Journal of Counseling Psychology
April 2001 Vol. 48, No. 2, 223-232
© 2001 by the American Psychological Association
For personal use only--not for distribution.
Family Caregiver Social Problem-Solving Abilities and Adjustment
During the Initial Year of the Caregiving Role
Timothy R. Elliott
Department of Physical Medicine and Rehabilitation, University of Alabama at
Birmingham
Richard M. Shewchuk
Department of Health Services Administration, University of Alabama at Birmingham
J. Scott Richards
Department of Physical Medicine and Rehabilitation, University of Alabama at
Birmingham
ABSTRACT
The authors examined the relation of social problem-solving abilities to trajectories of
adjustment of family caregivers in the initial year of their caregiving role. Persons
who recently assumed the caregiver role for a family member with a recent-onset
spinal cord injury completed measures of problem solving, depression, anxiety, and
health during the inpatient rehabilitation program and at 3 other times throughout the
year. Hierarchical linear modeling showed that negative problem orientation
explained significant variation in the rates of change in caregiver depressive behavior,
anxiety, and health complaints. Caregivers with a greater negative orientation were at
risk to develop psychological and health problems at a significantly higher rate over
the year. Implications for psychological interventions and health policy concerning
the needs of family caregivers and their care recipients are discussed.
Once regarded in a patronizing fashion as “informal” extensions of health care
programs (Kane & Kane, 1987), family caregivers are now responsible for a wide
variety of services and activities that were formerly and formally provided by some
traditional health service professions. As the number of chronic conditions escalate in
our society and health care programs continue to limit services to persons with these
conditions, more family members will assume caregiver roles (Hoffman, Rice, &
Sung, 1996). Dramatic cutbacks in formal health care services have intensified the
role of the family caregiver. Likewise, the emergence of managed care as the
predominant health care paradigm has also resulted in many negative effects for
families with a member who has a chronic disease or disability (Council on Scientific
Affairs, 1993). With these changes in health care allocation—and with increases in
the incidence of chronic disease and disability—more individuals will be compelled
to assume the role of primary caregiver for a family member who incurs a severe
physical disability.
Family members assume caregiving roles under different circumstances. In some
situations, caregivers may gradually evolve into their role in the face of chronic
conditions that slowly erode the capacities of a family member or loved one (e.g.,
Alzheimer's dementia, AIDS). In other cases, caregivers may be involved with care
recipients who may have considerable life expectancies (e.g., developmental
disabilities, multiple sclerosis). Sometimes caregivers are thrust into their role as
nonnormative, “off-time” life events (Neugarten, 1979)—such as a sudden-onset
disability (e.g., spinal cord injury, head trauma)—compete with and preclude the
pursuit of more normative personal, vocational, and social roles (Moen, Robison, &
Dempster-McClain, 1995). With the increasing incidence of chronic health problems
in American society and corresponding increases in the number of family caregivers,
greater emphasis is being placed on the needs of caregivers across the life span
(Schulz & Quittner, 1998). Essentially, individuals who begin caregiving assume a
role defined by the tasks in which they engage and the relationship they have with the
care recipient (Aneshensel, Pearlin, Mullan, Zarit, & Whitlatch, 1995).
Empirical research indicates that family caregivers are at an increased risk for
problems with distress and illness (for reviews, see Schulz et al., 1997; Vitaliano,
1997). Caregivers of persons with physical disability have higher levels of distress
than noncaregivers (e.g., Weitzenkamp, Gerhart, Charlifue, Whiteneck, & Savic,
1997). More elaborate research designs reveal that over time the health of the
caregiver may be compromised by disruptions in cardiovascular and immune
functioning (see Vitaliano, 1997). These psychological and physical problems may be
exacerbated as caregivers neglect their own health and care (Burton, Newsom, Schulz,
Hirsch, & German, 1997).
Psychological characteristics and related resources have been found to have a
greater impact on caregiver adjustment than objectively defined aspects of the
caregiver role or care recipient condition (Schulz & Quittner, 1998). Caregivers'
distress may be due in part to their subjective appraisal of caregiving as onerous and
personally distressing (Chwalisz, 1996; Haley et al., 1996). Further, some caregivers
appear to be predisposed to experiencing higher levels of emotional strain regardless
of how caregiving duties are objectively defined (Hooker, Monahan, Shifren, &
Hutchinson, 1992). Caregiving is often marked by a lack of available time for
personal pursuits (Quittner, Opipari, Regoli, Jacobsen, & Eigen, 1992) and a slow
erosion of social support with corresponding social isolation (Quittner, Glueckauf, &
Jackson, 1990). Caregiver coping strategies may be a better predictor of caregiver
adjustment than social support (Chwalisz, 1996).
In our ongoing, prospective study of caregiver adjustment and adaptation, we have
learned that the first year of caregiving is a dynamic process, characterized by
fluctuations in social support that can have pronounced effects on caregiver distress
and health (Shewchuk, Richards, & Elliott, 1998). However, simply modeling or
describing the caregiver distress process is insufficient. We must identify caregivers'
characteristics that predispose them and their care recipients to less than optimal
outcomes, so that we can develop strategic interventions for these persons.
Furthermore, we must consider theoretical models that delineate clear directives for
effective, low-cost interventions for family caregivers and their care recipients who
often have limited access, resources, and interest in formal psychological services
(Shewchuk & Elliott, 2000).
Several theorists believe that persons who provide care to those with chronic
disease and disability could benefit from cognitive–behavioral interventions that
equip them with the skills necessary for emotional regulation and task management
(Elliott & Shewchuk, 2000; Grant, 1999; Houts, Nezu, Nezu, & Bucher, 1996).
Contemporary models of social problem-solving abilities stipulate that individuals
differ in the cognitive–behavioral skills that influence the processing of information
about their problems, in their ability to regulate emotional experiences when problem
solving, and in their ability to implement effective strategies for resolving problems
(D'Zurilla & Nezu, 1999).
Social problem-solving abilities have been conceptualized as two components,
problem-orientation and problem-solving skills (D'Zurilla & Nezu, 1999). The
problem-orientation component encompasses beliefs and attitudes a person has about
their overall abilities, their level of confidence in problem solving, and their abilities
in regulating their emotions so that the individual is motivated to handle minor
problems efficiently and work diligently on more time-consuming problems. The
problem-solving skills component entails both effective (e.g., rational, logical) and
ineffective (e.g., impulsive, careless, avoidant) tendencies. This model is particularly
appealing to clinical researchers as evidence indicates that training in social problemsolving abilities has been effective in treating depression and distress, and in
improving self-management skills (Arean et al., 1993; Nezu & Perri, 1989; Richards
& Perri, 1978).
Programmatic research has demonstrated that the problem-orientation component
is a consistent predictor of emotional states and distress under routine and stressful
conditions (Elliott, Sherwin, Harkins, & Marmarosh, 1995) and among persons with
physical disability and other health conditions (Elliott, Godshall, Herrick, Witty, &
Spruell, 1991; Elliott, Shewchuk, Richeson, Pickelman, & Franklin, 1996). The
problem-orientation component has also been related to health complaints (Elliott &
Marmarosh, 1994; Godshall & Elliott, 1997), acceptance of disability among persons
with recent-onset spinal cord injuries (Elliott, 1999), and depressive behavior among
undergraduates under duress (Priester & Clum, 1993).
Cross-sectional studies have found the problem-orientation component predictive
of distress experienced by mothers of children with physical disabilities (Noojin &
Wallander, 1997) and of depression among family caregivers of persons recently
discharged from a stroke rehabilitation program (Grant, Elliott, Giger, & Bartolucci,
2001). Although a negative problem orientation was associated with caregiver distress
in these studies, the lack of prospective analyses hinders our ability to understand the
relation of social problem solving to caregiver adjustment over time. The social
problem-solving abilities of family caregivers may also affect the adjustment of care
recipients. Caregiver impulsive and careless problem-solving styles are significantly
associated with low acceptance of disability scores of care recipients being discharged
from medical rehabilitation (Elliott, Shewchuk, & Richards, 1999). These tendencies
also predicted the occurrence of a preventable skin ulcer a year later among persons
who returned for an annual medical examination (with 88% accuracy).
Unfortunately, cross-sectional designs reveal little about ongoing processes that
characterize caregiver adjustment over time, and we know that caregiver adjustment
in the initial year of this role can be particularly complex (Shewchuk et al., 1998).
Matthews (1993) astutely observed that we ultimately “know comparatively little
about the trajectory of caregiving” (p. 115). Statistical techniques such as growth
curve analyses and hierarchical linear modeling (HLM) are ideally suited for studying
trajectories of adaptation among families who have a member with a chronic health
condition (Drotar, 1997; Frank et al., 1998; Shewchuk & Elliott, 2000). These
techniques are well-suited for identifying specific characteristics of individuals who
may be at risk for adjustment problems over time.
In the present study, we prospectively examined the relationship between social
problem-solving abilities and the adjustment of caregivers throughout the first year of
the caregiving role. We measured three related but distinct aspects of caregiver
adjustment throughout the year (depressive behavior, anxiety, and physical health).
We expected the problem-orientation component to be significantly associated with
caregiver anxiety, depressive behavior, and health complaints throughout the course
of the initial year. We used data-analytic techniques that would permit examination of
different developmental trajectories on each of these three dimensions of adjustment.
Method
Participants
Participants included 11 men (5 Black, 6 Caucasian; M age = 42.91 years, SD =
15.74 years, range = 26–73 years; M years of education = 11.77, SD = 3.16, range =
6–16 years of formal education) and 55 women (21 Black, 34 Caucasian; M age =
41.76 years, SD = 13.09 years, range = 18–74 years; M years of education = 12.32,
SD = 2.47, range = 8–22 years of formal education). The preponderance of women
identified as caregivers in this sample is consonant with previous research.
Twenty of the caregivers were mothers of the care recipient, 12 were wives, 7 were
daughters, and 4 were sisters. Four husbands, 4 fathers, and 1 brother were also in
caregiver roles. Five caregivers were categorized as having other kinds of preinjury
relationships (e.g., aunts, close family friends, former in-laws). Forty-four caregivers
were married at the time they assumed their role, 15 had been previously divorced,
and 7 had never married. Thirty-two caregivers were not employed outside the home
prior to the injury incurred by the care recipient, 27 were employed full time, and 6
worked part time.
Following each consecutive admission to the inpatient rehabilitation program, a
trained research assistant reviewed each case to determine potential eligibility for
inclusion in the study. Individuals were eligible for the study if a family member
would be involved in providing some level of care and assistance in ongoing
caregiving activities to the person with spinal cord injury upon return to the
community (e.g., assistance with activities of daily living, adherence to self-care
regimens, and other supportive care). The research assistant then approached
prospective family caregivers and informed them of the study. Informed consent was
obtained from those who agreed to participate. The initial assessment was conducted
during the hospitalization. Subsequent assessments were conducted by mail 1 month,
6 months, and 1 year postdischarge. Questionnaires were mailed in envelopes in no
particular order, and participants completed the instruments in no particular sequence.
The research assistant maintained telephone contact with participants to discuss
receipt of materials and to address any questions or comments about the study.
Although we did not have information about the number of individuals who were
approached and declined to participate in this study over the course of this 5-year
project, our present research indicates that the number of eligible caregivers who have
refused to participate has been 16%. Moreover, of those who were approached, we
have noted that approximately 20% were unable to participate because they did not
meet our inclusionary criteria or were ineligible for a variety of other reasons (e.g.,
they were relocating to another state, there were multiple caregivers, or they had
cognitive impairments).
Measures
Social Problem Solving Abilities—Revised (SPSI–R; D'Zurilla, Nezu, & Maydeu-Olivares, in
press).
The SPSI–R is a 52-item, self-report measure of social problem-solving abilities
(Maydeu-Olivares & D'Zurilla, 1996). Each item is rated on a 5-point Likert-type
scale ranging from 0 (not very true of me) to 4 (extremely true of me). Higher scores
on each scale indicate a greater propensity in that facet of problem solving. The SPSI–
R is based on a five dimensional model of problem solving and provides five scales.
Two of the scales measure the problem-orientation dimensions: Positive Problem
Orientation (PO) and Negative Problem Orientation (NO). The remaining three scales
are considered problem-solving skills scales. These include Rational Problem Solving
(RPS), Impulsivity and Carelessness Style (IC), and Avoidance Style (AV). The PO
scale assesses a general cognitive set, which includes the tendency to view problems
in a positive light, to see them as challenges rather than threats, and to be optimistic
about one's ability to detect and implement effective solutions.
Sample items from the PO scale include “Whenever I have a problem, I believe
that it can be solved,” “When I have a problem, I try to see it as a challenge or
opportunity to benefit in some positive way from having a problem.” The NO scale
assesses a cognitive–emotional set indicative of a greater pessimism, a lack of
motivation toward problem solving, and a proclivity for negative moods that hinders
effective problem solving. Sample items on the NO scale include “I hate having to
solve the problems that occur in my life,” “When I am trying to solve a problem I get
so upset that I cannot think clearly,” and “When I am faced with a difficult problem, I
doubt that I will be able to solve it on my own no matter how hard I try.”
The RPS scale assesses the tendency to systematically and deliberately use
effective problem-solving techniques by defining the problem, generating alternatives,
evaluating alternatives, and implementing solutions and evaluating outcomes. Sample
items on the RPS scale include “Before I try to solve a problem, I set a specific goal
so that I know exactly what I want to accomplish” and “When I have a decision to
make, I weigh the consequences of each option and compare them to each other.” The
IC scale measures the tendency to solve problems in an impulsive, incomplete, and
haphazard manner. The IC scale has items such as “When making decisions, I do not
evaluate all my options carefully enough” and “When I am trying to solve a problem,
I go with the first good idea that comes to mind.” The AV scale assesses
dysfunctional patterns of problem solving characterized by putting the problem off
and waiting for problems to solve themselves. Sample items on this scale include “I
go out of my way to avoid having to deal with problems in my life” and “I spend
more time avoiding my problems than solving them.”
Internal consistency estimates for the scales with college students range from
alphas of .76 for PO to .92 for RPS, and test–retest (3 weeks) reliability ranges from
.72 for PO to .88 for NO for the same sample (D'Zurilla et al., in press). Significant
correlations between the SPSI–R scales and similar constructs on the Problem Solving
Inventory (Heppner, 1988) and with other theoretically related constructs as stress,
somatic symptoms, anxiety, depression, hopelessness, and suicidality, provide
evidence of construct validity (Chang & D'Zurilla, 1996; D'Zurilla et al., in press).
The SPSI–R scales have been predictably associated with self-esteem, life
satisfaction, extraversion, social adjustment, and social skills (D'Zurilla et al., in
press; Sadowski, Moore, & Kelley, 1994). The SPSI–R was administered at the initial
assessment.
Depressive behavior.
The Center for Epidemiological Studies Depression Scale (CES–D; Radloff, 1977)
was used to obtain an index of depressive behavior at each assessment. The CES–D
was developed to assess symptoms of depression in the general population (Radloff,
1977). This instrument contains 20 items that assess current levels of depressive
behavior, with a particular emphasis on the impact of depressed mood (e.g., “I felt
sad,” “I thought my life had been a failure,” “I had crying spells”). Items are scored
on a 4-point scale ranging from 0 (rarely or none of the time) to 3 (most or all of the
time) to indicate how often symptoms are experienced in the preceding week. Scores
range from 0 to 60. Higher scores indicate higher levels of depressive behavior; scores
greater than 16 have been found to differentiate depressed from nondepressed
community-residing adults (Craig & Van Natta, 1978). CES–D scores have been
associated across many studies with low levels of social support and social
integration, low perceptions of control over one's life, poor health, and social
discomfort (Elliott & Umlauf, 1995). The CES–D correlates well with other measures
of depression (e.g., r = .81; Weissman, Prusoff, & Newberry, 1975), and evidence
indicates that CES–D scores do not appear to be biased by somatic complaints (N =
1,060 persons aged 55 years and older; Foelker & Shewchuk, 1992). Alpha
coefficients have ranged from .84 to .90 in several field studies (Radloff, 1977). Test–
retest coefficients for this measure in the present study ranged from .73 to .89.
Anxiety.
The trait version of the State–Trait Anxiety Inventory (STAI; Spielberger,
Gorsuch, Lushene, Vagg, & Jacobs, 1983) was used to assess caregiver anxiety. The
STAI contains 20 items that assess how a person generally feels. Internal consistency
coefficients for the scale have ranged from .86 to .95; test–retest reliability
coefficients have ranged from .65 to .75 over a 2-month interval (Spielberger et al.,
1983). Test–retest coefficients for this measure in the present study ranged from .69 to
.89. Considerable evidence attests to the construct and concurrent validity of the scale
(Spielberger, 1989). Higher scores indicate greater anxiety. This measure was selected
to assess any ongoing problems with anxiety over the four assessment periods, rather
than to measure state-specific, and possibly transitory, periods of anxiety at each time.
Health complaints.
The general form of the Pennebaker Inventory of Limbic Languidness scale (PILL;
Pennebaker, 1982) was used to assess caregiver health complaints. The PILL contains
54 symptoms (e.g., back pains, headaches, indigestion, severe itching) that are rated in
a yes–no format. The PILL measures health complaints experienced by the individual
over the preceding 3 weeks. A total score is acquired by summing the reported
symptoms; higher scores reflect more health complaints. The PILL general form has
adequate internal consistency (.88), and test–retest reliabilities over a 2-month period
have ranged from .79 to .83 (Pennebaker, 1982). Test–retest coefficients for this
measure in the present study ranged from .51 to .84. Evidence of convergent and
predictive validity has been demonstrated in significant associations between higher
PILL scores and frequent physician visits, increased aspirin use within the past month,
more days of restricted activities because of illness, poor exercise habits, greater drug
and caffeine use, disturbed sleep and eating patterns, and scores on other symptom
inventories (Pennebaker, 1982).
Statistical Analysis
The principal research questions in this study were addressed by using the
multilevel modeling features of LISREL 8 (Jöreskog, Sörbom, du Toit, & du Toit,
1999) to examine time-ordered measures of caregiver depression, anxiety, and health
complaints reported by family caregivers over the first year of caregiving. The
multilevel approach was first used to estimate the parameters of curves that reflected
time-ordered changes in caregiver-specific adjustment (i.e., depression, anxiety, and
health complaints), and then to examine the between-caregiver variation in the
parameters defining the individual change curves. Finally, the multilevel modeling
approach was used to determine the extent to which such variation could be predicted
by hypothesized individual differences in social problem-solving abilities.
Conceptually, the repeated observations of our longitudinal study provided a
hierarchical or nested (i.e., a multilevel) data arrangement where the Level 1 data
units consisted of the time-ordered measures of adjustment that are nested within
Level 2 data units consisting of individual caregivers. In the Level 1 model, the
repeated measures of caregiver adjustment were used to estimate caregiver-specific
change trajectories as a linear function of time. These trajectories are defined by a
unique set of caregiver-specific intercept and slope parameters that describe the
intraindividual, or within-person, relationship between adjustment and time.
The Level 2 model examined the between-caregiver variability in the parameters
defining the performance curve for each participant. Conceptually, we hypothesized
that the variation in the within-caregiver growth curves (i.e., the variability of Level 1
change parameter π1j) could be modeled at Level 2 as a function of fixed and random
effects that included a set of social problem-solving abilities that were hypothesized to
vary between caregivers. In other words, the coefficients describing the Level 1
trajectories become the outcome variables in the Level 2 model. In using this
approach we assumed that observed levels of each adjustment measure reflected
ongoing change processes that could be represented by continuous time-dependent
curves at the individual-caregiver level. We also assumed that there would be
sufficient variation in these curves that could be modeled as a function of systematic
individual differences in measures that describe caregivers' problem-solving abilities.
A principal benefit of the multilevel modeling approach used in this study is the
ability to use information from both levels of the data structure to examine the
relationships that occur both within and between caregivers. By linking both levels of
a data structure, it is possible to identify systematic patterns of change and correlates
of change that would be difficult to discern when examining mean change levels
associated with aggregate level data. Another advantage of using an HLM, or
multilevel modeling approach, as opposed to an approach that uses a repeated
measures design in the analysis of variance (ANOVA) or multivariate analysis of
variance tradition—especially with longitudinal data—is that it is possible to obtain
statistically efficient solutions for data that include missing or nonsynchronous
observations (Bryk & Raudenbush, 1987; Tate & Hokanson, 1993). The statistical and
conceptual bases for HLM modeling of longitudinal data have been discussed in
considerable detail (Bryk, Raudenbush, & Congdon, 1996; De Leeuw & Kreft, 1986;
Francis, Fletcher, Stuebing, Davidson, & Thompson, 1991; Goldstein, 1986;
Goldstein, Healy, & Rasbash, 1994; Willett, 1988).1
Results
Table 1 displays the number of observations for each measure at each
measurement occasion. A comprehensive analysis of the missing values did not reveal
any pattern that would bias our analyses in any way. Moreover, the statistical
algorithm used to estimate the parameters of our models outweighs the cases with
more reliable data (Bryk & Raudenbush, 1992). Consequently, neither the missing
values nor the pattern of missing values compromised the integrity of our analyses.
Although we found no systematic biases in our pattern of missing values, we do not
know why some participants did not return all materials at each assessment. Means
and standard deviations for the self-report variables and their correlations are
presented in Table 2.
Unconditional Models
As the first step in our analyses, we estimated a fully unconditional or random
intercept model for each caregiver adjustment measure. The fully unconditional
model is actually the functional equivalent of a one-way random effects ANOVA and,
therefore, serves as a way to partition the total observed variability in caregiver
adjustment into within-caregiver and between-caregiver components (Bryk &
Raudenbush, 1992). By decomposing the overall variance in this manner, it is
possible to obtain a baseline measure for assessing the relative explanatory
contributions of models that include theory-relevant variables. The equations defining
the random intercept model at both Level 1 and Level 2 include only intercepts and
residual terms. At Level 1, each adjustment measure is modeled as a function of a
caregiver intercept parameter (i.e., π0j) and a random error term (i.e., rij). Because this
equation does not include covariates or predictors, each Level 1 intercept is an
estimate of the average level of adjustment (i.e., average level of depression, anxiety,
and health complaints) for a particular caregiver over the four measurement
occasions. In the Level 2 model, the estimates of the Level 1 intercepts were
estimated as a function of the grand mean of each adjustment measure (i.e., β00 =
19.61 for depression, β00 = 44.94 for anxiety, and β00 = 13.30 for physical health) for
all caregivers over all observation occasions, and a random residual term (i.e., u0j).
The variance of the Level 1 residuals for each adjustment measure (i.e., ς2 = 38.93 for
depression, ς2 = 42.23 for anxiety, and ς2 = 30.30 for physical health) provides an
estimate of the within-caregiver variation. The variance of the Level 2 residuals,
denoted as τ00, represents the level of between-caregiver variation. The values of τ00
were 113.15, 152.65, and 91.66 for depression, anxiety, and health complaints,
respectively.
Random Coefficient Regression Model
Before addressing our hypotheses concerning the problem-solving variables, we
also estimated a simple linear growth model for each measure of caregiver
adjustment. Because this model included only the measurement occasion variable
(i.e., time) as a Level 1 covariate and was specified without any Level 2 predictors, it
also can be considered an unconditional model (see Table 3). Generally, as the most
basic linear growth model specification, this unconditional model estimates the Level
1 initial status parameter (intercepts) at Level 2 as a function of the average intercept
over all participants, β00, and a residual, u0j. The estimates for β00 indicate that
caregivers had average scores of 19.48 and 45.90 for the depression and anxiety
measures, and an average of score of 11.74 for health complaints at the first
measurement occasion.
Similarly, the estimates for the Level 1 rate of change parameter (i.e., slope) for
each adjustment measure were modeled at Level 2 in terms of the overall average
slope across all caregivers, β10, and a residual, uij. The estimates obtained for the β10
parameters indicate that caregiver health complaints increased an average of 1.04 on
each successive measurement occasion (see Table 3). This was not a statistically
significant change. Caregivers on average also had lower depression (−.08) and
anxiety (−.66) scores from one measurement occasion to the next; these changes were
also not significant.
The absence of statistical significance associated with the β10 parameter for each
adjustment measure can likely be attributed to the fact that the parameter estimates
represent the average rate of change for caregivers in the aggregate. In addition, the
absence of significant changes for the group over time may be attributed to the
substantial and statistically significant variation that existed across caregivers for
these change parameters. However, the information provided by the statistically
significant estimates of variance (i.e., the random effects associated with each rate of
change parameter; see Table 3) indicated that the data provided sufficient variation to
warrant modeling of these parameters as a function of caregiver problem-solving
abilities. Given our primary focus on the rate of change parameter, the random effect
for the β00 parameter for each adjustment measure was not estimated for this model.
Final Model
A final model was formulated to examine the variation in the Level 1 rate of
change parameters (π11) as a function of problem-solving abilities that were measured
at the between-caregiver level (see Table 4 for estimates obtained for this model). The
high correlations between the rate of growth and the initial status parameters for each
measure of adjustment in this model indicated a near linear dependency. As such, a
second model was estimated wherein only the variability in the rate of change
parameter (π1j) across participants was examined as a function of caregiver level
problem-solving measures at Level 2. Although fixed effects estimates of the initial
level or intercept parameter (i.e., β00) were obtained for each adjustment measure, the
random effects associated with these parameters were not estimated for the final
model. The results of the final model indicated that of the five problem-solving
variables, only negative orientation had a statistically significant effect on rate of
change parameters for depression (β11 = .31, p < .001), anxiety (β11 = .48, p < .001),
and health complaints (β11 = .33, p < .001). The positive coefficients associated with
these parameters indicate that caregivers with a higher negative orientation were
likely to develop increasing levels of distress at a more rapid rate over the first year of
providing care than caregivers with a lower negative orientation. The effects of
negative orientation explained approximately 34%, 50%, and 21% of the variance in
the rate of change parameters of depression, anxiety, and health complaints,
respectively.
Because we measured the problem-solving variables at only one occasion, they
were specified as between-subjects (i.e., person-level) time-invariant covariates (i.e.,
problem-solving abilities were treated as if they did not vary over time). Given the
linkage between the Level 1 and Level 2 models, and the relationship between the
error terms of these models, the covariation between the outcome and the Level 2
covariates is implicit. Thus, by simple substitution it is possible to express each
outcome measure as a function of the combined Level 1 and Level 2 models (Singer,
1998). In effect, the linkage between the two models takes into consideration the level
of covariation between the individual difference variables (i.e., the Level 2 problemsolving predictors) and each outcome measure.
Discussion
Consistent with our predictions and with contemporary models of social problem
solving, an NO was predictive of the variability in the rates of change in caregiver
psychological and physical adjustment during the inaugural year of caregiving. As a
group, caregivers did not evince a change over time for the three indices of
adjustment. However, caregivers with a higher negative orientation were more likely
to experience more distress during the year at a greater rate than caregivers with a
lower negative orientation. Thus, caregiver negative orientation assessed during the
inpatient rehabilitation program was an important indicator of subsequent caregiver
adjustment over the first year of caregiving.
Individuals with a higher negative orientation toward problem solving may
experience greater distress for a variety of reasons. These persons are more likely to
use palliative, emotion-focused coping strategies and have ruminative, irrational
thoughts in time of stress that do not remedy problematic situations (D'Zurilla &
Chang, 1995; MacNair & Elliott, 1992). A higher negative orientation is also
associated with a more negative mood, and under duress the inability to regulate these
moods may make a person susceptible to pronounced experiences of depressive
symptomatology (Elliott et al., 1996). The lack of confidence and enduring pessimism
that characterize a negative orientation may also render a person vulnerable to
depression (Priester & Clum, 1993).
Heppner and Krauskopf (1987) believe persons who have a negative appraisal of
their problem-solving abilities are likely to encounter several difficulties processing
problem-related information. Pessimistic ruminations and negative moods can distort
available and pertinent information, interfere with retrieval and storage of
information, impair the implementation of solutions, and decrease cognitive
flexibility. Consistent with this position, we have found negative orientation impairs
cognitive problem-solving performance independently of negative affect (Shewchuk,
Johnson, & Elliott, 2000). Caregivers with a higher negative orientation might
experience an unfortunate yet circular situation. They may have difficulties managing
their own emotions and creatively meeting caregiving demands. When these problems
are unresolved over time, pessimistic beliefs about the self may be reinforced,
exacerbating their distress levels (Nezu, 1987; Nezu & D'Zurilla, 1989). This cycle
could contribute to the development of more emotional and health problems as the
caregiving role unfolds.
The present study converges with other research indicating that caregivers who
possess more cognitive–behavioral resources exhibit fewer emotional difficulties
adjusting to their role (Chwalisz, 1996; Haley et al., 1996). Moreover, it appears that
caregiver problem-solving abilities predict the emotional and physical health of the
caregiver and care recipient (Elliott et al., 1999), as observed over the course of a
year. Problem-solving interventions have demonstrated efficacy in the treatment of a
variety of psychological concerns, and this approach is well suited for programs
designed to help family caregivers adjust to their role and accompanying demands
(D'Zurilla & Nezu, 1999). Research investigating the effects of problem-solving
training with family caregivers is warranted.
There is a pressing need for interventions that help family caregivers address the
routines and tasks “essential to maintaining family functioning” (Altman, Cooper, &
Cunningham, 1999, p. 67). Psychologists can have an immense impact in developing,
evaluating, and delivering interventions that address the everyday needs of caregivers
and their care recipients. Intervention research in family health psychology indicates
that psychoeducational strategies are consistently more effective than other
modalities, presumably because these approaches address the specific needs of family
members and often actively involve family members (Burman & Margolin, 1992;
Campbell & Patterson, 1995). Programs that address the specific needs of families
may be more likely to succeed (Burman & Margolin, 1992). Likewise, effective
interventions are those that address the problems as experienced by families (Elliott &
Shewchuk, 2000). These interventions should help families to become more active
and expert in their self-management and to operate competently as extensions of the
formal health care system (Wagner, Austin, & von Korff, 1996). Counseling
psychologists can develop and implement the delivery of problem-solving training
programs for caregivers that can be provided in traditional and innovative formats that
promote adjustment for the caregiver and care recipient (cf. Kurylo, Elliott, &
Shewchuk, in press).
This study concerned a segment of family members who assume caregiver roles
for persons with spinal cord injury. Our results, therefore, may not apply to other
family caregivers of persons with other health conditions. In the future, a replication
with a larger sample of family caregivers—who provide care to a family member with
a condition other than spinal cord injury—would help us understand more about the
dynamic processes of caregiving over time. We did not examine the influence of other
possible mediating characteristics such as gender, ethnicity, or income; these are
issues that could also be addressed in studies with larger samples. Participant attrition
over time is a common problem in longitudinal field research; it may be advisable to
design research protocols that are more attentive to the special considerations and
demands that family caregivers are likely to experience that can hinder participation.
A longer time frame that includes more observations would permit examination of
complex patterns of adjustment that might emerge over the trajectories of the
caregiving role.
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1
A thorough description of the statistical procedures used in the current study is available from
Timothy R. Elliott. Results of alternate models in which attempts were made to specify the three
outcome measures as observed indicants of a single latent adaptation construct are also available on
request.
This study was supported by Grants H133B30025 and H133N5009 from the
National Institute on Disability and Rehabilitation Research and Grant
R49/CCR412718-01 from the National Center for Injury Prevention and Control and
the Disabilities Prevention Program, National Center for Environmental Health. Its
contents are solely the responsibility of the authors and do not necessarily represent
the official views of the funding agencies. This study is one in a series investigating
the adjustment of family caregiver and care recipients following the onset of spinal
cord injury.
Correspondence may be addressed to Timothy R. Elliott, Spain Rehabilitation Center
530, 619 19th Street South, Birmingham, Alabama 35249-7330.
Electronic mail may be sent to telliott@uab.edu
Received: February 2, 2000
Revised: July 27, 2000
Accepted: October 24, 2000
Table 1. Number of Observations for Outcome Measures
Table 2. Means, Standard Deviations, and Correlations Observed for Social
Problem-Solving Abilities and the Outcome Variables
Table 3. Unconditional Baseline Model of Estimated Caregiver Adjustment Curve
Parameters and Variance Components
Table 4. Final Model: Social Problem-Solving Abilities As Predictors of Rates of
Change in Caregiver Depressive Behavior, Anxiety, and Health Complaints Over the
Initial Year of Caregiving