When a romantic partner has a spinal cord injury: Caregiving tasks and resilience as moderators of support quality on psychosocial distress and relational closeness

Abstract

Using a sample of 312 people in a romantic relationship with a partner who has a spinal cord injury (SCI), this study examined the separate and combined effects of caregiving tasks, resilience, and received support on the participant’s level of psychosocial distress. We also tested whether such distress might mediate the effect of the predictors on romantic relationship closeness. Results supported the beneficial effects of both resilience and receiving high-quality support, although the timing of the injury moderated these effects. Injuries sustained after relationship initiation particularly threaten well-being and closeness and, along with the burden of caregiving tasks, alter the extent to which received support and resilience are associated with health and relationship benefits. These results suggest that support providers should be sensitive to the context of the SCI and, for scholars, indicate the importance of further theorizing context in the theory of resilience and relational load.

Keywords

Caregiving resilience romantic relationships social support spinal cord injury supportive communication well-being

Approximately 5.4 million Americans experience some form of paralysis, with spinal cord injuries (SCIs) serving as the second-leading cause (29% of all SCIs; Christopher and Dana Reeve Foundation, 2013). An SCI often renders people unable to perform daily tasks and in need of extensive caregiving, and in many cases, family and friends serve as their primary caregiver (Family Caregiver Alliance, 2015). For some with SCIs, their romantic partner often becomes that caregiver, a demanding role that often consumes many hours per day and may last for several decades, and threatens the caregiver’s social and psychological well-being (Angel & Buus, 2011; Weitzenkamp et al., 1997). Additionally, sustained caregiving responsibilities may erode relationship quality, particularly when the injury occurs after the onset of the romantic relationship (Kreuter, 2000; Simmons & Ball, 1984). Thus, it is of both theoretical and pragmatic importance to identify factors that might mitigate these health and relational risks among individuals caring for romantic partners with an SCI.

Historically, research suggests that social support can buffer against the deleterious effects of stressful life events, with higher quality support contributing to mental, physical, and relational well-being (see Vangelisti, 2009). In this study, we operationalize high-quality support as that which is helpful (i.e., pertaining to “informational and instrumental benefits” of support), supportive (i.e., pertaining to “relational loyalty” in the midst of the stressor), and sensitive (i.e., pertaining to “emotional awareness” in the offering of support; Goldsmith et al., 2000, p. 387). However, receiving social support can also be costly, as it may create a sense of social indebtedness and increase individuals’ awareness of their difficult life circumstances. Particularly salient for romantic partners of someone with an SCI, receiving support may be distressing when it suggests they cannot meet others’ expectations about how they should cope with the SCI (Hatchett et al., 1997). Although research has demonstrated that social support can guard against harmful outcomes associated with stressful events (Prati & Pietrantoni, 2009) such as long-term caregiving (Li et al., 1997), what remains less clear are the factors that could diminish or enhance the efficacy of such support (MacGeorge et al., 2011). Consequently, we considered how personal, situational, and relational components of providing care to an SCI partner frame partners’ experiences of social support.

First, the effect of received support quality may be influenced by the number of caregiving tasks performed by the partner (Pinquart & Sörensen, 2007). When number of caregiving tasks is relatively low, receiving high-quality support may be less necessary and, consequently, less beneficial. Second, resilience, or an individual’s successful adaption to adversity or stressful experiences (Zautra et al., 2010), might moderate supportive message quality because people who are more resilient may not have as much need for support to cope successfully, and thus derive less benefit from it when it is received. Third and perhaps most extensively, the timing of the injury may shape the experience of caregiving and response to receiving support. Experiencing an SCI after relational commitment significantly and drastically alters individuals’ expectations for their future; in contrast, those who enter a relationship after the SCI can anticipate (at least to some extent) the challenges that they will face (DeVivo et al., 1995). SCIs occurring after the start of the relationship may particularly diminish psychosocial health and relational quality, and thus such caregiving partners may particularly benefit from receiving support as they cope with this unexpected stressor.

The chief purpose of this project was to investigate how these individual and contextual factors such as (a) caregiving tasks, (b) resilience, and (c) timing of the SCI moderate the extent to which receiving social support predicts psychosocial distress among SCI caregiving romantic partners. Moreover, we also investigated the extent to which such distress mediates the effect of these predictors on romantic relationship closeness. This advances our understanding of social support by both clarifying factors that predict support efficacy and provides practical insight for SCI caregivers and those who support them.

Theoretical background

Caregiving tasks and the theory of resilience and relational load (TRRL)

According to one review of the literature on the health of SCI caregivers, we have “overwhelming evidence” that caregiving may harm the psychological, social, and physical health of the caregiver (Schulz et al., 2009, p. 2). Pinquart and Sörensen (2003) conceptualize caregiver burden as “the overall impact of physical, psychological, social, and financial demands of caregiving” (p. 112). They further distinguish caregiver burden from caregiving tasks, or the specific behaviors performed to provide for the well-being of the other person. For those taking care of someone with an SCI, such specific behaviors could include assisting the other with eating, bathing, putting on clothes, transportation, using the bathroom, and so forth. Their meta-analysis focused on dementia and care for older adults, finding that caregiving tasks are positively associated with the broader construct of caregiving burden. Among those caring for an SCI partner, we suspect that this association is particularly strong, as SCIs may greatly limit the tasks that the injured person can perform (Post et al., 2005), thus contributing to their relational partners’ experience of stress.

One theory useful in understanding how individuals and relationships are affected by chronic stress is Afifi et al.’s (2016) TRRL. Foundational to this theory is the assumption that engaging in ongoing validation of one’s relational partner accumulates beneficial emotional reserves, which allows partners to appraise stressors as shared or communal. In contrast, long-term stressors such as those associated with caring for an SCI partner contribute to fatigue or relational load. When partners experience sustained relational load, they are less likely to perceive a communal orientation toward relational stress, which also makes them more likely to engage in more threatening appraisals of stressors, resulting in depleted emotional and relational reserves. The relationship between emotional reserves and a communal orientation toward relational stressors is circular, such that ongoing investments in the relationship increase reserves, which increases communal orientation and reduces the threat appraisal of stressors. Chronic relational stress or depleted relational investments likely decreases communal orientation, increasing the detrimental individual and relational impact of stress.

In the context of providing care for an SCI partner, the type of chronic stress that is typically associated with caregiving tasks likely depletes emotional, psychological, and relational resources and contributes to stress and relational load. Thus, following previous research (Pinquart & Sörensen, 2007), we hypothesize that the amount of caregiving tasks will predict the caregiver’s psychosocial distress:

H1: Caregiving tasks positively predict the psychosocial distress of the caregiving partner.

Received support quality, distress, and well-being

Given the potential relational load and associated distress experienced by SCI partners, it is unsurprising that caregivers may desire support from others. According to Burleson and MacGeorge (2002), social support is a broader term referencing the feeling of being part of a supportive social network. In this investigation, we focus on partners’ received support, which refers to the nature, type, or amount of social support people perceive they get from their social network (Vangelisti, 2009). Received support is distinct from enacted support in that it focuses on individuals’ assessment of the support quality actually provided rather than evaluating the verbal and nonverbal behaviors that people engage in when providing support (i.e., enacted support).

The TRRL recognizes received support as a key buffer against psychosocial distress: “Effective social support, communication competence, affection, and other affirming communicative behaviors often act as stress buffers.…Individuals in supportive interactions could experience positive energy from the support they receive, helping them maintain (or potentially increase) their emotional reserves” (Afifi et al., 2016, p. 672). Thus, given the significant toll of caregiving (Schulz et al., 2009), receiving high-quality social support may help mitigate the negative psychosocial outcomes for SCI caregivers (Sheija & Manigandan, 2005; Vrabec, 1997). Several studies have documented the positive effect of social support on well-being (Krause et al., 1989) when coping with stressors such as long-term caregiving (Cunningham & Barbee, 2000). Social support and the associated sense of integration into a social network may diminish long-term caregivers’ evaluation of stress (Drentea et al., 2006), and the more connected a person is to a high-quality support network, the less likely that person is to experience poor mental and/or physical health (Uchino, 2006). Yet, for social support to be effective in mitigating stress, recipients must frame the support as helpful, beneficial, and attuned to their needs (Bodie & Burleson, 2008). Thus, we predict that receiving high-quality support will benefit the caregiving partner’s well-being:

H2: Received social support quality inversely predicts the psychosocial distress of the caregiving partner.

Resilience

The benefits of social support are not uniform across individuals and vary as a function of both their personal traits and characteristics (Pierce et al., 1997) and their previous responses to adversity (Zautra et al., 2010). One characteristic particularly salient in understanding how caregiving partners cope with SCIs is resilience (Simpson & Jones, 2012).

Due in large part to its interdisciplinary origin, the concept of resilience is difficult to uniformly define and measure (Afifi, 2018). At the individual/relational level, resilience can be defined as the ability to adapt positively when faced with adversity (Luthar, 2003). Increasingly, communication scholars have identified resilience as something that is “developed, shaped or framed, sustained, and grown” over the life span (Buzzanell & Houson, 2018, p. 2). However, the factors that affect this process of resilience development are varied and wide-ranging, including everything from individuals’ personality traits (Luthar et al., 2000), the type of adversity experienced, environmental and situational factors, and the extent of social support (Carr & Koenig Kellas, 2018). Although some people are likely more inherently resilient, others develop resilience through successfully adapting to previous adverse experiences. In sum, a person’s resilience is informed by both elements of their personality and the contextual, ongoing situation in which adversity occurs but is frequently measured at a discrete point in time. In light of this dual orientation, this study considers resilience as a combination of both personality and situational factors (which are so closely bound that they cannot be disentangled) that inform how individuals cope with stress and adversity.

The resiliency model describes the process by which people experience a hardship and respond to the challenge in a way that facilitates positive adaptation (Richardson, 2002). According to the model, this process begins prior to the occurrence of adversity with biopsychospiritual homeostasis or a point in time when a person has adapted to their life circumstances. Life changes (including adversity) may threaten this biopsychospiritual homeostasis, and when a person’s existing coping resources are inadequate or the disruption is significant, the process described by the resiliency model is activated. Ideally, a resilient person moves from assessing their adverse situation, to coping with the hardship, to restoring balance after the stressor, and potentially learning lessons from the experience such that they can more effectively navigate and overcome future challenges. As a result of these efforts, resilient people often experience fewer detrimental consequences from adversity, including less depression (Wingo et al., 2010), anxiety (Reinelt et al., 2014), and a more positive assessment of their overall well-being (Fredrickson & Joiner, 2002). Indeed, consistent with the TRRL (Afifi et al., 2016), resilient people are often able to appraise relational stressors from a more communal approach, minimizing the likelihood of physiological stress and fostering a more positive orientation toward adversity (Afifi, 2018). Therefore, we predict that caregiving partners’ resilience will inversely predict their level of psychosocial distress.

H3: Resilience inversely predicts the psychosocial distress of the caregiving partner.

Moderating the effect of support quality

Although the TRRL recognizes that receiving high-quality support generally aids well-being (Afifi et al., 2016), MacGeorge et al. (2011) noted that effective support is a contingent process, and they urged researchers to explain “why such effects may be variable across individuals and situations” (p. 342). In addition to their hypothesized direct effects, both the extent of SCI caregiving tasks and a person’s resilience might moderate the effect of support quality on psychosocial distress. Specifically, we contend that, for those with many caregiving tasks, receiving high-quality support may be especially helpful because it functions as a buffer from the stress typically associated with caregiving (Shewchuk et al., 1998). In contrast, partners facing fewer caregiving responsibilities may not accrue as much benefit from support because their relational load is not sufficient to produce distress. Likewise, when caregiving partners tend to respond to adversity in a resilient manner, their psychosocial well-being may be less negatively affected by situational stressors such as those associated with caregiving, and thus receiving support may not significantly contribute (or detract) from it. Thus:

H4: Caregiving tasks and resilience moderate the association between received support quality and psychosocial distress, such that (a) caregiving tasks increase the magnitude of the association and (b) resilience reduces the magnitude of the association.

One advantage of the TRRL is its tracing of the association between psychosocial distress and the health of interpersonal relationships, such that chronic stress wears away at relationships over time (Afifi et al., 2016). Indeed, scholarship predating the TRRL has long recognizes the threat that an SCI poses to the romantic relationship; as Kreuter (2000) noted, “often, the partner must play a dual role as lover and caregiver, which may create deleterious situations and conflicts” (p. 2). This places SCI caregivers at greater risk of relational separation (DeVivo et al., 1995) and diminished relational quality even if the relationship survives (Kreuter, 2000). Caregivers who reported feeling shocked, overwhelmed, torn, and personally harmed by their partner’s injury may pull away from their partners to distance themselves from distressing emotions (Angel & Buus, 2011), and per the TRRL, they may adopt a less communal orientation toward their partner. More generally, romantic partners caring for a partner with an illness may experience caregiver burnout (i.e., emotional exhaustion and tendency to depersonalize the ill partner), which is associated with diminished relational quality (Ybema et al., 2002). Thus, we predict:

H5: Psychosocial distress inversely predicts the non-SCI partner’s perception of relational closeness.

Figure 1 depicts the associations predicted thus far. To summarize, we have posited that caregiving tasks (H1), quality of received social support (H2), and resilience (H3) predict psychosocial distress, and such distress harms the quality of romantic relationships between SCI and caregiving partners (H5), both separately and jointly (H4). In turn, psychosocial distress functions as a mediator between these predictors and relational quality, such that diminished psychosocial health may serve as the mechanism by which these predictors influence the strength of the romantic bond (cf. Kreuter, 2000). This logic is consistent with the TRRL, which posits that depletion of coping resources increases stress, which, in turn, generates relational load (Afifi et al., 2016). To test this possibility, we predict:

H6: Psychosocial distress mediates the extent to which received support quality, caregiving tasks, and resilience predict relational closeness.

Figure 1.

Conceptual model predicting the caregiving partner’s psychosocial distress and relational closeness with the SCI partner. Note. H6 predicted that psychosocial distress would mediate the association between the three predictors (i.e., received support quality, caregiving tasks, and resilience), and RQ1 inquired whether these associations would differ across the pre- and post-injury groups. SCI = spinal cord injury.

As Figure 1 indicates, H4’s hypothesis of moderation and H6’s hypothesis of mediation indicate a conditional process model (i.e., moderated mediation). Therefore, we evaluated our hypotheses in a conditional process model following the guidance of Hayes (2013).

Timing of the injury

Finally, another critical moderator is timing of the injury. Previous research has demonstrated that pre-injury couples experience reduced relationship quality (Crewe et al., 1979; Simmons & Ball, 1984), greater caregiving burden for the noninjured partner (Chan et al., 2000), diminished frequency of sexual activity (Kreuter et al., 1994), and greater susceptibility to divorce (Feigin, 1994; Kreuter et al., 1998; perhaps due to the spouse’s caregiving burden, DeVivo et al., 1995). When an injury occurs after relationship initiation, the caregiving partner’s expectations may be shattered as the previously anticipated picture of their relational life with their partner has become unattainable. Thus, partners who were in a relationship with each other before the injury occurred may benefit from additional support to minimize the distress associated with that disruption (Angel & Buus, 2011). In contrast, if relational partners entered their relationship after the occurrence of the injury, functioning as a caregiver may feel less burdensome and disruptive because it was an expected part of their life together (Kreuter, 2000); on this point, the TRRL acknowledges that relational expectations shape the subsequent experience of stress and relational load (Afifi et al., 2016). However, despite the clear evidence that relational expectations and experiences differ depending on injury timing, we are not aware of any previous study that compares relational and psychosocial well-being in caregiving partners between pre- and post-injury relationships. Given that the timing of the injury has the potential to significantly affect the extent of the SCI’s impact on caregiving partners, we ask:

RQ1: Do the hypothesized patterns of association differ depending on whether the injury occurred before or after the beginning of the romantic relationship?

Method

Participants

After obtaining approval from the institutional review board, we solicited a snowball sample drawn primarily from online groups targeted at SCI caregivers. One of the study authors had developed trust with such groups over time and thus had natural access to them. The search for groups was limited to those in English and active in the 3 months before data collection (contact lead author for additional information about the groups contacted for this study). After indicating informed consent, participants completed a survey via the Qualtrics platform.

The final sample contained 312 participants which skewed female (n = 252, 80.8%), consistent with prior research indicating that those with an SCI are more likely to name a woman as one of their most helpful caregivers (Boschen et al., 2005). Most participants identified themselves as Caucasian (n = 271, 86.7%), with others identifying as Hispanic (n = 16, 5.1%), African American (n = 5, 1.6%), Native American (n = 2, 0.6%), or “other” (n = 7, 4.5%). Participant age ranged from 20 to 90 years (M = 42.3, SD = 11.9). Most reported their relationship with the injured partner as legally married (n = 207, 66.3%), in a long-term domestic partnership (n = 42, 13.4%), boyfriend or girlfriend (n = 42, 13.4%), in a common-law marriage (n = 15, 4.8%), or other (n = 6, 1.9%). Most participants reported on a heterosexual romantic relationship (n = 289, 92.6%). Slightly more than half (56.1%, n = 175) of the sample reported that the injury occurred after becoming romantic partners, with 43.9% (n = 137) reporting that the injury occurred before becoming romantic partners.

Measures

Caregiving tasks

To assess the partner’s engagement with caregiving tasks, we presented participants with a list of 10 common caregiving activities for people with an SCI: (a) dressing, (b) eating, (c) bathing, (d) bladder care, (e) bowel care, (f) transferring (i.e., moving them), (g) range of motion, (h) stretching, (i) driving, and (j) meal preparation. For each activity, participants indicated, via “yes” or “no” response, whether they helped the partner “on a regular recurring basis.” Responses ranged from no activities to all 10, and all items demonstrated acceptable internal reliability, α = .88.

Caregiver resilience

The Brief Resilience Scale (Smith et al., 2008) asked participants to evaluate how effectively they bounce back after adversity. The scale contained six statements (e.g., “I usually come through difficult times with little trouble”; “I have a hard time making it through stressful events,” reverse-coded) assessed on a 5-point Likert-type scale ranging from (1) strongly disagree to (5) strongly agree. The scale demonstrated acceptable reliability, α = .82.

Quality of received support

Goldsmith and her colleagues’ (2000) Social Support Quality Measure was adapted to assess participants’ perception of received support quality, measured via a series of 13 semantic-differential antonym pairs (e.g., “Selfish/Generous,” “Supportive/Unsupportive,” “Useless/Useful”). The measure achieved acceptable reliability in this study, α = .90.

Psychosocial distress

Following previous research (e.g., Schrodt & Ledbetter, 2007), three measures served as indicators of psychosocial distress. First, perceived stress was measured using the 4-item version of Cohen et al.’s (1983) Perceived Stress Scale (e.g., “In the last month, how often have you felt that you were unable to control the important things in your life?”), with items assessed on a frequency scale ranging from (1) never to (5) very often. The items obtained somewhat low but adequate reliability in this study, α = .65. Second, caregivers’ health symptoms were assessed using Dornbusch et al. (1991) instrument. This 14-item scale asked the caregiver to identify various feelings from the past 2 weeks, including fatigue, nervousness, irritability, and so forth. Responses were recorded using a 4-point frequency scale ranging from (0) never to (3) 3 or more times. The items obtained acceptable reliability in this study, α = .74. Finally, self-esteem was assessed using Rosenberg’s (1965) instrument. The measure contains 9 items (e.g., “I feel that I have a number of good qualities” and “I certainly feel useless at times”) assessed on a Likert-type scale ranging from (1) strongly disagree to (7) strongly agree, and the measure demonstrated acceptable reliability, α = .76. The esteem measure was reverse-coded for directional consistency with the other two indicators of psychosocial distress, and health symptoms and stress likewise were converted to a 1-to-7 metric. All three scales exhibited moderate intercorrelations (.49 ≤ r ≤ .62) and acceptable reliability as a whole, α = .80.

Relational closeness

Vangelisti and Caughlin’s (1997) 7-item measure assessed relational closeness with the SCI partner (e.g., “How satisfied are you with your relationship with your romantic partner?”; “How close are you to your romantic partner?”). Responses were solicited using a 7-point Likert-type scale ranging from (1) not at all to (7) very much. The measure obtained acceptable reliability, α = .82.

Data analysis

All study hypotheses and research questions were evaluated using multiple group confirmatory factor analysis (CFA) and structural equation modeling as implemented in the lavaan package for the R statistical computing environment (Rosseel, 2012). All latent constructs were identified by creating three parcels per construct (Little et al., 2002), with items assigned to each parcel by (a) conducting a principal components factor analysis that forced a unidimensional solution on each scale and (b) grouping items by thirds based on the strength of factor loadings. Additionally, four interaction term constructs were created to represent all possible two-way and three-way interactions among caregiving tasks, caregiver resilience, and received support quality. Interaction term parcels were created by orthogonalizing product terms relative to the first-order parcels (Little et al., 2007).

Multiple-group CFA was conducted across two groups: (a) those who began their relationship before the SCI (i.e., the pre-injury group) and (b) those who began the relationship after it (i.e., the post-injury group). Metric invariance was assessed using the procedures described by Little (1997). Nonparametric bootstrapping (Hayes, 2013) evaluated statistical mediation. Throughout, four common indices assessed model fit (Kline, 2016): (a) model chi-square, (b) the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the non-normed fit index (NNFI). Models were considered to have acceptable fit at RMSEA < .08 and CFI/NNFI > .90.

Results

Following both the TRRL (Afifi et al., 2016) and the broad tradition of social support research (Burleson & MacGeorge, 2002), the central empirical link examined in this study was the expected inverse relationship between support quality and psychosocial distress (and, in turn, the quality of the romantic relationship). Using a conditional process analysis model (Hayes, 2013), we evaluated three factors (i.e., resilience, caregiving tasks, and timing of the injury) that may serve as boundary conditions for this basic association. An initial confirmatory model that only included the first-order constructs (i.e., no interaction terms) demonstrated acceptable fit, χ²(16) = 272.38, CFI = 0.95, NNFI = 0.94, RMSEA = .067 (90% CI [.053, .081]).

We began by evaluating the timing of the injury (RQ1), testing for metric and structural invariance across the pre- and post-injury groups. Table 1 reports the results of this analysis. Although these tests indicated both weak and strong metric invariance (Little, 1997), they also indicated that the patterns of association differed between the pre- and post-injury groups, answering RQ1. Table 2 reports manifest and latent means, correlations, and standard deviations between groups. Of particular note, the post-injury group reported fewer caregiving tasks and greater relational closeness than did the pre-injury group.

Table 1.

Fit indices for the nested sequence in the multiple group confirmatory factor analysis.

Model	χ²	df	p	Δχ²	Δdf	p	RMSEA	RMSEA (90% CI)	NNFI	CFI	Constraint tenable
Configural invariance	562.60	600	>.05	—	—	—	.000	[.000, .015]	1.00	1.00	N/A
Loading invariance^a	587.48	618	>.05	—	—	—	.000	[.000, .017]	1.00	1.00	Yes
Intercept invariance^a	606.69	652	>.05	—	—	—	.000	[.000, .012]	1.00	1.00	Yes
Latent Mean invariance^b	663.39	657	>.05	56.71	5	<.01	.008	[.000, .026]	1.00	1.00	No
Homogeneity of variances and covariances^c	685.23	651	>.05	97.76	33	<.01	.018	[.000, .032]	1.00	1.00	No
Homogeneity of variances^c	636.18	627	>.05	48.71	9	<.01	.010	[.000, .027]	1.00	1.00	No
Equality of correlations^c	637.91	642	>.05	50.43	24	<.01	.000	[.000, .024]	1.00	1.00	No

Note. Each nested model contains its constraints, plus the constraints of all previous, tenable models. CFI = comparative fit index; RMSEA = root mean square error of approximation; NNFI = non-normed fit index; N/A = not applicable.

^a Evaluated with the ΔCFI and RMSEA model fit tests.

^b Evaluated with the χ² difference test, in comparison to the intercept (strong) invariance model.

^c Evaluated with the χ² difference test, in comparison to the loading (weak) invariance model.

Table 2.

Correlations and descriptive statistics for the combined, pre-injury, and post-injury groups.

Constructs	M ^a	SD ^a	1	2	3	4	5
1. Tasks	5.70 (6.68/4.62)**	3.31 (2.77/3.16)	1.00	.06/−.21*	−.003/.12	.05/.15	−.21**/.08
2. Resilience	4.05 (4.12/3.97)	0.79 (0.73/0.61)	−.04	1.00	.40/.31	−.79/−.63	.17*/.15
3. Support	4.92 (4.93/4.75)	1.29 (1.12/0.95)	.03	.25**	1.00	−.72/−.54	.30*/.20
4. Distress	3.05 (3.12/2.97)	0.82 (0.78/0.67)	.12*	−.53**	−.45**	1.00	−.41/−.33
5. Closeness	6.26 (6.08/6.39)**	0.89 (0.96/0.63)**	−.14*	.12*	.19**	−.32**	1.00

Note. Coefficients below the diagonal are manifest-level associations across the entire sample. Coefficients above the diagonal are for the pre-injury (before the slash) and post-injury (after the slash) groups modeled separately with the weak metric invariance constraint via phantom constructs (Little, 1997).

^a For the means and standard deviations of each variable, the first number represents the value in the combined group (N = 312), the second number represents the value in the pre-injury group (n = 175), and the third number represents the value in the post-injury group (n = 132). Latent means and standard deviations were obtained using the contrast coding method described by Little et al. (2006). Asterisks indicate statistically significant differences in means or standard deviations when constrained to equality across groups.

*p < .05; **p < .01.

Given the significantly different structural patterns between groups, the hypothesized structural model was run separately for each group, with the weak metric invariance constraint and phantom constructs (Little, 1997) attenuating for cross-group variance differences, χ²(618) = 587.48, p > .05, CFI = 1.00, NNFI = 1.00, RMSEA = .000 (90% CI [.000, .017]). Table 3 presents the regression coefficients obtained for each group. As posited by the TRRL (Afifi et al., 2016), across both groups, support quality served as an inverse predictor of psychosocial distress. We will now turn attention to resilience and caregiving tasks as moderators and, as we do, will consider how the boundary conditions provided by these variables differ based on the timing of the injury.

Table 3.

Regression coefficients for structural model, pre-injury, and post-injury groups.

	Distress		Closeness
Predictor	Pre-injuryB (β)	Post-injuryB (β)	Pre-injuryB (β)	Post-injuryB (β)
1. Tasks	0.21 (.08)	−0.03 (−.02)	−0.17 (−.14)	0.17 (.15)
2. Resilience	−1.52 (−.60)**	−0.83 (−.53)**	−0.57 (−.46)	−0.01 (−.01)
3. Support quality	−1.22 (−.48)**	−0.58 (−.37)**	−0.23 (−.19)	0.10 (.09)
4. Tasks × Res	−0.13 (−.05)	−0.12 (−.08)	−0.27 (−.22)*	−0.15 (.13)
5. Tasks × Sup	0.10 (.04)	0.28 (.18)*	0.25 (.21)*	−0.23 (−.20)
6. Res × Sup	0.09 (.04)	0.27 (.17)*	0.23 (.18)*	−0.16 (−.14)
7. Tasks × Res × Sup	−0.35 (−.14)*	−0.10 (−.07)	0.01 (.01)	0.21 (.19)*
8. Distress	n/a	n/a	−0.44 (−.91)**	−0.20 (−.28)
Variance explained	84.4%	58.6%	34.1%	22.5%

Note. n/a = not applicable.

*p < .05; **p < .01.

Resilience and tasks as boundary conditions when predicting distress

As with support quality, resilience inversely predicted psychosocial distress in both the pre-injury and post-injury groups. This indicates that, at mean levels of support quality and caregiving tasks, those who report greater resilience also tend to report lower distress. In contrast, caregiving tasks were not significantly associated with psychosocial distress in either group. Thus, at mean levels of support quality and resilience, tasks do not predict the psychosocial distress of the caregiving partner.

However, results also indicated significant moderating effects, with caregiving tasks and resilience separately moderating the effect of support quality in the post-injury group, and a significant three-way interaction among all three predictors emerging in the pre-injury group. These interactions were probed using the pick-a-point approach (Hayes, 2013) at low (−1.5 SDs), mean, and high (+1.5 SDs) levels of each predictor. Across both groups, the effect of received support quality on both dependent variables was a function of both caregiving tasks and resilience. Thus, all decompositions simultaneously accounted for the effect of both moderators. Figure 2 depicts these decompositions.

Figure 2.

Decomposition of caregiving tasks and resilience as moderators of the effect of support quality on psychosocial distress. Gray lines with gray marker fill are statistically nonsignificant. For pre-injury group, distress SD = 2.53; for post-injury group, distress SD = 1.55. All predictor SDs = 1.00.

The overall story of these results is that both support quality and resilience exhibit, in most cases, inverse associations with psychosocial distress. In both groups, distress was markedly high at low levels of support quality and resilience and markedly low when support quality and resilience were high. However, in some conditions, support quality did not serve as a significant predictor of distress and likewise for resilience.

Support as a predictor in the pre-injury group

In the pre-injury group, support quality did not emerge as a significant predictor for participants who reported low tasks and high resilience. In this case, they had low distress, regardless of the support quality; it would seem that high-quality support may be unnecessary when resilient caregiving partners face a lower caregiving burden. Conversely, caregiving partners with low resilience experienced heightened distress in the face of many caregiving tasks, and support quality did not significantly ameliorate that distress. In this condition, nonresilient caregiving partners may not be able to effectively utilize the support offered to them, even when it is high in quality. Support quality emerged as a robust inverse predictor particularly when tasks and resilience were high and also when both tasks and resilience were low.

Support as a predictor in the post-injury group

In post-injury groups, resilience weakened the strength of the association between support quality and distress; for example, at a mean level of tasks, support quality became a nonsignificant predictor of distress (and such distress was low) when the caregiving partner reported high resilience. Also, when caregiving tasks were high, support quality ceased to be a significant predictor of distress, regardless of the caregiving partner’s resilience; resilience, however, remained an inverse predictor in the presence of high caregiving tasks. Overall, then, as compared to support quality, resilience seems to be a more robust inverse predictor of distress in the post-injury group. This contrasts with the pre-injury group, where either support quality or resilience was associated with lower distress.

Resilience and tasks as boundary conditions when predicting relational closeness

Building from the TRRL (Afifi et al., 2016), we reasoned that distress would exert deleterious effects on the relational closeness between the caregiving partner and the person with an SCI. Perhaps the most dramatic difference between the two groups is that distress was inversely associated with closeness in the pre-injury group, but no significant association emerged in the post-injury group.

We used nonparametric bootstrapping (with 10,000 resamples) to evaluate the extent to which psychosocial distress mediated the association between support quality and relational closeness. Because results indicated that the effect of receiving high-quality support on closeness is conditional on caregiving tasks and resilience, the bootstrapping analysis calculated the indirect effect at all possible combinations of low (−1.5 SDs), mean, and high (+1.5 SDs) levels of each moderator.

Consistent with the nonsignificant direct path from psychosocial distress to closeness in post-injury relationships, no indirect effects emerged in that group. In the pre-injury group, support quality indirectly predicted relational closeness. Table 4 presents the coefficients and confidence intervals obtained from this analysis. We present results for both resilience and support as indirect predictors; no significant indirect effects emerged for caregiving tasks.

Table 4.

Summary of conditional indirect effects for structural model, pre-injury group.

Path (mediator = distress, outcome = closeness)	B [95% CI] (β)	SE
Predictor = received support quality
Tasks = low, resilience = low	1.00 [0.08, 1.93] (.81)*	.47
Tasks = low, resilience = mean	0.60 [0.05, 1.16] (.49)*	.28
Tasks = low, resilience = high	0.20 [−0.20, 0.61] (.17)	.21
Tasks = mean, resilience = low	0.60 [0.05, 1.14] (.48)*	.28
Tasks = mean, resilience = mean	0.54 [0.07, 1.01] (.44)*	.24
Tasks = mean, resilience = high	0.48 [0.03, 0.94] (.39)*	.23
Tasks = high, resilience = low	0.19 [−0.21, 0.59] (.15)	.20
Tasks = high, resilience = mean	0.47 [0.02, 0.93] (.39)*	.23
Tasks = high, resilience = high	0.76 [0.02, 1.50] (.61)*	.38
Predictor = resilience
Tasks = low, support quality = low	0.98 [0.05, 1.91] (.80)*	.47
Tasks = low, support quality = mean	0.58 [0.02, 1.14] (.47)*	.29
Tasks = low, support quality = high	0.18 [−0.23, 0.60] (.15)	.21
Tasks = mean, support quality = low	0.73 [0.06, 1.40] (.59)*	.34
Tasks = mean, support quality = mean	0.67 [0.07, 1.27] (.54)*	.31
Tasks = mean, support quality = high	0.61 [0.03, 1.19] (.50)*	.29
Tasks = high, support quality = low	0.47 [−0.09, 1.03] (.38)	.28
Tasks = high, support quality = mean	0.75 [0.06, 1.45] (.61)*	.35
Tasks = high, support quality = high	1.04 [0.07, 2.01] (.84)*	.49

Note. Effects at the mean of the moderators are the simple indirect effects.

*p < .05; **p < .01.

Taken overall, the mediation analysis revealed that the support quality (and resilience) produced significant and positive indirect effects at nearly all combinations of their moderators, providing general support for H6. For support quality, the only exceptions were nonsignificant effects when tasks were high and resilience was low (or vice versa). At low levels of caregiving tasks, receiving high-quality support and resilience exhibited something of a trade-off, such that increases in one variable reduced the potency of the other. When caregiving tasks were at the mean, support quality and resilience did not seem to meaningfully alter each other; in other words, they functioned as independent predictors with no moderating effects. At high levels of caregiving tasks, received support quality and resilience again moderated each other, but not as a trade-off; instead, increases in one variable appeared to amplify the effect of the other.

Remaining direct effects of tasks, support quality, and resilience on closeness

Although not directly hypothesized, the model also controlled for the direct effect of the exogenous predictors on closeness. Controlling for such direct effects is common in mediation analyses, and in this case, results indicated that direct effects on closeness remained even when controlling for psychosocial distress as a mediator, including a three-way interaction effect in the post-injury group and three two-way interaction effects in the pre-injury group (see Figure 3). In the post-injury group, the quality of received support predicted closeness only when caregiving tasks and resilience were both low, with a positive effect emerging. In contrast, high-quality received support in the pre-injury group demonstrated only inverse effects on closeness and then only at relatively low levels of resilience and caregiving tasks.

Figure 3.

Decomposition of caregiving tasks and resilience as moderators of the direct effect of support quality on relational closeness. Gray lines with gray marker fill are statistically nonsignificant. For pre-injury group, closeness SD = 1.23; for post-injury group, closeness SD = 1.14. All predictor SDs = 1.00.

Summary of results for study hypotheses

Overall, results indicated support for a number of the study’s hypotheses. In answer to the first research question, the associations among the study variables differed significantly across the pre- and post-injury groups. Although results did not generally support H1’s prediction that caregiving tasks would predict psychosocial distress, both support quality (H2) and resilience (H3) emerged as robust inverse predictors. However, the association between support quality and psychosocial distress was contingent on both resilience and caregiving tasks, supporting H4 to an extent (although the boundary conditions did not emerge exactly as hypothesized; we will consider this further in the Discussion section). H5 predicted that heightened psychosocial distress would inversely predict the caregiver’s perception of relational closeness, but a significant effect was obtained only in the pre-injury group. Likewise, only in the pre-injury group did distress mediate the association between support quality and closeness (H6). When taken collectively, these results suggest that the individual and relational impact of caring for an SCI partner varies greatly depending on when the injury occurred in the relationship and offers corresponding implications about the type of support they may need from their social networks.

Discussion

Using a sample of participants whose romantic partner has an SCI, the chief purpose of this project was to investigate the extent to which (a) caregiving tasks, (b) resilience, and (c) timing of the SCI might moderate the extent to which receiving high-quality social support predicts psychosocial distress in the caregiving partner. We also examined how distress mediated the effect of these predictors on romantic relationship closeness. Results generally supported the beneficial effect of receiving high-quality social support reported so frequently and consistently in the literature (High & Dillard, 2012) but also indicated that this effect is qualified by individual and situational factors. Particularly potent is the situational characteristic of when the SCI occurred. Given the central and practical importance of this variable for application of these findings, the discussion section will address implications for each group separately.

Pre-injury relationships

The TRRL posits that, like the human body, interpersonal relationships adapt to the levels of stress that they experience; however, when stress is excessive or prolonged, it may erode psychosocial and relational health (Afifi et al., 2016). With this theoretical backdrop in mind, it is hard to imagine a more devastating and all-encompassing relational change than that which occurs when a formerly able-bodied romantic partner becomes paralyzed as the result of an SCI (Dickson et al., 2010; Gill, 1999; Kreuter et al, 1998), and this change disrupts the couples’ preexisting biopsychospiritual homeostasis (Richardson, 2002). However, the TRRL also notes the role of high-quality social support in buffering against stress, and our results generally supported that claim, with two notable and related exceptions. First, caregivers who report relatively low caregiving tasks and already exhibit resilience may not gain much from support. One scope condition of the TRRL is that it “focuses on stress that affects relationships in the social system in some way” (Afifi et al., 2016, p. 676), and when resilient people face a light caregiving workload, they may not experience a stress response. Second and conversely, caregivers who report engaging in a significant number of caregiving tasks but who are not resilient may also find little benefit from receiving high-quality support, perhaps because support alone does little to ameliorate their distress. The TRRL emphasizes that resilience develops as a process over time, and those who have not developed resilience may not be able to effective utilize even high-quality support; relatedly, the dual-process model of supportive communication notes that support may be ineffective if the receiver lacks the ability to process the message, and perhaps a large number of caregiving tasks interferes with that ability (Burleson, 2009). In other words, in pre-injury relationships with high caregiving tasks, both high resilience and high-quality support may be needed to mitigate against psychosocial distress. Future research might investigate whether interventions designed to heighten resilience might enhance the efficacy of the support caregivers receive.

In turn, psychosocial distress emerged as a robust predictor of relational closeness, in accordance with both the TRRL (Afifi et al., 2016) and prior research indicating that compromised well-being in the caregiving partner also influences the quality of the romantic bond (Angel & Buus, 2011). Intriguingly, the closest romantic relationships seem to be those in which the caregiving burden is low, resilience is low, and support quality is low. Perhaps this unique combination creates an environment in which partners rely primarily on each other, which in turn fosters a sense of closeness. The TRRL adopts a systemic view of interpersonal relationships, and when neither outside support nor individual resilience is available to reduce distress, partners may adopt a communal orientation toward each other; such reliance contributes to their sense of closeness without adding to their distress because the caregiving burden is also relatively low. More generally, shared tasks serve as a relationship maintenance behavior in romantic relationships (Canary & Stafford, 1992), and to the extent that the low number of caregiving tasks and lack of external support enabled partners to accomplish them together, perhaps that served to reinforce relational closeness. Practically, this may suggest that support for such couples may undermine the strength of the romantic bond, and thus such support should be offered cautiously (e.g., to ensure it is truly needed and desired by the couple).

Additionally, the model supported the prediction that psychosocial distress would mediate the effect of resilience and support quality on closeness, with the level of caregiving tasks differentiating the patterns of mediation. When tasks were low, resilience and support quality functioned in an either/or manner, such that the presence of either high resilience or high-quality support aided relational well-being by improving the caregiving partner’s psychosocial well-being (see the top left graph in Figure 2). In contrast, when tasks were high, support quality and resilience functioned in a both/and manner, such that both were needed to produce the strongest effect on relational closeness via reduced distress (see the bottom left graph in Figure 2). It could be that a certain level of resilience is needed to effectively utilize high-quality support; alternatively, high-quality support may activate a person’s inherent ability to cope by encouraging a more communal orientation. Practically, this indicates that support interventions should consider the level of caregiving burden. When it is high, social support alone may not be enough, and helping efforts should aim to bolster the caregiving partner’s resilience as well.

Overall, these data are consistent with the TRRL’s central claim that stressors may wear away at psychosocial and relational health, but resilience and received support might buffer against such effects (Afifi et al., 2016). A cataclysmic health event such as an SCI marks the start of a long-term coping process that often changes over time (Richardson, 2002) as partners struggle to regain a sense of normalcy and to reestablish life as a couple. They must do so while also managing unfamiliar and often overwhelming caregiving tasks that were not anticipated at the start of the relationship (Angel & Buus, 2011).

Post-injury relationships

In contrast, post-injury relationships do not include the same disruption to biopsychospiritual homeostasis, as the able-bodied partner could consider the caregiving burden before committing to the relationship, and the injured partner may have had time to adjust to life with an SCI. Consequently, the quality of received social support, resilience, and their caregiving burden function differently in understanding psychosocial distress. People with an SCI may believe that their injury renders them less attractive as romantic partners (Yoshida, 1994), and indeed, marriage is less frequent and divorce more common among those with an SCI (DeVivo & Fine, 1985). Some have expressed incredulity regarding why someone who does not have a severe physical disability would want to enter a long-term romantic relationship with someone who does (DeLoach & Greer, 1981). Divorce is more common among those with more severe SCIs (DeVivo et al., 1995), and it stands to reason that those with particularly severe SCIs are less likely to (re)marry. Nevertheless, those with an SCI often do enter long-term romantic relationships after the injury (Crewe & Krause, 1990), and those relationships may follow generally accepted scripts for development (e.g., focusing on love and commitment; Milligan & Neufeldt, 1998). Resilience may be particularly important for caregiving partners entering a relationship with someone with an SCI, and indeed, resilience emerged as a more robust predictor of well-being than did support quality. The TRRL emphasizes the importance of relational expectations in shaping the experience of relational load (Afifi et al., 2016), and the expectations associated with pre-injury relationships (i.e., general health and wellness) differ potently from post-injury relationships (i.e., commitment to care for the health needs associated with the SCI).

Consistent with interpretation, post-injury participants reported higher relational closeness than did pre-injury participants, yet this investigation found few significant predictors of relational closeness in the post-injury group. Perhaps most notably, the caregiving partner’s psychosocial distress was unassociated with the closeness of the romantic relationship. Thus, although several factors predicted distress (as will be discussed shortly), the quality of the romantic relationship appeared to withstand such distress (although, of course, the cross-sectional nature of the sample precludes definitive claims regarding causation). Likewise, received support quality did not directly predict relationship closeness, and neither did the hypothesized moderators (i.e., caregiving tasks and resilience). A three-way interaction did emerge, but it revealed only one specific case where support may predict closeness: when both resilience and caregiving tasks are low. When caregivers have trouble coping with life challenges yet they face few of them in their caregiving tasks, receiving high-quality support may enhance romantic relationship quality, as it often does in relationships that do not involve an SCI (Sprecher & Felmlee, 1992). Prior work has indicated that higher marital quality is associated with caregiving partners deriving a sense of personal accomplishment from their caregiving, and perhaps low resilience and low tasks facilitates that sense (Ybema et al., 2002). Overall, however, post-injury relationships exhibited almost no associations between predictors and relational closeness.

Although predictor variables exhibited only a very limited association with relational closeness, they robustly predicted the caregiving partner’s psychosocial distress. Specifically, resilience and received support quality emerged as potent inverse predictors. Per existing research, those with greater resilience tend to possess healthier well-being (Werner & Smith, 1992), as do those who receive high-quality support (High & Dillard, 2012). However, both resilience and caregiving tasks qualified the beneficial effect of support quality, such that it diminished as the caregiver’s resilience increased. Because resilient people can withstand the challenges of life, they may not require (as much) emotional help, and thus do not reap (as much) benefit from receiving high-quality support, even if they appreciate the intent. It is worth noting that this differs from the pre-injury group, where both resiliency and support quality were needed for improved psychosocial well-being. Again, following the logic of the TRRL, we surmise that post-injury partners do not necessarily need both because they were able to anticipate the challenges of caregiving beforehand. Indeed, post-injury SCI couples may benefit from discussing future caregiving responsibilities that are likely to occur prior to entering a long-term partnership, and future research could seek to understand such conversations.

Pragmatically, for those supporting post-injury caregiving partners, these results suggest that support efforts may be more effective if they target the caregiving partner’s well-being rather than the strength of the romantic bond. Given cultural scripts that could undermine romantic bonds between those with a severe disability and those without (Manns & Chad, 2001; Yoshida, 1994), support efforts focused on the latter may sound as if they are questioning the wisdom of the relationship. These results also suggest theoretical boundary conditions for the efficacy of receiving social support (MacGeorge et al., 2011). Resilience represents one individual boundary condition, such that support messages will likely be less effective if the person has already developed their own ability to cope. A situational boundary condition is the size and scope of the challenge; when it is particularly daunting, even high-quality support may not mitigate the distress experienced by the target. Finally, the ability of a person to anticipate a stressor may shape the effectiveness of social support, as pre-injury relationships differed from post-injury relationships.

Theoretically, then, the findings of this study offer initial insight into contextual factors that might influence TRRL processes. The TRRL focuses primarily on interaction within the dyad (or family unit), with outside influences identified as contextual factors that may exert effects across the entire model (see figure 1 in Afifi et al., 2016). We have interpreted our results to indicate that the timing of the injury serves as one such contextual factor, speculating that it may function as such because it (re)shapes expectations for the relationship. However, our data also suggest support quality from network members may function together with resilience to mitigate against relational load and enhance health outcomes. Although it is beyond the scope of this article to incorporate network member processes into the TRRL, these results point toward the potential utility of such expansion of the theory. In turn, for scholars investigating relational outcomes of SCIs, results indicate that injury timing is a critical factor influencing relational processes and therefore should be included in future investigations.

Limitations and conclusion

All studies must be interpreted in light of their limitations. This sample was predominantly White/Caucasian and female, and although this may represent the fact that women are more likely to take on a caregiving role than are men (Boschen et al., 2005), only future research can determine whether or to what extent these results generalize to male and/or non-Caucasian caregivers. This study also did not collect data from the partner with the SCI, and such dyadic analysis may illuminate processes of support that cannot be observed by collecting data from only one partner. The model did not control for the severity of the SCI or myriad other variables that might mitigate or enhance caregiving burden (e.g., availability and type of health insurance, equipment available to the couple, proximity to and quality of medical care, availability of a paid caregiver, relational maintenance behaviors exchanged within the couple, length of time since the injury, etc.).¹ We chose to measure caregiving tasks as the participant’s report of their activities, and it is possible that more perceptual measures of caregiving burden might yield stronger associations with outcomes variables. And, of course, cross-sectional studies provide evidence of causation that is weak at best, and so we offer the routine caution that readers should exercise discretion when interpreting correlational results as causal in nature.

Taken collectively, these results illuminate the complexity of the caregiving experience and demonstrate the utility of the TRRL in understanding it. Although the results generally support the benefits of receiving high-quality social support (Komproe et al., 1997) and of resilience (Richardson, 2002), the extent of that help may depend on the specific circumstances faced by the couple, including the level of caregiving tasks and, especially, the timing of the SCI. In some cases, a more challenging context may enhance the efficacy of coping resources by encouraging a more communal orientation toward stress, as it did for the psychosocial distress of those in the pre-injury group. In other cases, such difficulties may reduce beneficial effects, as appeared to be the case for psychosocial distress in the post-injury group. This highlights the practical importance of considering both the timing of the injury and amount of caregiving tasks as critical contextual factors that shape the experience of caregiving for a partner with an SCI and the associated benefit of received social support.

Footnotes

Authors’ note

The data were collected for Gentry Lynn’s master’s thesis research, and a previous version of this article was presented at the 2018 meeting of the National Communication Association.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Andrew M. Ledbetter

Open research statement

This research was not pre-registered. The data and materials used in the research are available upon request by emailing the lead author at a.ledbetter@tcu.edu.

Note

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