Factors Associated With Working Caregivers’ Well-Being: Comparisons Between Black and White Working Caregivers in the United States

Abstract

This study investigated stressors and strains, resources, and well-being among Black working caregivers (BWC) and White working caregivers (WWC) who participated in the Midlife in the United States study (Black: n = 49, White: n = 250). Comparisons were made between BWC and WWC for primary caregiving stressors, secondary strains, resources, and well-being, and hierarchical regression models tested associations among these factors. BWC reported less negative work-to-family spillover, more perceived control and religious/spiritual coping, and higher positive affect than WWC, complementing existing evidence of greater resilience among BWC. Secondary strains stemming from the workplace had stronger associations with well-being than primary caregiving stressors, confirming that the workplace deserves greater attention in stress research and interventions for working caregivers. Finally, perceived control, optimism, and family support were important resources for well-being for both BWC and WWC, substantiating their valuable role in interventions for working caregivers.

Keywords

negative spillover coping resources life satisfaction affect

Introduction

Prioritizing caregiver well-being is imperative for cultivating societies that can effectively support the growing aging population. Increased caregiver burden, distress, and negative appraisals of caregiving have been linked to detrimental outcomes for care recipients, including increased all-cause mortality, hospitalizations, loneliness, and risk of institutionalization (Iecovich, 2016; Kuzuya et al., 2011; Montgomery et al., 2011; Stall et al., 2019). It is particularly important to examine factors associated with the well-being of diverse groups of working caregivers in the United States, given the rising percentage of caregivers simultaneously working for pay while providing care (i.e., working caregivers; Stone, 2015; Toosi, 2015), and the projected growth of older adults in minoritized racial and ethnic groups, who may have more functional impairments and greater needs for care (National Academies of Sciences, Engineering, and Medicine [NASEM], 2016).

Caregiving stress process models guide us to identify potential factors important for caregiver well-being (Haley et al., 1987; Pearlin et al., 1990). According to these models, multiple factors may influence caregiver well-being, including demographic characteristics, primary caregiving stressors (e.g., hours of care provided), secondary role strains (e.g., stressors from the workplace), and internal and external resources (e.g., family support, appraisals of caregiving). Indeed, decades of longitudinal research have established that caregivers with high levels of primary caregiving stressors, more secondary strains, and low levels of resources exhibit worse well-being (Allen et al., 2000; Ang & Malhotra, 2018; Bom et al., 2019; Dickson, 2008; Frone et al., 1996; Gareis et al., 2009; Grzywacz and Marks, 2000; Haley et al., 2020; Lavner and Clark, 2017; Meyer, 2014; Pinquart & Sörensen, 2003; Roth et al., 2009; Templeman et al., 2019). In some cases, this may even result in ceasing care provision (NASEM, 2016).

Research examining racial differences in specific aspects of the stress process for caregivers has found that Black/African American (AA) caregivers report more primary caregiving stressors, including more hours of care, higher intensity care, co-residing with care recipient, and caring for a care recipient with dementia than White caregivers (Fabius et al., 2020; NAC and AARP PPI, 2020). However, resources like mastery, optimism, reporting positive aspects of caregiving, and religious and spiritual coping, which have protective effects for caregiver well-being, are often higher in Black/AA caregivers (Haley et al., 1996, 2004; Liu et al., 2021), whereas resources like the amount of social support, including family support, do not tend to differ by race in the working adult population or among caregivers (Bailey et al., 1996; Haley et al., 1996). Furthermore, Black/AA caregivers tend to have lower levels of depression and anxiety and fewer negative caregiving-related emotional impacts than White caregivers (Fabius et al., 2020; Haley et al., 2004; Liu et al., 2021; Nápoles et al., 2010; Pinquart & Sörensen, 2005; Sörensen & Pinquart, 2005). Unfortunately, research has paid less attention to racial differences in secondary stressors, like the combination of caregiving and working.

There is also ample literature that addresses ways in which aspects of the stress process relate to well-being for caregivers. While primary caregiving stressors have been linked to emotional strain and poorer quality of life for caregivers (Kang, 2006, 2016; Litzelman et al., 2015; NAC and AARP PPI, 2020; NASEM, 2016; Pinquart & Sörensen, 2003; Schulz et al., 2009), arguably more detrimental to working caregivers are secondary strains like caregiving to work conflict, which is associated with more emotional, physical, and financial strain for this group (Templeman et al., 2019). Internal and external resources tend to have positive associations with well-being. Examples include personal mastery, which minimizes psychological distress resulting from caregiving strain and work-family conflict (Adams et al., 2005; Badawy & Schieman, 2020; Mausbach et al., 2007; Pearlin et al., 1990; Pioli, 2010); optimism, which improves quality of life and reduces psychological distress in caregivers (Díaz et al., 2020; Lamont et al., 2019); religious and spiritual coping/support, which reduces caregiver burden, depression (especially among Black/AA caregivers), negative family-to-work spillover, and increases resilience (Ellison, 1983; Haley et al., 2004; Heo & Keoske, 2011; Pearce, 2005; Selvarajan et al., 2020a, 2020b; Spurlock, 2005; Wilks et al., 2018); religious involvement, which buffers the effects of negative work-family spillover on life satisfaction among Black/AA working adults (Henderson, 2016); family support, which buffers the effects of objective caregiving stressors on well-being (Kaufman et al., 2010; Verbakel et al., 2018); and expressive social support, which buffers the impact of caregiving-related work interruptions on depression (Ang & Malhotra, 2018).

Despite these important findings, the literature on caregiver stress and coping does not always distinguish between working and non-working caregivers, and the work-family literature does not tend to focus explicitly on caregivers. Further, while important differences between Black/AA and White caregivers overall have been illuminated, little attention has been paid to the differences in the stress process between Black/AA working caregivers (BWC) and White working caregivers (WWC). To address these gaps, the current study investigates well-being among BWC and WWC through the lens of caregiving stress process models. Of note, these issues are also of great interest and importance across other racial and ethnic groups, but the present study focuses on Black/AA and White caregivers due to limited availability of other groups in the present data set.

Aims and Hypotheses

The first aim of the current study was to identify differences between BWC and WWC in aspects of the stress process and the outcome of subjective well-being. Consistent with prior research, we hypothesized that BWC would report more primary caregiving stressors, and due to the scarcity of literature examining racial differences in secondary strains, we explored whether BWC would also report more secondary strains than WWC. We also hypothesized that BWC would report higher levels of all resources except social support, and better subjective well-being than WWC, both before and after accounting for primary caregiving stressors.

The second aim was to examine associations between demographic characteristics, primary caregiving stressors, secondary strains, resources, and subjective well-being for BWC and WWC. Consistent with prior research findings, and with stress process models, we hypothesized that for both BWC and WWC, higher levels of primary caregiving stressors and secondary strains would be associated with poorer caregiver subjective well-being, and that more resources would be associated with better caregiver subjective well-being.

Method

We obtained the study data from the Midlife in the United States (MIDUS) 2 study and MIDUS Refresher samples, and an oversample of Black/AA individuals from the Milwaukee data corresponding to each main sample (n = 9,640; Ryff et al., 2017; Ryff et al., Mar. 2018, Sept. 2018, Oct. 2018). The exclusion criteria included not completing both portions of the study (self-administered questionnaire and computer-assisted phone interview) since key variables were obtained from both instruments (n = 2,290); reporting a primary racial identity other than Black/AA and White (n = 468); not reporting working at the time of the study or for at least 50 weeks over the last year (n = 2,850); not reporting caregiving (providing personal care now or for a period of at least 1 month over the last year to an adult family member or friend; n = 3,645); and not reporting working and caregiving concurrently (either at the time of the study or during the past year, n = 40). The overall sample consisted of 347 working caregivers. Of these, 48 participants did not provide complete data for key study variables. Participants with no missing data (n = 299) did not differ from those with missing data (n = 48) in age, sex, race, or education (data available upon request), so we conducted listwise deletion of those with missing data. Our final analytic sample was 299 working caregivers (49 BWC and 250 WWC).

Measures

Demographic Characteristics

Participant demographic characteristics included age (in years), sex (1 = male, 2 = female), race (1 = White, 2 = Black/AA), and level of education (1 = no college degree, 2 = college degree).

Primary Caregiving Stressors

We used three measures to assess primary caregiving stressors: high or low hours of care provided (0 = under 20 hr/week, 1 = 20 or more hr/week; NAC and AARP PPI, 2020); caregiver co-residence with care recipient (0 = no, 1 = yes); and caregiver assistance with ADL and IADL, measured by computing the sum of four yes/no questions: “Because of his/her limitations do/did you provide him/her with (1) personal help with bathing, dressing, eating, or going to the bathroom, (2) getting around inside the house or going outside, (3) shopping, cooking, housework, or laundry, and (4) managing money, making phone calls, or taking medications?”

Secondary Strains

Negative Work-Family Spillover

Participants were asked how often they experienced eight different circumstances (four representing negative work-to-family spillover, four representing negative family-to-work spillover) in the past year using a five-point scale ranging from all of the time to never (Ryff et al., 2017). Scores on each sub-scale represented the sum of the scores on the four items, ranging from one to 20, where higher scores represented more negative spillover. Internal reliability for the negative work-to-family spillover measure was .83 and for the negative family-to-work spillover measure, .80.

Workplace Discrimination

Participants were asked how often they experienced six different circumstances of workplace discrimination using a five-point scale ranging from once a week or more to never (Williams et al., 1997). Scores represented the sum of the six ratings, ranging from one to 30, where higher scores represented more workplace discrimination. Participants who did not have valid values on at least three items were coded as missing. Internal reliability for the six-item measure was .96.

Resources

Perceived Control

Participants were asked about their own personal mastery (four items) and perceived constraints (eight items) using a seven-point scale ranging from strongly agree to strongly disagree (Lachman & Weaver, 1998). A composite variable – perceived control – was created by calculating the mean of the responses to the 12 items, where higher scores represented higher levels of perceived control. Internal reliability for the 12-item measure was .87.

Religious/Spiritual Coping

Participants were asked two questions about the frequency of their religious/spiritual coping using a four-point scale ranging from often to never (Garfield et al., 2001). Scores represented the sum of the two items, ranging from one to eight, where higher scores represented more religious/spiritual coping. Internal reliability for the two-item measure was .87.

Optimism

Participants were asked three questions about their optimism from the Revised Life Orientation Test (Scheier et al., 1994) using a five-point scale ranging from a lot agree to a lot disagree. Scores represented the sum of all items, ranging from one to 15, where higher scores represented higher levels of optimism. Internal reliability for the three-item measure was .74.

Family Support

Participants were asked four questions about the degree to which their family supports them using a four-point scale ranging from a lot to not at all (revised from Schuster et al., 1990). Scores represented the mean of the responses on the four items, ranging from one to four, where higher values represented more support. Internal reliability for the four-item measure was .83.

Well-Being

We selected life satisfaction and positive and negative affect as measures of well-being because combined, they constitute hedonic well-being (Ryff et al., 2021).

Life Satisfaction

Participants were asked to rate their lives in five domains using a 10-point scale ranging from the worst possible to the best possible (Prenda & Lachman, 2001). Scores represented the mean of responses across all domains. Internal reliability for the five-item measure was .62.

Affect

Participants were asked how often they felt a certain way (six positive, six negative) during the past 30 days using a five-point scale ranging from all of the time to none of the time (Mroczek & Kolarz, 1998). Scores represented the mean, where higher scores represented higher levels of positive and negative affect. Internal reliability for the positive affect measure was .91 and for the negative affect measure, .86.

Statistical Analyses

We performed statistical analyses using SPSS, Version 27.0 (IBM Corp., 2020). We calculated frequencies and means for the final analytic sample on all study variables and conducted t-tests and chi-square tests to compare BWC and WWC. To evaluate collinearity, we conducted point-biserial correlation analyses. Finally, we conducted ordinary least squares hierarchical regression analyses to determine relationships between demographic characteristics (first step), primary caregiving stressors (second step), secondary strains (third step), resources (fourth step), and well-being outcomes. We inspected the variance inflation factor (VIF) to detect multicollinearity. Statistical significance was set at p < .05.

Results

Description of the Sample

Descriptive statistics for the final analytic sample are displayed in Table 1. The average age of participants was 51 years, more than half were female, and just over half had a college degree. The t-tests revealed significant differences by race for age, t(297) = 2.50; negative work-to-family spillover, t(297) = 2.20; perceived control, t(50) = −3.51; religious/spiritual coping, t(89) = −4.17; and positive affect, t(297) = −2.63). BWC were younger, reported less negative work-to-family spillover, more control, more religious/spiritual coping, and higher positive affect than WWC. Chi-square tests also revealed significant differences by race for sex, χ²(1) = 3.90 and education, χ²(1) = 2.19. BWC were more likely to be female and less likely to have a college degree than WWC. Finally, correlation analyses revealed that all coefficients were under 0.80, and VIFs were less than two for all predictors, indicating no issues with multicollinearity.

Table 1.

Caregiver Demographics, Primary Caregiving Stressors, Secondary Strains, and Resources.

	Black/AA caregivers n = 49		White caregivers n = 250		p	Total sample n = 299
Demographics
Age	48.41	9.77	52.03	9.18	.013	51.44	9.36
Sex (n/% female)	36	74	146	58	.048	182	61
Education (n/% w/o college degree)	43	88	124	50	<.001	167	56
Primary caregiving stressors
High/low hours of care (n/% 20 + )	23	47	95	38	.242	118	39.5
Co-residence with CR (n/% yes)	16	33	103	41	.264	119	40
ADL/IADL assistance (out of 4)	2.80	1.12	2.72	1.13	.684	2.74	1.13
Secondary role strains
Negative WTF spillover	9.90	3.10	10.94	2.99	.029	10.77	3.03
Negative FTW spillover	8.86	3.08	9.07	2.78	.621	9.04	2.82
Workplace discrimination	12.36	6.28	10.92	4.51	.133	11.15	4.86
Hours worked/week	35.81	11.40	39.40	15.31	.125	38.81	14.78
Resources
Internal
Perceived control	7.23	3.33	5.54	1.15	<.001	5.81	1.81
Optimism	12.26	2.66	11.61	2.58	.111	11.71	2.60
Religious/spiritual coping	6.65	1.56	5.56	2.18	<.001	5.74	2.13
External
Family support	3.45	0.67	3.52	0.57	.439	3.51	0.59
Well-being
Life Satisfaction	7.50	1.29	7.68	1.14	.317	7.65	1.16
Positive Affect	3.63	0.75	3.32	0.74	.009	3.37	0.75
Negative Affect	1.75	0.78	1.54	0.57	.074	1.58	0.61

Note. Results are means and standard deviations unless otherwise noted; AA = African American; w/o = without; CR = care recipient; ADL = activity of daily living; IADL = instrumental activity of daily living; WTF = work-to-family; FTW = family-to-work; spillover items scaled from one to 20; workplace discrimination scaled from zero to 30; perceived control scaled from zero to seven; optimism scaled from zero to 15; religious/spiritual coping scaled from zero to eight; family support scaled from zero to four; life satisfaction scaled from zero to 10; positive affect scaled from one to five; negative affect scaled from one to five; for all stressors, resources, and outcomes, higher = more.

Hierarchical Regression Analyses

Life Satisfaction

Results from the first hierarchical regression analysis for the outcome of life satisfaction are displayed in Table 2. The first step showed that caregiver demographics were not significantly associated with life satisfaction, although being older was independently associated with greater life satisfaction. The second step showed that primary caregiving stressors were also not significantly associated with life satisfaction after adjusting for demographics, but the association between age and life satisfaction remained. The third step showed that secondary strains were significantly associated with life satisfaction after controlling for demographics and primary caregiving stressors, accounting for 28% of variance in life satisfaction. More negative work-to-family spillover, negative family-to-work spillover, and workplace discrimination were independently associated with lower life satisfaction. The association between age and life satisfaction disappeared in this step. The final step showed that resources were significantly associated with life satisfaction after controlling for demographics, primary caregiving stressors, and secondary strains, accounting for an additional 10% of variance in life satisfaction. More perceived control, optimism, and family support were independently associated with higher life satisfaction. In this step, all three secondary strains remained significantly negatively associated with life satisfaction, and a significant association appeared between race and life satisfaction. Being Black/AA was associated with lower life satisfaction. The final model accounted for 41% of the total variance in life satisfaction.

Table 2.

Regression Analyses Predicting Caregiver Life Satisfaction.

	Model 1		Model 2		Model 3		Model 4
Predictor	B	β	B	β	B	β	B	β
Demographics
Age	.018	.142*	.017	.137*	.000	.001	.003	.021
Sex	.106	.045	.086	.036	.135	.057	.078	.033
Race	−.123	−.039	−.140	−.045	−.216	−.069	−.424	−.135*
Education	.028	.012	.033	.014	.166	.071	.150	.064
Primary caregiving stressors
High/low hours of care			.063	.027	.060	.025	.130	.055
ADL/IADL assistance			−.029	−.028	.031	.030	.038	.037
CG lives with CR			−.189	−.080	−.019	−.008	−.071	−.030
Secondary Role Strains
Negative WTF spillover					−.107	−.278**	−.095	−.248**
Negative FTW spillover					−.106	−.258**	−.070	−.169*
Workplace discrimination					−.045	−.189**	−.032	−.132*
Resources
Perceived control							.099	.154*
Religious/spiritual coping							.009	.017
Optimism							.082	.183**
Family support							.302	.152*
R ²	.026		.033		.314		.414
ΔR²	.026		.007		.281		.100
ΔF	1.957		.684		39.336**		12.103**

Note. n = 299; for sex, male = 1, female = 2; for race, White = 1, Black/African American = 2; for education, no college degree = 1, college degree = 2; for high/low hours of care, low = 0, high = 1; ADL = activity of daily living; IADL = instrumental activity of daily living; CG = caregiver; CR = care recipient; for co-residence with CR, no = 0, yes = 1; WTF = work-to-family; FTW = family-to-work; for all stressors, resources, and outcomes, higher = more; *p < .05, **p < .001.

Positive Affect

Results from the second hierarchical regression analysis for the outcome of positive affect are displayed in Table 3. The first step showed that caregiver demographics were significantly associated with positive affect, accounting for 4% of variance in positive affect. Being older and being Black/AA were independently associated with more positive affect. The second step showed that primary caregiving stressors were not associated with positive affect after adjusting for demographics, although higher hours of care was independently associated with less positive affect. The association between age and positive affect disappeared in this step, but race remained significantly independently associated. The third step showed that secondary strains were significantly associated with positive affect after controlling for demographics and primary caregiving stressors, accounting for an additional 19% of variance in positive affect. Less negative work-to-family and family-to-work spillover were independently associated with more positive affect. Race and hours of care remained significantly associated with positive affect. The fourth step showed that resources were significantly associated with positive affect after controlling for demographics, primary caregiving stressors, and secondary strains, accounting for an additional 17% of variance. More optimism and family support were independently associated with more positive affect. The associations between race and positive affect, and negative work-to-family spillover and positive affect disappeared in this step, but negative family-to-work spillover remained significantly independently associated with positive affect. The final model accounted for 42% of the total variance in positive affect.

Table 3.

Regression Analyses Predicting Caregiver Positive Affect.

	Model 1		Model 2		Model 3		Model 4
Predictor	B	β	B	β	B	β	B	β
Demographics
Age	.009	.116*	.009	.110	.000	.005	.002	.021
Sex	−.080	−.052	−.021	−.014	.028	.019	−.026	−.017
Race	.314	.156*	.324	.161*	.273	.136*	.169	.084
Education	−.092	−.061	−.101	−.067	−.020	−.013	−.026	−.017
Primary caregiving stressors
High/low hours of care			−.097	−.129*	−.195	−.128*	−.125	−.082
ADL/IADL assistance			−.040	−.061	−.007	−.010	−.005	−.007
CG lives with CR			.043	.028	.135	.089	.085	.056
Secondary strains
Negative WTF spillover					−.039	−.160*	−.026	−.106
Negative FTW spillover					−.088	−.333**	−.060	−.226**
Workplace discrimination					−.007	−.048	.003	.022
Resources
Perceived control							.039	.094
Religious/spiritual coping							.002	.006
Optimism							.091	.317**
Family support							.252	.198**
R ²	.040		.063		.250		.423
ΔR²	.040		.023		.187		.173
ΔF	2.062*		2.426		23.932**		21.221**

Negative Affect

Results from the third hierarchical regression analysis are displayed in Table 4. The first step showed that caregiver demographics were significantly associated with negative affect, accounting for 4% of the variance in negative affect. Younger age was associated with more negative affect. The second step showed that primary caregiving stressors were not associated with negative affect after controlling for demographics. Age remained independently associated with negative affect. The third step showed that secondary strains were significantly associated with negative affect after controlling for demographics and primary caregiving stressors, accounting for an additional 20% of the variance. More negative work-to-family and family-to-work spillover were associated with more negative affect. Age was no longer associated with negative affect, but an association between race and negative affect appeared. Being Black/AA was independently associated with more negative affect. The fourth step showed that resources were significantly associated with negative affect after controlling for demographics, primary caregiving stressors, and secondary strains, accounting for an additional 7% of the variance. More optimism and family support were independently associated with less negative affect. The independent associations between negative affect and race, negative work-to-family spillover, and negative family-to-work spillover and negative affect remained significant. The final model accounted for 32% of the total variance in negative affect.

Table 4.

Regression Analyses Predicting Caregiver Negative Affect.

	Model 1		Model 2		Model 3		Model 4
Predictor	B	β	B	β	B	β	B	β
Demographics
Age	−.010	−.154*	−.010	−.149*	−.003	−.045	−.004	−.058
Sex	.039	.031	.024	.019	−.013	−.011	.005	.004
Race	.176	.107	.180	.109	.233	.141*	.265	.160*
Education	.017	.013	.018	.015	−.054	−.043	−.053	−.043
Primary caregiving stressors
High/low hours of care			.066	.053	.064	.051	.024	.019
ADL/IADL assistance			.024	.045	−.004	−.008	−.004	−.007
CG lives with CR			.081	.065	.010	.008	.046	.037
Secondary strains
Negative WTF spillover					.043	.212**	.035	.175*
Negative FTW spillover					.066	.305**	.052	.238**
Workplace discrimination					.004	.029	−.003	−.023
Resources
Perceived control							−.022	−.063
Religious/spiritual coping							.016	.057
Optimism							−.042	−.176*
Family support							−.172	−.165*
R ²	.040		.051		.247		.320
ΔR²	.040		.011		.196		.073
ΔF	2.087*		1.128		24.979**		7.655**

Discussion

This is a unique study addressing racial differences in stressors and resources and their associations with well-being among working caregivers. Our findings show the utility of caregiving stress process models (Haley et al., 1987; Pearlin et al., 1990) in examining well-being among diverse working caregivers. First, they reinforce that primary caregiving stressors alone are not sufficient in predicting well-being, and that strains from other roles must also be considered when studying well-being among working caregivers (Templeman et al., 2019). They also extend prior findings regarding Black/AA caregivers’ resilience in coping with caregiving stress to BWC, who reported greater internal resources, which were associated with better subjective well-being. Finally, they suggest that for both BWC and WWC, resources including control, optimism, and family support are excellent tools to target in interventions for working caregivers.

Hypotheses for our first aim of identifying differences between BWC and WWC in aspects of the stress process and the outcome of subjective well-being were partially supported. Surprisingly, BWC did not report more primary caregiving stressors than WWC, which is contrary to our hypothesis based on prior research that consistently finds that Black/AA caregivers provide more hours of care than White caregivers (NAC and AARP PPI, 2020). However, because this sample consisted exclusively of working caregivers, who tend to provide fewer hours of care than non-working caregivers, differences in primary caregiving stressors may have been attenuated (NAC and AARP PPI, 2020). Our exploration of racial differences in secondary strains found that BWC reported less negative work-to-family spillover than WWC, extending similar findings in a recent study of working women (Rung et al., 2021). This may relate to the lower education levels of the BWC in this study, as higher educational attainment has been tied to more work-family conflict (Schieman et al., 2009). It may also reflect cultural differences in the value of caregiving and the prioritization of work and family (Dilworth-Anderson et al., 2002). We found support for our hypothesis that BWC would report greater internal, but not external resources than WWC. While there were no racial differences in the external resource of family support, BWC reported more of all three internal resources, although for optimism, the difference was not statistically significant. Finally, our hypothesis that BWC would report better well-being than WWC was partially supported, with BWC only reporting more positive affect than WWC.

Hypotheses for our second aim of examining relationships between various aspects of the stress process and well-being for BWC and WWC were also partially supported. We hypothesized that both primary caregiving stressors and secondary strains would be associated with worse well-being, but we found that this was only true for secondary strains. Specifically, more negative work-to-family conflict was associated with worse subjective well-being, as measured by all three outcomes; more negative family-to-work conflict was associated with lower life satisfaction and more negative affect; and workplace discrimination was associated with lower life satisfaction. Further, secondary strains accounted for the highest percentage of explained variance in all three models. This finding confirms prior research suggesting that for working caregivers, secondary strains from the workplace domain may be more detrimental than primary stressors from the caregiving domain (Templeman et al., 2019).

As hypothesized, resources were found to be an important aspect of the stress process for both BWC and WWC, but not uniformly. More optimism was associated with better subjective well-being, as measured by all three outcomes, and perceived control was associated with greater life satisfaction. This is aligned with prior research on caregiver well-being and suggests that interventions aimed at improving caregiver well-being should focus on increasing internal resources (Litzelman et al., 2017; Montgomery et al., 2011). Religious/spiritual coping, on the other hand, was not associated with subjective well-being, contrary to former research that has found religiosity to be an important cultural resource that protects against the negative effects of caregiving stress, especially for Black/AA caregivers (Dilworth-Anderson et al., 2005, 2007, Haley et al., 2004; Heo & Keoske, 2011) and Black/AA workers (Henderson, 2016; Selvarajan et al., 2020a, 2020b). However, the small percentage of BWC in our analytic sample (16%) might have resulted in a lack of power to detect the relationship between religious/spiritual coping and caregiver well-being that may be more apparent in the BWC group. Finally, more family support was associated with better well-being, as measured by all three outcomes, illuminating the importance of including not only the caregiver, but also their family members, in interventions for working caregivers.

Finally, our hypothesis that BWC would report better well-being than WWC after accounting for primary caregiving stressors was partially supported. Being Black/AA was associated with higher positive affect in group comparisons and after accounting for primary caregiving and secondary strains. However, interestingly, after adjusting for resources, this association disappeared. This suggests that BWC in our sample generally have greater internal resources to cope with caregiving than WWC (see Table 1), but when holding these resources equal between the groups, they do not differ in positive affect. On the other hand, race was not associated with negative affect or life satisfaction after accounting for primary caregiving stressors, but after accounting for secondary strains and resources, being Black/AA became associated with more negative affect and lower life satisfaction. Together, our results suggest that racial disparities in well-being among working caregivers may be due to differences in the extent of secondary strains from the workplace and internal coping resources.

Limitations

The main limitation of this study is its cross-sectional design, precluding a determination of temporal relationships between concepts. Another limitation is that subjective measures of caregiving, including those representing appraisals of burden or satisfaction with caregiving, were not available in the data. Adding these, in addition to objective measures of the impact of caregiving on work, including going in late, leaving early, or taking time off to provide care (Templeman et al. 2019), would broaden the scope of our understanding of how stressors and resources are related to the well-being of working caregivers and what kinds of policies might be useful to support them. The small sample size for BWC is also a limitation, as it may have made additional relationships among the variables undetectable. Finally, there was a lack of data for caregivers identifying with other minoritized racial and ethnic groups, so we were unable to extend our study to include a more diverse sample. Future research should prioritize collecting ample data on all racial and ethnic groups since the minoritized racial and ethnic older adult population is increasing at four times the rate of the non-Hispanic White older adult population in the United States (Administration for Community Living, 2021).

Conclusion

This study extends the caregiving stress literature by showing differences in stressors and resources between BWC and WWC and how those differences are associated with subjective well-being. Overall, our findings reinforce the importance of identifying and building upon internal and external resources that working caregivers can employ to cope and thrive when faced with multiple role stressors. An excellent place to build those resources is the workplace. Such an investment in employees is worthwhile, as this study suggests that if working caregivers, and especially BWC, are not equipped with appropriate resources, they may be at risk for negative outcomes.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

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

ORCID iD

Maureen E. Templeman

Author Biographies

Maureen E. Templeman is a Professor of Gerontology at Missouri State University. She has a PhD in Aging Sciences and a Graduate Certificate in Hospice, Palliative Care, and End of Life Care from the University of South Florida. Her research examines the work-family interface among informal (unpaid) caregivers of older adults and end of life issues.

Soomi Lee is an Associate Professor in the Department of Human Development and Family Studies at the Pennsylvania State University. Her research focuses on sleep, stress, pain, and lifestyle factors that underlie healthy aging.

William E. Haley is a Distinguished University Professor in the School of Aging Studies at the University of South Florida. His research focuses on stress and coping in older adults, including an emphasis on social relationships, such as family caregiving and social isolation and loneliness.

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