Abstract
Objectives:
Caregivers of individuals with Alzheimer’s Disease and related dementias experience significant burden and adverse outcomes. Enhancing caregiver self-efficacy has the potential to mitigate these negative impacts, yet little is known about its relationship with other aspects of caregiving. This study examined the relationship between self-efficacy and outcomes; identified factors associated with self-efficacy; examined the mediating role of self-efficacy, and analyzed whether there were racial/ethnic differences.
Methods:
Data from caregivers (N=243) were collected from the Caring for the Caregiver Network study. Participants’ level of self-efficacy, depression, burden, and positive aspects of caregiving was assessed using validated measures.
Results:
Two self-efficacy subscales predicted caregiver depression, burden, and positive aspects of caregiving. Being White, a spouse, or having a larger social network predicted lower self-efficacy for obtaining respite. Higher income and lower preparedness predicted lower self-efficacy for controlling upsetting thoughts and responding to disruptive behaviors. Self-efficacy for controlling upsetting thoughts mediated the relationship between preparedness and depression along with the relationship between preparedness and burden. Race/ethnicity did not improve model fit.
Conclusion:
Self-efficacy plays an important role in caregiver outcomes. These findings indicate that strategies to improve caregiver self-efficacy should be an integral component of caregiver interventions.
Keywords: Caregiving, self-efficacy, preparedness, ADRD, dementia
Introduction
Family caregivers play a critical role in maintaining the health and well-being of patients with Alzheimer’s Disease and related dementias (ADRD), a growing patient population in the United States (US) and worldwide. They provide a significant number of hours of unpaid care which saves the US billions of dollars in health care costs (e.g., Langa et al., 2001). Given their value to the care recipient and to society, it is critically important that we continue to better understand and identify modifiable factors that impact the caregiving experience.
Caregiver self-efficacy (e.g., Bandura, 1997) is defined as the caregiver’s belief in their capability to successfully complete specific caregiving tasks. The caregiver self-efficacy measure, developed by Steffen et al. (e.g., Steffen, McKibbin, Zeiss, Gallagher-Thompson, & Bandura, 2002), captures three domains of self-efficacy that include: obtaining respite, responding to disruptive behaviors, and controlling upsetting thoughts. Understanding the construct of caregiver self-efficacy and how it varies across different populations and contexts is important because of its association with caregiver outcomes, such as depression (e.g., Gilliam & Steffen, 2006), caregiver burden (e.g., Gallagher et al., 2011), and positive aspects of caregiving (e.g., Cheng, Lam, Kwok, Ng, & Fung, 2013; Semiatin & O’Connor, 2012) (e.g., caregivers’ sense that their caregiving experience is generally satisfying and rewarding).
Despite its recognized importance, few studies have examined the role of caregiver self-efficacy in mediating caregiver outcomes. Grano et al. (2017) found that caregivers’ perceived ability to control upsetting thoughts mediated the effect of caregiver burden on depressive symptoms (e.g., Grano, Lucidi, & Violani, 2017). Au et al. (2010) found that caregiver self-efficacy mediated the relationship between physical health and depression (e.g., Au et al., 2010). However, these studies did not examine predictor variables such as caregiver preparedness or social support, which are variables that have shown to correlate with important caregiver outcomes (e.g., Li et al., 2020; Shyu et al., 2010). Understanding whether variables important to the caregiving role and caregiver outcomes, such as preparedness and social supports, are mediated by caregiver self-efficacy can help to define the mechanisms underlying these relationships. For example, based on the findings of Grano et al. (2017), the authors found that the relationship between caregiver burden and depression was mediated in part by one’s ability to control upsetting thoughts.
Furthermore, these studies were conducted in Italy and Hong Kong respectively, with relatively small sample sizes, thus limiting their generalizability to the diverse group of caregivers in the US (e.g., Epps, 2014). With an increasing number of diverse older adults living with ADRD, understanding the impact that race/ethnicity plays in the relationship between self-efficacy and caregiver outcomes can shed light on whether interventions should be tailored for caregivers from different racial/ethnic backgrounds.
Accordingly, this exploratory study examines the role of self-efficacy in a diverse population of caregivers caring for individuals with ADRD in the US. Study objectives include: (1) examining the relationship between caregiver self-efficacy and caregiver outcomes, specifically depression, caregiver burden, and positive aspects of caregiving, (2) identifying factors associated with caregiver self-efficacy, (3) examining caregiver self-efficacy as a mediator of caregiving outcomes, and (4) ascertaining whether these relationships vary as a function of caregiver race/ethnicity.
Materials and Methods
This study is a secondary analysis of baseline data generated as part of the Caring for the Caregiver Network study. Study data collected at 6 months and 12 months were not included in the analysis to avoid the impact on self-efficacy that caregivers may have received from the intervention. The research protocol was approved by the institutional review board at the University of Miami Miller School of Medicine (Protocol number 20130460). Participants were compensated for taking part in the study.
The dataset includes information on 243 caregivers. Caring for the Caregiver Network is a multicomponent, technology-based psychosocial intervention designed to provide education, skills training, support, and to reduce caregiver stress. The intervention was delivered via interactive computer technology for six-months and targeted diverse racial/ethnic caregivers. During the intervention, participants completed individual skill-building sessions, participated in video-conferencing support groups, and accessed expert videos and a resource guide. Control group participants also received the technology and training in topics related to nutrition and wellness. Additional information regarding the intervention protocol has been reported in a previous paper (e.g., Falzarano, Moxley, Pillemer, & Czaja, 2021).
The parent study employed a randomized control trial design, where inclusion criteria included: a) English/Spanish speaking caregiver, b) ≥ 18-years of age, c) provides care to a friend/family member with Alzheimer’s disease for a minimum of 15-hours a week over a 6-month period prior to enrollment, d) contact with the care recipient at least five times a week, e) resides with or in close proximity to the care recipient, f) non-cognitively impaired based on age/education adjusted scores on the Telephone Interview for Cognitive Status (e.g., Knopman, 2018), and g) reported at least 2 stressors related to caregiving.
Measures
Caregiver Self-Efficacy
This scale (e.g., Steffen et al., 2002) measures caregivers’ perceived self-efficacy in 3 specific domains: 1) obtaining respite, 2) ability to respond to disruptive patient behaviors, and 3) ability to control upsetting thoughts about caregiving. Caregivers are asked to rate their level of confidence to perform each activity from 0% to 100%, when giving their best effort. Each subscale score is based on a mean of scores. The total score is the sum of all 15 items. In this study, we report the total score as the average of the three subscales. Lower scores denote decreased levels of self-efficacy. The scale has acceptable reliability (SE- Obtaining Respite: r12 = .76, SE-Responding to Disruptive Patient Behaviors: r12 = .70, SE-Controlling Upsetting Thoughts: r12 = .76).
We analyzed the subscales of caregiver self-efficacy rather than the overall scale as the data indicated that the subscales were not highly correlated and thus appeared to assess distinct aspects of self-efficacy as opposed to a univariate construct. The correlations among the subscales ranged from an r(225)=.06, p=.37 for the correlation of self-efficacy for disruptive behaviors and obtaining respite, r(239)=.17, p=.01, for obtaining respite and controlling upsetting thoughts, and r(225)=.41, p<.001, for responding to disruptive behaviors and controlling upsetting thoughts.
Caregiver Depression
The Center for Epidemiologic Studies - Depression (CES-D) scale (e.g., Radloff, 1977) is a widely-used instrument for the measurement of depression. This questionnaire contains 10 statements describing different feelings and emotional states. The caregiver is asked to indicate how often he or she felt that way during the past week. Scores range from 0 to 30, with higher scores indicating greater levels of depressive symptomatology.
Caregiver Burden
The 12-item version of the Zarit Burden Interview is a commonly used measure of caregiver burden. A higher score indicates greater level of caregiver burden. This version has been shown to have excellent correlation (0.96) with the full version of the instrument (e.g., Bédard et al., 2001). A couple questions in this survey include, (1) do you feel that because of the time you spend with the care recipient, that you don’t have enough time for your self and (2) do you feel stressed between caring for the care recipient and trying to meet other responsibilities.
Positive Aspects of Caregiving
This questionnaire (e.g., Tarlow et al., 2004) asks respondents about possible positive feelings they may get from caregiving (e.g, feeling more useful, feeling good about myself, feeling important or needed). Scores range from 0–36, with higher scores indicating more positive feelings about caregiving.
Potential Predictor Variables
We examined the following potential predictors of caregiver self-efficacy: caregiver and care recipient characteristics (i.e., age, race/ethnicity, caregiving experience, income, relationship between caregiver and care recipient), functional status of the care recipient (i.e., activities of daily living, instrumental activities of daily living), an 8-item caregiver preparedness survey (e.g., feeling prepared to take care of the various needs of the care recipient) (e.g, Archbold, Stewart, Greenlick, & Harvath, 1990), and three social support measures (i.e., negative interactions aspect of social support, social network size, satisfaction with support).
The negative interactions scale (e.g., Krause, 1995) included four items that assessed the frequency of negative social interactions over the past month ranging on a scale from 0 (never) to 3 (very often). Social network size included three items from the Lubben Social Network Index (e.g., Lubben, 1988) measuring the overall size of participants’ social network on a 5-point Likert scale. Satisfaction with support (e.g., Krause, 1995) included three items measuring satisfaction with emotional, tangible, and informational support, and one item assessing global satisfaction with received support. Responses were based on a 4-point Likert scale.
Lastly, we created two additional categories of support (i.e., support for health needs and support for caregiving needs) based on questions derived from the formal care and services questionnaire. Questions for these categories were selected by the study team (VP, CR, RA, SC) through discussion and based on the team’s clinical and research experience. The support for health needs included four items examining use of various health services (e.g., physician visits, hospitalization, psychiatric care) within the past month that were measured as a yes/no response. Support for caregiving needs included three questions looking at use of home or community services (e.g., home health aides, visiting nurse, senior day care). Participants were asked to provide a yes or no response to these questions. The support for caregiving needs differs from the caregiver self-efficacy subscale of obtaining respite care in that it focuses on formal care services whereas the subscale looks at informal care (e.g., families and friends).
Data Analysis
We initially computed descriptive statistics of caregivers and care recipients to examine demographic data, data on potential predictor variables and mediating factors (e.g., preparedness and social support), and outcome variables. Multiple regression analyses were conducted to examine the predictors of the self-efficacy subscales. The mediation model included any variable that marginally significantly predicted self-efficacy and each self-efficacy subscale. We maintained every marginally significant path predicting aspects of caregiver self-efficacy, and then used all variables to predict the chosen primary outcomes: depression, burden, and positive aspects of caregiving. To estimate the model, 10000 bootstrap samples were used. Since we used bootstrap estimates, we only report 95% confidence intervals and attribute results as significant if the interval did not overlap with 0. Overall, the model fit was strong C2(14)=22.87, p=.06, CFI=.98, SRMR=.03. While there have been concerns about type 1 error rate of mediation models particularly bootstrapping, which minimize type 2 error much better than older methods, in small samples of less than 80 (e.g. Koopman, Howe, Hollenbeck, & Sin, 2015) this study is well outside that range should achieve both reasonable power while avoiding unacceptable type 1 error risks.
Results
Descriptive statistics are shown in Table 1. The sample was diverse from a race/ethnicity standpoint, with Hispanic caregivers (n=109, 45%) making up the largest portion of participants followed by White caregivers (n=79, 32%) and Black caregivers (n=55, 23%). The majority of caregivers were female (n=204, 84%) with an average age of 61 years. Care recipients had an average age of 80 years and an average Mini-mental State Examination (MMSE) score of 14.6 (SD +/− 8.9).
Table 1.
Caregiver (CG) and care recipient (CR) demographic data
| M (SD) | n (%) | |
|---|---|---|
|
| ||
| CG Age | 61.3 (12.9) | 244 |
| CG Race/ethnicity | ||
| Black | 55 (22.5) | |
| Hispanic/Latino | 109 (44.7) | |
| Other | 1 (.44) | |
| White | 79 (32.4) | |
| CG Gender | ||
| Female | 204 (83.6) | |
| CG Experience (years) | 5.3 (5.7) | 244 |
| CR Age | 80 (9.5) | 244 |
| CR Race/ethnicity | ||
| Black | 53 (21.7) | |
| Hispanic/Latino | 112 (45.9) | |
| Other | 6 (2.5) | |
| White | 79 (32.4) | |
| CR Gender | ||
| Female | 140 (57.4) | |
| CR MMSE | 14.6 (8.9) | 153 |
| CR IADL | 6.9 (1.6) | 241 |
| CR ADL | 3.1 (2.2) | 243 |
| Income | ||
| Less than 30000 | 59 (25.8) | |
| Income $30,000 – $49,999 | 47 (20.5) | |
| Income $50,000 – $99,999 | 69 (30.1) | |
| Income $100,000 or more | 40 (17.5) | |
| CG relationship to CR | ||
| Spouse | 99 (40.6) | |
| CG self-efficacy | 62.1 (16.4) | 228 |
| SE -UT | 69.5 (20.1) | 242 |
| SE -CB | 74.3 (21.3) | 228 |
| SE-R | 43.8 (31.3) | 243 |
| CG Depression | 11.1 (6.4) | 244 |
| CG Burden | 19.1 (8.3) | 244 |
| Positive Aspects of CG | 23.9 (8.7) | 243 |
| CG Preparedness | 2.3 (0.76) | 244 |
| Negative Interactions | 3.5 (2.7) | 244 |
| Social Network Size | 8.4 (3.3) | 244 |
| FCS Health Support | 0.29 (0.24) | 244 |
| FCS CG Support | 0.23 (0.15) | 244 |
| FCS Other Support | 0.29 (0.29) | 244 |
Note. SE-UT= SE for Controlling Upsetting thoughts
SE-CB= SE for Controlling Upsetting thoughts, SE-R= SE for Obtaining Respite
The mean caregiver self-efficacy score for the entire sample was 62.1 (SD = 16.4). Average self-efficacy score for the ability to control upsetting thoughts about caregiving was 69.5 (SD = 20.1), 74.3 (SD = 21.3) for responding to disruptive patient behaviors, and 43.8 (SD 31.3) for obtaining respite.
Associations between Self-efficacy Subscales and Caregiver Outcomes
Table 2 shows the results of the multiple regression analysis for the self-efficacy subscales and caregiver outcomes (i.e., depression, burden, positive aspects of caregiving). Lower self-efficacy in one’s ability to control upsetting thoughts was associated with higher caregiver depression (p<.001), higher caregiver burden (p<.001), and less positive feelings about caregiving (p=.006). Lower self-efficacy for obtaining respite was also associated with higher caregiver depression (p=.001) and burden (p=.003).
Table 2.
Multiple regression analysis predicting self efficacy subscale and caregiver outcomes.
| SE for Controlling Upsetting thoughts | SE for Responding to Disruptive Behavior | SE for Obtaining Respite | Depression | Burden | PAC | |
|---|---|---|---|---|---|---|
|
|
||||||
| β (p-value) | β (p-value) | β (p-value) | β (p-value) | β (p-value) | β (p-value) | |
|
| ||||||
| SE-UT | −.44 (<.001) | −.42 (<.001) | .20 (.006) | |||
| SE-CB | .05 (.72) | .01 (.99) | .08 (.29) | |||
| SE-R | −.24 (.001) | −.18 (.003) | .12 (.08) | |||
| Preparedness | .32 (<.001) | .30 (<.001) | .11 (.06) | |||
| Negative Interactions | −.11 (.099) | .02 (.77) | −.03 (.63) | |||
| Social Network Size | .08 (.28) | .06 (.41) | .38(<.001) | |||
| FCS Health Support | .04 (.59) | .02 (.77) | .004 (.95) | |||
| FCS CG Support | .06 (.40) | .01 (.85) | −.09 (.18) | |||
| FCS Other Support | −.09 (.19) | .05 (.46) | −.06 (.35) | |||
| Age | −.09 (.30) | −.12 (.22) | .05 (.60) | |||
| CG Experience | .06 (.38) | −.11 (.11) | .07 (.31) | |||
| Race=Black/AA | −.10 (.22) | −.16 (.07) | .22 (.008) | |||
| Race=White | .01 (.92) | .09 (.33) | −.20 (−.02) | |||
| IADL | −.03 (.63) | −.04 (.63) | −.13 (.05) | |||
| ADL | −.13 (.098) | .13 (.11) | .04 (.63) | |||
| Income | −.17 (.01) | −.10 (.13) | .08 (.20) | |||
| Spouse | −.08 (.36) | −.13 (.14) | −.21 (.01) | |||
Note. SE-UT= SE for Controlling Upsetting thoughts, SE-CB= SE for Controlling Upsetting thoughts, SE-R= SE for Obtaining Respite
Predictors of Caregiver Self-efficacy Subscales
Table 2 shows the standardized beta coefficients and p-values generated from the multiple regression analyses examining predictors of the self-efficacy subscales. As shown in the table, lower caregiver preparedness was associated with lower self-efficacy for controlling upsetting thoughts (p<.001) and lower self-efficacy for responding to disruptive behavior (p<.001). Caregivers with higher levels of income had lower self-efficacy for controlling upsetting thoughts (p=.01).
White/Caucasian caregivers (p=.02) had lower self-efficacy for obtaining respite as compared to Hispanic caregivers. In contrast, Black/African American caregivers (p=.008) had higher self-efficacy for obtaining respite as compared to Hispanic caregivers. Furthermore, being a spouse (p=.01) or having a larger social network size (p<.001) was associated with lower self-efficacy for obtaining respite.
Mediation Model
Figure 1 shows the path model used to examine the mediating role of caregiver self-efficacy and Figure 2 presents the significant mediation model. Table 3 shows the regression path’s beta and bootstrap confidence intervals. With respect to mediation effects, self-efficacy for upsetting thoughts β=−.12 [−.19, −.06] accounted for a significant amount of the association between caregiver preparedness and depression. Self-efficacy for upsetting thoughts β=−.10 [−.15, −.04] also accounted for a significant amount of the association between caregiver preparedness and burden. In addition, self-efficacy for controlling upsetting thoughts β=.05 [.01, .09] accounted for a significant amount of the association between income and depression, and for a significant amount of the association between income and burden β=.04 [.01, .07] scores. Lastly, self-efficacy for obtaining respite mediated the association between social network size and burden β=−.05 [−.10, −.01].
Figure 1.
Path model tested to find mediating role of CG self-efficacy
Figure 2.
Paths showing significant mediation. Dashed line is a non-significant path.
Table 3.
Bootstrap standardized betas and 95% CI for regression paths of mediation model.
| SE-UT | SE-CB | SE-Respite | Depression | Burden | PAC | |
|---|---|---|---|---|---|---|
|
|
||||||
| β [95% CI] | β [95% CI] | β [95% CI] | β [95% CI] | β [95% CI] | β [95% CI] | |
|
| ||||||
| SE-UT | −0.35 [−0.48, −0.22] | −0.28 [−0.40, −0.15] | 0.12 [−0.04, 0.29] | |||
| SE-CB | 0.06 [−0.08, 0.2] | 0.01 [−0.11, 0.14] | 0.01 [−0.16, 0.18] | |||
| SE-R | −0.12 [−0.25, 0] | −0.14 [−0.25, −0.02] | 0.09 [−0.04, 0.21] | |||
| Preparedness | 0.34 [0.23, 0.46] | 0.30 [0.18, 0.43] | 0.09 [−0.02, 0.19] | −0.21 [−0.34, −0.08] | −0.21 [−0.35, −0.07] | 0.11 [−0.05, 0.26] |
| Negative Interactions | −0.10 [−0.21, 0.01] | 0.21 [0.11, 0.31] | 0.02 [0.08, 0.31] | −0.03 [−0.15, 0.10] | ||
| Social Network Size | 0.39 [0.28, 0.49] | −0.16, [−0.29, −0.04] | −0.06, [−0.18, 0.07] | 0.03, [−0.11, 0.17] | ||
| Black/AA | −0.06 [−0.22, 0.1] | 0.20 [0.06, 0.33] | −0.08 [−0.22, 0.07] | −0.07 [−0.21, 0.07] | 0.09 [−0.06, 0.24] | |
| White | −0.02 [−0.16, 0.13] | −0.14 [−0.28, 0.01] | 0.06 [−0.10, 0.21] | 0.11 [−0.04, 0.26] | −0.06 [−0.23, 0.11] | |
| IADL | −0.09 [−0.21, 0.02] | −0.02 [−0.12, 0.08] | 0.08 [−0.03, 0.19] | −0.01 [−0.13, 0.12] | ||
| ADL | −0.10 [−0.21, 0.01] | 0.05 [−0.07, 0.16] | 0.06 [−0.06, 0.18] | 0.03 [−0.11, 0.16] | ||
| Income | −0.14 [−0.25, −0.03] | −0.05 [−0.18, 0.07] | 0.18 [0.06, 0.30] | −0.19 [−0.34, −0.05] | ||
| Spouse | −0.14 [−0.26, −0.01] | 0.04 [−0.08, 0.15] | −0.10 [−0.21, 0.01] | −0.10 [−0.23, 0.03] | ||
Finally, we examined if the model varied as a function of race/ethnicity. We found that model fit did not vary significantly as a function of race/ethnicity.
Discussion
This study examined the relationship between caregiver self-efficacy and salient outcomes along with factors associated with self-efficacy in a racially diverse group of family caregivers of individuals with ADRD. Our findings indicate that certain domains of caregiver self-efficacy are associated with caregiver depression, caregiver burden, and perceptions regarding the positive aspects of caregiving. In addition, we identified specific caregiver-related factors that were independently associated with these domains of self-efficacy. A unique aspect of the study was the development and testing of a mediation model. Our path model showed that self-efficacy was a significant mediator of the relationship between variables such as preparedness and social network size for both caregiver depression and caregiver burden.
In particular, the model highlights that self-efficacy of controlling upsetting thoughts was a significant mediator between caregiver preparedness and depression and between caregiver preparedness and burden. The relationship between preparedness and depression confirms the findings of Shyu et al (2010) (e.g., Shyu et al., 2010), which demonstrated that among Taiwanese caregivers, higher preparedness was associated with better mental health. Our study showed that controlling upsetting thoughts mediates this relationship, which makes intuitive sense since caregivers who are better able to manage negative thoughts and feelings will likely have better mental health. This is an important finding as self-efficacy and preparedness are potentially modifiable and thus could be enhanced through targeted interventions such as caregiver education and skill building. However, it is also important that the development of these interventions be based on an understanding of a caregiver’s needs and the caregiving context.
Our results further indicate that self-efficacy in obtaining respite mediated the relationship between social network size and caregiver burden. We hypothesize that caregivers with a larger social network have more opportunities (or knowledge) to obtain respite care as they may have contact with others who share caregiving responsibilities. These opportunities could potentially lead to more utilization of services that can reduce caregiver burden. Furthermore, having a large social network may also provide caregivers with an opportunity to discuss their emotions and frustrations, which can alleviate some of the psychological burden of the caregiving experience. Additional research on how to improve the social network of caregivers and what types of social supports is most helpful to caregivers is needed given the risk of loneliness and lack of social interactions that can be imposed by the caregiving role (e.g., Victor et al., 2021).
A surprising result from our analysis was that self-efficacy for controlling upsetting thoughts mediated the relationship between income and depression and burden. While past studies have shown that caregiver depression is associated with lower income (e.g., Covinsky, Newcomer, Fox, & Wood, 2003), we hypothesize that caregivers with higher income in this cohort may have more stressors balancing work and caregiving and less bandwidth in managing these issues. We conducted a post hoc analyses which in fact showed a relationship between working status (being employed) and higher income and working status (being employed) and increase in burden. Further studies that focus on understanding how caregivers balance work, finances, and the caregiving role may provide better insights into our findings. Regardless, strengthening programs for working and non-working caregivers who are depressed or burdened is critical to better support caregivers’ mental health and well-being.
Lastly, we examined the role of race and ethnicity in our models. In our regression analysis we found differences between Whites, African American, and Hispanic caregivers in self-efficacy for obtaining respite care (e.g., help from friends and families). The concept of familism, a sense of collectivism and family attachment, may play an important role in explaining these results. Knight et al. (e.g., Knight et al., 2002) has shown that Black caregivers have a high degree of familism. More recently, Falzarano et al. (e.g., Falzarano et al., 2021) found that African American and Hispanic caregivers report higher levels of familism compared to Whites. This high degree of familism may lead to African American and Hispanic caregivers feeling more confident in reaching out to extended family or friends for help when compared to their White counterparts. We also examined whether our model fit improved when considering race/ethnicity. We did not find that model fit improved and thus our mediation model held for all race/ethnic groups in our sample. However, continuing to explore how race/ethnicity, culture, and other factors such as religion/spirituality play a role in caregiving outcomes is critical given the expanding diversity of caregivers in the US.
While our analysis examined three self-efficacy subscales, it is important to acknowledge the heterogeneity of caregiver tasks and that additional measures are needed to assess self-efficacy for performing other important caregiver roles such as managing the care recipient’s medical care, providing emotional and social support, and managing finances. The National Academies of Sciences, Engineering, and Medicine’s report (e.g., Schulz, Eden, & Division, 2016) on family caregiving provides a list of caregiver tasks that can be used as a framework to help develop additional measures to assess self-efficacy. Understanding how to capture caregivers’ perceptions of their abilities to perform these tasks is an important step to developing strategies to support the caregiving role. As underscored by the Stress Process Model of Caregiving (e.g., Pearlin, Mullan, Semple, & Skaff, 1990), caregivers’ perceptions of overload can contribute to caregiver stress, which in turn can contribute to adverse health outcomes.
This study has some limitations. First, we analyzed cross-sectional (baseline) data generated as part of the Caring for the Caregiver Network study, which makes it difficult to make causal inferences. While we believe that this is the direction of the relationships, we don’t know for sure, but the precision of our estimates about the covariation of these variables is sound. In addition, while this study included underrepresented groups (i.e., Hispanic and Black), some racial/ethnic groups were not included (i.e., Asians, Native Americans), limiting the generalizability of the results to those populations.
In conclusion, this study confirms the associations between heightened caregiver self-efficacy and lower levels of both depression and caregiver burden, as well as an association with more positive perceptions of caregiving. Our study adds new knowledge by demonstrating that caregiver self-efficacy acts as a mediator between caregiver variables (i.e., preparedness, income, and social networks) and outcomes of depression and burden. Additional research is needed to better understand these nuanced relationships and future interventions targeting some of these modifiable factors may be appropriate to examine as a way of improving the well-being of caregivers caring for a care recipient with ADRD.
FUNDING DETAILS
This work was supported by the National Institute of Nursing Research (NINR) under grant 5R01NR014434–05.
Footnotes
DECLARATION OF INTEREST STATEMENT
The authors report there are no competing interests to declare.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions (e.g., containing information that could compromise the privacy of research participants).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions (e.g., containing information that could compromise the privacy of research participants).