rise to the change in question. Third and finally, participants were asked to
discuss how they believed the change had affected their life.
Procedure
Participants were interviewed as part of a larger study, consisting of five sessions
lasting two to three hours in length. During an initial telephone contact, participants'
clinical case managers were contacted to verify eligibility and appropriateness for the
study. Following, participants were contacted to request their participation, and an initial
appointment was scheduled at a community mental health clinic. Participants were paid
$50 for each study session.
The proposed study was integrated into the first and fifth sessions of the larger
research project, separated in time by approximately nine to twelve weeks. During the
initial interview (Time 1), participants were administered the SADS in order to validate
diagnoses and the PANSS in order to establish baseline symptom severity. Patients were
also asked questions pertaining to their psychiatric history, medication use, and
demographics. During this initial session, participants were administered the BDI-II and
STAI in order to establish baseline levels for depression and anxiety.
45
During the follow-up session (Time 2; fifth session of the larger study),
participants were rated on the PANSS for psychosis symptom severity, and were
administered the BDI-II and STAI in order to assess for changes in levels of depression
and anxiety. Participants were administered the COR-E to retrospectively probe for
stressful life experiences that may have occurred over the preceding six weeks. Losses,
threats-of-loss, and gains in resources were assessed. Afterwards, participants were
asked follow-up questions for the COR-E, in which narrative detail was elicited regarding
the nature of any specific resource changes that had been indicated.
The COR-E follow-up interviews were recorded, and were later transcribed by an
undergraduate research assistant. Each transcription was checked for accuracy by a
second research assistant. Based on the transcriptions, trained research assistants served
as objective third-person raters for the COR-E, generating ratings of resource changes
parallel to those made by participants. Raters based their ratings on the contextual
narrative information provided by the participant, and exhibited acceptable reliability,
r (ICC) = 0.77, p < .001. For each specific event, raters also assessed the independence
or dependence of causes, relative to the behavior of the participant. Classifications of
dependence/independence were important in order to establish that the direction of
causality was in the event-to-symptom-change direction (e.g., events that are behaviordependent may, by definition, occur as an effect of symptom change, rather than as a
cause of symptom change). A conservative rating approach was adopted: only clearly
independent events were classified as independent, whereas events for which
independence was unclear, or was merely probable based on the information, were
46
classified as dependent. Raters exhibited excellent inter-rater reliability for these
classifications, κ = 0.95, p < .001. For the dataset as a whole, 24.8% of items for which a
resource change was indicated were classified as independent.
Based on the above information, four versions of scores for each of the three
COR-E change variables (loss, threat-of-loss, or gain) were generated: (1) self-reported
resource changes; (2) self-reported independent resource changes, where the variable was
computed based only on items that were classified as independent; (3) third-person rated
independent resource changes, where the variable was computed based on objective
third-person ratings, which were in turn limited to only those items that were classified as
independent; and (4) third-person rated independent non-subjective resource changes, in
which COR-E items that refer to the subjective condition of the participant (e.g., ―Sense
of pride in myself;‖ ―Feeling that I am successful;‖ ―Motivation to get things done‖) were
excluded. These are resource changes for which it is impossible to rule out illness-related
causal mechanisms, even when the target events to which the resource changes are
attributed are, in themselves, clearly independent of the participant‘s behavior. A total of
20 subjective resource items were excluded (see Appendix G for the full list of excluded
questions), and the rated independent loss variable was recalculated. Due to the cognitive
impairments and biases associated with clinical psychosis, the third-person ratings for
resource changes were considered to be more trustworthy than participant self-report.
Therefore, the variables computed based on third-person rated independent resource
changes (versions 3 and 4 above) were considered the most methodologically
conservative.
47
Finally, a medication adherence review was conducted at the community mental
health clinic from which participants were recruited. The purpose of this review was to
determine if results changed significantly as a function of medication adherence. The
review was conducted by a paid staff member, who had access to participants‘ paper and
electronic medical records (only clinic staff had access to the electronic records). Clinic
records were found to include specific information from caseworkers, nurses, and
psychiatrists regarding known or suspected medication adherence issues, but did not state
whether or not clients were considered to be medication adherent. Therefore, participants
were classified as to whether there was, or was not, evidence of medication nonadherence.
Analyses
Change scores were created based on subtracting Time 1 scores from Time 2
scores for the BDI-II, STAI-Y1, and the PANSS scales (PANSS-PS, PANSS-CPS,
PANSS-NS, and PANSS-T). Change scores were also computed a second way, by
residualizing scores at Time 1 out of the scores at Time 2. While not as direct a
computation, this second method offers the advantage of controlling for large withinsubjects variability at baseline.
The analysis was done in four parts. First, correlations and two-way analyses of
variance (ANOVAs) were computed between predictor and outcome variables and key
demographic variables. Second, correlations were computed between predictor and
outcome variables.
48
In the third part of the analysis, Baron and Kenny‘s (1986) procedure was used to
test the mediation hypothesis. In the first step of this procedure, the predictor must be
significantly related to the outcome variable. In the second step, the predictor must be
significantly related to the proposed mediating variable. In the third step, the proposed
mediator must be significantly related to the outcome variable. In the fourth step, full
mediation is suggested if – after both the predictor and the mediator are entered into a
regression model as independent variables – the effect of the predictor variable on the
outcome variable is no longer statistically significant, whereas partial mediation is
suggested when this effect is substantially reduced.
In the fourth and final part of the analysis, moderated multiple regression analyses
were performed, following the recommendations of Baron and Kenny (1986). In this
procedure, the main effects for the predictor and putative moderating variables are
entered in step 1. Next, the interaction product term of these two variables is entered in
step 2. Whereas statistical significance of the moderator variable in step 1 suggests a
direct effect, statistical significance of the interaction term in step 2 suggests a
moderating effect.
CHAPTER V
Results
Patient Descriptive Variables
Table 1 contains descriptive information for the sample, expressed in terms of
age, gender, race, education, employment status, diagnosis, and medication compliance.
None of these variables were significantly related to scores on the COR-E.
One descriptive variable, diagnosis, was found to be associated with significant
differences in core positive symptoms on the PANSS (both at Time 1 and at Time 2).
Looking to the means for PANSS-CPS, there were two easily discernible clusters:
schizophrenia-spectrum disorders (higher means) and bipolar disorders (lower means).
The diagnosis variable was recoded into these two clusters, and group differences were
reassessed. As shown in Table 2, significant differences emerged for the following four
variables: PANSS-PS, PANSS-CPS, PANSS-NS, and PANSS-T. Given these
differences, supplementary hypothesis tests were carried out based on diagnostic
category, and are reported at the end of this section.
Reliability Analyses
Internal consistency reliability was assessed for measures purporting to assess a
unitary construct, namely, the SFS, the BDI-II, and STAI-Y1. As shown in Table 3,
internal consistency was assessed using Cronbach‘s alpha for both Time 1 and Time 2
test administrations, as well as for change scores between Time 1 and Time 2. Reliability
scores ranged from acceptable to excellent.
49
50
Table 1. Descriptive Information for Sample (n=77).
n
38.3 (± 7.62)
Mean Age (± SD)
Gender
Female
Male
No information
38
38
1
Race
African American
Caucasian
Hispanic/Latino
No information
48
27
1
1
Diagnosis
Schizophrenia Spectrum Disorders
Schizophrenia
Paranoid subtype
Disorganized subtype
Undifferentiated subtype
Schizoaffective Disorder
Depressive subtype
Bipolar subtype
Bipolar Disorders
Bipolar I
Bipolar II
64
33
26
4
3
31
15
16
13
12
1
Education Level
Less than high school
Some high school
High school graduate or GED
Some college
College graduate or above
No information
4
24
26
18
4
1
Employment Status
Employed at least part time
Unemployed
No information
12
64
1
Medication Adherence
Evidence of non-adherence
No evidence of non- adherence
No information
5
53
19
51
Table 2. Means and Standard Deviations for Dependent Variables.
Time 1
n
Mean (±SD)
Time 2
n Mean (± SD)
7 – 49
75
63
11
16.89 (± 5.74)
17.56 (± 5.73)a
12.91 (± 4.42)a
All
Spectrum Disorders
Bipolar Disorders
2 – 14
75
63
11
6.03 (± 2.80)
6.54 (± 2.72)c
3.00 (± 0.78)c
Negative Symptoms
All
Spectrum Disorders
Bipolar Disorders
7 – 49
75
63
11
13.17 (± 4.46)
13.67 (± 4.43)e
10.64 (± 3.93)e
PANSS-CNS
Core Negative Symptoms
All
Spectrum Disorders
Bipolar Disorders
2 – 14
75
63
11
3.10 (± 1.23)
3.10 (± 1.27)
3.18 (± 0.98)
PANSS-T
Global Psychopathology
All
Spectrum Disorders
Bipolar Disorders
30 – 210
75
63
11
62.83 (± 16.65)
64.81 (± 16.64)g
50.18 (± 10.63)g
BDI-II
Depression
All
Spectrum Disorders
Bipolar Disorders
0 – 63
77
63
13
17.61 (± 11.86)
18.46 (± 12.22)
13.92 (± 9.89)
STAI-Y1
State Anxiety
All
Spectrum Disorders
Bipolar Disorders
20 – 80
76
62
13
42.66 (± 14.22)
43.92 (± 13.88)
36.54 (± 15.31)
7
65
12
2
7
65
12
2
7
65
12
2
7
65
12
2
7
65
12
2
7
68
14
3
7
67
13
3
Variable
Construct
Diagnostic Group
Range
PANSS-PS
Positive Symptoms
All
Spectrum Disorders
Bipolar Disorders
PANSS-CPS
Core Positive Symptoms
PANSS-NS
16.99 (± 5.75)
18.10 (± 5.41)b
11.67 (± 4.44)b
5.92 (± 2.77)
6.56 (± 2.55)d
2.92 (± 1.51)d
13.12 (± 4.47)
13.50 (± 4.53)f
10.67 (± 3.20)f
3.16 (± 1.28)
3.18 (± 1.28)
3.08 (± 1.20)
62.93 (± 16.22)
65.27 (± 16.02)h
50.67 (± 12.32)h
18.6 (± 13.06)
19.17 (± 13.21)
15.23 (± 12.61)
43.95 (± 12.78)
44.11 (± 12.24)
43.31 (± 16.12)
Note: PANSS-PS = Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS = Positive and Negative Symptoms Scale-Core Negative Symptoms;
PANSS-NS = Positive and Negative Symptoms Scale-Negative Symptoms; PANSS-CNS = Positive and Negative Symptoms Scale-Core Negative Symptoms; PANSS-T
= Positive and Negative Symptoms Scale-Total Score; BDI-II = Beck Depression Inventory-II; STAI-Y1 = State-Trait Anxiety Inventory, State Version.
b,c,d,g,h
a,e,f
Mean differences significant at the 0.01 level.
Mean differences significant at the 0.05 level.
51
52
Table 3. Reliability Coefficients for Dependent Variables Measuring Unitary Constructs.
α
SFS
Baseline (Time 1)
0.70
BDI-II
Time 1
Time 2
Change Scores
0.92
0.93
0.84
STAI-Y1
Time 1
Time 2
Change Scores
0.94
0.93
0.86
Note: SFS = Social Functioning Scale; BDI-II = Beck Depression Inventory-II; STAI-Y1 = State-Trait
Anxiety Inventory, State Version.
53
Means and Standard Deviations
Means and standard deviations for the predictor variables are displayed in Table
4. Because distributions of scores on the COR-E were substantially positively skewed, a
logarithmic transformation was applied before conducting comparisons.
There was one participant for whom COR-E self-report data were missing, but for
whom the recorded narrative COR-E data were not missing. Therefore, as shown in
Table 4, self-report data on the COR-E were only available for 76 of the total sample of
77 participants. For analyses based on third-person rated data, including all analyses for
which resource changes were limited to events classified as independent, only 64 of the
77 total participants were included. Narrative follow-up data were missing for 13
participants.
Means and standard deviations for the Time 1 and Time 2 dependent variables are
displayed in Table 2. Distributions for the Time 2 – Time 1 change variables were
approximately normally distributed, as were distributions for the SFS, and the STAI-Y1
at Time 2. The BDI-II at Time 2 was moderately positively skewed, and therefore a
square root transformation was applied prior to analysis.
Resource Change and Current Emotional Functioning
Table 5 displays correlations of COR-E losses, threats-of-loss, and gains with
current (Time 2) emotional functioning. Self-reported losses were positively associated
with scores on the BDI-II, r = 0.33, p < .01, and the STAI-Y1, r = 0.31, p < .01; there
were no significant associations for self-reported independent losses, rated independent
losses, or rated independent non-subjective losses. A similar but inverse pattern of
54
Table 4. Means and Standard Deviations for Independent Variables.
n
Mean (± SD)
COR-E Resource Losses
Self-reported Losses
Self-reported Independent Losses
Rated Independent Losses
Rated Independent Non-subjective Losses
76
64
64
64
14.59 (± 21.04)
4.98 (± 10.52)
4.61 (± 10.51)
3.47 (± 5.66)
COR-E Resource Threats-of-Loss
Self-reported Threats
Self-reported Independent Threats
Rated Independent Threats
Rated Independent Non-subjective Threats
76
64
64
64
2.26 (± 6.59)
0.98 (± 2.23)
1.78 (± 3.2)
1.56 (± 2.73)
COR-E Resource Gains
Self-reported Gains
Self-reported Independent Gains
Rated Independent Gains
Rated Independent Non-subjective Gains
76
64
64
64
33.00 (± 36.09)
2.48 (± 4.9)
2.11 (± 4.2)
1.91 (± 3.66)
55
Table 5. Correlations of Predictor Variables with Time 2 Emotion Variables.
BDI-II
STAI-Y1
COR-E Resource Losses
Self-reported Losses
Self-reported Independent Losses
Rated Independent Losses
Rated Independent Non-subjective Losses
0.33**
0.17
0.20
0.18
0.31**
0.20
0.16
0.13
COR-E Resource Threats-of-Loss
Self-reported Threats
Self-reported Independent Threats
Rated Independent Threats
Rated Independent Non-subjective Threats
0.10
0.03
0.11
0.10
-0.09
-0.11
-0.02
-0.04
COR-E Resource Gains
Self-reported Gains
Self-reported Independent Gains
Rated Independent Gains
Rated Independent Non-subjective Gains
-0.30**
-0.09
-0.03
-0.03
-0.47**
-0.07
-0.06
-0.03
Note: BDI-II, Beck Depression Inventory-II; STAI-Y1, State-Trait Anxiety Inventory, State Version.
** Significant at the 0.01 level (2-tailed).
56
associations was found for resource gains. Self-reported gains were negatively associated
with scores on the BDI-II, r = -0.31, p < .01, and the STAI-Y1, r = -0.47, p < .01; there
were no significant associations for self-reported independent gains and rated
independent gains. Resource threats-of-loss showed no associations with current
emotional functioning. These results show partial support for Hypotheses 1 and 2.
Resource Loss and Symptom Change
As shown in Table 6, self-reported losses, self-reported independent losses, rated
independent losses, and rated independent non-subjective losses from the COR-E showed
a similar pattern of relationships whereby resource loss was significantly predictive of
symptom exacerbations for all seven dependent variables. The only variable for which
there were mixed significant results was negative symptoms. For Δ PANSS-NS, selfreported losses and rated independent losses were significantly related to exacerbations,
whereas the associations for self-reported independent losses and rated independent nonsubjective losses did not reach statistical significance. For Δ PANSS-CNS, self-reported
losses did not significantly predict symptom exacerbations, whereas the other three
versions of the resource loss variable were significantly associated with exacerbations.
Overall, these findings support Hypotheses 3A, 3B, and 3C.
As also shown in Table 6, resource threats-of-loss exhibited an overall weak and
nonsignificant pattern of associations with the outcome variables. Self-reported threats
and rated independent threats were not related to any outcome variables. For selfreported independent threats, two significant associations emerged: Δ PANSS-PS: r = -
57
Table 6. Correlations of Predictor Variables with Outcome Variables Computed as Time 2 – Time 1 Change Scores (Δ).
Δ PANSSPS
Δ PANSSCPS
Δ PANSSNS
Δ PANSSCNS
Δ PANSS- T
Δ BDI-II
Δ STAIY1
Resource Losses
Self-reported Losses
Self-reported Independent Losses
Rated Independent Losses
Rated Independent Non-subjective Losses
-0.41**
-0.38**
-0.39**
-0.36**
-0.41**
-0.40**
-0.38**
-0.38**
-0.28*
-0.21
-0.26*
-0.19
-0.16
-0.28*
-0.35**
-0.31*
-0.48**
-0.38**
-0.39**
-0.34**
-0.42**
-0.27*
-0.31*
-0.30*
-0.38**
-0.33**
-0.36**
-0.32**
Resource Threats-of-Loss
Self-reported Threats
Self-reported Independent Threats
Rated Independent Threats
Rated Independent Non-subjective Threats
-0.22
-0.28*
-0.05
-0.01
-0.16
-0.26*
-0.09
-0.05
-0.05
-0.06
-0.00
-0.05
-0.16
-0.09
-0.10
-0.06
-0.11
-0.18
-0.05
-0.01
-0.17
-0.06
-0.21
-0.17
-0.05
-0.05
-0.08
-0.01
Resource Gains
Self-reported Gains
Self-reported Independent Gains
Rated Independent Gains
Rated Independent Non-subjective Gains
-0.13
-0.32*
-0.20
-0.20
-0.09
-0.32*
-0.18
-0.16
-0.20
-0.10
-0.14
-0.15
-0.23
-0.29*
-0.30*
-0.30*
-0.20
-0.20
-0.11
-0.11
-0.22
-0.03
-0.01
-0.00
-0.26*
-0.15
-0.12
-0.09
Rated Independent Losses x Mean SFS
Mean SFS
-0.40**
-0.07
-0.45**
-0.04
-0.23
-0.11
-0.43**
-0.21
-0.43**
-0.07
-0.42*
-0.02
-0.37**
-0.01
Note: PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS, Positive and Negative Symptoms Scale-Core Negative
Symptoms; PANSS-NS, Positive and Negative Symptoms Scale-Negative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core
Negative Symptoms; PANSS-T, Positive and Negative Symptoms Scale-Total Score; BDI-II, Beck Depression Inventory-II; STAI-Y1, State-Trait
Anxiety Inventory, State Version; SFS, Social Functioning Scale.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
57
58
0.28, p < .05; and Δ PANSS-CPS: r = -0.26, p < .05. These associations were in the
opposite direction from what had been hypothesized.
Supplementary correlations were computed in which the change variables were
calculated by residualizing scores at Time 1 out of the scores at Time 2 (i.e., using the
standardized residuals), rather than as Time 2 – Time 1 change scores. Results of these
analyses were highly similar to those using change scores, with the following exceptions.
For resource loss, Δ BDI-II was no longer significantly predicted by self-reported
independent losses (r = 0.21, p = .10), by rated independent losses (r = 0.24, p = .06), or
by rated independent non-subjective losses (r = 0.23, p = .07). For resource threats-ofloss, Δ PANSS-PS was no longer significantly predicted by self-reported independent
threats (r = -0.24, p = .06).
Loss Spiral
The interaction of low social functioning at Time 1 with subsequent resource
losses was calculated as the product of mean scaled scores on the SFS with rated
independent losses on the COR-E. Associations for this interaction variable with the
outcome variables are shown in Table 6, as are the associations for the main effect of the
SFS. Overall, the associations were slightly stronger than those found for rated
independent losses, although these differences did not approach statistical significance.
The SFS itself was not significantly related to any of the outcome measures. These
results are consistent with Hypothesis 4.
59
Change in Emotional Functioning as Mediator
Two series of regression analyses were conducted to test the mediation models for
emotional functioning. The first series of regression analyses assessed change in levels
of depression as a mediator of the relationship between resource loss and exacerbated
psychosis symptoms, as shown in Table 7. The second series of regression analyses
assessed change in levels of state anxiety as the proposed mediator, are shown in Table 8.
Neither variable was a significant mediator. The results of these regression analyses do
not support Hypothesis 5, which proposed that changes in emotion would mediate the
effect of resource loss on psychosis symptoms.
Resource Gains and Symptom Change
Associations between resource gains and the outcome variables are shown in
Table 6. Self-reported gains were significantly negatively associated with Δ STAI-Y1,
while self-reported independent gains were significantly negatively associated with Δ
PANSS-PS, Δ PANSS-CPS, and Δ PANSS-CNS. In addition, both rated independent
gains and rated independent non-subjective gains were significantly negatively associated
with Δ PANSS-CNS, such that the most consistent predictive relationship for resource
gains (across three of the four versions of the resource gains variable) was with
improvements in core negative symptoms.
As shown in Table 9, a series of regression analyses was conducted to test the
moderation model. Using rated independent resource gains as the proposed moderator,
60
Table 7. Mediation Analyses for Depression as Proposed Mediator Between Resource
Loss† and Psychosis Symptoms.
IV
Δ PANSS-PS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-CPS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-NS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-CNS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-T
Step 1
Step 2
Step 3
Step 4
DV
Δ R2

t
p
COR-E Losses
COR-E Losses
Δ BDI-II
Δ BDI-II
COR-E Losses
Δ PANSS-PS
Δ BDI-II
Δ PANSS-PS
Δ PANSS-PS
-
-0.391
-0.313
-0.162
-
-3.260
-2.594
-1.377
-
0.002**
0.012*
0.173
-
COR-E Losses
COR-E Losses
Δ BDI-II
Δ BDI-II
COR-E Losses
Δ PANSS-CPS
Δ BDI-II
Δ PANSS-CPS
Δ PANSS-CPS
-
-0.384
-0.313
-0.225
-
-3.196
-2.594
-1.930
-
0.002**
0.012*
0.058
-
COR-E Losses
COR-E Losses
Δ BDI-II
Δ BDI-II
COR-E Losses
Δ PANSS-NS
Δ BDI-II
Δ PANSS-NS
Δ PANSS-NS
0.082
0.032
-0.257
-0.313
-0.320
-0.228
-0.188
-2.047
-2.594
-2.846
-1.758
-1.445
0.045*
0.012*
0.006*
0.084
0.154
COR-E Losses
COR-E Losses
Δ BDI-II
Δ BDI-II
COR-E Losses
Δ PANSS-CNS
Δ BDI-II
Δ PANSS-CNS
Δ PANSS-CNS
-
-0.348
-0.313
-0.129
-
-2.855
-2.594
-1.087
-
0.006**
0.012*
0.281
-
COR-E Losses
COR-E Losses
Δ BDI-II
Δ BDI-II
COR-E Losses
Δ PANSS-T
Δ BDI-II
Δ PANSS-T
Δ PANSS-T
0.092
0.100
-0.394
-0.313
-0.268
-0.201
-0.332
-3.292
-2.594
-2.340
-1.625
-2.681
0.002**
0.012*
0.022*
0.109
0.010*
Note: PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS, Positive and
Negative Symptoms Scale-Core Negative Symptoms; PANSS-NS, Positive and Negative Symptoms ScaleNegative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core Negative Symptoms;
PANSS-T, Positive and Negative Symptoms Scale-Total Score; BDI-II, Beck Depression Inventory-II;
COR-E, Conservation of Resources-Evaluation.
† Resource loss calculated using rated independent losses.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
61
Table 8. Mediation Analyses for State Anxiety as Proposed Mediator Between Resource
Loss† and Psychosis Symptoms.
IV
Δ PANSS-PS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-CPS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-NS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-CNS
Step 1
Step 2
Step 3
Step 4
Δ PANSS-T
Step 1
Step 2
Step 3
Step 4
DV
Δ R2

t
p
-
-0.391
-0.364
-0.120
-
-3.260
-3.051
-0.994
-
0.002**
0.003**
0.324
-
Δ PANSS-CPS
Δ STAI-Y1
Δ PANSS-CPS
Δ PANSS-CPS
0.067
0.110
-0.384
-0.364
-0.248
-0.161
-0.345
-3.196
-3.051
-2.110
-1.288
-2.756
0.002**
0.003**
0.039*
0.203
0.008**
COR-E Losses
COR-E Losses
Δ STAI-Y1
Δ STAI-Y1
COR-E Losses
Δ PANSS-NS
Δ STAI-Y1
Δ PANSS-NS
Δ PANSS-NS
-
-0.257
-0.364
-0.148
-
-2.047
-3.051
-1.238
-
0.045*
0.003**
0.220
-
COR-E Losses
COR-E Losses
Δ STAI-Y1
Δ STAI-Y1
COR-E Losses
Δ PANSS-CNS
Δ STAI-Y1
Δ PANSS-CNS
Δ PANSS-CNS
-
-0.348
-0.364
-0.164
-
-2.855
-3.051
-1.374
-
0.006**
0.003**
0.174
-
COR-E Losses
COR-E Losses
Δ STAI-Y1
Δ STAI-Y1
COR-E Losses
Δ PANSS-T
Δ STAI-Y1
Δ PANSS-T
Δ PANSS-T
0.073
0.106
-0.394
-0.364
0.260
-0.173
-0.340
-3.292
-3.051
2.224
-1.383
-2.714
0.002**
0.003**
0.029*
0.172
0.009**
COR-E Losses
COR-E Losses
Δ STAI-Y1
Δ STAI-Y1
COR-E Losses
Δ PANSS-PS
Δ STAI-Y1
Δ PANSS-PS
Δ PANSS-PS
COR-E Losses
COR-E Losses
Δ STAI-Y1
Δ STAI-Y1
COR-E Losses
Note: PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS, Positive and
Negative Symptoms Scale-Core Negative Symptoms; PANSS-NS, Positive and Negative Symptoms ScaleNegative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core Negative Symptoms;
PANSS-T, Positive and Negative Symptoms Scale-Total Score; BDI-II, Beck Depression Inventory-II;
COR-E, Conservation of Resources-Evaluation.
† Resource loss calculated using rated independent losses.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
62
Table 9. Moderation Analyses for Resource Gain as a Proposed Buffer Between
Resource Loss and Outcome Variables: Resource Gain Calculated Using Rated
Independent Gains.
IV
Δ PANSS-PS
Step 1
Step 2
Δ PANSS-CPS
Step 1
Step 2
Δ PANSS-NS
Step 1
Step 2
Δ PANSS-CNS
Step 1
Step 2
Δ PANSS-T
Step 1
Step 2
Δ BDI-II
Step 1
Step 2
Δ STAI-Y1
Step 1
Step 2

t
p
Predictor
Moderator
Predictor x Moderator
-0.388
-0.192
-0.133
-3.281
-1.626
-0.644
0.002**
0.109
0.522
Predictor
Moderator
Predictor x Moderator
-0.382
-0.175
-0.160
-3.205
-1.469
-0.770
0.002**
0.147
0.445
Predictor
Moderator
Predictor x Moderator
-0.256
-0.131
-0.056
-2.033
-1.042
-0.252
0.047*
0.302
0.802
Predictor
Moderator
Predictor x Moderator
-0.344
-0.295
-0.051
-2.944
-2.524
-0.250
0.005**
0.014*
0.804
Predictor
Moderator
Predictor x Moderator
-0.392
-0.101
-0.126
-3.271
-0.839
-0.603
0.002**
0.405
0.549
Predictor
Moderator
Predictor x Moderator
-0.315
-0.035
-0.026
-2.589
-0.289
-0.127
0.012*
0.774
0.899
Predictor
Moderator
Predictor x Moderator
-0.357
-0.096
-0.007
-2.981
-0.800
-0.036
0.004**
0.427
0.971
Note: PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS, Positive and
Negative Symptoms Scale-Core Negative Symptoms; PANSS-NS, Positive and Negative Symptoms ScaleNegative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core Negative Symptoms;
PANSS-T, Positive and Negative Symptoms Scale-Total Score.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
63
there was no indication of a direct or moderating effect for any of the outcome variables.
Given that self-reported independent gains showed a different pattern of associations than
rated independent gains (see Table 6), a parallel series of analyses were conducted using
this version of the resource gains variable. Again, as shown in Table 10, there was no
indication of a moderating effect. These results are contrary to Hypothesis 6A.
A combined gain-loss variable was created by subtracting rated independent
resource losses from rated independent resource gains (net gains were denoted by
positive values, and net losses by negative values). This variable was then correlated
with the outcome variables. The following associations were found: Δ PANSS-PS: r =
0.42, p < .01; Δ PANSS-CPS: r = 0.40, p < .01; Δ PANSS-NS: r = 0.28, p < .05; Δ
PANSS-CNS: r = 0.46, p < .01; Δ PANSS-T: r = 0.36, p < .01; Δ BDI-II: r = 0.22, ns;
and Δ STAI-Y1: r = 0.34, p < .01. These results support the expectation (Hypothesis 6B)
that gains-minus-losses would be significantly associated with changes in psychosis
symptoms and emotional functioning.
Supplementary Analyses
Because there were significant mean differences for diagnostic category—
specifically, between the schizophrenia-spectrum and bipolar diagnostic clusters—on
four of the PANSS variables, supplementary analyses were conducted. It was not
feasible to run separate analyses for the bipolar cluster, due to the small size of this subsample (n = 13). Therefore, supplementary analyses were limited to the schizophreniaspectrum participants (n = 64). The overall pattern of associations was similar to that
shown in Tables 5 and 6 for the entire sample. Correlations were generally stronger
64
Table 10. Moderation Analyses for Resource Gain as a Proposed Buffer Between
Resource Loss and Outcome Variables: Resource Gain Calculated Using Self-Reported
Independent Gains.
IV
Δ PANSS-PS
Step 1
Step 2
Δ PANSS-CPS
Step 1
Step 2
Δ PANSS-NS
Step 1
Step 2
Δ PANSS-CNS
Step 1
Step 2
Δ PANSS-T
Step 1
Step 2
Δ BDI-II
Step 1
Step 2
Δ STAI-Y1
Step 1
Step 2

t
p
Predictor
Moderator
Predictor x Moderator
-0.361
-0.295
-0.203
-3.099
-2.538
-1.098
0.003**
0.014*
0.277
Predictor
Moderator
Predictor x Moderator
-0.376
-0.289
-0.308
-3.248
-2.501
-1.695
0.002**
0.015*
0.096
Predictor
Moderator
Predictor x Moderator
-0.203
-0.086
-0.177
-1.567
-0.669
-0.854
0.123
0.506
0.397
Predictor
Moderator
Predictor x Moderator
-0.255
-0.271
-0.154
-2.083
-2.213
-0.785
0.042*
0.031*
0.436
Predictor
Moderator
Predictor x Moderator
-0.367
-0.169
-0.241
-3.041
-1.401
-1.257
0.004**
0.167
0.214
Predictor
Moderator
Predictor x Moderator
-0.281
-0.063
-0.119
-2.254
-0.505
-0.628
0.028*
0.615
0.532
Predictor
Moderator
Predictor x Moderator
-0.318
-0.114
-0.087
-2.587
-0.931
-0.465
0.012*
0.356
0.644
Note: PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS, Positive and
Negative Symptoms Scale-Core Negative Symptoms; PANSS-NS, Positive and Negative Symptoms ScaleNegative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core Negative Symptoms;
PANSS-T, Positive and Negative Symptoms Scale-Total Score.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
65
when limited to the schizophrenia-spectrum participants (e.g., for rated independent
losses on PANSS-PS, r = 0.46, p < .01, compared to r = 0.39, p < .01 for the entire
sample), but these differences were not statistically significant. Results of mediation and
moderation analyses were essentially unchanged.
CHAPTER VI
Discussion
Summary
In summary, the results of this study provide support for the hypothesis that
cumulative resource losses are causally related to psychosis symptom exacerbations and
increased levels of depression and anxiety. There was also support for the hypothesis
that the interaction of baseline social functioning with resource losses would predict
symptom exacerbations, as well as for the hypothesis that a combined gains-minus-losses
variable would predict symptom improvements. Mixed support was found for other
hypotheses. There was very little evidence that changes in depression and anxiety
mediated the associations between resource loss and psychosis symptoms, or that
resource gains buffered the adverse effect of resource losses. Resource loss was
positively associated with current depression and anxiety, while resource gain was
negatively associated, but only when using self-reported changes that were inclusive of
both dependent and independent events.
General Discussion
The predictive associations between resource loss and symptom exacerbations
were the most robust findings in this study. As expected, these associations were found
as a function of the combination of self-reported dependent and independent change
events (i.e., self-reported losses). Importantly, these associations also held when resource
loss was calculated using only independent events, despite the fact that this criterion
66
67
eliminated over 75% of reported losses from consideration. The associations also held
when the magnitude of independent changes was assessed by objective, blind raters based
on participants‘ narrative descriptions of the events in question, thereby helping to
control for possible idiosyncrasies and biases in participant ratings. Further, even when
rated independent losses were calculated after excluding the 20 COR-E items pertaining
to subjective psychological resources (almost 33% of total items), the pattern of
significant associations was largely unchanged. Taken together, these findings provide
evidence that the associations between resource loss and symptom exacerbations were in
the expected causal direction.
Resource loss, it should be emphasized, represents a particular conceptualization
and operationalization of psychological stress. Evidence that psychological stress is
causally related to psychotic symptom exacerbations is not new. However, there are a
number of considerations that make the present results noteworthy.
First, most of the studies reviewed in this paper only included individuals
diagnosed with schizophrenia, and excluded individuals diagnosed with other psychotic
disorders, thereby limiting the generalizability of findings. The present results are based
on a diagnostically inclusive sample of patients diagnosed with schizophrenia,
schizoaffective disorder, and bipolar (I and II) disorders with psychotic features. While
stronger results were found when analyses were limited to schizophrenia-spectrum
participants, differences were not statistically significant. The overall pattern of results
was similar to that found for the sample as whole.
68
Second, almost all of the published studies of stress and psychosis reviewed in
this paper incorporate psychotic relapse or psychotic episodes as binary dependent
variables (e.g., Bebbington et al., 1993; Hultman et al., 1997; Mazure et al., 1997). The
criteria used to define the onset of a psychotic episode differed among these studies,
although most commonly the focus was on acute exacerbations of positive psychotic
symptoms. In this regard, Beiser, Erikson, Fleming, and Iacono (1993) commented that
there was no ―standardized, replicable method for establishing illness onset‖ (p. 1349),
further noting that the criteria used in research studies often differ from DSM criteria.
The situation continues to the present day, calling into question the comparability of
studies that use different criteria (Phillips et al., 2007). The most common definition of
the onset of an acute episode used in retrospective studies of life events and psychosis is
the initiation of treatment or hospital admission (e.g., Day et al., 1987; Gift, Strauss,
Harder, Kokes, & Ritzler, 1981; Johnstone, Crow, Johnson, & MacMillan, 1986).
However, criteria used for hospital admission are variable, reflecting not only the level of
psychotic symptoms that are being experienced but also other issues, such as potential for
self-harm or harm to others, or lack of suitable supports in the community. One early
psychosis intervention center reported that up to one-third of its patients were treated
without requiring inpatient admission during the first three months of treatment (Power et
al., 1998), suggesting that hospitalized inpatients are not representative of the entire
group of individuals experiencing a recent-onset psychotic disorder. By way of contrast,
the present study examined symptom change among a sample of stable outpatients.
Results add to the small body of evidence that subtle (continuous) changes in psychosis
69
symptoms occur as a function of stressful life experience (see also Docherty et al., 2008).
Similar results were found in an ESM study discussed earlier in this paper (MyinGermeys, Delespaul, & van OS, 2005), where subjects at increased risk for psychosis
showed continuous variation in the intensity of subtle psychotic experiences as a function
of minor stresses in daily life.
Third, the resource loss operationalization of stress used in the present study is
unique in the study of psychosis. In contrast to the studies of life events and daily hassles
that have predominated, this approach is explicitly grounded in a theoretical model of
psychosocial stress—the conservation of resources (COR) model (Hobfoll, 1989). The
emphasis here is not on events per se, but rather on the effects of events as captured by
the resource change construct. From the perspective of COR theory, focusing on
resource change is a ‗bottom-line‘ approach. An equivalent event—say, the loss of a
friend who moved away—can have disparate resource implications, not as a function of
appraisal per se, but rather as a function of the individual‘s baseline resource inventory.
Hobfoll (2001) cites evidence that the impact of resource loss on psychological distress is
greater than the impact of negative life events assessed as such. He reasons that this
greater impact is observed because life events are too broad a unit of analysis, and must
be unpacked into the losses and gains that they contain (see also Dohrenwend, Raphael,
Schwartz, Stueve, & Skodol, 1993). In a review of the literature, no other studies were
found that have specifically examined the impact of resource loss on psychotic
symptoms. Results of the present study are noteworthy in that they demonstrate the
utility of resource-based operationalizations of stress—and in particular, that based on
70
COR theory—in explaining symptom exacerbations among severely mentally ill
participants.
Fourth, the study found significant associations between resource loss (stress) and
increased negative symptoms. These results run contrary to established findings in the
literature, and should be interpreted cautiously. Negative symptoms have been found to
be stable over time, in contrast to positive symptoms, which are more variable in course
(Arndt et al., 1995; Harrow et al., 1997; Johnstone et al., 1987). In this vein, MyinGermeys and colleagues (2002) speculate, following Crow (1985) and others, that there
may be two primary forms of schizophrenia that underlie the manifest heterogeneity of
symptom presentations found by clinicians and researchers: an episodic, stress-reactive,
good outcome form characterized by positive psychotic symptoms; and a more chronic
form characterized by high levels of negative symptoms (see also Carpenter, Heinrichs,
& Wagman, 1988; Cohen, Brown, & Minor, 2010). The latter, more chronic, subtype is
thought to be characterized by primary negative symptoms that are, by definition,
endogenous to the underlying schizophrenia disease process. Primary negative
symptoms were originally conceptualized in terms of Kraepelin‘s dementia praecox
construct (Carpenter et al., 1988), reflecting a breakdown in volition and emotional
functioning. In contrast, secondary negative symptoms are thought to be more transient
in nature, and are attributable to exogenous influences such as depression, substance
abuse, intellectual disability, social isolation, paranoia, and so forth. While the PANSS
negative symptoms subscale incorporated in the present study does not inherently
distinguish between primary and secondary negative symptoms, the more narrowly
71
defined core negative symptoms variable was incorporated in this study to represent
primary negative symptoms, following the influential work of Crow (1980, 1985).
Core negative symptoms, operationalized using Crow‘s (1985) definition of flat
affect and poverty of speech, have been found to be stable over time, to respond poorly to
neuroleptic medications, and to be associated with intellectual impairments and poorer
long-term outcomes. The present study found that the predictive associations for the
resource loss variables were more consistently associated with core negative symptoms
than they were with the less stringently defined PANSS negative symptoms subscale. At
first glance, these findings are striking. However, even using the core negative symptoms
variable, it should be emphasized that there remains an inherent ambiguity in evaluating
whether observed negative symptoms are primary or secondary in origin (Flaum &
Andreasen, 1995). This ambiguity is heightened when using a measure, such as the
PANSS, that is not specifically designed to distinguish between primary and secondary
symptoms (Roy et al., 2001; Subotnik et al., 1998). While the symptoms of flat affect
and poverty of speech, which together define the core negative symptoms variable, have
been shown to be highly effective in distinguishing the negative from the positive
syndromes (Crow, 1985), the targeted behaviors can nevertheless plausibly be attributed
to alternative mechanisms, such as to depression, or to exacerbations of hallucinations,
delusions, and paranoia.
In considering the findings regarding core negative symptoms, a further
distinction can be made between the presence of such symptoms, which varies from mild
to extreme on the PANSS, and the presence of the symptomatically defined negative
72
syndrome, variously referred to as Type II schizophrenia (Crow, 1985) and, more
commonly, as the deficit syndrome (Carpenter et al., 1988). The deficit syndrome is a
schizophrenia subtype that is defined by core negative symptoms that are primary, severe,
and enduring, typically in the absence of elevated positive symptoms. The important
point here is that while the present study provides some evidence that psychosocial stress
can produce exacerbations in the very symptoms that define the deficit syndrome, this
does not mean that these changes were produced in patients who actually meet symptom
criteria for the deficit syndrome itself. Indeed, although the core negative symptoms
variable used in the present study had a ceiling of 14 points, the maximum score found
for any participant was exactly half that (7 points), falling within the mild to moderate
range of PANSS symptom severity. Thus, it can be plausibly inferred that the present
findings provide evidence that core negative symptoms may, at lower levels, be subject to
variability as a function of psychosocial stress, even though they may not be at the high,
sustained levels that define the deficit syndrome.
In contrast to the findings regarding resource loss, the findings regarding threatsof-loss were contrary to expectations. Overall, the four versions of the threats-of-loss
variables were ineffective in predicting symptom change. The notable exceptions were
that self-reported independent threats-of-loss predicted improvements in both the positive
and core positive symptoms variables. These results are puzzling, and should be weighed
with caution. The notion that threatened loss of psychosocial resources would produce
improvements in psychological symptoms runs contrary to theory, as well as to relevant
empirical findings from other domains (e.g., Asarnow, 1999). It can be conjectured that
73
the negative associations between self-reported independent threats-of-loss and changes
in positive symptoms represent a chance statistical artifact resulting from multiple
comparisons. Alternatively, evaluating the data in context, it is evident that the symptom
improvement associations for resource threats are isolated findings: they held only when
using self-reported independent threats, and not when the third-person rated variables
were used. The latter, more methodologically conservative, threats-of-loss variables
exhibited null (near-zero) associations with all the dependent variables, including
changes in positive symptoms. Considering that the discrepant findings occurred
specifically with a self-report variable, it may be that the COR-E threats-of-loss
questions, because they refer to feared events that did not actually occur, were too
abstract or hypothetical for a sample of seriously mentally ill participants to answer
meaningfully. Suggestively, while the associations between threats-of-loss and positive
symptoms are quite discrepant between the rated and self-reported versions of the threats
variable (ranging between 0.01 and -0.28), such discrepancies are much smaller with
actual resource loss as the predictor (ranging between 0.36 and 0.41).
A rather more interesting (and arguably more speculative) conjecture can also be
offered. It may be that participants who had experienced improvements in positive
symptoms feared loss more than other participants, precisely because they were
conscious of having more to lose. The expectation would then be that, as a function of
perceived improvements, participants tended to rate their threats-of-loss in a biased or
exaggerated manner. To explore this idea, post hoc associations were computed between
self-reported independent threats-of-loss and the corresponding loss and gain variables.
74
Self-reported independent threats were not significantly associated with the self-reported
independent losses, but were significantly positively associated with self-reported
independent gains, r = 0.38 , p < .01. These findings are consistent with the ―more to
lose‖ hypothesis, namely, that participants who have experienced improvements are
defensively attuned to perceived threats, thereby exaggerating threats-of-loss.
The unique influence of COR theory in the present study is highlighted in the
hypotheses regarding loss spiral and resource gains. Loss spiral refers to the idea that
those with fewer resources are more vulnerable to resource loss, and thus vulnerable to
―loss cycles‖ in which ―loss begets future loss‖ (Hobfoll, 2001, p. 354). Based on this
COR theory corollary, it was hypothesized that the interaction of baseline interpersonal
resources with subsequent resource loss would significantly predict symptom
exacerbations. Baseline interpersonal resources were assessed indirectly, through the
social functioning construct. This indirect assessment is certainly a weakness of the
study. Nevertheless, the interaction of baseline social functioning with rated independent
resource loss was found to have significant associations with the outcome variables.
These associations lend some support to the importance of considering moderating
variables that influence the impact of stressors. The importance of social support, in
particular, has been highlighted elsewhere in the psychosis literature. For example, level
of social support has been shown to be negatively correlated with increased emotional
reactivity towards daily stressors (Affleck, Tennen, Urrows, & Higgins, 1994; DeLongis,
Folkman, & Lazarus, 1988), while poor social support is associated with more severe
symptoms and more frequent hospital admission rates (Hultman et al., 1997). At a
75
broader level, the putative loss cycle phenomenon is a particularly interesting feature of
COR theory, as it maps well onto the difficult life experiences and challenges faced by
many psychosis patients. Based on this idea, the interaction of baseline resources with
subsequent psychosocial stressors should be the most powerful predictor of stressinduced symptom change. This result was not borne out in the present study; however, it
should be emphasized that the assessment of baseline resources that was used (social
functioning) was both indirect and highly incomplete. Future research is required in this
area.
With respect to resource gains, there was some evidence that gains, on their own,
were predictive of improved symptoms. However, these relationships were mixed.
Improvements in positive symptoms were only found as a function of self-reported
independent gains, whereas self-reported gains (dependent and independent) and rated
independent gains showed no such associations. Similarly, improvements in anxiety
symptoms were only found as a function of self-reported gains, a rather tenuous finding
on its own. A more consistent pattern of associations was found for changes in core
negative symptoms, which were negatively predicted by three of the four resource gains
variables, including both the third-person rated versions. Notably, the negative
symptoms variables are the only outcome variables that were based on observed behavior
rather than on reported information. Considering this unique feature in light of the
significant associations found for resource gains, it can be conjectured that the beneficial
effect of gains manifested in behavioral improvements prior to manifesting in
improvements to which participants were consciously aware.
76
Other findings for resource gains were similarly mixed. Using the rated
independent gains and self-reported independent gain variables, resource gains failed to
show a buffering effect for the impact of resource loss on any of the outcome variables.
These null findings for a buffering effect of gains are inconsistent with COR theory, as
well as with findings outside the psychosis literature (e.g., Wells, Hobfoll, & Lavin,
1997, 1999), and cannot plausibly be attributed to issues of low sample size. It may be
that the effect of resource gains is dampened among psychosis patients, given the severity
of their psychopathology and the high prevalence of anhedonia among this population
(Cohen & Minor, 2010). While there is a dearth of research that speaks directly to this
idea, there are some relevant inferences that can be made from the broader literature on
emotional experience and affective processing in schizophrenia. In a meta-analysis of
laboratory emotion induction studies, Cohen and Minor (2010) reported that positive and
neutral valenced stimuli induced relatively high levels of aversive emotion in patients,
relative to controls, while also inducing increases in hedonic emotion. In interpreting this
result, Cohen and Minor make use of the construct of ambivalence, noting that early
schizophrenia theorists focused on ambivalence—defined as a co-occurrence of hedonic
and aversive emotional states—as an important feature of schizophrenia pathology (e.g.,
Bleuler, 1950; Meehl, 1990). Recent research, moreover, has found ambivalence to be a
useful construct for investigating psychosis proneness (Kwapil, Mann, & Raulin, 2002;
Kwapil, Raulin, & Midthun, 2000). Might ambivalence be a useful way to conceptualize
the study‘s null findings regarding resource gains as a buffer against psychological
stress? This suggestion is offered cautiously. More research is required in this area, as
77
this is the first study to examine the effects of resource gains on psychosis patients, and
the findings, in themselves, are quite preliminary.
An alternative, more speculative, explanation was also considered. It may be that
the resource gains reported in this study were insufficiently grounded in reality,
occurring, perhaps, as a function of defensive responding (i.e., wanting to perceive one‘s
situation as having improved more than it actually has). In this regard, it is noteworthy
that participants overall reported 2.3 times more gains than losses. This disparity in favor
of positive events appears implausible for such a large group of community serviceseeking, severely mentally ill patients, suggesting that a pronounced reporting bias may
indeed be at play with respect to gains. Unfortunately, a review of other studies that have
used that have used the COR-E failed to uncover relevant information or precedents that
would help to clarify this issue.
The analysis of resource gains was taken a step further through the creation of the
combined ―gain-loss‖ or ―gains-minus-losses‖ variable. This variable was created by
subtracting losses from gains for each participant, and was then correlated with the
outcome variables. A pattern of associations emerged in which net gains were associated
with improvements across all psychosis variables (accounting for as much as 16% of
variance, in the case of positive symptoms). These findings further support the
incorporation of data on positive experiences in the study of symptom change. It should
be emphasized, however, that the absolute associations for the gains-minus-losses
variable did not significantly improve on the direct associations found for resource losses.
Given the relative magnitudes of the direct predictive relationships between resource
78
gains and losses, on the one hand, and the outcome variables, on the other, it is evident
that resource loss would have accounted for most of the variance in symptom change
accounted for by gains-minus-losses.
Viewed in total, the present findings regarding resource gains, while by no means
conclusive, suggest that positive life experiences may play a distinct, salutary role in
symptom fluctuations in psychosis patients. This conjecture is consistent with research
outside the psychosis literature, such as a study of depression which found that positive
affect buffered the impact of stress on negative affect (Marilee et al., 2005), and studies
of resilience in which it has been shown that some individuals use positive emotions to
help themselves recover from negative emotional experiences (Strand et al., 2006;
Tugade & Fredrickson, 2004; Zautra et al., 2005). The findings regarding resource gains,
while tentative, are both novel and conceptually significant. The study of resource gains
in clinical psychosis is relevant to the understanding of underlying pathophysiological
process, and has direct analogues in the psychosis treatment literature, where resources
such as sense of well-being, hope, and personal meaning are emphasized in terms of
quality of life and illness recovery (Corrigan & Ralph, 2005)
The most unexpected findings in this study are those pertaining to the mediation
models. Neither anxiety nor depression was shown to significantly mediate the effect of
resource loss on the outcome variables. There was some indication of a trend level
mediation effect in the case of depression and negative symptoms, but in every other case
where there was evidence of statistical mediation, findings were contrary to expectation
(resource loss was the mediator). To put these data in perspective, it is helpful to
79
consider relevant findings in the literature. Research using naturalistic symptom
assessment via ESM has found that stressful experiences in the flow of daily life are
associated with decreases in positive affect and increases in negative affect (MyinGermeys et al., 2001), as well as with subtle increases in the intensity of psychotic
experiences (Myin-Germeys, Delespaul, & van Os, 2005). Lataster and colleagues
(2009), also using ESM, found significant associations between negative affect and
subclinical psychotic symptoms as a function of daily life stress. Consistent with these
results, Myin-Germeys and colleagues (2005) suggest that findings regarding the
momentary associations between daily life stress and psychosis symptoms may not
represent a direct effect of stress, but rather an effect of the increased negative mood
produced by daily stress; i.e., the changes are mediated by negative emotion. This
conjecture is intuitively appealing, and can be broadened in terms of the dual route
cognitive model proposed by Garety and colleagues (2001). In this model, the impact of
stress on psychotic symptoms is influenced both by cognitive and emotional processes.
The most common pathway is believed to be one whereby stress-induced cognitive
changes directly lead to anomalous (psychotic) experiences, which are simultaneously
moderated by emotional processes. The cognitive changes are defined as a breakdown in
'willed intention' (p. 190) activity, such that the individual's own thoughts and intentions
are experienced as alien, and a range of anomalous conscious experiences are produced
(e.g., thoughts appearing to be broadcast). With respect to present results, it may be that
the cognitive changes, which were not assessed, play the most significant mediatory role
between stress and symptom exacerbations. Even so, the lack of a mediation effect for
80
the two emotion variables is challenging, as the model defines a role for emotional
changes in direct response to triggering events, as well as in response to the anomalous
experiences that are produced later.
In weighing this study‘s failure to find a mediation effect for negative emotion, it
should be noted that, to date, the only published research that has shown a direct effect of
emotions on psychotic symptoms has been either ESM (e.g., Lataster et al., 2009) or
experimental in nature. In a recent study, for example, Lincoln and colleagues (2009)
used an experimental paradigm in which one group of participants was exposed to a
stressor while completing a challenging cognitive task, and another group was similarly
tasked, but without being exposed to the stressor. The authors found that not only did the
participants in the stress condition exhibit significantly greater momentary paranoia, but
also that this increase was mediated by an increase in anxiety. Experimental studies
reported earlier in this paper by Docherty and colleagues (e.g., Cohen & Docherty, 2004;
Docherty et al., 1998) have also shown a direct impact of negative emotions on
symptoms. Considering present results in light of such findings, it can be speculated that,
to the extent that negative emotion does mediate the effect of stressors on psychotic
symptoms, this mediation occurs within the immediate (proximal) time-frame in which
the negative emotion is aroused, rather than as an ongoing effect of increased negative
emotionality persisting over time (such as would have been captured at Time 2 in the
present study). Thus, it may be an error to suppose that the effect of negative emotion on
psychotic symptoms can be broadly captured by looking at the totality of emotional
changes between two time points as mediating the totality of stress-induced psychotic
81
symptom changes between said time points. This methodological approach may be too
coarse to capture the hypothesized mediation process. With respect to the Myin-Germeys
et al. (2001) study, for example, it may be questionable whether or not the changes in
affect following stressful experiences in the ebb and flow of daily life, to the extent that
these changes produce heightened levels of psychotic symptoms, would be adequately
represented by scores on measures of depression and anxiety administered days or weeks
in the future. Rather, to capture the hypothesized mediation effect, it may be necessary to
assess these changes in-the-moment, as has been done using ESM and experimental
approaches.
Limitations
This study possesses a number of limitations. One limitation was the use of social
functioning as a proxy variable for assessing baseline interpersonal resources. A better
approach would have been to directly assess baseline resources at Time 1, perhaps
administering a modified version of the COR-E. Another limitation was the failure to
include focal assessment of the deficit syndrome using a specialized instrument, which
would have helped to clarify ambiguities regarding apparent changes in core negative
symptoms. The most important limitation to this study is retrospective design.
Participants at Time 2 were asked to recall, rate, and describe resource changes that had
occurred over an approximate 6 week period. This design raises two issues. First, there
are reasonable concerns that can be raised about the accuracy of participant recall (see
Heinrichs & Zakzanis, 1998), and thus also of the accuracy and completeness of the
narrative descriptions upon with objective ratings were later based. In fact, participants
82
who were blind to the larger objectives of the study were able to provide information on
the COR-E interview that was meaningfully related to symptom changes, often at high
levels of statistical significance. It can nevertheless plausibly be suggested that present
results may have been attenuated as a function of recall errors and omissions occurring
during the assessment of resource change. Second, the retrospective design of this study
limits the extent to which causal relationships can be imputed. Efforts were made to
address this limitation, particularly through the separate analyses of resource changes
based on independent events, and through, in one series of analyses, the removal from
consideration of the 20 COR-E items that involve subjective resources, such as pride and
hope. Because of this analytic approach, causal relationships are more easily discernible,
and can be inferred with a fair degree of confidence. Despite such efforts, it remains that
a longitudinal design would have inherently allowed for the derivation of stronger causal
inferences.
Future Research
In addition to addressing specific limitations, as noted above, there are a number
of promising directions for future research. One important question pertains to whether
resource loss is associated with increased sensitivity to stress. Future research could
examine changes in stress-reactivity as a function of resource loss, either through selfreport measures, psychophysiological reactivity measures, or neuroimaging measures of
brain activity under stress-relevant conditions. Future research could also examine the
influence of other factors, such as childhood trauma or trait characteristics, that may
moderate stress sensitivity within the context of resource loss. Following Garety et al.,
83
(2001), another promising direction for future research is the purported cognitive
mediation that occurs between stress and psychosis exacerbations. An important
challenge here is to operationalize ―‗willed intention‘ activity‖ (p. 190), a phenomenon
that may not readily lend itself to self-report. Nevertheless, in order to better understand
and intervene in this process, it is important to understand how and in what way cognitive
changes produced by stress may, in turn, produce exacerbated symptoms. If patients
could learn to attend to such changes as early warning signs, it might be possible to
implement interventions well in advance of psychotic relapse. Another worthwhile
direction for future research would be to more closely examine the relationships between
resource gains, stress reactivity, and illness course. In the treatment arena, psychosocial
resources have been emphasized as playing an important part in recovery and quality of
life, but such resources—and improvements related thereto—have not been extensively
studied in psychosis patients.
84
APPENDICES
85
APPENDIX A
Schedule for Affective Disorders and Schizophrenia (SADS)
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
APPENDIX B
Positive and Negative Syndrome Scale (PANSS)
104
APPENDIX C
Beck Depression Inventory-II (BDI-II)
105
106
APPENDIX D
State-Trait Anxiety Inventory – Form Y1 (STAI-Y1)
107
APPENDIX E
Social Functioning Scale (SFS)
108
109
110
111
112
113
114
115
116
APPENDIX F
Conservation of Resources Evaluation (COR-E)
117
118
119
120
121
122
123
APPENDIX G
Subjective Resources Items From COR-E
2.
Feeling that I am successful.
6.
Feeling valuable to others.
10.
Sense of pride in myself.
13.
Feeling that I am accomplishing my goals.
17.
Hope.
21.
Feeling that my future success depends on me.
25.
Sense of optimism.
29.
Sense of humor.
33.
Feeling that I have control over my life.
35.
Ability to communicate well.
37.
Feeling that my life is peaceful.
39.
Ability to organize tasks.
41.
Sense of commitment.
44.
Self-discipline.
47.
Motivation to get things done.
51.
Feeling that I know who I am.
54.
Feeling independent.
57.
Knowing where I am going with my life.
60.
Feeling that my life has meaning/purpose.
124
61.
Positive feeling about myself.
125
APPENDIX H
Correlation Matrix for All Study Variables
SR Losses
SR Losses
SR Ind. Losses
Rated Ind. Losses
Rated Ind. NS Losses
SR Threats
SR Ind. Threats
Rated Ind. Threats
Rated Ind. NS Threats
SR Gains
SR Ind. Gains
Rated Ind. Gains
Rated Ind. NS Gains
Gains-minus-Losses
Mean SFS
RIL x Mean SFS
Δ PANSS-PS
Δ PANSS-CPS
Δ PANSS-NS
Δ PANSS-CNS
Δ PANSS-T
Δ BDI-II
Δ STAI-Y1
T1 BDI-II
T1 STAI-Y1
T2 BDI-II
T2 STAI-Y1
1
.668**
.708**
.676**
0.022
-0.162
0.133
0.1
-0.139
-0.16
-0.074
-0.06
-.556**
-0.028
.596**
.406**
.406**
.283*
0.16
.483**
.420**
.377**
-0.015
-0.014
.320**
.311**
SR Ind.
Losses
.668**
1
.921**
.890**
0.008
-0.093
0.245
0.2
-0.182
-0.125
0.005
0.007
-.659**
-.313*
.745**
.382**
.397**
0.209
.275*
.379**
.274*
.331**
-0.04
-0.175
0.17
0.139
Rated Ind.
Losses
Rated Ind.
NS Losses
.708**
.921**
1
.972**
-0.028
-0.116
0.24
0.189
-0.202
-0.189
-0.073
-0.075
-.771**
-0.223
.833**
.391**
.384**
.257*
.348**
.394**
.313*
.364**
-0.035
-0.185
0.207
0.163
.676**
.890**
.972**
1
-0.016
-0.091
0.242
0.226
-0.116
-0.175
-0.045
-0.039
-.734**
-0.198
.790**
.357**
.378**
0.194
.314*
.336**
.296*
.321*
-0.045
-0.181
0.184
0.129
SR
Threats
0.022
0.008
-0.028
-0.016
1
.847**
.533**
.564**
0.182
.327**
.282*
.259*
0.2
0.133
-0.123
-0.215
-0.159
-0.046
-0.159
-0.107
0.166
0.054
-0.025
-0.146
0.114
-0.093
SR Ind.
Threats
-0.162
-0.093
-0.116
-0.091
.847**
1
.636**
.667**
0.185
.384**
.330**
.296*
.294*
-0.065
-0.115
-.275*
-.260*
-0.062
-0.092
-0.178
0.056
-0.051
0.04
-0.075
0.079
-0.081
Rated Ind.
Threats
Rated Ind.
NS
Threats
SR Gains
0.133
0.245
0.24
0.242
.533**
.636**
1
.975**
0.117
0.148
0.213
0.207
-0.038
-0.102
.364**
0.048
0.094
0.002
0.099
0.053
0.207
0.081
-0.011
-0.115
0.148
-0.017
0.1
0.2
0.189
0.226
.564**
.667**
.975**
1
0.142
0.187
.256*
.249*
0.026
-0.101
.264*
-0.011
0.045
-0.045
0.055
-0.008
0.165
0.005
0.012
-0.069
0.137
-0.037
-0.139
-0.182
-0.202
-0.116
0.182
0.185
0.117
0.142
1
.378**
.354**
.327**
.371**
.286*
-0.16
-0.134
-0.093
-0.199
-0.229
-0.195
-0.22
-.261*
-0.162
-0.164
-.305**
-.472**
SR Ind.
Gains
-0.16
-0.125
-0.189
-0.175
.327**
.384**
0.148
0.187
.378**
1
.885**
.844**
.699**
0.002
-0.148
-.321*
-.316*
-0.101
-.289*
-0.196
0.028
-0.152
-0.083
0.031
-0.054
-0.131
Rated Ind.
Gains
Rated Ind.
NS Gains
-0.074
0.005
-0.073
-0.045
.282*
.330**
0.213
.256*
.354**
.885**
1
.986**
.691**
-0.089
-0.072
-0.198
-0.181
-0.135
-.300*
-0.106
0.012
-0.12
-0.006
0.072
0.004
-0.055
-0.06
0.007
-0.075
-0.039
.259*
.296*
0.207
.249*
.327**
.844**
.986**
1
.684**
-0.076
-0.065
-0.198
-0.157
-0.154
-.303*
-0.112
0.004
-0.09
0.004
0.069
0.007
-0.028
GainsminusLosses
-.556**
-.659**
-.771**
-.734**
0.2
.294*
-0.038
0.026
.371**
.699**
.691**
.684**
1
0.105
-.648**
-.417**
-.400**
-.278*
-.456**
-.356**
-0.219
-.341**
0.022
0.18
-0.147
-0.153
Note: SR, Self-reported; Ind., Independent; NS, Non-subjective; SFS, Social Functioning Scale; RIL, Rated Independent Losses; PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS,
Positive and Negative Symptoms Scale-Core Negative Symptoms; PANSS-NS, Positive and Negative Symptoms Scale-Negative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core Negative
Symptoms; PANSS-T, Positive and Negative Symptoms Scale-Total Score; BDI-II, Beck Depression Inventory-II; STAI-Y1, State-Trait Anxiety Inventory, State Version.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
125
126
SR Losses
SR Ind. Losses
Rated Ind. Losses
Rated Ind. NS Losses
SR Threats
SR Ind. Threats
Rated Ind. Threats
Rated Ind. NS Threats
SR Gains
SR Ind. Gains
Rated Ind. Gains
Rated Ind. NS Gains
Gains-minus-Losses
Mean SFS
RIL x Mean SFS
Δ PANSS-PS
Δ PANSS-CPS
Δ PANSS-NS
Δ PANSS-CNS
Δ PANSS-T
Δ BDI-II
Δ STAI-Y1
T1 BDI-II
T1 STAI-Y1
T2 BDI-II
T2 STAI-Y1
Mean
SFS
RIL x
Mean
SFS
-0.028
-.313*
-0.223
-0.198
0.133
-0.065
-0.102
-0.101
.286*
0.002
-0.089
-0.076
0.105
1
-0.092
-0.067
-0.044
-0.113
-0.21
-0.071
0.022
0.012
-.509**
-.456**
-.458**
-.525**
.596**
.745**
.833**
.790**
-0.123
-0.115
.364**
.264*
-0.16
-0.148
-0.072
-0.065
-.648**
-0.092
1
.404**
.450**
0.232
.322*
.426**
.416**
.371**
-0.087
-0.121
0.246
0.216
Δ
PANSSPS
Δ
PANSSCPS
Δ
PANSSNS
Δ
PANSSCNS
.406**
.382**
.391**
.357**
-0.215
-.275*
0.048
-0.011
-0.134
-.321*
-0.198
-0.198
-.417**
-0.067
.404**
1
.835**
.390**
.352**
.828**
0.162
0.12
-0.148
0.003
0
0.112
.406**
.397**
.384**
.378**
-0.159
-.260*
0.094
0.045
-0.093
-.316*
-0.181
-0.157
-.400**
-0.044
.450**
.835**
1
.338**
.323**
.760**
0.225
.248*
-0.184
-0.017
0.01
0.194
.283*
0.209
.257*
0.194
-0.046
-0.062
0.002
-0.045
-0.199
-0.101
-0.135
-0.154
-.278*
-0.113
0.232
.390**
.338**
1
.648**
.685**
.291*
0.148
-0.133
0.087
0.109
.281*
0.16
.275*
.348**
.314*
-0.159
-0.092
0.099
0.055
-0.229
-.289*
-.300*
-.303*
-.456**
-0.21
.322*
.352**
.323**
.648**
1
.503**
0.129
0.164
-0.012
-0.075
0.09
0.17
Δ
PANSS-T
.483**
.379**
.394**
.336**
-0.107
-0.178
0.053
-0.008
-0.195
-0.196
-0.106
-0.112
-.356**
-0.071
.426**
.828**
.760**
.685**
.503**
1
.291*
.260*
-0.211
0.005
0.044
.265*
Δ BDI-II
Δ STAIY1
T1 BDI
T1 STAI
T2 BDI
T2 STAI
.420**
.274*
.313*
.296*
0.166
0.056
0.207
0.165
-0.22
0.028
0.012
0.004
-0.219
0.022
.416**
0.162
0.225
.291*
0.129
.291*
1
.411**
-.311**
-0.137
.503**
.262*
.377**
.331**
.364**
.321*
0.054
-0.051
0.081
0.005
-.261*
-0.152
-0.12
-0.09
-.341**
0.012
.371**
0.12
.248*
0.148
0.164
.260*
.411**
1
-.254*
-.537**
0.1
.351**
-0.015
-0.04
-0.035
-0.045
-0.025
0.04
-0.011
0.012
-0.162
-0.083
-0.006
0.004
0.022
-.509**
-0.087
-0.148
-0.184
-0.133
-0.012
-0.211
-.311**
-.254*
1
.641**
.665**
.485**
-0.014
-0.175
-0.185
-0.181
-0.146
-0.075
-0.115
-0.069
-0.164
0.031
0.072
0.069
0.18
-.456**
-0.121
0.003
-0.017
0.087
-0.075
0.005
-0.137
-.537**
.641**
1
.496**
.601**
.320**
0.17
0.207
0.184
0.114
0.079
0.148
0.137
-.305**
-0.054
0.004
0.007
-0.147
-.458**
0.246
0
0.01
0.109
0.09
0.044
.503**
0.1
.665**
.496**
1
.648**
.311**
0.139
0.163
0.129
-0.093
-0.081
-0.017
-0.037
-.472**
-0.131
-0.055
-0.028
-0.153
-.525**
0.216
0.112
0.194
.281*
0.17
.265*
.262*
.351**
.485**
.601**
.648**
1
Note: SR, Self-reported; Ind., Independent; NS, Non-subjective; SFS, Social Functioning Scale; RIL, Rated Independent Losses; PANSS-PS, Positive and Negative Symptoms Scale-Positive Symptoms; PANSS-CPS,
Positive and Negative Symptoms Scale-Core Negative Symptoms; PANSS-NS, Positive and Negative Symptoms Scale-Negative Symptoms; PANSS-CNS, Positive and Negative Symptoms Scale-Core Negative
Symptoms; PANSS-T, Positive and Negative Symptoms Scale-Total Score; BDI-II, Beck Depression Inventory-II; STAI-Y1, State-Trait Anxiety Inventory, State Version.
** Significant at the 0.01 level (2-tailed).
* Significant at the 0.05 level (2-tailed).
126
127
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