Geriatric Nursing xx (2016) 1e5
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The role of interpersonal sensitivity, social support, and quality of life in rural older adults Monika Wedgeworth, EdD a, *, Michael A. LaRocca, MA b, William F. Chaplin, PhD c, Forrest Scogin, PhD b a
Capstone College of Nursing, University of Alabama, USA Department of Psychology, University of Alabama, USA c Department of Psychology, St. John’s University, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 23 March 2016 Received in revised form 28 June 2016 Accepted 2 July 2016 Available online xxx
The mental health of elderly individuals in rural areas is increasingly relevant as populations age and social structures change. While social support satisfaction is a well-established predictor of quality of life, interpersonal sensitivity symptoms may diminish this relation. The current study extends the ﬁndings of Scogin et al by investigating the relationship among interpersonal sensitivity, social support satisfaction, and quality of life among rural older adults and exploring the mediating role of social support in the relation between interpersonal sensitivity and quality of life (N ¼ 128). Hierarchical regression revealed that interpersonal sensitivity and social support satisfaction predicted quality of life. In addition, bootstrapping resampling supported the role of social support satisfaction as a mediator between interpersonal sensitivity symptoms and quality of life. These results underscore the importance of nurses and allied health providers in assessing and attending to negative self-perceptions of clients, as well as the perceived quality of their social networks. Ó 2016 Elsevier Inc. All rights reserved.
Keywords: Interpersonal sensitivity Rural Older adults Quality of life Social support
Introduction The mental health of elders living in rural areas is increasingly relevant due to aging populations and changing social structures. The outward migration of younger family members may leave rural older adults isolated and susceptible to issues related to physical, mental, and economic well-being.1 Older adults in rural areas often rely heavily on informal support from friends and family due to the lack of formal infrastructures that are generally present in urban areas.2 The reliance on friends and family in providing physical and emotional support is complicated by the inherent challenges of living in rural areas, such as lack of local health care services, social isolation, and poverty. Rural older adults may also be vulnerable to decreased physical and mental well-being due to transportation barriers that limit access to informal and formal support services or fewer informal social ties. Broadly deﬁned, social support encompasses both the perception of support and various forms of assistance from both informal
This research was supported by National Institute on Aging Grant AG16311. Allan Kaufman provided valuable assistance in collecting data for this manuscript. * Corresponding author. The University of Alabama, Capstone College of Nursing, Box 870358, Tuscaloosa, AL 35487-0358, USA. Tel.: þ1 205 331 6120. E-mail address: [email protected]
(M. Wedgeworth). 0197-4572/$ e see front matter Ó 2016 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.gerinurse.2016.07.001
and formal social networks.3e5 The quality of social relations, which are based on factors such as conﬂictual relationships, criticism, stressful social interactions; and the presence of conﬁdants, are also associated with depressive symptoms.5 One of the most signiﬁcant and consistent predictors of the quality of social relationships is relationship satisfaction.3 A social relationship in which there is a non-reciprocal exchange and low levels of emotional support is a risk factor for poor mental health.6,7 Similarly, stressful social interactions, criticism from family members, or poor social interactions are signiﬁcantly associated with higher levels of depression.5,7,8 Although satisfaction with social support is related to the psychological well-being of older adults, feelings of inadequacy or inferiority, particularly in relation to others, may hinder this relationship.9 The concept of interpersonal sensitivity, the undue and exaggerated sensitivity to rejection, behaviors, and emotions of others, often leads to preoccupation with social relationships, increased sensitivity to criticism, and modiﬁcations in behavior to meet other’s expectations. It involves the person’s ability to correctly observe and interpret their environment and provide appropriate social and emotional responses.10 Negative cognitive tendencies, such as interpersonal sensitivity, have been strongly correlated with poor psychological functioning and identiﬁed as an underlying trait in anxiety disorders.11e13 Increased interpersonal sensitivity has been correlated with low self-esteem,
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leading to poor social relationships.14 In elder studies in which social cognitive styles have been hypothesized, interpersonal characteristics were clear risk factors for depression.15 Given these complexities, perceptions of interpersonal interactions have considerable effects on older adults’ satisfaction with their social networks. Researchers and clinicians have often targeted quality of life as a critical overall outcome for rural elders. The current study utilizes quality of life as conceptualized by Frisch, encompassing a comprehensive array of domains including self-perceptions, social relations, health, and community.16 Previous research suggests a positive relation between social support and the various facets of subjective well-being. For example, the perception of social support, or the satisfaction with support exchanges or anticipated support, is a signiﬁcant predictor of quality of life among older adults.3 Based upon the public health burden associated with poor quality of life in older adults, it is important to identify the underlying processes to inform intervention strategies. However, the literature examining social support and quality of life related to interpersonal sensitivity, particularly in rural elders, is limited. The current study is based upon ﬁndings of The Project to Enhance Aged Rural Living (PEARL), a randomized control trial conducted to assess the effects of home-based cognitive behavior therapy (CBT) on quality of life in an ethnically diverse sample of rural older adults.17 In the original study, in-home CBT signiﬁcantly improved quality of life and reduced negative psychological symptoms. The PEARL study data have presented multiple opportunities to conduct secondary data analyses. The current study investigates the relationship among interpersonal sensitivity, social support satisfaction, and quality of life among rural older adults. Speciﬁcally, we predict that interpersonal sensitivity will be negatively related to quality of life, while social support satisfaction will be positively related to quality of life. Moreover, interpersonal sensitivity is expected to be negatively related to social support satisfaction. Finally, we also explore the mediating role of social support in the relation between interpersonal sensitivity and quality of life. Method The current study is a secondary analysis of data collected by Scogin et al as part of a randomized controlled trial that examined the efﬁcacy of home-delivered CBT in improving the quality of life in rural older adults (see Scogin et al for additional details regarding methods and primary treatment outcomes).17 In the current study (N ¼ 128), data are from baseline only. Participants Participants were recruited for the original study through advertisements, public and private home health care agencies, senior centers, church organizations, hospitals, and service providers such as physicians and pharmacists. Inclusion criteria were as follows: (a) age of 65 years or older, (b) a T score of 55 or lower on the Quality of Life Inventory (QOLI), (c) a T score of greater than 45 on the Global Severity Index (GSI) of the Symptoms Checklist-90Revised (SCL-90-R) using non-patient adult norms, and (d) residence outside the cities of Tuscaloosa (AL) and Montgomery (AL).18,19 Exclusion criteria were (a) self-reported history of bipolar disorder, schizophrenia, or current substance abuse; (b) receiving psychotherapy currently; or (c) signiﬁcant cognitive impairment indicated by a score of 23 or less (16 or less for those with less than a ninth-grade education) on the MMSE.20
Measures Background information Background participant characteristics included age, sex, race, marital status, education, self-rated health, and income adequacy. Participant characteristics that predicted the outcome variable of quality of life were used as controls in the main analysis. Quality of life The Quality of Life Inventory was used to measure self-reported overall quality of life.18 The QOLI contains sixteen domains of assessment: health, self-regard, philosophy of life, standard of living, work, recreation, learning, creativity, helping, love relationship, friendships, relationships with children and relatives, home, neighborhood, and community. Participants rate the importance of each domain on a 3-point Likert scale (0, “not at all important” to 2, “very important”), and a 6-point Likert scale is used to rate satisfaction with the domain (3, “very dissatisﬁed” to 3, “very satisﬁed”). The cross-product of these ratings are then summed, and this score is converted to T scores based on adult, community-dwelling norms. Cronbach’s alpha in the normative study was .79 and .71 in the current study.18 The mean T score for the sample was 42.2 (SD ¼ 9.3), which is in the low average range. Social support satisfaction This variable was created from the social support scale in the original study.17 These measures consist of multiple dimensions and were based on a measure of social support developed for the Resources for Enhancing Alzheimer’s Caregiver Health I (REACH I) project.21 Four items from this scale were used in the current study: “Overall, how satisﬁed have you been in the last month with the help you have received from friends, neighbors, or family members?”; “Overall, how satisﬁed have you been in the last month with the help you have received with transportation, household and yard work, and shopping?”; “In the past month, how satisﬁed have you been with the support received during difﬁcult times, comforting from others, how others have listened, and interest and concern from others?”; and “Overall, how satisﬁed in the last month have you been with the suggestions, clariﬁcations, and sharing of similar experiences you have received from others?” Item responses ranged from 0 (“Not at all”) to 3 (“Very”). Items were summed to create the social support satisfaction variable. The range of the social support satisfaction scale is 0e12, and the Cronbach’s alpha value for the sample is .77. The mean score for the sample was 7.5 (SD ¼ 3.0). Interpersonal sensitivity The Symptoms Checklist-90-Revised (SCL-90-R) is a 90-item inventory of nine primary psychological symptom dimensions (somatization, obsessive compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism).19 The IS dimension of the SCL-90-R focuses on feelings of inadequacy and/or inferiority in comparison to others. The person may experience self-doubt and discomfort in interpersonal interactions and have negative expectations about interpersonal relationships. Items addressed in the IS subscale of the SCL-90-R include (1) feeling critical of others (2) feeling shy of the opposite sex (3) feeling easily hurt (4) others are unsympathetic (5) people dislike you (6) feeling inferior to others (7) feeling uneasy when others are watching you (8) self-conscious around others and (9) being uncomfortable eating or drinking in public (Urban, 2014). Item responses range from 0 (“Not at all”) to 4 (“Extremely”). The mean T score for the sample was 56.8 (SD ¼ 11.2), which is in the average range. The Cronbach’s alpha value for the sample is .78.
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Procedure Participants in the original study were randomized to a CBT treatment or minimal support condition (MSC), and participants in each group were compared on quality of life score at posttreatment. See Scogin et al for procedural details of the randomized controlled trial.17 In the current study, only baseline data were analyzed, and these data were analyzed as a single group (i.e., prior to any treatment). Statistical analyses Correlation was used to test interpersonal sensitivity, social support satisfaction, and demographic variables as predictors of quality of life. Categorical variables (e.g., marital status) were tested using ANOVA. Variables signiﬁcantly correlated with quality of life were then added to a hierarchical regression model of predictors of quality of life. Hierarchical regression is a type multiple regression, which is a correlation-based analysis that presents a linear model of the interrelationship among variables. In hierarchical regression, predictor variables are tested step-by-step for their prediction of the outcome variable, in which previously entered variables have been controlled for. Finally, signiﬁcant predictors from this model were tested in a mediation model of quality of life using bootstrapping resampling.22 Bootstrapping resampling calculates average 95% conﬁdence intervals through a minimum of 1000 repeated tests of indirect effects. Results of this analysis yield regression coefﬁcients of the relation among variables, as well as a 95% conﬁdence interval of the role of a variable as a mediator between two other variables. In our analysis, we tested the role of social support satisfaction as a mediator between interpersonal sensitivity and quality of life. Results Participant characteristics See Table 1 for participant characteristics. The mean age of participants was 76.0 years (SD ¼ 7.5). Participants were predominantly female (81.5%) and African-American (55.9%). Correlation and ANOVA Demographic variables, interpersonal sensitivity, and social support satisfaction were tested for correlation with quality of life. Education, interpersonal sensitivity, and social support satisfaction were signiﬁcantly correlated with quality of life and were entered into the regression model. The categorical variable of marital status was tested as a predictor of quality of life using ANOVA. Post-hoc testing revealed that marital statuses of “widowed” and “never married” predicted quality of life. Therefore, marital status was dummy coded and added as an additional control variable to the regression model. Hierarchical regression Hierarchical regression analysis was used to test whether interpersonal sensitivity and social support satisfaction uniquely predicted quality of life (see Table 2). The variables of education and marital status were entered in step 1 as demographic controls. Interpersonal sensitivity was entered in step 2 and was signiﬁcant in negatively predicting quality of life (DR2 ¼ .05, p < .01). In step 3, social support satisfaction was signiﬁcant in positively predicting quality of life (DR2 ¼ .09, p < .01). The model explained 33.2% of the
Table 1 Participant characteristics. Variable
Sex Men Women Race/ethnicity Caucasian African-American Years of education 0e8 9e11 12 13e16 17e20 Income adequacy Not difﬁcult Not very difﬁcult Somewhat difﬁcult Very difﬁcult Marital status Never Married Married Widowed Divorced Separated Self-reported level of health Poor Fair Good Very good Excellent
22 (18.5) 97 (81.5) 56 (44.1) 71 (55.9) 42 (35.3) 32 (26.9) 26 (21.8) 16 (13.4) 3 (2.5) 14 15 42 44
(12.2) (13.0) (36.5) (38.3)
8 (6.7) 24 (20.2) 68 (57.1) 12 (10.1) 7 (5.9) 43 (35.2) 57 (46.7) 15 (12.3) 6 (4.9) 1 (.8)
Age MMSE score Interpersonal sensitivity Social support satisfaction Quality of life
76.0 (7.5) 24.8 (3.5) 56.8 (11.2) 7.5 (3.0) 42.2 (9.3)
N ¼ 128. Missing participant data are as follows: age (36), sex (9), race (1), education (9), income adequacy (13), marital status (9), self-reported health (6), MMSE score (2), Interpersonal Sensitivity (5), Social Support Satisfaction (8), and Quality of Life (4). MMSE ¼ Mini-Mental State Examination; Interpersonal Sensitivity ¼ SCL-90 Interpersonal Sensitivity T score; Social Support Satisfaction ¼ score out of 12; Quality of Life ¼ Quality of Life Inventory T score.
variance F(4, 107) ¼ 13.30 (p < .001). Thus, interpersonal sensitivity and social support satisfaction uniquely predicted quality of life beyond the effects of education and marital status. Mediation We predicted that social support satisfaction would mediate the relation between interpersonal sensitivity and quality of life. To test this hypothesis, mediation modeling using bootstrapping resampling was used (see Fig. 1).22,23 In the current analysis, 5000 indirect effects were tested. Controlling for education and marital status, path coefﬁcients among interpersonal sensitivity, social support
Table 2 Hierarchical multiple regression analysis predicting quality of life from interpersonal sensitivity and social support satisfaction. Predictor Step 1 Education Marital status Step 2 Interpersonal sensitivity Step 3 Social support satisfaction *p < .05; **p < .01.
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quality of life is sparse.3 These ﬁndings underscore the role of interpersonal sensitivity as an important area of intervention to facilitate these relationships. Study limitations This study has several limitations. Results from a sample of rural older adults may not generalize to other populations. In addition, the sample was also overwhelmingly female, making it difﬁcult to fully assess the role of sex in the variables of interest. Moreover, the correlational approach to analysis obscures the nature of causality. For example, while it appears that social support predicts higher quality of life, it is possible that both social support and quality of life are inﬂuenced by variables other than interpersonal sensitivity. Fig. 1. Analysis of social support satisfaction as a mediator of the relation between interpersonal sensitivity and quality of life. Numbers are unstandardized regression coefﬁcients. *p < .05; **p < .01.
satisfaction, and quality of life were statistically signiﬁcant. Consistent with the hierarchical regression above, interpersonal sensitivity was negatively related to social support satisfaction (path a; B ¼ .06, SE ¼ .03, p < .05), social support satisfaction was positively related to quality of life (path b; B ¼ 1.03, SE ¼ .26, p < .01), and interpersonal sensitivity was negatively related to quality of life (path c (total effect of interpersonal sensitivity on quality of life); B ¼ .22, SE ¼ .07, p < .01). The direct effect of interpersonal sensitivity on quality of life (path c0 ), although weaker than the total effect, remained signiﬁcant (B ¼ .16, SE ¼ .07, p < .05). The indirect effect of interpersonal sensitivity on quality of life (i.e., the total effect minus the direct effect, which is the mediator of social support satisfaction) was signiﬁcant. According to Hayes, rather than inferring indirect effects based on statistical signiﬁcance of path coefﬁcients, asymmetric bootstrap conﬁdence intervals provide a more explicit quantiﬁcation of the indirect effect along with a statistical test accounting for the nonnormality of the sampling distribution of the indirect effect.23 Using this method, the indirect effect of interpersonal sensitivity on quality of life was negative and statistically different from zero, as indicated by the point estimate of .06 and a 95% bias-corrected bootstrap conﬁdence interval that was below zero (.15 to .01). The statistical signiﬁcance of the total, direct, and indirect effects of interpersonal sensitivity on quality of life indicates that social support satisfaction is a partial mediator. Accordingly, through the partial mediator of social support satisfaction, each measured unit of interpersonal sensitivity predicted lower quality of life scores by an average of .06. Discussion The most important goal of this study was to examine the relation among an individual’s sensitivity to others, his or her social networks, and overall quality of life. Speciﬁcally, this study found that interpersonal sensitivity and social support satisfaction uniquely predicted quality of life among rural older adults. Through mediation analysis, this relation was explained by social support satisfaction, which illustrated the link between interpersonal sensitivity and quality of life. Greater interpersonal sensitivity was found to predict lower satisfaction with social networks. However, social networks signiﬁcantly predicted quality of life. In sum, high interpersonal sensitivity predicted low quality of life, with dissatisfaction with social support as the underlying mechanism. While these ﬁndings add to the body of research suggesting that perceived social support is related to quality of life, research relating interpersonal sensitivity to social support satisfaction and
Clinical implications Increased life expectancies and the rising numbers of older adults emphasize the need to focus on the variables related to quality of life, including targeted interventions to improve mental health outcomes. Nurses and other health care providers may be unaware of the concept of interpersonal sensitivity and its relationship to quality of life and social support satisfaction. As high interpersonal sensitivity predicted low quality of life in this study, simple observations during the nursing visit, such as interactions with the provider or caregivers, communication skills, and eye contact provide insight into the client’s interpersonal ability. Assessment of meaningful client relationships, such as encouraging or validating behaviors and communication between family members or caregivers should be ongoing throughout the therapeutic relationship. Nurses can enhance interpersonal care via simple interventions such as remaining attentive, maintaining eye contact, verbally reassuring the client, utilizing therapeutic touch, and providing care that is personalized, delivered with respect and emotional openness, and lacking judgment.24 Additionally, nurses can assist rural clients by setting up social networks through available community and religious programs. Interpersonal sensitivity and social relationship issues that are noted by the nurse during the client visit should be communicated to the interdisciplinary care team. Validated assessment tools, such as the SCL-90-R, can be utilized by the nurse or health care team members to directly assess interpersonal sensitivity as well as depression, anxiety, hostility, paranoia, and psychotic symptoms. Findings should be reported to the care team, and referral to social work or psychology services may be necessary to provide speciﬁc and targeted interventions beyond the scope of the nurse. Targeted cognitive-behavioral interventions may address a client’s negative appraisals of others and perceptions of personal inferiority in social situations, which in turn may enhance satisfaction with social networks as well as quality of life. For example, Scogin et al found that home-delivered CBT, which included cognitive restructuring, relaxation, and activity scheduling, improved the overall quality of life of rural older adults.17 In today’s technology-rich environment, these interventions may be delivered via a variety of methods, including the use of telehealth and face-to-face Internet connection applications commonly available on most smart phones and tablets. The nurse may assist with establishing these connections and educating clients and family members on their use. Conclusion Research of the role of interpersonal sensitivity is scarce, and this study emphasized the role of interpersonal sensitivity in social support and quality of life. Social and geographic isolation, coupled
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with a lack of formal social support services, places rural elders at risk for insufﬁcient positive social experiences. The results of this study underscore the importance of allied health care providers in assessing and attending to negative self-perceptions of clients, as well as the perceived quality of their social networks. Mental health promotion is an interdisciplinary concept. However, the role of health care providers, including nurses, is crucial as nurses are often the ﬁrst and most frequent providers of care to rural elders living at home. It is imperative that nurses are educated to assess the quality of social support in their clients, that they develop therapeutic communication techniques to aid in the assessment and intervention for rural elders, and that a clear pathway exists for interdisciplinary referral. The assessment of an older adult’s engagement in positive activities and the resulting enhancement in quality of life should be of particular importance to all health care practitioners providing care in rural areas. References 1. Rogers CC. Growth of the oldest old population and future implications for rural areas. Rural Dev Perspect. 1999;14:22e26. 2. Mair CA, Thivierge-Rikard RV. The strength of strong ties for older rural adults: regional distinctions in the relationship between social interaction and subjective well-being. Int J Aging Hum Dev. 2010;70(2):119e143. http://dx.doi.org/ 10.2190/AG.70.2.b. 3. Siedlecki KL, Salthouse TA, Oishi S, Jeswani S. The relationship between social support and subjective well-being across age. Soc Indic Res. 2014;117(2):561e 576. http://dx.doi.org/10.1007/s11205-013-0361-4. 4. Antonucci TC. A life-span view of women’s social relations. In: Turner B, Troll LE, Turner B, Troll LE, eds. Women Growing Older: Psychological Perspectives. Thousand Oaks, CA, US: Sage Publications, Inc; 1994;239e269. 5. Schwarzbach M, Luppa M, Forstmeier S, König HH, Riedel-Heller SG. Social relations and depression in late life-A systematic review. Int J Geriatr Psychiatry. 2014;29(1):1e21. http://dx.doi.org/10.1002/gps.3971. 6. von dem Knesebeck O, Siegrist J. Reported nonreciprocity of social exchange and depressive symptoms: extending the model of effortereward imbalance beyond work. J Psychosom Res. 2003;55(3):209e214. http://dx.doi.org/10.1016/ S0022 3999(02)00514-7. 7. Hessel A, Geyer M, Brähler E. Psychiatric problems in the elderlystandardization of the Symptom Check List SCL-90-R in patients over 60 years of age. Z Gerontol Geriatr. 2001;34(6):498e508. 8. Shin JK, Kim KW, Park JH, et al. Impacts of poor social support on general health status in community-dwelling Korean elderly: the results from the Korean
longitudinal study on health and aging. Psychiatry Investig. 2008;5(3):155e162. http://dx.doi.org/10.4306/pi.2008.5.3.155. Beckner V, Howard I, Vella L, Mohr DC. Telephone-administered psychotherapy for depression in MS patients: moderating role of social support. J Behav Med. 2010;33(1):47e59. http://dx.doi.org/10.1007/s10865-009-9235-2. Boyce P, Parker G. Development of a scale to measure interpersonal sensitivity. Aust N Z J Psychiatry. 1989;23:341e351. Urbán R, Kun B, Farkas J, et al. Bifactor structural model of symptom checklists: SCL-90-R and Brief Symptom Inventory (BSI) in a non-clinical community sample. Psychiatry Res. 2014;216(1):146e154. http://dx.doi.org/10.1016/ j.psychres.2014.01.027. Butler JC, Doherty MS, Potter RM. Social antecedents and consequences of interpersonal rejection sensitivity. Pers Individ Dif. 2007;43(6):1376e1385. http://dx.doi.org/10.1016/j.paid.2007.04.006. Wilhelm K, Boyce P, Brownhill S. The relationship between interpersonal sensitivity, anxiety disorders, and major depression. J Affect Disord. 2004;79: 33e41. http://dx.doi.org/10.1016/S0165-0327(02)00069-1. McCabe RE, Blankstein KR, Mills JS. Interpersonal sensitivity and social problem solving: relations with academic and social self-esteem, depressive symptoms, and academic performance. Cognit Ther Res. 1999;23:587e604. Liu RT, Kraines MA, Massing-Schaffer M, Alloy LB. Rejection sensitivity and depression: mediation by stress generation. Psychiatry. 2014;77(1):86e97. http://dx.doi.org/10.1521/psyc.2014.77.1.86. Frisch MB. Quality of life therapy and assessment in health care. Clin Psychol Sci Pract. 1998;5(1):19e40. http://dx.doi.org/10.1111/j.1468-2850.1998.tb00132.x. Scogin F, Morthland M, Kaufman A, Burgio L, Chaplin W, Kong G. Improving quality of life in diverse rural older adults: a randomized trial of a psychological treatment. Psychol Aging. 2007;22(4):657e665. http://dx.doi.org/10.1037/ 0882-79126.96.36.1997. Frisch MB. Use of the quality of life inventory in problem assessment and treatment planning for cognitive therapy of depression. In: Freeman A, Dattilio FM, eds. Comprehensive Casebook of Cognitive Therapy. New York: Plenum; 1992;27e52. Derogatis LR, Rickels K, Rock AF. The SCL-90 and the MMPI: a step in the validation of a new self-report scale. Br J Psychiatry. 1976;128:280e289. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189e198. Wisniewski SR, Belle SH, Coon DW, et al. The Resources for Enhancing Alzheimer’s Caregiver Health (REACH): project design and baseline characteristics. Psychol Aging. 2003;18:375e384. Hayes AF. An Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach. New York, NY: Guilford Press; 2013. Hayes AF. PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling. Retrieved from: http:// www.afhayes.com/public/process2012.pdf; 2012. Finfgeld-Connett D. Meta-synthesis of presence in nursing. J Adv Nurs. 2006;55(6):708e714. http://dx.doi.org/10.1111/j.1365-2648.2006.03961.x.