Social Work Research

Williams, James Herbert;Vieyra, Miguel Joseph

2018 Social Work Research

doi: 10.1093/swr/svy001

I consider social work to be undergoing a renaissance period. There is great energy, activity, and discourse within and across the profession. I know that the following statement may sound like a hyperbole. It appears that the profession is on the precipice of making a much-needed transition in defining itself and exploring new substantive areas for research, training, and education. There continues to be growth in our research enterprise. The seminal 1991 report of the National Institute of Mental Health (NIMH) Task Force on social work research provided strong justification for the investment in research resources within the field of social work (Austin, 1999; Task Force on Social Work Research, 1991). In addition to this investment other national development initiatives, dissemination, publications, conferences, and research development in social work education programs were established. Significant research domains are identified, the status of doctoral education is analyzed, and major sources of current funding for social work research are identified (Austin, 1999; Task Force on Social Work Research, 1991). Since 1993, there has been a precipitous growth in social work research. The profession has seen an increase in both the number of articles published and the number of social work journals (Perron et al., 2017; Victor, Hodge, Perron, Vaughn, & Salas-Wright, 2017). Shortly after the release of the NIMH Task Force report in 1993, the Society for Social Work and Research (SSWR) was founded. SSWR is committed to the advancement of social work research. The taskforce report, the increase in infrastructure, having visionary leaders in the profession, the improvement in doctoral education, and the founding of SSWR are just a few of the antecedents that have energized the overall growth in the social work research enterprise. In addition to the growth in research, publications, and social work journals, there has also been growth in social work education. The profession has witnessed steady growth in the number of BSW, MSW, and PhD programs. As of 2017, there are 518 accredited BSW programs, 255 accredited MSW programs, and 17 BSW and 23 MSW programs in candidacy (Council on Social Work Education [CSWE], 2017). There has been a significant increase in offering both BSW and MSW degrees in the online format. We have seen the reemergence of offering the practice doctorate in both face-to-face and online formats. Over the five-year period of 2011 to 2015, the full-time enrollment for BSW students and MSW students increased by 5.2% and 25.7%, respectively, whereas the full-time enrollment for doctoral (practice doctorate and PhD) students decreased by 1.4%. During the same time period, the part-time enrollment for BSW students and MSW students increased by 9.1% and 16.1%, respectively, whereas the part-time enrollment of doctoral (practice doctorate and PhD) students decreased by 37.9% (CSWE, 2015). We should take some satisfaction in the growth of our profession. The growth in research, knowledge development, and education can be remarkable foundations for our continued efforts to define and refine the profession. The growth in research has prompted discourse regarding evidence-based practice and the function of science in social work. The profession is attentively exploring better methods for addressing the perceived disconnect between research and practice. There are members of the academy and practice community acknowledging concerns regarding the research agenda of the profession. Overall, are we asking the right research questions, are we using the methods, and is our research having the expected impact for the communities we serve and society? These are big questions our profession will continue to grapple with moving forward. Our growth in social work education is necessitating that the profession become more knowledgeable about curriculum, practice behaviors, skills and competencies, better gatekeeping, educational outcomes, and alternative educational delivery formats. There is great momentum in social work to stake our claim in the core areas of our professional turf in the national social, psychological, and health services development and delivery agenda. The increased collaborative partnerships between national social work organizations on research, education, and policy advocacy support this momentum. There continue to be gaps in our knowledge base about the social work workforce. Having quality comprehensive knowledge about the social work workforce would strengthen this momentum. Given this growth, strong organizational collaborations, and national initiatives for social work, our profession has been harmed by the deprofessionalization of the sectors in which we traditionally have been leaders (for example, medical social work, child welfare, criminal justice, and juvenile delinquency). Although social work research and education have expanded positively in size and scope, some would argue that we are still frighteningly disengaged from current events and have ceded our voice to other professions in the realms of policy and management. This is a stark contrast to the Progressive Era 100 years ago, when our profession came together for the first time to stake our claim, draw our boundaries, and speak out on the very relevant issues affecting our fellow citizens. At the conclusion of the 2018 SSWR Annual Scientific Meeting, I attended a symposium addressing social work in its second century. The symposium was sponsored by the Social Service Review in collaboration with SSWR and the American Academy of Social Work and Social Welfare (AASWSW). This symposium, “Social Service Review Symposium: Whither American Social Work in its Second Century?” invited social work scholars to reflect on the state of scholarship and the profession (for example, social welfare policy, administration, macro practice, and direct practice). The symposium acknowledged the 100th anniversary of the inaugural meeting of the National Conference of Social Work in 1917. Building on the profession’s accomplishments, scholars examined promising directions for the profession as we enter the second century and the kinds of scholarship necessary to sustain our profession. Given the overall changes occurring in our profession, this symposium is absolutely timely. As we move into the second century of our profession, it is timely for the profession to consider the state of the profession and how we are managing across the very diverse fields of practice. The 1917 National Conference was described as the greatest gathering of social workers on the continent. Attendees included a very broad range of practitioners (for example, public relief officials, institution officers, parish workers, charity organization secretaries, probation officers, nurses, settlement workers, medical social services workers, prison heads, truant officers, teachers of special groups, tenement inspectors, public welfare directors, social investigators, and executives of agencies for social legislation) and many others (Courtney, 2017). Defining fields of practice for the profession continues to challenge social work. This symposium sought to answer questions related to the current state of scholarship in core areas of social work, what social work brings to these areas that is unique or essential that other professions do not, and what are promising directions for social work moving forward? Social work has made tremendous progress in defining itself as a profession internally; however, defining the profession to external audiences and delineating the boundaries of our profession continues to require our endeavors. I find that other applied professions (for example, nursing, physical therapy, occupational therapy, and medicine) collect greater amounts of comprehensive data on their profession. These professions understand that regular workforce studies are critical for informing future directions, needs, and capacity of the profession. Currently, the annual report from CSWE is about the extent of our knowledge about the social work workforce. Recognizing this deficiency, CSWE in collaboration with the Association of Baccalaureate Social Work Program Directors, SSWR, AASWSW, Association of Social Work Boards, Group for the Advancement of Doctoral Education, National Association of Deans and Directors of Schools of Social Work, and National Association of Social Workers contracted with the George Washington University Health Workforce Institute to conduct a preliminary workforce study. Results from this preliminary study reinforced the woeful lack of data we have on our profession (Salsberg et al., 2017). Here are three examples of how allied professions collect and use data to plan and improve their workforce: (1) The Association of American Medical colleges (AAMC) commits to publish annual updates of national physician workforce projections and assess the capacity of the nation’s future health care workforce in general and physician workforce in particular. AAMC believes these data to be important for both the public and the private sector. Up-to-date data are needed to guide investments in health care systems and to develop physicians to transform the system (AAMC, 2017). The overall focus of AAMC’s workforce studies is to have a better understanding of how clinicians and care settings will respond to economic and other trends and to understand how the growth in nonphysician personnel (for example, hospitalists, physician assistants, and advanced practice registered nurses) will affect demand for physicians and improve workforce projections (AAMC, 2017). (2) Physical therapists conducted a workforce study to develop a strategy for modeling future workforce projections to serve as a basis for analyzing annual supply of and demand for physical therapists across the United States into 2020 (Landry et al., 2016). (3) Every two years, the National Council of State Boards of Nursing (NCSBN) partners with the National Forum of State Nursing Workforce Centers to conduct the only national-level survey specifically focused on the U.S. nursing workforce. This survey generates information on the supply of nurses. These data are critical to planning for sufficient numbers of adequately trained nurses for a safe and effective health care system (NCSBN, 2015). We do not have a systematic approach to scan the environment and look toward the future. We lag behind other professions in our ability to analyze trends (Lein, Uehara, Lightfoot, Lawlor, & Williams, 2017). Workforce studies in social work are practically nonexistent. In 2004, NASW conducted a benchmark national survey of licensed social workers. The survey used licensed social workers because they represent frontline practitioners and because state licensing lists provided an opportunity to reach social workers who may not have had any other identifiable professional affiliation (Center for Health Workforce Studies [CHWS], 2006). This study followed similar methods as workforce studies conducted in other professions: collection of demographic characteristics, practice settings, job tasks, and compensation and benefits (CHWS, 2006). As mentioned earlier, CSWE in collaboration with other social work organizations has conducted a preliminary workforce study (Salsberg et al., 2017). The scope of the data from this preliminary study was not at a scale for defining our profession to external audiences and delineating our boundaries. Over the years, several federal agencies and foundations have invested in social work workforce development. These investments were not based on data from a workforce study. Two noticeable examples have been in the areas of child welfare and gerontology. Historically, child welfare is a practice area in which social workers have been leaders. Protecting children from abuse and neglect means addressing the complex needs of families who experience poverty, cope with trauma, and struggle with substance use. The stakes are high and the work is hard: Social workers in child welfare carry demanding caseloads and may face threats to their safety, while making critical decisions regarding the removal and reunification of children. When workers burn out, turnover creates costly vacancies in agencies struggling to meet the demand for services at a time that multiple issues strain the child welfare system (Lachman, 2017; Pecora, Whittaker, Maluccio, Barth, & DePanfilis, 2009; Wulczyn, Orlebeke, & Haight, 2009). During a crisis in the child welfare system, the response from policymakers and practitioners is often to expand services to children and families. Less attention is paid to who is best positioned to provide those services and how they should be trained and supported in delivering those services. Although partnerships between child welfare agencies and universities have made strides in professionalizing the child welfare workforce, fewer than 40% hold degrees in social work (Barth, Lloyd, Christ, Chapman, & Dickinson, 2008). Relevant education and training alone will not ensure a qualified child welfare workforce without also addressing the complex structural, organizational, and financial challenges that also affect social services systems. In the context of a rapidly changing practice environment, some child welfare agencies are implementing comprehensive workforce strategies focused on recruiting, training, and retaining workers. But assessing the workforce also requires dialogue with collaborating disciplines to better forecast and understand emerging service needs of families that the next generation of social workers will need to be prepared to address. The John A. Hartford Foundation (JAHF) invested in social work by launching the Geriatric Social Work Initiative (GSWI). GSWI had three major components. One component was intended to transform social work curricula with aging and gerontological content. More than 1,500 social work faculty members participated in gerontological competency-based training, and 250 social work programs infused aging and gerontological competencies into their curricula and program structure (Hooyman & Lubben, 2009; JAHF, 2012; Lubben, 2009). The second component of the GSWI was the Geriatric Social Work Faculty Scholars program. This program’s focus was to increase the number of social work faculty members committed to research and teaching about the needs of older adults. The third component was to cultivate outstanding social work students to pursue an academic career in gerontology. The Hartford Doctoral Fellows in Geriatric Social Work program supported research, mentoring, and professional development (JAHF, 2012; Lubben, 2009). Many social work students were introduced to a range of settings and practice opportunities in gerontology through coursework and an innovative practicum model. The workforce investments in both child welfare and gerontology have been instrumental in influencing the practice areas of social work. The continued success of social work is contingent on being able to better define the profession to external audiences, delineate the boundaries of our profession, and meticulously explore new areas of practice. The ongoing assessment of social work is needed to understand trends and adequately prepare a workforce to address the changing social, economic, psychological, health, and environmental needs of society. Social work carefully examines the methods our colleagues from nursing, physical therapy, occupational therapy, and medicine use to systematically collect workforce data and use those data to inform curriculum, practice, and policy. The current social work workforce data are limited and not adequate for the profession to successfully plan for the future. A concerted effort is required from social work organizations, schools, and programs to collect quality workforce data as we move into the second century. We need both workforce investment and workforce data. I think it is clear that we need a seminal workforce study now to make the same bold decisions our profession made 100 years ago. James Herbert Williams, PhD, is director and Arizona Centennial Professor of Social Welfare Services, and Miguel Joseph Vieyra, MSW, is associate director for community engagement and strategic initiatives and clinical associate professor, School of Social Work, Arizona State University, Phoenix. 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New York: Author. Retrieved from https://www.johnahartford.org/ar2012/JAHF_2012AR.pdf Lachman, S. ( 2017, December 29). The opioid plague’s youngest victims. New York Times , p. A23. Landry, M. D., Hack, L. M., Coulson, E., Freburger, J., Johnson, M. P., Katz, R., et al.  . ( 2016). Workforce projections 2010–2020: Annual supply and demand forecasting models for physical therapists across the United States. Physical Therapy,  96, 71– 80. Google Scholar CrossRef Search ADS   Lein, L., Uehara, E. S., Lightfoot, E., Lawlor, E. F., & Williams, J. H. ( 2017). A collaborative framework for envisioning the future of social work research and education [Editorial]. Social Work Research,  41, 67– 71. Google Scholar CrossRef Search ADS   Lubben, J. E. ( 2009). Cultivating a new generation of scholars: The Hartford Doctoral Fellows Program. In N. R. Hooyman (Ed.), Transforming social work education: The first decade of the Hartford Geriatric Social Work Initiative  (pp. 79– 97). Alexandria, VA: Council on Social Work Education Press. National Council of State Boards of Nursing. ( 2015). The 2015 National Nursing Workforce Survey: Executive summary . Chicago: Author. Retrieved from https://www.ncsbn.org/2015ExecutiveSummary.pdf Pecora, P. J., Whittaker, J. K., Maluccio, A. N., Barth, R. P., & DePanfilis, D. ( 2009). The child welfare challenge  ( 3rd ed.). Piscataway, NJ: Aldine-Transaction Books. Perron, B. E., Victor, B. G., Hodge, D. R., Salas-Wright, C. P., Vaughn, M. G., & Taylor, R. J. ( 2017). Laying the foundations for scientometric research: A data science approach. Research on Social Work Practice,  27, 802– 812. Google Scholar CrossRef Search ADS   Salsberg, E., Quigley, L., Mehfoud, N., Acquaviva, K., Wyche, K., & Sliwa, S. ( 2017). Profile of the social work workforce: A report to Council on Social Work Education and National Workforce Initiative Steering Committee . 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McHale, James P;Negrini, Lisa S

2018 Social Work Research

doi: 10.1093/swr/svx024

Abstract Despite incontrovertible evidence documenting effects of fathering on child outcomes, social work practice has unyieldingly resisted the pursuit of father engagement as a requisite outcome of competent clinical child intervention. Reasons behind this resistance are already well understood, and several promising programs have provided reassuring evidence that inclusion of fathers in clinical work with young children in higher-risk families is not only possible, but transformative. Yet despite the fact that the soil has been tilled and essential professional competencies needed for effective work with fathers identified, it is unlikely that comprehensive changes will ever take root without the social work profession embracing a coparenting and triangular perspective in all efforts on behalf of children and their families. In this article, main conceptual distinctions between triangular and “2 + 1” models are drawn, with accounts provided differentiating perfunctory from persistent assumption of a triangular model, calling on an inventive coparenting intervention for unmarried expectant parents as a case in point. This article also focuses on parallel changes in the institutional and community contexts in which social work practice is embedded that will be needed to better support the coparenting family once interventions have met with success in solidifying family triangles in children’s best interests. Although evidence establishing father effects on child outcomes—for good or ill, and whether fathers are attended to clinically, or not—has been accessible for decades, improvements in fathering have not been identified as compulsory outcomes of proficient social work interventions with families. Obstacles impeding this remaking of the definition of capable practice have been articulated (D’Andrade & Sorkhabi, 2016), and professional competencies needed to address these obstacles have been identified (Coakley, Kelley, & Bartlett, 2014). And although the landscape is now dotted with promising programs demonstrating that incorporating fathers in case planning and management with higher-risk families is not, as many professionals feared, unmanageable or disrupting (Malm, Murray, & Geen, 2006; Marczak, Becher, Hardman, Galos, & Ruhland, 2015), efforts to serve fathers and their families are destined to continue meeting with only meek success until one important paradigmatic transformation is embraced. In this article we address this needed conversion: the presumption, and assumption, of a coparenting and triangular perspective as frame for all social work with children and their families. Our aims in this article are threefold. First, we identify what coparenting actually is—for the published literature is, unfortunately, muddied with misunderstandings and distortions of the core concept of coparenting that have led to misuses of time, effort, and energy. Second, we identify why a coparenting and triangular frame in social work practice with young children promises the kinds of advances that marriage and fatherhood programming alone have rarely delivered. And third, we provide illustrative data from an ongoing initiative that has taken the triangular frame seriously and begun to redefine practice, while at the same time contending with challenges inevitably faced when charting previously unknown waters. We close with recommendations about more immediately and more effectively altering existing training programs and agency methodologies so that fathers might truly become, in the words of Zanoni, Warburton, Bussey, and McMaugh (2013), a “core business” for social work practice. Conceptual Underpinnings: What Is Coparenting? Coparenting is a family systems notion that refers to the degree of support and coordination among any two or more adults responsible for a given child’s care and upbringing (McHale, 1995; McHale & Irace, 2011). Seven years ago, the field’s first volume on coparenting as it occurs across diverse family systems and structures was released by the American Psychological Association (McHale & Lindahl, 2011). This milestone work provided both conceptual and empirical arguments for why case formulation, assessment, and intervention that is guided by a coparenting model maximizes the likelihood of favorable outcomes in work with children and families—and why ignoring coparenting can reasonably be viewed as professional negligence. This is a rather dramatic departure from most clinical practice—typically, one caregiver only is chosen as a target for intervention, and that person engages in individual and sometimes dyadic child–parent work with the interventionist on behalf of the child and family. From a family systems standpoint, dyadic relationships in families, be they mother–child, father–child, or mother–father relationships, are each important elements of family functioning and can all be useful intervention targets. But mothers’ and fathers’ individual—and combined—impact on children’s coping and adjustment (Al-Yagon, 2011; Diener, Isabella, & Behunin, 2008; Flouri & Buchanan, 2003), as well as the direct (see, for example, Cummings, Davies, & Simpson, 1994) and indirect (see, for example, Hinde & Stevenson-Hinde, 1988) influences of husband–wife marital quality on child adjustment, constitute in S. Minuchin’s (1974) terms the effects of relationship subsystems. Coparenting systems—one key thrust of S. Minuchin’s (1974) theory of family structure—are always at least triangular in nature. Mother–father–child triangles reflect more than just the sum of their constituent dyadic parts; triangles are distinctive emotional systems (Bowen, 1978; Fivaz-Depeursinge & Corboz-Warnery, 1999), and empirical studies find that triangular systems are irreducible, impossible to describe in individual or dyadic terms without losing their quintessence (McHale, Kuersten-Hogan, Lauretti, & Rasmussen, 2000). Originally studied empirically in families where parents had divorced (Ahrons, 1981), it was the mid-1990s before coparenting in families where parents had not divorced first became a focus of enthusiastic study. Early clinical observations of family triads and groups (Belsky, Crnic, & Gable, 1995; Belsky, Putnam, & Crnic, 1996; McHale, 1995; McHale, Kuersten, & Lauretti, 1996) uncovered several distinguishable coparenting and family-level processes: coparenting cooperation, coparenting competition, family warmth, verbal sparring, degree of disparity in levels of parental engagement (with disconnection and disengagement at the extreme), child-centeredness, and attunement and sensitivity (and at the other extreme, parent-directedness and overstimulation). It is important to note that such dynamics were always documented with reference to a particular child; in families with multiple children the same two coparenting adults can behave cooperatively and exhibit high warmth in their dealings with one child, yet coparent in a more fractured and conflictual manner—and to exhibit a greater imbalance in their levels of mutual emotional engagement and involvement—with that child’s sibling (McHale, 2007). This vital point often eludes practitioners who are attempting to understand each child’s sense of contentment, safety, and security in their family: Any given child’s reality is uniquely shaped by the exclusive coparent–coparent–child interactions the child overtly experiences with his or her two or more coparents (McHale, Johnson, & Sinclair, 1999) and by the child’s exposure to covert communications from each parent concerning the other coparent and the coparenting alliance (McHale, 1997). From the child’s perspective, the triangular (or multicoparent) system that affects and matters to them is inexorably dissimilar from any other sibling’s triangle. This is why an accurate assessment of coparenting for each child in the family is so important when one or both parents have children by multiple partners (Baxter, 2012; Carlson, 2015; Cooper, Beck, Högnäs, & Swanson, 2015). From a child-centric frame, what the clinician needs to understand is how that child’s “people” coordinate, or fail to coordinate, as coparents to him or her. How a child’s half-sibling’s father or mother work together is not really of any concern to the child. Although fathers (and mothers) are our primary focus in this article, responsible coparenting evaluation extends beyond just biological fathers and mothers. The coparenting system in millions of American families involves a kin caregiver or someone else besides just mother and father. When a mother or father is entirely absent from the child’s life (with other important adults taking on responsibility for sharing in the child’s care and upbringing), it is the triangular relationships between these coparenting adults with reference to the child that are every bit as salient and formative for child development. Coparenting is hence decidedly not a dynamic limited just to married or divorced heterosexual mother–father family systems; indeed, it can be argued that between birth and young adulthood, all children will be coparented (McHale, 2009; McHale & Irace, 2011; McHale et al., 2002; McHale & Phares, 2015). Social workers guided by this advanced and more accurate child-centric perspective (that to each child, the relevant coparenting alliance is that set of adults with whom the child has cultivated closest attachment and affiliative bonds) will find themselves better positioned to recognize and support every family system that evolves to coparent children. Although immensely diverse in nature, all coparenting systems can be seen as sharing common core components: (a) degree of support and solidarity between parenting figures, (b) degree of consistency and predictability in the approaches that the different caregivers take in the child’s life, (c) security and integrity of the child’s home base (regardless of whether that home base is a single domicile or spans multiple residences), and (d) degree of mutual empathy and attunement to the child’s needs. By taking pains to see coparenting through the child’s eyes and paying attention to how these core elements function in the child’s family, social workers put themselves in a position to build up coparenting alliances—even in family circumstances where an important coparent is separated from a child, as in the case of incarceration, immigration, deportation, or military deployment of parents. The power and reach of the coparenting metaphor is perhaps best illuminated by the pioneering work of Patricia Minuchin (P. Minuchin, Colapinto, & Minuchin, 2007). Her work with families entangled in the child welfare system, since expanded by the Annie E. Casey Family-to-Family initiative, creatively engendered coparenting alliances between biological and foster families after a child had been removed from the family, but with the anticipation of later reunification. This family-to-family coparenting model defied previous views of biological parents as dangers from which children had to be safeguarded until such time that the parents could prove themselves deserving of contact once again. Moreover, it was borne of attunement to children’s own sensibilities. Young children experience unremitting grief when precipitously separated from their families of origin. By embracing the family that the children saw and loved, interventionists were then able to design means to help bring foster and biological coparents into early and ongoing positive, collaborative contact. By so doing, they tempered children’s deepest fears about their vanished parents and disintegrating families. In summary, coparenting formulations view communication, collaboration, and cooperation among the important coparenting adults in any child’s life as essential to improving the child’s mental health and adaptation. Evidence for this tenet is addressed in the next section. What Is the Evidence That Coparenting Affects Children? The notion that coparenting dynamics within families affect children’s development dates back to the 1950s, when the family therapy movement arose as a new clinical paradigm. Lidz, Cornelison, Fleck, and Terry (1957), studying triadic (mother–father–child) patterns in families with schizophrenic young adults, articulated two dysfunctional patterns that entangled the youths. The first was openly antagonistic, wherein partners undermined one another’s efforts with the child and competed openly for the child’s affection and loyalty. The second was an imbalance wherein one parent displayed overbearing parenting that was not countered, but rather acquiesced to, by the other. From this work grew both theory and research positing the detrimental aftereffects of failures in coparenting solidarity, epitomized in S. Minuchin’s (1974) structural family theory. Minuchin’s work with urban poor families was both inclusive and appropriately flexible, honoring multigenerational family systems and structures as much as mother–father coparenting systems. Minuchin viewed the coparenting adults, be they two biological parents, parent and stepparent, or parent and grandparent as heads of the family hierarchy. These coparents operated as an “executive subsystem” that ideally possessed and wielded mutual and shared decision-making authority, rather than triangulating children into inappropriate positions of influence. When cross-generational coalitions involving minor children did materialize, problem behaviors inevitably ensued. Resolution of the family balance required returning the child to the position of child and letting the adults reclaim shared leadership and authority, exemplified by open and continuous communication about and on behalf of the child and his or her best interests. The term “coparenting” first gained widespread usage in the late 1970s and early 1980s, as children in postdivorce family systems began developing behavior problems owing to stalemates their parents had in working together as parents (Ahrons, 1981; Hetherington, 1989). By the mid-1990s, these lines of thought had prompted empirical study of the influences and aftereffects of both positive and problematic coparenting for children in nonreferred community families. The first study to show that child adjustment problems are foreshadowed by hostile and competitive coparenting dynamics in the family discovered cross-time links between antagonistic coparenting during the infant years and poor impulse control, dysregulation, and aggression once children reached the preschool years (McHale & Rasmussen, 1998). That same study linked imbalanced parental involvement with the child during infancy to higher levels of anxiety and depression during the preschool years. From the viewpoint of infant mental health, the notion that coparents’ unsupportive and undermining behavior kindles dysregulated behavior in the child is expectable. Young children, particularly from birth to age three, require consistent and predictable responsivity from parents to develop internal rhythms, self-soothe, manage frustration, and regulate behavior and emotions under duress. Dissimilar child-rearing and disciplinary practices by caregivers can undermine children’s self-regulation (Harvey, 2000). For these reasons, clinical interventions that target just a single caregiver and ignore other caregivers who are also coparenting the child often run into problems. If no effort is made to develop intercaregiver consistency, the child’s situation can remain erratic and unpredictable. Since McHale and Rasmussen’s (1998) report, over two dozen studies have replicated the original findings that poor coparenting awakens problem behavior in young children (see Mangelsdorf, Laxman, & Jessee, 2011, and Teubert & Pinquart, 2010, for comprehensive reviews of this work). The link between greater discrepancies in parental engagement and children’s sadness and anxiety has also been echoed in relevant related reports on the impact of father absence (East, Jackson, Power, Woods, & Hutchinson, 2014; McLanahan, Tach, & Schneider, 2013; Stover, Van Horn, Turner, Cooper, & Lieberman, 2003), although it is also clear that other situational factors and family adaptations can amplify or mitigate this connection. Absenteeism in general and paternal incarceration in particular are associated with increases in child aggression and attentional problems, even for children who did not live with the father prior to his incarceration (Geller, Cooper, Garfinkel, Schwartz-Soicher, & Mincy, 2012). In studies of incarcerated mothers, child symptomatology postrelease has been linked to quality of coparenting between the incarcerated mother and the coparenting grandmother (McHale, Salman, Strozier, & Cecil, 2013), suggesting that negative effects of parental incarceration on children’s postreunification adjustment might be mitigated if dedicated attention were to be paid to improving coparenting alliances between the incarcerated and custodial coparents during the weeks and months the parent is away. We emphasize that what we are focused on here is evidence that the coparenting alliance, and not the typically studied fathering or mothering effects, is what is crucial. In virtually all of the work summarized here, quality of coparenting explained unique variance in child outcomes over and above effects of parenting. That is, the dynamics of the triadic family unit were not simply echoing or replicating processes obvious in dyadic assessments of mother with child or father with child. In practice, this means that if coparenting difficulties are identified, targeted interventions are in order to help address the impediments to coparenting. Interventions with just mother, or just father, are not sufficient. Despite all we have learned thus far, the field of coparenting theory and research is in many ways still in its infancy. Most empirical studies of coparenting have enrolled coresidential married heterosexual families and divorced heterosexual families. Far less information is available about coparenting in nonresidential and unmarried families, as studies typically only examine how support by fathers, and not coparenting per se, benefits children. But fathering studies (summarized by Zanoni et al., 2013) do provide requisite evidence that higher-risk fathers not identified as violent or unfit to parent can play a protective role and be a resource in the lives of children, even in child protection–involved families (Coady, Hoy, & Cameron, 2013; Lee, Bellamy, & Guterman, 2009; Malm & Zielewski, 2009; Storhaug & Øien, 2012). A noteworthy exception to the virtual absence of studies of coparenting by unmarried and noncoresidential fathers and mothers, guided by a truly triangular father–mother–infant frame, is a novel intervention supporting unmarried African American parents in the cultivation of an intentional and positive coparenting alliance. Figuring It Out for the Child (FIOC), a six-session adaptation of McHale and Irace’s (2010) Focused Coparenting Consultation, is described next. Strengthening Coparenting Alliances It is rather troubling that even in the face of undeniable evidence that children growing up in father-absent families face exponentially greater risk for poorer life outcomes, standard social work and nursing practices direct nearly all education and supports to higher-risk unmarried mothers, neglecting their babies’ fathers (Olds et al., 2007). For men, Responsible Fatherhood groups do exist in most urban areas, but initiatives that intentionally bring nonresidential mothers and fathers together to talk about and plan their family situation are uncommon (although initial efforts are afoot to try to include otherwise marginalized men in home visiting; see McHale & Phares, 2015). The dearth of family-sensitive coparenting programming is certainly not to say that there have been no attempts to bring mothers and fathers together as couples. In 2012, McHale, Waller, and Pearson summarized the state of the field with respect to interventions for unmarried parents. Their review devoted considerable attention to the federal Healthy Marriages initiative, and in particular Relationship and Marriage Education (RME) programs, including the Building Strong Families (BSF) program. Alhough BSF was not without some minor successes, the overall conclusion from the work was that intervention families fared no better as coparents than did control group families (Wood, Moore, Clarkwest, & Killewald, 2014)—a situation further complicated by the fact that the majority of families receiving Healthy Start and Healthy Families services (mothers-to-be who did not self-identify as being in a committed relationship with their baby’s father) were actually ineligible to take part in BSF at all (Dion, Avellar, & Clary, 2010). Given the now well-documented positive impact of harmonious, coordinated coparenting on child development, the failure of BSF and of other small- and larger-scale efforts to successfully bring nonresidential parents together around issues of coparenting (rather than relationship enhancement) has been disheartening. Positive coparenting alliances between unmarried parents who are not in committed relationships are attainable, but such alliances are achieved most readily if and when parents bridge impasses to communicate, coordinate, and problem solve in the child’s best interests. It is curious that although noncoresidential parents themselves have articulated more need for help with coparenting relationships than programs currently offer, the use of existing services such as mediation—especially by custodial parents—has been poor (Martinson & Nightingale, 2008). Parents harbor mistrust of governmentally sponsored programs, and interadult conflict in on-again, off-again relationships is a further impediment to parents using services well-meaningly designed to help promote better communication. Perhaps for these reasons FIOC, designed as a preventive family-strengthening alternate method to mediation, Responsible Fatherhood, and RME programs, has struck a new chord. FIOC eschews an educational stance in favor of a combined experiential and skill-building approach, and takes on the real-life circumstances confronting lower-income, unmarried and uncoupled parents that impede them from coordinating to coparent their babies. Details of the FIOC intervention are described elsewhere so will be only briefly summarized here; readers may refer to informative reports by Gaskin-Butler and colleagues (Gaskin-Butler et al., 2015; McHale, Gaskin-Butler, McKay, & Gallardo, 2013). When family interventions are culturally grounded, they have greater credibility with parents. Perhaps key to the FIOC intervention’s early successes is that it was codesigned by experienced African American activists, interventionists, and educational leaders in the community where the intervention was piloted. Collaborating with university-based family clinicians experienced in couples and family therapy approaches, community leaders helped design a program that honored fathers and mothers equally as permanent partners in the care and upbringing of their shared child. Unfortunately, the “father and mother coparenting as a team, regardless of residential and life circumstance” dispatch was a foreign one that did not readily resonate within the social services community where FIOC was field-tested. Considerable groundwork was needed for potential referral agents to feel at ease communicating to both the mother and the father not only that father participation in the family-strengthening work of FIOC was required—but that the work itself could not proceed, at all, without his presence. Questions from several frontline professionals serving pregnant mothers quickly surfaced, as anticipated, about what precautions the FIOC project was taking to safeguard mother and fetus from dangers the father may introduce. Addressing these concerns took time and effort; collaboration with respected leaders in the domestic violence community and with individuals who specialized in batterer interventions and in anger management were necessary to help assure questioners that there were robust safety plans in place should any concerns arise during the course of the intervention. Moreover, following best practice guidelines, we did not bring parents together for the mother–father intervention in circumstances where a preintervention screening completed with the mother alone signaled levels of danger exceeding the degree of support that could be provided within a brief coparenting consultation. Mothers and fathers were also not seen in the intervention together if mothers voiced concerns for their own safety participating with the father. Safety became the primary focus for screening. As it happened, however, the field test disqualified no family because of disclosed concerns about danger; this may have been because parents contending with intimate partner violence (IPV) elected not to present themselves for a dyadic intervention. Among the families actually served, over 90% completed the intervention with no safety issues raised. This was one important lesson learned from the field test; the expectant fathers who came forward to participate in the dyadic FIOC intervention to figure things out for their child were in fact all motivated, optimistic men who saw the project as an opportunity to bring benefits to their child’s life. As a group, they did not pose dangers to their children, as referral agents had anticipated they might. Having plans in place to address IPV in the event it did surface unexpectedly was necessary to enjoin the goodwill of referral agents in guiding parents to the intervention. A more arduous and ongoing challenge to the work has been persuading parents themselves to take part in the intervention. Unlike Responsible Fatherhood programs, FIOC is brought to the attention of mothers and not fathers as the point of first contact. Fathers are contacted only after mothers deliberate the pros and cons and then decide to take part. For many frontline professionals, the notion of connecting a mother to programming that will serve both herself and her baby’s father simultaneously remains a foreign one, so agents who actually have the opportunity to help mothers ponder about coparenting often fail to do so. When mothers are made aware of programming and do self-refer, engaging fathers is often even more challenging. Motivated mothers are frequently unable to persuade skeptical fathers to answer phone calls from program staff poised to provide more information about FIOC. Unless referral agents have direct access to fathers and mothers (which few do), many families who might benefit from the intervention are not reached. As McHale and Phares (2015) have detailed, men regularly receive implicit messages from service systems and agents that services actually only exist for mothers, not for them. Further disinclining fathers from self-referring is their wariness over misinformation promulgated about family-support programs that, unfortunately, often end up portraying the father as a negligent family provider or incompetent parent. An important reason why FIOC outreach to men is successful, when it is, is that FIOC outreach staff are fully sold on the value of engaging fathers with their children. This personal value underlies any success social workers are likely to have in addressing fathers’ questions and concerns and attracting them to take part in a family service with their baby’s mother. Interventionists who deliver FIOC include both social workers and paraprofessionals (supervised by a licensed clinician). We learned early on that not all interventionists were themselves fully sold on messages that they were giving parents about the importance of fathers to children, for many had themselves grown up without involved fathers or knew others doing well without having had an active father or father figure. Hence new interventionists learning the curriculum partake in the same experiential exercises parents do to raise consciousness about the role and meaning of fathers to children. This step may be indispensable for interventionists who will be working with both fathers and mothers to promote solidarity. Fathers are able to perceive the lack of sincerity when professionals are saying one thing about the essential nature of father involvement and coparenting but actually believing another. Convincing parents of fathers’ importance must begin with convincing the interventionist. The FIOC approach makes use of both male and female interventionists, working together. It also begins the interventionist–parent relationship in a unique fashion. Rather than commencing with the joint mother–father work immediately, one or more individual informal mentorship sessions are scheduled between male interventionists and fathers and between female interventionists and mothers. Only once parents indicate that they feel ready to begin do the four individuals meet. And when they do, they break bread together. Although an unfamiliar occurrence in customary social work practice, the meal introduces an effective means of building relationship trust and rapport. At the outset, parents and interventionists all commit to creating a safe environment for the work, and all sign commitment statements to this effect. The statements are not legally binding documents, but rather symbolize each person’s commitment to the child and family. Sessions then proceed through the three stages of Focused Coparenting Consultation—consciousness-raising, skill building, and guided enactments. Interventionists actively intervene when parents get stuck but respect parents’ needs to go at their own pace and individually tailor exercises to meet their own preferences and communication styles. The genuine respect for fathers and the dogged determination to see parents through were key elements in the first test of the FIOC intervention. Perhaps as a result, that field test found tenacity and commitment among the parents. Specifically, no parents who completed a first FIOC session dropped from the intervention, and one in four completers referred friends or family members to participate in the project. Acceptability surveys completed by independent assessors after the intervention established consistently high levels of maternal and paternal buy-in and satisfaction. Earlier, we commented on implicit and explicit messages that parents have received from existing programming about their own worth as parents and about the halfhearted reception that fathers receive if they do express interest in being part of family life. By parents’ own accounts, the FIOC model was unlike any support service they had previously experienced. What ultimately mattered to parents? Key components included the welcoming and honoring of both father and mother, the interventionists’ stance of accepting and meeting both of the parents “where they were,” the benign acceptance and offering of help to work through resistance, the opportunity to talk openly and honestly with interventionists knowledgeable and savvy about the community in which the intervention was being delivered, and above all the unwavering message that there was no challenge too great to be surmounted if there was shared resolve and goodwill to stick it out for the child. Collectively, these elements cut through many common challenges that professionals face in engaging fathers in any kind of sustained way in family-centered programming with their child’s mother. As a result, consistent with findings from Marriage and Relationship Enhancement programs that have largely served committed couples (see McHale et al., 2012, for a review), this coparenting-only focused intervention yielded statistically significant and meaningful gains in parents’ communication skills (decreases in verbal aggressiveness and coercion; greater support and solidarity observed during videotaped problem-solving discussions; McHale, Salman-Engin, & Coovert, 2015). Most important, beyond communicating respectfully about the child, all FIOC completers were also actually coparenting together at three months postpartum, regardless of whether the parents were now living together or not. Moreover, systematic observations of triangular interactions between father, mother, and baby revealed discernable signs of strength and comradery. They also revealed affinity for and knowing of the father by the three-month-old, attunement and reciprocity by the father to his baby’s overtures, and support of father–baby interactions by mothers (McHale & Coates, 2014). The family dynamic was a cohesive one. A randomized controlled trial to test the efficacy and longitudinal impact of the intervention—not only on family but on infant mental health outcomes—is now underway in St. Petersburg, Florida, with a new cohort of unmarried, first-time African American parents. In summary, although FIOC is an intervention guided by a manualized curriculum, it is the manner in which families are first approached, and subsequently engaged, that epitomizes the paradigmatic shift in social work practice we advocate in this article. The very frame for the FIOC intervention is triangular in nature. Both coparents are presumed to be essential to the work, and messaging is consistent from the beginning that fathers need to be involved. Both parents not only hear this message, but they hear it lovingly, and relentlessly. When challenges to the work occur, and they inevitably do, interventionists stand ready to allow parents to work through the impediments. This sometimes necessitates a temporary moratorium on sessions until one or both parents can resolve substantive issues. Appropriate referrals are made when individual or couples counseling, anger management, or other services will benefit the family—the focus and thrust of the FIOC work stays on the child and on successfully resolving issues that prevent the adults from coparenting effectively together. The improvements in coparenting resolve and resilience generated in the FIOC pilot provide promising evidence of what is possible given a change in attitude and in practice. Challenges and Recommendations As we have reiterated, approaches that welcome fathers to traditional mother-only service systems have not always been well-received by fathers. Men as a rule are less apt to seek help than women, fathers rightfully view many community programs as support services intended for mothers, and mothers who themselves appreciate having individual supports do not always disabuse fathers of their viewpoints. One analysis indicates that satisfaction or gains by fathers involved in existing parenting services are less than those realized by mothers (Lundahl, Tollefson, Risser, & Lovejoy, 2008). McHale and Phares (2015) have provided a detailed analysis of ways that services can transform to appreciate the sensibilities of men and fathers—shoehorning men into service delivery programs designed by and for women will not accomplish desired aims. Rather, a stance that an intervention cannot begin without both mother and father being present signals from the start that both are of equal importance and value to the child and that neither is considered “less than.” Fathers may also be troubled by unemployment, insufficient income, child support demands, and lack of housing (Goldberg & Carlson, 2015)—in part because culturally they are expected to shoulder responsibility for resolving these issues (Gadsden, Davis, & Johnson, 2015). Hence practical connections with community supports must be researched and available to support the family’s sustenance needs (Dion, Zaveri, & Holcomb, 2015). Equally, however, we caution that social workers’ maintenance of stereotypical views of men as benefactors first and fathers second will remain damaging, reinforce the 2 + 1 status quo, and stifle efforts made to strengthen the triangle and promote coparenting. Moreover, purposeful efforts to respect and involve fathers (and mothers) as coparents even as they are going through mental health or substance abuse treatment or face incarceration, although challenging, can pay off if the structure guiding the work remains a robust coparenting frame (Loper, Phillips, Nichols, & Dallaire, 2014). Enhancements in technology (FaceTime, Skype), improved sensitization among leadership in many institutions and facilities to the needs of children to stay connected to coparents, and innovations in connecting parents to children through personal visits or through distance methods and media are now raising the possibilities that the strains young children experience during prolonged separations from parents can be lessened. But practitioner efforts to help at-home caregivers connect children to coparents while they must be separated have enduring effects only to the extent that due attention is also given to solidifying coparenting during the separation at the same time as attention is given to parenting. Efforts to strengthen family functioning must obviously never give up on or replace parent–child programming, even as they embrace a coparenting frame. Many fathers who do successfully connect and team with their children’s mothers also need interventions that strengthen their own parenting efficacy, skills, and confidence (Dubowitz, Black, Kerr, Starr, & Harrington, 2000). And discounting the mental health needs of young and new fathers can have serious ramifications; interviews with fathers who participated in the national Fragile Families and Child Well-Being study revealed that depressed fathers were approximately three times more likely to report having spanked their one-year-old infant in the last month compared with nondepressed fathers (Davis, Davis, Freed, & Clark, 2011). Maternal postpartum depression is now on most agencies’ radar; paternal depression, far less so. Although fathers are sometimes affronted by the suggestion that they possess less parenting knowledge and skill than mothers (O’Donnell, Johnson, D’Aunno, & Thornton, 2005), fathers who took part in the FIOC intervention who had grown up without fathers themselves were open to furthering their own competencies as fathers after completing the program. Trust had been built, and with that trust came self-reflection and greater openness. Unfortunately, as it currently stands, social services programs, child care programs, mental health services, and other community agencies may not themselves stand fully ready to embrace such fathers’ motivations or welcome them as equal and contributing partners in their child’s health, education, and welfare. Although recommendations in this article speak to adjustments in the individual practices of social workers themselves, a parallel and broader community dialogue about supporting the triangle also needs to be kept front and center. Certainly, the conversation must address the need for more father-specific resources and interventions. The need for such resources is an acute one (Saleh, 2013) as men favor activity-based services designed specifically for fathers that afford them opportunities to interact with their children, preferring skills-based exercises and approaches (Maxwell, Scourfield, Holland, Featherstone, & Lee, 2012). But as we have emphasized, community parenting services for fathers—although as essential as services for mothers—are not the same as supports for coparenting. Hence in a closing section, we briefly describe some of the community-level changes that can nurture these broader systems adjustments, changes that will be necessary if the positive gains realized through programming such as that described in this article are to have lasting benefit for the families and children. Embedding Coparenting Initiatives in a Broader Community Conversation In Pinellas County, Florida, at the University of South Florida (USF) St. Petersburg’s Family Study Center, we have been working assiduously for over a decade to broadly educate community partners about coparenting and how coparenting work differs from the 2 + 1 models that guide most agencies’ policies and operations. Although the going has sometimes been slow, we have seen community-level change in openness to the future paradigm shift. Individual practitioners’ and agencies’ definitions of coparenting sometimes do tend to be idiosyncratic, and coparenting efforts are implemented in some programs, but not others. Some of the community-level messaging that has helped move the needle are described in the following sections. Messaging about Coparenting at One-Off and Recurring Trainings and Consortia Family Study Center efforts have involved over 100 in-services, agency trainings, day-long conferences, Grand Rounds, and other consultations for local agencies, running the full gamut from Healthy Start to Head Start to child welfare to pediatric, nursing, and medical consortia, and all other entities serving children and families. For the better part of 10 years, we have chaired the county’s Early Childhood Mental Health Committee, which meets monthly and includes representatives from all infant- and toddler-serving agencies in the county. Family Study Center staff message about coparenting at community gatherings, neighborhood events, task force meetings, and legislative forums. Yet even with all this coordinated effort, significant changes often come from lone one-on-one conversations at opportune times; a Healthy Start–sponsored Community Baby Shower in St. Petersburg, which had historically drawn only mothers, transformed dramatically in October 2015 when the agency staff who invited the babies’ mothers began asking them to bring fathers along to the shower. Nearly 40% of attending women had the baby’s father with them at the event—which also had a dedicated table and giveaways for the men in honoring their attendance. We reiterate here that father friendliness is not the same as development and provision of comprehensive coparenting support, but it is a prerequisite. A Community-Based Infant–Family Mental Health Center Although preventive services abound in Pinellas County, more intensive services for families already upset by toxic stress and trauma have been few and far between and seldom accessible to lower-income families. Moreover, when such services have been offered on a limited scale basis, they have tended to exclude fathers, again guided by the premise that the toxic stress or trauma experienced by the child may have been at the father’s hands. Although data do not support the presumption of widespread paternal threat, it is not the policy or approach of most infant mental health programming to involve the entire family in case formulation, planning, and intervention. A new Infant–Family Center that we established in partnership with Johns Hopkins All Children’s Hospital offers families in the community a first-of-its-kind service including coparenting-centered consultation and therapeutic support to all families as a matter of standard practice (except when there are imminent safety concerns that would preclude engagement of one of the parents). Noncoresidential and residential fathers are engaged; other live-in coparents involved with the care and upbringing of the child are also explicitly sought for intake assessments and included in case planning (see McHale & Phares, 2015). Although work with abusive men is beyond the scope of services that the center’s hospital-based outpatient clinic is positioned to provide, partnerships with other community agencies allow abusive behavior to be addressed with focus on the men’s role as fathers, a powerful motivator to change (Featherstone & Peckover, 2007; Fox, Sayers, & Bruce, 2001; Rivett, 2010; Stover, 2015). Batterer programs designed specifically for men who are fathers (Crooks, Scott, Francis, Kelly, & Reid, 2006; Pennell, 2012; Scott & Lishak, 2012) are promising intervention services (Bancroft & Silverman, 2002; Featherstone, Rivett, & Scourfield, 2007), addressing men’s control-based parenting, sense of entitlement, and failures of empathy for their children (Scott, Francis, Crooks, Paddon, & Wolfe, 2006). As Zanoni and colleagues (2013) eloquently argued, engaging with domestically violent fathers and holding them fully culpable for their behavior and its effect on their children will provide better outcomes for children and mothers, and can potentially benefit the abusive fathers themselves (Douglas & Walsh, 2010; Featherstone & Peckover, 2007; Fox et al., 2001). Conversely, avoiding biological fathers who are perpetrators of IPV places children at sizable risk, for fathers most often remain an ongoing existence in their lives. In one study by Israel and Stover (2009), 68% of women who had been victims of domestic violence reported an attachment between their child and the aggressive father. Other work found higher levels of depression and anxiety among preschool-age children who had limited or no contact with their previously violent fathers than among preschoolers who had frequent (at least weekly) visits (Stover et al., 2003). Of particular note, preschool-age children, especially boys, who saw their fathers more regularly had fewer negative representations of their mothers (Stover, Van Horn, & Lieberman, 2006). These data highlight the reality that perpetrator fathers often continue their presence within the family following domestic violence and play an important role not just in parenting their children, but in supporting or undermining coparenting even when not physically associated with children’s mothers. To adequately protect children, it is crucial to identify and engage all relevant coparenting and father figures in family interventions (Cavanagh, Dobash, & Dobash, 2007; Klevens & Leeb, 2010). In most cases an Infant–Family Center will stand ready to meet this challenge; when it is not, strong partnerships with collaborating community agencies will help to serve children’s and families best interests by seeking family system–level, and not just mother–infant, recovery and transformation. Intensive Training in Infant–Family Mental Health In a survey examining the educational preparation that Canadian undergraduate students receive for work with fathers, specifically the fathering content found in the required readings of child welfare, family practice and family therapy, human development and human behavior, Aboriginal studies, and child and youth social work courses (Walmsley, Strega, Brown, Dominelli, & Callahan, 2009), explicit content on fathers and fathering was found to be minimal. Perhaps not surprising, frontline personnel acknowledge a need for training in how to engage fathers and address father-specific issues—although few relevant training curricula exist (Huebner, Werner, Hartwig, White, & Shewa, 2008). In 2013, USF St. Petersburg opened its doors to a first-of-its-kind year-long infant–family mental health graduate certificate program, offered fully online. Although several excellent infant mental health certificate programs, notably in Minnesota and in Boston, also exist, the USF St. Petersburg program is unique in being guided fully by a coparenting framework from beginning to end. Case conceptualization, assessment, intervention, and work within systems are all approached from a coparenting frame. With a new Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (Zeanah et al., 2017) now calling on those who work with infants and young children to provide an assessment of the family’s coparenting system (on Axis II) as part of routine diagnostic practice, high quality and state-of-the-field training in coparenting and family systems frameworks are needed more than ever. The time has finally arrived to revolutionize training of all clinicians and professionals to provide each practitioner with more adequate skills to carry out the work of strengthening coparenting in diverse family systems. Concluding Comments As attested to by an upsurge of topical articles calling for the transformation of clinical practice to involve fathers in clinical case formulations and intervention in all work with young children, we are approaching a new tipping point wherein a long-sought paradigmatic shift may soon occur. But to transcend current social work practices that already invite fathers to partake of services if they are readily reachable and accessible, but move methodically forward without them if children’s mothers (but not they) are promptly available, concerted effort will be needed. To transform from a dyadic to a family systems model, where all social work efforts seek to integrate fathers and other coparenting adults in standard care, comprehensive examination of standing policies, practices, and procedures is called for. In the overwhelming majority of cases, the embracing of a triangular framework that treats mothers and fathers as coparenting partners and allies will better serve children. The questioning of existing practices that systematically exclude fathers from the mother–father–child triangle must come from every agency, institutional and organizational leader, male and female alike, or the transformation needed will never come to fruition. We hope that this article provides a road map that might help this change to finally begin taking hold. James P. McHale, PhD, is director and Lisa S. Negrini, LCSW, is assistant director, Family Study Center, University of South Florida St. Petersburg. Address correspondence to James P. 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2018 Social Work Research

doi: 10.1093/swr/svy002

Correction: The article “Symptom Profiles of Major Depressive Disorder and Their Correlates among a Nationally Representative Sample” (September 2017, Vol. 41, No. 3, pp. 145–153, doi:10.1093/swr/svx013) should have appeared with Claudette L. Grinnell-Davis listed as the fourth author and with the following information: Claudette L. Grinnell-Davis, PhD, MSW, MS, MTS, is assistant professor, Grace Abbott School of Social Work, University of Nebraska Omaha. © 2018 National Association of Social Workers.

Hong, Philip Young P;Choi, Sangmi;Key, Whitney

2018 Social Work Research

doi: 10.1093/swr/svx025

Abstract The purpose of this study was, first, to validate the factor structure of psychological self-sufficiency (PSS) and, second, to investigate the extent to which PSS affects economic self-sufficiency (ESS) among low-income job seekers. PSS is conceptualized as a transformative process-driven psychological capital that comprises employment hope and perceived employment barriers. Using a sample of 802 low-income job seekers from two different local job training programs in Chicago, a multisample confirmatory factor analysis tested the factor structure of PSS, and a structural equation modeling analysis was conducted to test the hypothesized pathways to ESS, examining employment hope and perceived employment barriers individually and taking the difference score between the two. Findings revealed that PSS significantly contributes to ESS. Workforce development practitioners need to focus on clients’ PSS when working with them to achieve ESS. Benchmarking PSS, providing adequate supportive services, and engaging employers are warranted as ways to build a system that generates successful employment and retention paths and outcomes. Self-sufficiency is regarded in policy, research, and practice primarily as economic self-sufficiency (ESS) (Hong, Choi, & Polanin, 2014; Hong, Hodge, & Choi, 2015). ESS is a unifying federal and state social policy goal that is promulgated by politicians and government officials, evaluated by policy analysts and social scientists, and benchmarked by local agency administrators and practitioners. However, there is no single definition when it comes to operationalizing ESS (Hawkins, 2005). The array of definitions ranges anywhere from having financial independence or economic security to having an annual income above the 200% federal poverty line, being able to pay 100% of necessary bills without any help from government or other people, achieving one year job retention, leaving poverty by way of steady employment, and attaining job stability (Cain, 1998; Cancian, 2001; Caputo, 1997; Fleischer, 2001; Johnson & Corcoran, 2003). However, the general philosophical agreement in the literature is that ESS is a labor market outcome conditioned by adequate earned income that does not require any external financial support (Hong, Sheriff, & Naeger, 2009). In practice, this ESS outcome includes a must condition of being independent from any government subsidies and financial assistance (Bratt & Keyes, 1997; Caputo, 1997; Mulroy & Lauber, 2004; Taylor & Barusch, 2004) and hopefully a desirable condition of gaining long-term employment above a certain wage rate (Cancian, 2001; Johnson & Corcoran, 2003). The must condition incurs government enforcement to end public assistance if one overstays his or her welcome beyond five years and exchanges welfare checks with earned income through employment—an active measure. The desirable condition on the other hand is left at the mercy of the market forces for matching the labor supply and demand and for one to sustain the job once attained—a passive measure. In this case, the ESS outcome on the aggregate can only be as successful as the maximum number of quality jobs available to absorb low-income job seekers in the labor market, thereby giving community-based workforce development agencies and programs little power vis-à-vis the market when it comes to controlling the level of success. Particularly when funders primarily emphasize the bottom line ESS outcome for evaluation, agencies and programs are forced to use short-term market-driven outcome measures—that is, welfare exit and employment placement outcomes—as primary benchmarks of ESS (Harvey, Hong, & Kwaza, 2010). They face a catch-22 situation—fail if they do not report on the market-prescribed ESS benchmarks or face funding cuts when the market does not perform to bring about a good return on investment on these success metrics (Hong, 2013, 2016). Therefore, by ignoring measures that capture what are key process variables inside the “black box” as referred to by Weigensberg et al. (2012)—mediators of direct input and output within the agency practice context—agencies are disempowered to only rely on market performance to demonstrate success (Harvey et al., 2010; Hong, 2013). Comprehensive supportive services offered by agencies as input that tap into the process of empowering low-income job seekers to become job-ready are typically ignored and not included as intermediate measures in current practice of outcome-based evaluations (Hong, 2013; Hong, Choi, & Polanin, 2014; Hong, Hodge, & Choi, 2015). This signals a new era of market dependency in the face of a dissipating social contract between individuals and the state—government inaction to publicly tackle the deep-seated barriers that exacerbate welfare dependency by resorting to private market-based solutions (Hong & Crawley, 2015). Social workers within workforce development agencies are in a position to lead a win–win solution to this self-sufficiency dilemma by recuperating the importance of mission-based work that provides added value to agency performance and sustainability. To complement ESS as an outcome measure, psychological self-sufficiency (PSS) represents the human-centered ecology of work perspective that focuses on the bottom-up process of “developing the workforce” from the perspective of the worker by honoring low-income job seekers’ empowerment, strength, self-determination, motivation, and growth (Daugherty & Barber, 2001). PSS is the answer to the question of how one becomes economically self-sufficient—by arduously and meaningfully trotting the path, by engaging in a forward process, and by switching from perceived barriers to employment hope (Hong, 2013). Therefore, the purpose of this study is twofold. First, validate PSS as the process element of self-sufficiency, which was conceptualized and operationalized by Hong and his colleagues to include perceived employment barriers and employment hope (see Figure 1) (Hong, 2013; Hong, Choi, & Polanin, 2014; Hong, Polanin, Key, & Choi, 2014; Hong, Polanin, & Pigott, 2012). Second, examine the extent to which the PSS process contributes to ESS outcome. By achieving these aims, the study will help enhance workforce development program evaluation efforts by assessing client progress and documenting aggregate outcomes in programs. Figure 1: View largeDownload slide Conceptual Model of Psychological Self-Sufficiency and Economic Self-Sufficiency Figure 1: View largeDownload slide Conceptual Model of Psychological Self-Sufficiency and Economic Self-Sufficiency Literature Review Giving peripheral attention to the psychological dimension of self-sufficiency forms a gap in policy implementation (Hong & Crawley, 2015). Although lawmakers argued that psychological barriers were the root cause of welfare dependency, leading up to welfare reform, no deliberate policy investment has been made to reduce psychological barriers as part of a comprehensive action plan to promote self-sufficiency (Cooney, 2006; Harvey et al., 2010). In research, only a handful of authors have considered the multidimensionality of self-sufficiency by highlighting its psychological dimension—for example, personal and family sustainability as self-sufficiency in terms of achieving economic, physical, psychological, and social well-being (Hawkins, 2005); self-sufficiency as a personal process of acquiring money and resources, psychological power, and skills (Gowdy & Pearlmutter, 1994); and PSS as driven by employment hope anchoring on one’s perceived employment barriers (Hong, 2013, 2016). Research has found that welfare-to-work policies are most effective when comprehensively approached with supportive services that encourage job retention by focusing on both PSS and ESS (Kazis & Miller, 2001). Previous conceptualizations of the psychological dimension of self-sufficiency have focused on human agency—individual actions being determined not only by structure, but also by one’s decisions, free will, and choices being enacted onto the structure (Bandura, 2001, 2006). Orme-Johnson (1988) defined PSS as “the ability to maintain a confident, balanced, happy, productive frame of mind capable of providing for one’s own needs without dependence on others” (p. 188). Mellor (2009) maintained that PSS is positive self-appraisals made about one’s abilities, talents, skills, and efficacy to provide for oneself. PSS is philosophically supported by the capabilities approach (Nussbaum, 1988, 1992; Sen, 1993, 1999) and the social work practice model of a strengths-based approach (Saleebey, 2013) and empowerment (Gutiérrez, 1994, 1995). Informed by positive psychology, such goal-directed motivational concepts as self-efficacy, positive selves, self-regulation, positive emotions, positive psychological capital, grit, growth mind-set, and so on have primarily focused on the positive drives (Hong, 2016). Using a grounded theory approach, however, qualitative findings from local focus groups of low-income job seekers suggested that PSS is “a dynamic process of overcoming perceived employment barriers along the goal-oriented path to individualized success and developing employment hope within the new realities of career goals” (Hong, 2013; Hong, Polanin, et al., 2014, p. 693). To maintain this balance between the negative and positive on the path to ESS, it is posited that one has to recognize employment barriers as such and be able to transform this to a motivational outlook and put effort toward realistic goals as one becomes an empowered worker (Hong, 2013). As illustrated in Figure 1, the bottom-up theory hypothesizes that PSS as a necessary condition positively affects ESS (Hong, 2013; Hong, Stokar, & Choi, 2016). As such, this process of developing PSS can be theoretically supported by social cognitive career theory (SCCT) and the theory of mental contrasting. SCCT hypothesizes that taking into consideration the environmental obstacles to achieve targeted goals, self-efficacy and outcome expectations contribute positively to the development of vocational hope (Brown, Lamp, Telander, & Hacker, 2013). Mental contrasting combines the negative and positive assessments, which involves concurrently focusing on a positive outcome and the obstacles that block the path to the outcome (Duckworth, Grant, Loew, Oettingen, & Gollwitzer, 2011). By engaging in the process of contrasting the barrier-filled reality with the desired future outcome, one generates positive energy toward goals (Oettingen, 2000; Oettingen, Pak, & Schnetter, 2001). Making a strong association between future and reality signals the need to overcome the obstacles to attain the desired future. Paralleling these theories, PSS comprises the negative perceived employment barriers and the positive employment hope (Hong, 2013). Perceiving employment barriers first as barriers is the starting point in this psychological process to transform the negative self-assessment into a positive one and channel this toward the desired future economic outcome. PSS is not a byproduct of ESS but a centerpiece to lasting economic success. Perceived employment barriers coincide with dwelling—reflecting on the current reality that obstructs the path to one’s desired future, and employment hope closely matches with indulging—imagining a desired future and mentally elaborating its benefits. Dwelling and indulging individually by themselves do not necessitate action toward goals, but mental contrasting generates energy toward goals. Previous research has validated the two measures that comprise PSS—the Perceived Employment Barrier Scale (PEBS) (Hong, Polanin, et al., 2014; Hong, Song, Choi, & Park, 2015) and the Employment Hope Scale (EHS) (Hong, Choi, & Polanin, 2014; Hong et al., 2012; Hong, Song, Choi, & Park, 2016)—and has found them to be valid across multiple settings within the United States and South Korea. Perceived employment barriers are conceptually clustered into five dimensions—physical and mental health, labor market exclusion, child care, human capital, and soft skills—and were found to be negatively associated with employment hope (Hong, Polanin, et al., 2014; Hong, Song, Choi, & Park, 2015). Employment hope comprises psychological empowerment, futuristic self-motivation, utilization of skills and resources, and goal-orientation and was found to be positively associated with ESS (Hong, Choi, & Polanin, 2014; Hong, Song, Choi, & Park, 2016). The gap in the literature that this study addresses is conceptualizing PSS as having not only the positive attribute, but also the negative internal factor—how perceived employment barriers and employment hope interact—and quantitatively validating the concept of PSS. The study also seeks to answer how the two key components of PSS contribute to ESS. Low PSS would make it difficult for an individual to navigate the labor market and find employment, which then would make it more difficult to achieve ESS. For example, someone with low PSS may find it difficult to follow through with myriad tasks associated with identifying job leads, requesting information, updating a resume, and so on. In this regard, as depicted in Figure 1, it is hypothesized that perceived barriers negatively affect employment hope (hypothesis 1) and employment hope positively contributes to ESS (hypothesis 2). Also, it is hypothesized that employment hope mediates the relationship between perceived employment barriers and ESS (hypothesis 3). Method Sample and Data Collection This study used two independent samples from local community-based agencies in Chicago. The first sample was collected from participants of a social services agency in the West Haven community of Chicago between October 2008 and March 2009 (sample 1). West Haven is a neighborhood facing the side effects of transformed high-rise public housing; the clients of this agency have received assistance in the areas of job preparation, life skills training, financial literacy, public benefits, and other support services. The second sample was collected from participants in a nationally recognized job training program in Chicago (sample 2) between June 2009 and August 2010. The agency provides services in the areas of intensive job readiness training, job search, placement, and yearlong retention services. The size of each sample is similar (sample 1 = 390, sample 2 = 411), and an equivalent proportion of the participants were receiving Temporary Assistance for Needy Families benefits (sample 1 = 42.3%; sample 2 = 41.4%). However, other demographic characteristics of the two independent samples were slightly different. Sample 1 had a higher percentage of women (62.4%) than sample 2 (54.3%); the average age for sample 1 was lower (40.5) than that for sample 2 (42.09). Sample 1 was mostly African American (97.9%), with 24.9% having less than a high school education; sample 2 was a bit more diverse (87.2%), and 14.7% did not have a high school degree. Sample 1 was less employed (20.3%), with a lower percentage having more than 10 years of job training experience (41.7%) compared with sample 2’s employment rate (28.4%) and job training experience (57.9%). In addition, there was a difference in the average individual income across the two samples (μ: sample 1 = $14,595, sample 2 = $8,325). Measures Hong et al. (2009) originally conceptualized the EHS with a 24-item six-factor structure derived from qualitative findings. This measure uses a Likert-type scale ranging from 0 = strongly disagree to 10 = strongly agree. Each factor was constructed to have four items. An exploratory factor analysis (EFA) procedure decreased the 24-item six-factor model into a 14-item two-factor model; four items loaded on the first factor labeled psychological empowerment, and 10 items loaded on the second factor of goal-orientation pathways (Hong et al., 2012). This 14-item two-factor model preliminarily identified using an EFA was then validated as a 14-item four-factor model using a confirmatory factor analysis (CFA) (Hong & Choi, 2013) and subsequently a multisample CFA (Hong, Choi, & Polanin, 2014). The four factors are (1) psychological empowerment (four items), (2) futuristic self-motivation (two items), (3) utilization of resources and skills (four items), and (4) goal-orientation (four items). EHS was cross-nationally revalidated using a national sample of Self-Sufficiency Program (SSP) participants in South Korea (Hong, Song, Choi, & Park, 2016). PEBS was developed by Hong, Polanin, et al. (2014) as an empowerment-based measure capturing the level of barriers to securing a job as perceived by low-income job seekers. The study suggested a five-factor 20-item PEBS generally covering the range of individual, family, human capital, and structural factors—(1) physical and mental health (four items), (2) labor market exclusion (three items), (3) child care (three items), (4) human capital (five items), and (5) personal balance and soft skills (five items). Respondents were asked to rank each employment-related barrier item by circling a number on a five-point Likert-type scale ranging from 1 = not a barrier to 5 = strong barrier, according to how the item affects one’s securing a job. PEBS was also cross-culturally validated in the South Korean context from a nationally representative sample of SSP participants (Hong, Song, Choi, & Park, 2015). ESS was measured using the Women’s Employment Network (WEN) Economic Self-Sufficiency Scale (Gowdy & Pearlmutter, 1993) to capture the multidimensionality of ESS. This scale measures the self-assessed level of economic and financial independence with four factors: (1) autonomy and self-determination, (2) financial security and responsibility, (3) family and self well-being, and (4) basic assets for community living. Analysis Procedure The analysis goals of this study were twofold: (1) to validate PSS as a two-factor—EHS and PEBS—measurement model across two independent samples and (2) to investigate the pathways among PEBS, EHS, and ESS. To achieve the first goal, we conducted a CFA on PSS as a higher order latent measure that includes EHS and PEBS. We compared the proposed two-factor PSS with the one-factor model, following P. Kline’s (1994) suggestion. While the one-factor baseline model represents a global hypothesis where all of the items form one factor, the proposed second model is composed of EHS and PEBS based on the theoretical framework. These two nested models are compared using appropriate model-fit statistics. Traditional chi-square model-fit statistics were not considered due to the large sample size (Meade, Johnson, & Braddy, 2008). Instead, several model-fit indices were used to reduce the plausibility of chance fit and to increase the robustness of the conclusions—that is, the root mean square error of approximation (RMSEA) (Steiger & Lind, 1980), the comparative fit index (CFI) (Bentler, 1990), the non-normed fit index (NNFI) (Hu & Bentler, 1999), and Akaike information criterion (AIC) (Akaike, 1987). The values of CFI and NNFI above .90 are considered a good fit (Bentler & Bonett, 1980; R. B. Kline, 2011) and conservatively above .95 are an excellent fit (Hu & Bentler, 1999). RMSEA values up to .08 indicate an acceptable fit (R. B. Kline, 2011) and up to .06 a close fit (Hu & Bentler, 1999). Regarding the AIC, the model with the lowest value is preferred. To achieve the second analysis goal after conducting construct validation of PSS across two samples, the authors proceeded to test the theoretical model using structural equation modeling (SEM). Following Anderson and Gerbing’s (1988) procedures, a CFA was first conducted to assess the proposed dimensionality through the fit of the individual items to their respective scales. Next, the hypothesized model was analyzed using SEM to indicate the pathways from perceived employment barriers to ESS mediated by employment hope. AMOS (Version 7.0) (Arbuckle, 2006) was used to perform CFA, multigroup CFA, and SEM, using a maximum likelihood estimation method. Full-information maximum likelihood was used to handle missing data. Finally, we used the Sobel (1982) test to examine the indirect effect of perceived employment barriers on ESS. Results Descriptive Statistics The descriptive and bivariate statistics for the latent construct of PSS—EHS and PEBS—and ESS are presented in Table 1. The correlation between employment hope and perceived employment barriers was negative and the correlation between employment hope and ESS was positive as expected. All the variables were found to meet the assumptions for normality with the values of skewness between −1.261 and 1.68 and the values of kurtosis between −0.745 and 2.808. The values of skewness within −2 and +2 (Field, 2009; Gravetter & Wallnau, 2014; Trochim & Donnelly, 2006) and the values of kurtosis within −3 and +3 (Byrne, 2010) are considered to be normally distributed (see Table 1). Table 1: Descriptive and Bivariate Statistics for the Study Variables (Sample 1/Sample 2) Variable  M (SD)  Range  Skewness  Kurtosis  1  2  3  EHS  7.63 (2.43)/8.79 (1.36)  0.00–10.00  −1.261/−1.688  0.949/2.808  (.968/.934)  −.131**  .146**  PEBS  2.30 (1.03)/1.92 (0.84)  1.00–5.00  0.775/1.400  −0.216/1.778  −.143**  (.939/.924)  .019  ESS  2.84 (1.02)/2.44 (0.97)  1.00–5.00  0.092/0.295  −0.705/−0.745  .285**  .046  (.915/.914)  Multivariate kurtosis (critical ratio)        0.392 (0.707)/1.032 (1.910)        Variable  M (SD)  Range  Skewness  Kurtosis  1  2  3  EHS  7.63 (2.43)/8.79 (1.36)  0.00–10.00  −1.261/−1.688  0.949/2.808  (.968/.934)  −.131**  .146**  PEBS  2.30 (1.03)/1.92 (0.84)  1.00–5.00  0.775/1.400  −0.216/1.778  −.143**  (.939/.924)  .019  ESS  2.84 (1.02)/2.44 (0.97)  1.00–5.00  0.092/0.295  −0.705/−0.745  .285**  .046  (.915/.914)  Multivariate kurtosis (critical ratio)        0.392 (0.707)/1.032 (1.910)        Notes: Cronbach’s alpha coefficients are reported in parentheses on the diagonal. Correlations: lower diagonal = sample 1, upper diagonal = sample 2. In multivariate kurtosis, values of critical ratio are presented in parentheses. EHS = Employment Hope Scale, PEBS = Perceived Employment Barrier Scale, ESS = economic self-sufficiency. **p < .01. Because SEM requires that the multivariate normality assumption is met, the study proceeded to test the assumption using multivariate kurtosis (Mardia, 1970, 1974). As presented in Table 1, evidence of multivariate normality was found with multivariate kurtosis values (critical ratio [CR]) of 0.392 (0.707) in sample 1 and 1.032 (1.910) in sample 2. CR values of 1.96 or less indicate that multivariate kurtosis is not significant; therefore multivariate normality can be assumed. Validation of the PSS Scale This study examined whether one common model of PSS fits the data well across two samples using a CFA. Two alternative models were compared in each group: A one-factor model and a two-factor model. The one-factor model is a baseline model where all nine subfactors—four for EHS and five for PEBS—are loaded onto one general factor. The second one is a two-factor model in which four factors are loaded onto one factor (EHS) and the other five factors are loaded onto the other factor (PEBS). Given the well-known problem of the chi-square test being sensitive to sample size (Anderson & Gerbing, 1988; Marsh & Grayson, 1990; Steenkamp & Baumgartner, 1998), model fit was evaluated using several fit indices to quantify the degree of fit to supplement the chi-square test. The model comparison is presented in Table 2. Based on the fit indices of CFI, NNFI, RMSEA, and AIC, the two-factor model reported significantly better fits than the baseline model across two samples, indicating the superiority of the two-factor model. The factor loadings for the PSS model are presented in Figure 2. Table 2: The Result of CFA on PSS across Two Samples: Two-Factor Model versus One-Factor Baseline Model Data  PSS Model  χ2(df)  RMSEA (90% CI)  NNFI  CFI  AIC  Sample 1  One-factor  2,120.914(519)  .089 (.085–.093)  .833  .854  2,340.914  Two-factor  1,376.640(518)  .065 (.061–.069)  .910  .922  1,598.640  Sample 2  One-factor  1,952.439(519)  .082 (.078–.086)  .809  .833  2,172.439  Two-factor  1,315.242(518)  .061 (.057–.065)  .894  .907  1,537.242  Data  PSS Model  χ2(df)  RMSEA (90% CI)  NNFI  CFI  AIC  Sample 1  One-factor  2,120.914(519)  .089 (.085–.093)  .833  .854  2,340.914  Two-factor  1,376.640(518)  .065 (.061–.069)  .910  .922  1,598.640  Sample 2  One-factor  1,952.439(519)  .082 (.078–.086)  .809  .833  2,172.439  Two-factor  1,315.242(518)  .061 (.057–.065)  .894  .907  1,537.242  Notes: CFA = confirmatory factor analysis, PSS = psychological self-sufficiency, RMSEA = root mean square error of approximation, CI = confidence interval, NNFI = non-normed fit index, CFI = comparative fit index, AIC = Akaike information criterion. Figure 2: View largeDownload slide Standardized Factor Loadings for the Psychological Self-Sufficiency Model Notes: EH = employment hope, PEB = perceived employment barriers. Figure 2: View largeDownload slide Standardized Factor Loadings for the Psychological Self-Sufficiency Model Notes: EH = employment hope, PEB = perceived employment barriers. Theoretical Model from PSS to ESS Given the verified structure of PSS across the two samples, the study tested the theoretical model with the path from PSS to ESS. In keeping with PSS representing the process of mental contrasting of employment barriers to hope, it was hypothesized that perceived employment barriers affect ESS mediated by employment hope. Prior to testing the hypothesized pathways from perceived employment barriers to employment hope and from employment hope to ESS, the study tested the individual item reliability to assess the dimensionality of the proposed model. As a result of CFA, the measurement model fits the data reasonably well, χ2(654, N = 801) = 2132.233, p = .000, CFI = .935, NNFI = .926, RMSEA [95% confidence interval (CI)] = .053 [.051–.056]. Baring verified latent factor structure of the measurement model, the proposed model was tested using SEM. As reported in Table 3, all fit indices indicate that the hypothesized model has a good fit to the data, χ2(655, N = 801) = 2141.453, p = .000, CFI = .935, NNFI = .926, RMSEA [95% CI] = .053 [.051–.056]. Perceived employment barriers are negatively associated with EHS, supporting hypothesis 1; employment hope is positively related to ESS, supporting hypothesis 2 (see Figure 3). To estimate the indirect effect of perceived employment barriers (that is, the mediation effect of EHS), a Sobel test was used. The result indicates that perceived employment barriers have a negative indirect effect on ESS through employment hope, z = −3.54, p = .000. The total effect size from PEBS to ESS, which is also the indirect effect from perceived employment barriers to ESS, is −.019; the direct effect from perceived employment barriers to employment hope and from employment hope to ESS are −.125 and .154, respectively. The assessment of hypotheses is presented in Table 3. Table 3: The Result of Structural Equation Modeling on the Hypothesized Model (N = 801)   Regression Coefficients      Hypotheses  B  β  SE  CR  PEBS → EHS  −.450  −.206***  .088  −5.127  EHS → ESS  .098  .193***  .020  4.971  χ2(655) = 2,141.453 (p = .000), CFI = .935, NNFI = .926, RMSEA [95% CI] = .053 [.051–.056]    Regression Coefficients      Hypotheses  B  β  SE  CR  PEBS → EHS  −.450  −.206***  .088  −5.127  EHS → ESS  .098  .193***  .020  4.971  χ2(655) = 2,141.453 (p = .000), CFI = .935, NNFI = .926, RMSEA [95% CI] = .053 [.051–.056]  Notes: CR = critical ratio, PEBS = Perceived Employment Barrier Scale, EHS = Employment Hope Scale, ESS = economic self-sufficiency, CFI = comparative fit index, NNFI = non-normed fit index, RMSEA = root mean square error of approximation, CI = confidence interval. ***p < .001. Figure 3: View largeDownload slide Structural Equation Modeling Model of Psychological Self-Sufficiency and Economic Self-Sufficiency Figure 3: View largeDownload slide Structural Equation Modeling Model of Psychological Self-Sufficiency and Economic Self-Sufficiency As another way to examine the mental contrasting process, PSS was operationalized as the difference in normalized scores between employment hope and perceived employment barriers. The proposed model was tested using SEM. As reported in Table 4, all fit indices indicate that the hypothesized model has a good fit to the data, χ2(5, N = 801) = 19.274, p = .000, CFI = .992, NNFI = .977, RMSEA [95% CI] = .060 [.033–.089]. Validation of this hypothesis—that observed variable PSS is positively associated with the latent variable ESS—is presented in Table 4. Table 4: The Result of SEM on the Hypothesized Model (N = 801) Hypotheses  Regression Coefficients  SE  CR  B  β  PSS (EHS and PEBS) → ESS  .046  .109**  .016  2.912  χ2(5) = 19.274 (p = .000), CFI = .992, NNFI = .977, RMSEA [95% CI] = .060 [.033–.089]  Hypotheses  Regression Coefficients  SE  CR  B  β  PSS (EHS and PEBS) → ESS  .046  .109**  .016  2.912  χ2(5) = 19.274 (p = .000), CFI = .992, NNFI = .977, RMSEA [95% CI] = .060 [.033–.089]  Notes: SEM = structural equation modeling, CR = critical ratio, PSS = psychological self-sufficiency, EHS = Employment Hope Scale, PEBS = Perceived Employment Barrier Scale, ESS = economic self-sufficiency, CFI = comparative fit index, NNFI = non-normed fit index, RMSEA = root mean square error of approximation, CI = confidence interval. **p < .01. Discussion and Conclusion This study validated the factor structure of PSS—comprising employment hope and perceived employment barriers—and examined how PSS affects ESS among low-income job seekers. Results from CFA on PSS revealed that PSS is a valid measure across two independent samples. The hypothesized path from PSS to ESS using SEM was also confirmed with good fit indices, indicating that the theoretical model has a good fit with the data. Employment hope was found to be a full mediator between perceived employment barriers and ESS. Moreover, PSS measured as the difference score between employment hope and perceived employment barriers was also found to have a significant path to ESS. Limitations of this study need mentioning. First, PSS primarily captures individual attitudes and behavioral attributes, but ESS is not affected only through change at the person level. In fact, employment and retention outcomes rest more on the structural conditions embedded within the low-wage labor market in various complex ways. Second, although this study examined perceived barriers as one overarching concept, perceived employment barriers include both individual and structural barriers. Barriers, as perceived by low-income job seekers, represent individual articulation as rational agents the degree to which personal and socioeconomic conditions are blocking their paths to achieving economic success in the labor market (Hong, Polanin, et al., 2014). Therefore, employment hope represents a self-assessment of individual identity, capacity, motivation, future possibilities, resources and skills, and goal orientation within the context of individual and structural barriers (Hong, Choi, & Polanin, 2014). The PSS process could then be better understood as activating employment hope against how limiting or enabling these barriers may be when moving toward ESS. When it comes to the labor market processes and outcomes, employment hope could parallel the mediating effects of culture as described by Wilson (2010)—bringing together the effects of individual and structural barriers negatively affecting hope and hope negatively affecting ESS. PSS is a concept that denotes empowerment-based, goal-oriented individual drive against all odds that one might face. It places the locus of control on individuals as much as possible against individual and structural barriers that limit their drive to reach the ESS outcome (Hong, Stokar, & Choi, 2016). However, PSS is not all about internally motivating oneself with hope within the confines of existing barriers. Rather, it is more about keeping hope alive despite the barriers and actively sustaining it greater than the negative barrier-filled assessment of reality in the labor market—as the name of a PSS-based intervention Transforming Impossible into Possible would suggest (Hong, 2016). Therefore, future studies should examine how the individual and structural barriers dynamically affect employment hope in the PSS process. In the case where structural barriers may have strong presence in reality, they may forcefully limit the possibility of low-income job seekers overcoming their individual barriers. It is also possible that when barriers are less real and a person only perceives them to be higher due to social isolation, he or she might adjust the scores on these barriers downward to those that more adequately represent the reality through personal reflection in job readiness training and individual coaching sessions. Based on the findings, it is recommended that workforce development practitioners focus on PSS to achieve ESS when working with low-income job seekers at the individual level and with employers and policymakers at the structural level. Adhering to the newly emerging transformative social work practice framework (Schott & Weiss, 2015), PSS can provide an integrative multisystems approach to labor market supply–demand matching and quality job development (Hong, 2016). The former would involve some form of direct social work practice—casework or group work—to prepare individuals to become job ready. And the latter would entail using PSS as a framework to engage in organizational development within workforce development and job placement organizations and companies that recruit and hire from the low-wage labor market. Also, it would be vital through advocacy and policy entrepreneurship to incentivize employers and engage them in human-centered hiring, training, and retention support. At the individual level, benchmarking PSS and providing adequate supportive services along successful employment and retention paths are warranted. Workforce development organizations should use PEB and EHS measures to track the human-centered developmental process in becoming job ready (Hong, 2013). As such, applying PSS as the main self-sufficiency process goal would require “developing relationships based on respect for clients” and evaluating individual progress on each goal (Bratt & Keyes, 1998, p. 807). Specifically, hope building and maintaining strategies would involve (a) an individualized employment and retention plan, (b) support services to remove barriers that block the drive and pathway, (c) reassessing and revising goals, and (d) an evaluation based on the short-term achievement of the process. PSS as psychological capital is also relevant in terms of how it manifests as soft skills that employers tend to find as key in hiring decisions—that is, employee motivation; self-presentation; and interpersonal skills such as cheerful demeanor, effective communication skills, and emotional self-regulation (Carnochan, Taylor, Pascual, & Austin, 2014). These inter- and intrapersonal abilities pertain to “personality, attitude and behavior” (Moss & Tilly, 2001) and “personality traits, goals, motivations, and preferences” (Heckman & Kautz, 2012, p. 451). Heckman and Kautz (2013) have argued that these attributes are character and that character is a noncognitive skill and not a trait. Character skills—personality traits, goals, motivations, and preferences—or soft skills are important predictors of success and sources of inequality in the labor market, in school, and in many other areas of economic and social life (Heckman & Kautz, 2012). They suggest that these skills can be changed by interventions that promise to improve manifest behaviors. At the organizational level, workforce development agencies and employers can jointly invest in pre- and post-employment support for PSS as a precursor to win–win solutions—job placement and retention outcomes for the former and reduction of turnover rates and quality human resource management for the latter. With some creativity, community agencies can use curricula, counseling, coaching, and other support services to carve out spaces for their clients—their inventors of hope—to be partners in developing and implementing the most appropriate intervention. Liu, Huang, and Wang (2014), in their meta-analytic review, found that the job search intervention programs were largely more successful in facilitating employment for participants compared with those in the control group. Interventions that contained such components as job search skills, self-presentation, self-efficacy, proactivity, goal setting, and social support were found to be more successful than those that did not include them. Programs that coupled skill development with motivation enhancement were found to be more successful in finding employment for job seekers. At the structural level, engaging employers as partners to work together on providing more comprehensive and holistic approaches to hiring and retention support would strengthen the labor market. It is crucial to involve employers in these programs because of the financial investment they put into their employees. By being involved, employers can not only understand the type of transformation their prospective employees have made to exemplify strong soft skills, but also see the dividends of motivation and performance—that is, lower turnover rate and higher productivity—that the employees have once on the job site. Incorporating a PSS framework into the workplace will yield a higher job retention rate. Philip Young P. Hong, PhD, is Lucian and Carol Welch Matusak endowed professor and director, Center for Research on Self-Sufficiency, School of Social Work, Loyola University Chicago, 1 E. Pearson Street, Maguire Hall 528, Chicago, IL 60611; e-mail: [email protected]. Sangmi Choi, PhD, is assistant professor, Department of Social Welfare Counseling, Dongguk University, Seoul, South Korea. Whitney Key, MSW, MPH, is a doctoral student, School of Social Work, Loyola University Chicago. This research was supported by the University Partnership Research Grants for the Health Profession Opportunity Grants Program under the Affordable Care Act, Grant #90PH0018, from the Office of Planning, Research and Evaluation of the Administration for Children and Families, U.S. Department of Health and Human Services, and the Korea Foundation of the Republic of Korea. References Akaike, H. ( 1987). 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Kiley, Kimberly A;Sehgal, Ashwini R;Neth, Susan;Dolata, Jacqueline;Pike, Earl;Spilsbury, James C;Albert, Jeffrey M

2018 Social Work Research

doi: 10.1093/swr/svx026

Abstract Mental health professionals’ exposure to clients’ traumatic experiences can result in elevated stress, including compassion fatigue and burnout. Experiencing symptoms of these types of stress can hinder workers’ ability to provide effective services. If a tool can reduce these symptoms, there is potential benefit for workers as well as those receiving their services. The purpose of this study was to examine the effects of prerecorded guided imagery (GI) on compassion fatigue and state anxiety. A total of 69 employees of a mental health nonprofit organization participated in this two-arm randomized controlled trial. Participants completed the Professional Quality of Life Scale, the Perceived Stress Scale, and question 6 from the Pittsburgh Sleep Quality Index at baseline and follow-up, and completed State Trait Anxiety Inventory short form before and after each activity (GI or taking a break). Results revealed statistically significant differences in change scores between the control and experimental groups for state anxiety and sleep quality. The results suggest that GI may be useful for reducing stress for mental health professionals, which could have positive implications for quality of service delivery. A growing body of literature suggests that exposure to clients’ traumatic experiences can have negative effects on helping professionals and that such exposure may constitute an occupational hazard (Bride, 2004; Figley, 1995; McCann & Pearlman, 1990). Stress resulting from this exposure is variously labeled “compassion fatigue,” “secondary traumatic stress” (STS), or “vicarious trauma.” In this article, we will refer to it as “compassion fatigue.” The literature is sparse in regard to randomized controlled trial (RCT) measurement of the effectiveness of self-care interventions for compassion fatigue. Those who work with traumatized individuals or are exposed to traumatic narratives might benefit from a tool shown to reduce compassion fatigue or general stress symptoms. Consequently, there may be potential for improvement in the quality of care provided by helping professionals utilizing such a tool. Literature Review Compassion Fatigue The Professional Quality of Life Scale (ProQOL) is the most widely used measure of compassion fatigue (Stamm, 2010). According to the theory from which ProQOL is derived, helping professionals’ quality of life has both positive and negative aspects. The positive aspect, compassion satisfaction, is the pleasure that one gains from helping others (Stamm, 2010). The negative aspect, compassion fatigue, is the “stress resulting from wanting to help a traumatized or suffering person” (Figley, 1995, p. 7). Compassion fatigue is broken into two components—burnout and STS. Burnout is the physical, mental, and emotional exhaustion resulting from long-term engagement in emotionally demanding conditions (Pines & Aronson, 1988), leading to difficulty coping with one’s environment, especially the work environment (Maslach, 1982). Burnout is characterized by frustration, anger, and feelings of hopelessness, and symptoms usually manifest gradually (Stamm, 2010). STS is a harmful reaction to work-related trauma and can occur as a result of exposure to clients’ accounts of traumatic experiences (Figley, 1995; Mathieu, 2011). Until the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (American Psychiatric Association [APA], 2013), the symptoms of STS and posttraumatic stress disorder (PTSD) had been viewed as nearly identical, with the exception that indirect trauma exposure caused STS symptoms and direct exposure caused PTSD symptoms (Figley, 1995, 2002; Mathieu, 2011). However, they may now be viewed synonymously, because the DSM-5 addresses the possibility of developing PTSD from indirect trauma exposure. In addition to directly experiencing or witnessing a traumatic event, one can be diagnosed with PTSD if he or she experiences “repeated or extreme exposure to aversive details of the traumatic event” (APA, 2013, p. 271) and meets the other criteria to qualify for a PTSD diagnosis (having symptoms of re-experiencing, avoidance, negative thought and mood, and arousal). The indirect exposure “does not apply to exposure through electronic media, television, movies, or pictures unless this exposure is work related” (APA, 2013, p. 271). Considerable research documents the prevalence of compassion fatigue and burnout among mental health professionals. In a survey of 460 mental health providers, 56% reported moderate to high levels of emotional exhaustion, a key component of burnout, and 50% considered quitting their jobs (Acker, 2011). In another study of 282 licensed social workers, 82% of whom had moderately to highly traumatized clients on their caseloads, 55% met at least one of the PTSD criteria (other than exposure), 20% met two of the criteria, and 15.2% met all three of the criteria for a DSM-IV diagnosis of PTSD (Bride, 2007). In a sample of community mental health workers, 17% met criteria for STS disorder, and 18% had significant subclinical levels of psychopathology (Meldrum, King, & Spooner, 2002). The literature suggests factors that correlate with degree of severity of compassion fatigue and burnout. Higher levels of compassion fatigue correlate with having less sense of control over the workplace, more overinvolvement with clients, and higher amounts of secondary exposure to clients’ traumatic memories (McKim & Smith-Adcock, 2014). Research indicates that working with clients with severe mental illness is predictive of depersonalization and emotional exhaustion, two components of burnout (Acker, 1999) and high levels of stress (Oberlander, 1990). In a sample of 213 mental health counselors, participants who reported less maladaptive coping, more positive perceptions of the workplace, and higher mindfulness attitudes also reported less burnout (Thompson, Amatea, & Thompson, 2014). Support from supervisors and coworkers can have a buffering effect on burnout (Himle, Jayaratne, & Thyness, 1991; McFadden, Campbell, & Taylor, 2015), and usage of evidence-based practices for trauma treatment correlates with lower burnout and compassion fatigue, and higher compassion satisfaction (Craig & Sprang, 2010). Worksite Stress Management Initiatives Some research documents the effectiveness of worksite stress management initiatives. Two interventions found to reduce employee stress are yoga (Chu, Koh, Moy, & Müller-Riemenschneider, 2014) and meditation courses (Cohen-Katz, Wiley, Capuano, Baker, & Shapiro, 2005; Shapiro, Astin, Bishop, & Cordova, 2005). Although such interventions may be effective, they may not be feasible for some mental health agencies to implement because of cost and time constraints. Guided Imagery RCTs Guided imagery (GI) is a relaxation technique that relies on descriptive language to facilitate listener visualization of detailed, calming images, with the goal of achieving a relaxation response (National Center for Complementary and Integrative Health, 2016). No RCTs using only guided imagery as an employee stress management tool were located in the literature. GI RCTs often focus on medically associated pain and stress management. Several of these studies have found GI effective. In comparison to controls, GI has decreased intensity, frequency, and duration of chronic tension-type headaches (Abdoli, Rahzani, Safaie, & Sattari, 2012). In an RCT of 208 cancer patients undergoing chemotherapy, the treatment group (GI with progressive muscle relaxation) had significantly greater decreases in anxiety and depression and lower salivary cortisol levels than the control group (Charalambous, Giannakopoulou, Bozas, & Paikousis, 2015). In a study measuring GI’s effects on smoking cessation, the GI group had over double the abstinence rate (26%) at 24 months postintervention, compared with that of the control group (12%) (Wynd, 2005). In a 10-week RCT, 72 women with fibromyalgia experienced significant increases in self-efficacy and significant decreases in pain, fatigue, stress, and depression in comparison with the women in the control group (Menzies, Lyon, Elswick, McCain, & Gray, 2014). The purpose of this study was to determine if a brief and inexpensive GI intervention could be an effective self-care tool for mental health employees. This project used MP3 players containing prerecorded GI. The GI tracks relied on pleasant imagery, with descriptions of being in peaceful settings like beaches and forests. Because imagining an activity and completing an activity can result in similar physiological responses (Morewedge, Huh, & Vosgerau, 2010; Pascual-Leone et al., 1995), pleasant imagery has the potential to elicit a physical response similar to responses elicited by actual situations of calm and peacefulness. This RCT was designed to determine whether listening to GI three times per week for four weeks while at work was superior to taking a break as usual in the same frequency and duration. This pilot study’s primary focus was to determine GI’s effect on compassion fatigue. However, we also sought to determine GI’s effect on other harmful stress (perceived stress, poor sleep quality, and state anxiety) because there is a lack of RCTs documenting GI on its own as a worksite stress management initiative and its effect on these types of stress. Reducing these types of stress would be beneficial to mental health workers, and therefore it was deemed important to determine under what circumstances GI may be useful. This project may be the first RCT to determine if GI on its own can be an effective self-care tool to combat the effects of compassion fatigue and stress in a mental health agency and the first RCT to use pleasant GI alone as a worksite wellness initiative. What may be appealing about the use of prerecorded GI for nonprofits, which typically do not have significant financial resources, is the low cost, portability, and brevity of the intervention. If listening to GI during breaks is superior to doing what workers normally do, it is reasonable to conclude that GI can serve as a useful, low-cost self-care tool for mental health workers, who often have little free time and schedules dictated by client needs. Method Participants The study recruited participants from a nonprofit mental health agency that has over 26 programs working with several overlapping populations, including clients who are homeless, suffer from mental illness, experience mental health crises, and are trauma survivors. The agency employs approximately 250 full-time staff, consisting of licensed social workers and counselors, psychiatrists, case managers, support staff, and management. The study recruited participants face-to-face and by e-mail. We opened the study to both direct and indirect service staff, as well as middle and upper management. This pilot study’s primary focus was compassion fatigue, which is commonly attributed to direct service workers. However, we also sought to determine GI’s effect on other harmful work-related stress—burnout, perceived stress, poor sleep quality, and state anxiety. All positions in the agency have the potential to engender these types of stress. We used a broad definition of “helping professional” to include all employees because all employees, in various roles, help clients. It has been our experience that those who do not directly help can still develop PTSD symptoms through exposure to traumatic material, whether it is through clinical supervision; upper management handling of major unusual incidents; or support staff entering data, conducting research, or reviewing records to ensure adherence to accreditation standards. Although the literature is limited, there is evidence to support the possibility of developing PTSD symptoms without directly helping trauma survivors (Kiyimba & O’Reilly, 2016; Perez, Jones, Englert, & Sachau, 2010; Regehr, Chau, Leslie, & Howe, 2002). Furthermore, the DSM-5 criteria state that PTSD can be caused by “repeated or extreme exposure to aversive details of the traumatic event” (APA, 2013) and does not specify that the exposure must result from directly helping the trauma survivor. Participation eligibility required staff to work a minimum of three shifts per week and be willing and able to spend 10 to 15 minutes of three lunch breaks per week for the purpose of the study. Ineligibility criteria included the following: substance abuse, suicidal ideation or another serious mental health issue while not under the care of a trained mental health professional, or the use of GI at the time of recruitment. Two of this article’s coauthors are employees of the mental health agency where the study took place. To avoid potential discomfort due to disclosure of sensitive information to a fellow employee, we asked prospective participants not to consent to the study if they met any of the ineligibility criteria. Before participants completed baseline measures, project staff obtained informed consent. We discussed project procedures, the minimal risks of the study, and the compensation that participants would receive for completing the study. It was also emphasized, both during recruitment and with the informed consent process, that project participation would not affect employment status at the agency, and that no employee at the agency would have access to participants’ responses that could be linked to their identities. All participants had the opportunity to ask questions and received a copy of the informed consent form with the investigator’s and Case Western Reserve University’s institutional review board contact information. Sixty-nine employees enrolled in the study. Before randomization, we stratified the subjects by the median score on the Social Readjustment Rating Scale (SRRS) and the Neuroticism scale of the Big Five Inventory-10 (BFI-10). This ensured that the control and treatment groups had equal representation of people with high levels of stressful life events and people who tend to respond to stress with higher levels of anxiety and depression. Although the differences between the job characteristics of direct service, indirect service, and management positions were a concern, we determined to allow the randomization to distribute the job types between the control and GI groups. From the subgroups created by participants falling above or below the median score, we randomized them by using a random assignment table. We assigned 35 participants to the treatment group and 34 to the control group. One participant in each group withdrew from the study. In each group, 28 completed all follow-up measures; the remaining were lost to follow-up or completed only some of the required measures. There were no significant differences between those who did not complete measures and the rest of the sample in regard to baseline scores on the ProQOL and the Pittsburgh Sleep Quality Index (PSQI) question 6. However, the difference between those who completed the Perceived Stress Scale (PSS) and those who did not was marginally significant (p = .057). The mean baseline PSS score of those who did not complete it was 21.92, whereas the mean baseline of those who completed was 17.93. This may suggest that those with higher levels of perceived stress felt overwhelmed and unable to complete the study’s tasks. Equipment MP3 players containing six GI tracks were used for the study. Table 1 lists the tracks used. Tracks of differing lengths (between six and 15 minutes) were chosen to conform to the needs of staff with various amounts of free time on any given day. All tracks were downloaded from the Internet, and for the tracks that were not free of cost, a track was purchased for each MP3 player. The GI tracks were all under 15 minutes, free or low cost, aimed at a goal of relaxation and stress relief, and included a script describing a peaceful setting (pleasant imagery). Table 1: Guided Imagery Tracks Used in the Study Track Name  Length of Track  Web Site Where Track Was Obtained  “Meadow Visualization”  5 minutes, 50 seconds  http://prtl.uhcl.edu/portal/page/portal/COS/Self_Help_and_Handouts/Visualization  “The Forest”  5 minutes, 41 seconds  http://www.dartmouth.edu/~healthed/relax/downloads.html#guided  “Trip to the Beach”  9 minutes, 42 seconds  http://www.mckinley.illinois.edu/Units/Health_Ed/relax_relaxation_exercises.htm  “A Walk in the Forest”  11 minutes, 37 seconds  http://www.amazon.com/A-Walk-in-the-Forest/dp/B0026GDC1U/ref=sr_1_5?ie=UTF8&qid=1424897686&sr=8-5&keywords=mp3+ken+goodman  “Mountain Lake”  14 minutes, 14 seconds  http://www.amazon.com/Guided-Imagery-Mountain-Lake/dp/B005CS53FW/ref=sr_1_1?ie=UTF8&qid=1424897528&sr=8-1&keywords=mp3+michael+olpin  “Floating through Colors”  14 minutes, 40 seconds  http://www.amazon.com/Guided-Imagery-Floating-Through-Colors/dp/B005CS534S/ref=sr_1_9?ie=UTF8&qid=1424897528&sr=8-9&keywords=mp3+michael+olpin  Track Name  Length of Track  Web Site Where Track Was Obtained  “Meadow Visualization”  5 minutes, 50 seconds  http://prtl.uhcl.edu/portal/page/portal/COS/Self_Help_and_Handouts/Visualization  “The Forest”  5 minutes, 41 seconds  http://www.dartmouth.edu/~healthed/relax/downloads.html#guided  “Trip to the Beach”  9 minutes, 42 seconds  http://www.mckinley.illinois.edu/Units/Health_Ed/relax_relaxation_exercises.htm  “A Walk in the Forest”  11 minutes, 37 seconds  http://www.amazon.com/A-Walk-in-the-Forest/dp/B0026GDC1U/ref=sr_1_5?ie=UTF8&qid=1424897686&sr=8-5&keywords=mp3+ken+goodman  “Mountain Lake”  14 minutes, 14 seconds  http://www.amazon.com/Guided-Imagery-Mountain-Lake/dp/B005CS53FW/ref=sr_1_1?ie=UTF8&qid=1424897528&sr=8-1&keywords=mp3+michael+olpin  “Floating through Colors”  14 minutes, 40 seconds  http://www.amazon.com/Guided-Imagery-Floating-Through-Colors/dp/B005CS534S/ref=sr_1_9?ie=UTF8&qid=1424897528&sr=8-9&keywords=mp3+michael+olpin  Design and Procedure The study was designed as a two-arm RCT. We instructed the treatment group to listen to one GI track three times per week for four weeks and did not give participants set guidelines for determining which tracks to choose. Because the purpose of the study was to determine if staff usage of GI during a work break was superior to doing what workers would normally do on a break, we determined that participants should choose a track most suited for that day based on length and personal preference. A major concern during project design was difficulty finding time during the work day. If staff were asked to adhere to a set schedule but did not have time to listen to that day’s track, the activity itself could induce stress or not be completed. The control group received treatment as usual. Participants were asked to take a 10-minute break (the average of the GI track lengths) and instructed to do what they normally do during a break. These activities were completed at the same dosage and duration as the experimental group. Usage of a treatment-as-usual control group is supported in the literature on GI RCTs (Abdoli et al., 2012; Baider, Peretz, Hadani, & Koch, 2001; Haase, Schwenk, Hermann, & Miller, 2005; Maddison et al., 2012; Wynd, 2005). Because of the nature of the group tasks, a blinded design was not possible. Baseline Measures The following measures were completed only at baseline. Job Classification The variable measured was level of exposure to clients (indirect service, direct service, management with less than 25% time in direct client contact, and management with more than 25% time in direct client contact). Demographics Variables measured were highest level of education, years of experience in the social services field, gender, race and ethnicity, and age. SRRS The SRRS is an inventory of 43 stressful life events. Based on its potential to induce stress, each event is assigned a numeric value (Holmes & Rahe, 1967). Cronbach’s alpha is not reported for this measure. However, a study testing the reliability of the SRRS found that the rank ordering used remained consistent for both healthy adults (r = .096–.089) and psychiatric outpatients (r = .091–.070) (Gerst, Grant, Yager, & Sweetwood, 1978). BFI-10 The BFI-10 is a 10-item version of the full scale, the BFI-44, and was used to reduce burden on participants. The BFI-44 is a personality inventory measuring five personality traits—neuroticism, extroversion, conscientiousness, openness, and agreeableness (John & Srivastava, 1999). Both the full and 10-item scales have acceptable reliability and validity (Rammstedt & John, 2007). Cronbach’s alpha is not reported for the BFI-10. However, the BFI-44 has a Cronbach’s alpha of .82 (John & Srivastava, 1999), and the BFI-10’s neuroticism scale, which was used as a stratification tool, has acceptable correlation to the full scale (α = .86) (Rammstedt & John, 2007). Baseline and Follow-Up The following measures were completed at baseline and at the end of the four-week period. All measures have adequate validity and reliability. ProQOL-V The Pro-QOL-V is a 30-item Likert-scale questionnaire containing three subscales measuring burnout and STS (the two components of compassion fatigue) and compassion satisfaction. The questions address symptoms experienced within the last 30 days. Reliability alphas are reported as .75 for the burnout scale, .81 for the STS scale, and .88 for the compassion satisfaction scale. Stamm (2010) asserted good construct validity with over 200 published papers and reported discriminant validity between the compassion satisfaction scale and the other two scales (STS and burnout). She reported a 2% shared variance (r = –.23) between the compassion satisfaction and the STS scales, and a 5% shared variance (r = –.14) between the compassion satisfaction and the burnout scales (Stamm, 2010). PSS The PSS (Cronbach’s α = .78) is the most widely used instrument to measure perception of stress, assessing how unpredictable, overloaded, and uncontrollable one finds his or her life within the last month. The 10 Likert-scale questions address thoughts and feelings, and can be used with any population (Cohen, Kamarck, & Mermelstein, 1983). PSQI, Question 6 The sixth question on the PSQI measures general subjective sleep quality. Respondents rate their sleep as “very good,” “fairly good,” “fairly bad,” or “very bad” (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). Before and After Each Activity Participants also completed the following measure before and after each activity (listening to GI or taking a break). State Trait Anxiety Inventory (STAI) Six-Item Short Form The STAI short form was developed from the full STAI, which consists of two 20-item scales. The state anxiety scale measures current transient anxiety, whereas the trait anxiety scale measures the more enduring individual tendencies leading to anxious responses to perceived stress (Speilberger, 1983). The six-item short form of STAI was used to reduce burden on participants who have limited free time during their work day. The short form has acceptable correlation to the full STAI, with a correlation coefficient of .95. The STAI has acceptable validity and reliability (Cronbach’s α = .82) (Marteau & Becker, 1992; Speilberger, 1983). Break Detail Form To document their compliance with protocol, subjects filled out a form documenting the break length, the break activity (control) or GI track to which they listened (treatment), and whether or not the break was interrupted. Data Analysis For each response variable, we calculated the average change score from pretest to posttest for each group. The STAI measurements, which involved multiple pre- and posttest measurements, were summarized as the total change over the six-scale items, averaged over the 12 time points. Unpaired t tests were calculated to determine the statistical significance of the difference between the two groups’ mean change scores. An intent-to-treat approach was used whereby all randomized subjects were included in the analysis where possible; thus, the analysis made use of all available outcome measurements. We surveyed the literature to determine a sample size goal for the study. Because of the small dosage of the intervention and short duration of the treatment period, we hypothesized that any changes on the measures completed before and after the four-week period would be of moderate size. We hypothesized that the largest effect would be realized through a change in anxiety as assessed by baseline and follow-up scores on the STAI short form. As a result, we powered the study for state anxiety (STAI) rather than compassion fatigue (ProQOL). Because we found no literature documenting the standard deviation of STAI change scores with GI or other short-term wellness approaches such as meditation, we decided to assign a moderate standardized effect size of .5. Using a two-tailed alpha and beta of .2, we determined that a sample size of 126 would therefore be needed to achieve statistically significant results with a moderate effect size. Results Table 2 represents the demographics, job characteristics, and baseline scores for both groups, including participants who did not complete the study. Both groups were similar in constitution, except in regard to race and ethnicity. Table 2: Demographics   Guided Imagery (n = 35)  Control (n = 34)    Demographic  n (%)  n (%)  p  Gender      .742   Female  25 (71)  27 (79)     Male  7 (20)  5 (15)     Missing data  3 (9)  2 (6)    Race and ethnicity      .011   African American  6 (17)  12 (35)     White  24 (69)  19 (56)     Multiracial  2 (6)  1 (3)     Hispanic  0 (0)  0 (0)     Other  0 (0)  1 (3)     Missing data  3 (8)  1 (3)    Age (years)      .323   18–34  16 (46)  9 (26)     35–54  13 (37)  18 (53)     55+  4 (11)  6 (18)     Missing data  2 (6)  1 (3%)    Job classification      .077   Indirect service  3 (9)  3 (9%)     Direct service  25 (71)  15 (44%)     Management (<25% direct service)  2 (6)  8 (24%)     Management (>25% direct service)  4 (11)  8 (24)     Missing data  1 (3)  0 (0)    Highest level of education      .510   High school/associate degree  4 (11)  6 (18)     Bachelor’s/some master’s work  12 (34)  9 (26)     Master’s degree  16 (46)  17 (50)     Missing data  1 (3)  0 (0)      Guided Imagery (n = 35)  Control (n = 34)    Demographic  n (%)  n (%)  p  Gender      .742   Female  25 (71)  27 (79)     Male  7 (20)  5 (15)     Missing data  3 (9)  2 (6)    Race and ethnicity      .011   African American  6 (17)  12 (35)     White  24 (69)  19 (56)     Multiracial  2 (6)  1 (3)     Hispanic  0 (0)  0 (0)     Other  0 (0)  1 (3)     Missing data  3 (8)  1 (3)    Age (years)      .323   18–34  16 (46)  9 (26)     35–54  13 (37)  18 (53)     55+  4 (11)  6 (18)     Missing data  2 (6)  1 (3%)    Job classification      .077   Indirect service  3 (9)  3 (9%)     Direct service  25 (71)  15 (44%)     Management (<25% direct service)  2 (6)  8 (24%)     Management (>25% direct service)  4 (11)  8 (24)     Missing data  1 (3)  0 (0)    Highest level of education      .510   High school/associate degree  4 (11)  6 (18)     Bachelor’s/some master’s work  12 (34)  9 (26)     Master’s degree  16 (46)  17 (50)     Missing data  1 (3)  0 (0)      M (SD)  M (SD)    Years of experience  11.68 (9.36)  11.43 (9.71)    Baseline score   Perceived Stress Scale  19.66 (5.25)  17.56 (5.99)  .126   Compassion satisfaction  38.51 (6.02)  39.47 (4.88)  .472   Burnout  22.49 (5.01)  22.59 (4.86)  .932   Secondary traumatic stress  21.69 (5.13)  20.79 (5.42)  .485   Self-reported sleep quality  2.40 (0.60)  2.56 (0.75)  .334   Big Five Inventory neuroticism scale  5.85 (1.96)  5.59 (1.97)  .593   Social Readjustment Rating Scale  319.53 (399.64)  277.80 (212.19)  .588    M (SD)  M (SD)    Years of experience  11.68 (9.36)  11.43 (9.71)    Baseline score   Perceived Stress Scale  19.66 (5.25)  17.56 (5.99)  .126   Compassion satisfaction  38.51 (6.02)  39.47 (4.88)  .472   Burnout  22.49 (5.01)  22.59 (4.86)  .932   Secondary traumatic stress  21.69 (5.13)  20.79 (5.42)  .485   Self-reported sleep quality  2.40 (0.60)  2.56 (0.75)  .334   Big Five Inventory neuroticism scale  5.85 (1.96)  5.59 (1.97)  .593   Social Readjustment Rating Scale  319.53 (399.64)  277.80 (212.19)  .588  Because the randomization failed to evenly distribute the various job characteristics among the control and GI groups, we analyzed the baseline scores of all the four-week measures used in the study. There were no statistically significant differences between baseline scores of direct service staff and baseline scores of all other participants (management and indirect service staff). To further investigate, we analyzed a subsample of direct service workers completing the study. The differences between the change scores of the GI and control groups were not statistically significant but remained similar to the differences found in the analysis of the groups in their entirety. These analyses suggest that in this sample, the differences between the two groups were not likely due to group composition. However, the results should be interpreted with caution. STAI Short Form Each participant completed up to 12 activities (GI or break) and completed the STAI short form before and after each activity. Each participant’s mean change score of all activities was calculated, and this mean STAI change score was then used to calculate the overall group mean STAI change score. Table 3 illustrates the overall mean change score for all 12 activities of each group and the standard deviations of those change scores. On an 18-point scale, the GI group saw an average decrease of 3.56 STAI (state anxiety) points per person, whereas the control group score decreased by an average of 1.75 STAI points per person. This 1.81 point difference between the two groups was statistically significant (p = .001). It is important to note that the GI group spent an average of 8.02 minutes per activity, whereas the control group spent an average of 14.79 minutes per activity. Table 3: Means and Standard Deviations of Change Scores   Guided Imagery  Control Group    Measure  M (n)  SD  M (n)  SD  p  State Trait Anxiety Inventory short form  –3.56 (31)  2.18  –1.75 (31)  1.42  .001**  Perceived Stress Scale  –1.83 (29)  4.16  –0.35 (29)  4.85  .217  Compassion Satisfaction  0.71 (28)  3.47  –0.10 (29)  3.41  .373  Burnout  –1.46 (28)  3.28  0.17 (29)  3.53  .075  Secondary Traumatic Stress  –1.39 (28)  3.26  –0.24 (29)  4.32  .262  Self-Reported Sleep Quality  0.45 (29)  0.74  0.03 (29)  0.82  .048*    Guided Imagery  Control Group    Measure  M (n)  SD  M (n)  SD  p  State Trait Anxiety Inventory short form  –3.56 (31)  2.18  –1.75 (31)  1.42  .001**  Perceived Stress Scale  –1.83 (29)  4.16  –0.35 (29)  4.85  .217  Compassion Satisfaction  0.71 (28)  3.47  –0.10 (29)  3.41  .373  Burnout  –1.46 (28)  3.28  0.17 (29)  3.53  .075  Secondary Traumatic Stress  –1.39 (28)  3.26  –0.24 (29)  4.32  .262  Self-Reported Sleep Quality  0.45 (29)  0.74  0.03 (29)  0.82  .048*  *p < .05. **p < .01. ProQOL-V, PSS, and Sleep Before and after the four-week intervention, both groups filled out the ProQOL, the PSS, and the PSQI question 6. We calculated the change scores for each participant and compared the mean change scores of the two groups. Table 3 illustrates these mean change scores and standard deviations of the change scores. Regarding the measures completed before and after the four-week period, there was one statistically significant difference between the two groups’ mean change scores. The GI group increased an average of .45 points on a four-point rating of sleep quality, whereas the control group increased an average of .03 points on this scale. This .42 point difference between the groups’ change in self-reported sleep quality was statistically significant (p = .048). It is noteworthy that this sample was not normal in comparison with the ProQOL’s norms. According to the ProQOL norms, 25% of a sample should score low on burnout, STS, and compassion satisfaction; 50% of a sample should score average on these scales; and 25% of a sample should score high on these scales. On the compassion satisfaction scale, the sample scored as follows at baseline: 0% low, 67% average, and 33% high. At baseline, 57% of participants scored low on burnout, 43% reported average burnout, and 0% reported high burnout. Regarding baseline STS, 67% scored low, 33% reported average STS, and 0% reported high STS. A surprising result was the increase in sleep quality. The treatment group reported the following: 7% reported very bad sleep at baseline and 0% reported very bad sleep at follow-up; 45% reported fairly bad sleep at baseline and 21% reported fairly bad sleep at follow-up; 48% reported fairly good sleep at baseline and 72% reported fairly good sleep at follow-up; 0% reported very good sleep at baseline and 7% reported very good sleep at follow-up. There was little difference between the control group’s pre and post reports: 7% reported very bad sleep at baseline and follow-up; 41% reported fairly bad sleep at baseline and follow-up; 45% reported fairly good sleep at baseline and 41% reported fairly good sleep at follow-up; 7% reported very good sleep at baseline and 10% reported very good sleep at follow-up. Because the intervention was low dosage, low duration, and conducted during work hours, this finding was unanticipated; it is notable that such a short intervention could affect sleep to a point where only 21% of those in the treatment group indicated poor sleep after the four weeks, as opposed to individuals in the control group, whose sleep quality remained almost unchanged. Some treatment adherence issues must be considered. Several participants did not complete all activities because they were too busy, sick, or took vacation days. On average, each participant in the GI group completed 9.42 activities (SD = 2.53), and the control group completed 10.34 activities (SD = 2.22). This difference between the two groups was not statistically significant. To gain insight into what might be creating the sleep effect, a subsample of those completing nine or more activities was analyzed (see Table 4). In this subsample, the sleep trends persist; the differences between the two groups increase slightly. Table 4: Mean and Standard Deviations of Change Scores for Participants Completing Nine or More Activities   Guided Imagery (n = 20)  Control Group (n = 25)    Measure  M (SD)  M (SD)  p  Perceived Stress Scale  –2.20 (3.44)  –0.16 (5.10)  .133  Compassion satisfaction  0.30 (3.31)  –0.08 (3.63)  .715  Burnout  –1.90 (3.29)  0.00 (3.20)  .058  Secondary traumatic stress  –0.50 (2.72)  0.32 (4.10)  .420  Self-reported sleep quality  0.55 (0.76)  0.00 (0.76)  .020*    Guided Imagery (n = 20)  Control Group (n = 25)    Measure  M (SD)  M (SD)  p  Perceived Stress Scale  –2.20 (3.44)  –0.16 (5.10)  .133  Compassion satisfaction  0.30 (3.31)  –0.08 (3.63)  .715  Burnout  –1.90 (3.29)  0.00 (3.20)  .058  Secondary traumatic stress  –0.50 (2.72)  0.32 (4.10)  .420  Self-reported sleep quality  0.55 (0.76)  0.00 (0.76)  .020*  *p < .05. Discussion In this sample, participants who listened to GI during breaks over a four-week period achieved significant reductions in state anxiety and significant increases in sleep quality when compared with those of the control group participants. Furthermore, the GI group’s beneficial results were achieved in almost half of the time. Because the intervention was implemented over a relatively short period of time and not time intensive, it was hypothesized that the greatest effect would be with short-term state anxiety, and that small decreases in compassion fatigue and perceived stress would occur. Generally speaking, this hypothesis was supported. On an 18-point scale, the GI group saw a decrease of 1.81 more STAI points than the control group. The STAI results are promising, as stress is cumulative. If short-term benefits are consistent, there may also be benefits in reducing long-term stress, such as compassion fatigue, through regular use of the intervention for longer periods of time. The improvements in self-reported sleep quality warrant further investigation using more rigorous measures. Because improvement in sleep quality was unanticipated, the full PSQI was not used, to reduce burden on participants. In future studies, we recommend using the full PSQI or a noninvasive biological measure of sleep quality. There are limitations to consider while interpreting these data. Our study was powered to detect a moderate effect size of .5, but we were unable to reach our sample size goal of 126 participants. However, we were still able to find statistically significant differences in state anxiety and sleep quality. It is possible that the differences in burnout, STS, and perceived stress may have achieved statistical significance if the sample goal was met, as their trends were similar to those trends that did reach significance. It is inconclusive whether significant results would have been achieved with a larger sample; therefore, more research needs to be conducted to determine GI’s effects on these constructs. It is possible that this sample is biased, as no participants scored high on burnout or STS, or low on compassion satisfaction. According to ProQOL norms, 25% of a sample should score within these ranges. It is suspected that there were people within this agency who did score within these ranges on these scales, but they did not participate. One supposition is that the study drew people already familiar with utilization of self-care activities. Another possibility is that those with higher levels of stress may have felt overwhelmed and unable to adhere to the study’s guidelines, and this is supported by the marginally significant difference (p = .057) in PSS baseline scores of those who did not complete (21.92) versus those who completed the study (17.93). Because the sample was not generally normative, it is inconclusive whether these results are generalizable. Further research needs to be conducted with samples that more closely mirror ProQOL’s norms. The randomization failed to evenly distribute direct service staff and supervisors among the control and GI groups. There were no significant differences in baseline scores between direct service and other employees, but we recognize that there are inherent differences between job classes, and the results should be interpreted with caution. We recommend that future studies recruit homogeneous samples or stratify by job classification. Difficulty in treatment adherence occurred. This is partly due to the study’s implementation during the summer months when traditionally many employees take vacations. However, once those who completed less than nine activities were removed from the analysis, the differences between the two groups’ mean change in sleep remained consistent, lending support to GI as the cause of the effects, rather than the vacations taken. Implications Because of the study’s limitations, the results should be interpreted with caution. This pilot study provides some support for the use GI as a promising self-care tool for mental health workers. As this study has shown, compared to the control group, the GI group had significant improvements in state anxiety and sleep quality. Further research needs to address GI’s efficacy in larger samples that reflect ProQOL norms. We also suggest using more rigorous measures of sleep quality and ensuring that both groups are congruent in regard to direct service workers. If further research supports this study’s findings, GI may be a useful self-care tool for mental health agencies with minimal time and resources. Such utilization may yield significant organizational impact, as poor sleep quality correlates with negative health outcomes (Gallicchio & Kalesan, 2009). GI may have the potential to not only help workers who are experiencing sleep problems and anxiety, but also positively affect the mental health agencies that employ them and the clients that they serve. Kimberly A. Kiley, MA, is a research and evaluation specialist, FrontLine Service, 1744 Payne Avenue, Cleveland, OH 44114; e-mail: [email protected]. Ashwini R. Sehgal, MD, is codirector and Duncan Neuhauser Professor of Community Health Improvement, Center for Reducing Health Disparities, Case Western Reserve University, Cleveland. 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Forrest-Bank, Shandra S;Cuellar, Matthew J

2018 Social Work Research

doi: 10.1093/swr/svx023

Abstract Path analysis was used to assess direct and mediating relationships of an a priori mediation model. Data were collected from 210 black, Hispanic/Latino, and Asian undergraduate college students. Authors found that microaggression was positively associated with ethnic identity. Microaggression had a significant positive association with psychological distress but no other direct relationships with the outcome variables. Ethnic identity was negatively associated with psychological distress but positively associated with self-esteem and academic self-efficacy. A positive effect was found between ethnic identity and substance abuse. Ethnic identity mediated the effect of microaggression on psychological distress. Moreover, including ethnic identity in the equation revealed that microaggression has a positive effect on self-esteem and academic self-efficacy through participants’ reported degree of ethnic identity. The results suggest that racial microaggressions have damaging impacts on the emotional health of racial and ethnic minority young adults. However, microaggressive experiences may also elicit stronger ethnic identity, which appears to serve as a protective factor to the negative influence of microaggression on psychological well-being. Post hoc exploratory multigroup analysis revealed some differential findings for each group. The article concludes with a discussion of the implications for social work practice, education, and research. Young adults who enroll in college often become exposed to increased and different racial and ethnic diversity than they were previously accustomed to, which is likely to stimulate growth in ethnic identity (Brittian et al., 2015). Unfortunately, numerous research investigations have found that racial and ethnic minority college students tend to report that they experience racial discrimination (Hwang & Goto, 2008; Solorzano, Ceja, & Yosso, 2000; Utsey, Ponterotto, Reynolds, & Cancelli, 2000). In addition, there is substantial evidence demonstrating inverse relationships between racial discrimination and indicators of emotional well-being (Chen, Szalacha, & Menon, 2014; Hwang & Goto, 2008; Utsey et al., 2000) and behavioral health (Borrell, Jacobs, Williams, Pletcher, & Houston, 2007; Gibbons et al., 2012; Park, Schwartz, Lee, Kim, & Rodriguez, 2013). Although exposure to racial discrimination has been established as a causal factor for negative outcomes of behavioral and emotional well-being among people from racial and ethnic minority groups (Pascoe & Smart Richman, 2009; Pieterse, Todd, Neville, & Carter, 2012), much less is understood about the processes and dynamics by which the impacts occur, or the specific forms of racial discrimination involved (Pascoe & Smart Richman, 2009; Williams & Mohammed, 2009). There is a critical need for research that elucidates the mechanisms by which racial discrimination contributes to compromised psychological well-being among racial and ethnic minority young adults. It is hard to imagine a social work role that would not be better equipped to serve vulnerable populations by understanding how racial discrimination influences behavioral and emotional health, and what factors could promote resilience and interrupt those processes. Furthermore, this issue is a matter of public health and social justice and aligns with the social work grand challenge to eradicate health inequities set forth by the American Academy of Social Work and Social Welfare (Walters et al., 2016). Many experts agree that racial microaggression may be a particularly harmful form of discrimination due to the complex and stressful dynamics involved (Noh, Kaspar, & Wickrama, 2007; Sue et al., 2007). Conversely, there is considerable evidence that ethnic identity tends to have strong positive relationships with indicators of health and well-being. In fact, research has found that ethnic identity often plays a protective role against impacts of discrimination (Brittian et al., 2015; Pascoe & Smart Richman, 2009). However, the research is inconclusive about the nature of the relationship between racial discrimination and ethnic identity (Branscombe, Schmitt, & Harvey, 1999; Sellers & Shelton, 2003), and whether or not ethnic identity is a protective factor against the impacts of racial discrimination on indicators of psychological well-being (Brittian et al., 2015; Pascoe & Smart Richman, 2009). In addition, little research specifically measured the relationship between discrimination in the form of microaggression and indicators of well-being such as psychological distress, self-esteem, self-efficacy, and substance abuse, and very little research has been conducted to examine ethnic identity in the pathway between microaggression and these indicators. Furthermore, since every racial and ethnic group in the United States has unique qualities and histories of oppression, there might be variations in the mechanisms involved in how perceived discrimination influences psychological well-being (Brittian et al., 2015). We used cross-sectional data from a sample of 210 racial and ethnic minority undergraduate college students to examine the extent to which racial discrimination in the form of microaggression, and ethnic identity, predict participants’ psychological distress, self-esteem, academic self-efficacy, and substance abuse. Moreover, indirect effects of ethnic identity on the influence that experiences of racial microaggression had on psychological well-being were also assessed. In addition, we explored the relationships among the variables in each of the three racial and ethnic groups in the sample: Asian, Latino/Hispanic, and black. Racial and Ethnic Microaggression A paradox exists in the United States between social norms of racial equality and justice and the persistence of racism and discrimination. In other words, even though most people express that they are opposed to racism, and racial discrimination is prohibited by law, racial discrimination and inequities persist across all major social institutions (Wise, 2013). To illustrate, racial and ethnic minority groups are disproportionately represented in official indicators of poverty (DeNavas-Walt & Proctor, 2014), and racial disparities are found in housing, lending, and residential segregation (Shapiro, Meschede, & Osoro, 2013); employment (A. B. Smith, Craver, & Turner, 2011); education (Gregory, Skiba, & Noguera, 2010); health care (Pascoe & Smart Richman, 2009; Smedley & Smedley, 2005); and the criminal justice system (Stevenson, 2011). Racism in the United States can be defined as the system of oppression that categorizes people into groups based on skin color and stratifies the groups in a hierarchy according to an ideology of inferiority such that white people at the top are afforded preference and privilege and everyone else assumes reduced status (Bonilla-Silva, 1996). The concept of race operates as though it is based on biological differences. However, race in modern U.S. culture is socially constructed according to skin color; in reality there is very little biological difference that distinguishes one racial group from another. In fact, each racial group represents a conglomeration of numerous ethnic backgrounds. For example, consider that the racial categories of “Hispanic/Latino” or “Asian” refer to numerous different ethnic backgrounds as well as a range of extent of acculturation into U.S. culture. Racial discrimination can be understood as the unequal and unfair restriction by judgment or action of people due to their race (Krieger, 1999). In effect, racism is perpetuated and reinforced through racial discrimination. Ahmed, Mohammed, and Williams (2007) explained that racial discrimination functions at many levels. It is institutionalized such that racial and ethnic minority groups are more likely to live in poverty and residential environments that present exposure to risk factors for compromised health and well-being. Racial discrimination occurs systemically such that people from racial and ethnic minority groups have reduced access to resources like affordable housing and health care. In addition, racial discrimination occurs at the individual level where people are recipients of racist attitudes and discriminatory behaviors. Sometimes racial discrimination occurs as major traumatic events that are violent or aggressive and relay overt messages of hatred. In modern culture, however, racial discrimination is more likely to occur as small acts of discrimination called racial microaggressions. The term “racial microaggressions” was first coined by psychiatrist Chester Pierce in 1970 to refer to insults and slights containing negative stereotypes that are frequently experienced by people from racial and ethnic minority groups in their everyday lives. Microaggression may occur through interpersonal exchanges or through environmental messages (Sue et al., 2007). Although microaggressions may be manifested by verbal or physical actions intended to inflict harm (Sue et al., 2007), much of the time they occur as subtle insults toward people of color that are automatic, nonverbal, and unintended in nature (Solorzano et al., 2000; Sue et al., 2007). Experts have noted that these subtle occurrences of discrimination are harder to interpret, creating situations in which recipients are confused about the intent and how to best respond. The complexity of the dynamics involved may cause more psychological distress than blatant forms of discrimination (Noh et al., 2007; Sue, 2010). Perceived Racial and Ethnic Discrimination and Psychological Well-Being There is unequivocal evidence that racial discrimination plays a significant role as a determinant of psychological well-being on people of color in the United States (Paradies, 2006; Williams & Mohammed, 2009). For example, a review of research from population studies found that discrimination consistently was associated with detrimental mental health indicators including increased depression and anxiety and lower happiness, life satisfaction, and self-esteem (Williams, Neighbors, & Jackson, 2003). Substance use has also been found to be affected by experiences of perceived discrimination (Borrell et al., 2007; Gibbons, Gerrard, Cleveland, Wills, & Brody, 2004). In addition, negative association to belief in one’s academic competence has also been substantiated (Wong, Eccles, & Sameroff, 2003). The negative influence of racial discrimination on psychological well-being has been demonstrated across samples of various racial and ethnic groups and appears to have similar effects across groups (Chou, Asnaani, & Hofmann, 2012; Pascoe & Smart Richman, 2009). Ethnic Identity and Psychological Well-Being Ethnic identity is defined as the part of an individual’s self-concept that comes from membership in a social group (or groups) combined with the value and emotional significance attached to that membership (Phinney, 1990; Tajfel, 1981). An individual’s ethnic identity develops over the life course. Adolescence usually marks the most intensive period of ethnic identity development, but growth often continues into young adulthood and beyond (Phinney & Ong, 2007). The formation of ethnic identity has been described as a complex process that involves an ongoing exchange between the internal view one has of oneself with the external perceptions others possess based on race and ethnicity (Nagel, 1994). Phinney and Ong (2007) explained that ethnic identity is achieved over time through a complex process involving an interaction between exposure to experiences and various choices and actions made by individuals. The conceptualization of ethnic identity is complex and involves many dimensions. For example, self-categorization, or identifying with a particular group or groups, in itself is one facet of the concept. Perhaps the most important dimensions of ethnic identity involve affective qualities, or the sense of belonging people have toward their ethnic groups, including aspects like the extent to which a person feels attachment, pride, and commitment associated with that identification. Also important, however, are the active processes people take to explore their ethnic identities and participate in ethnic-oriented events, such as reading information about ethnicity relevant to them, seeking experiences with people from specific ethnic groups, and participating in cultural rituals. Ethnic identity has been consistently found to be important to people across all ethnic groups (Phinney, 1992). A substantial body of research demonstrates a positive relationship between ethnic identity and psychological well-being in samples of adolescents and young adults (T. B. Smith & Silva, 2011). Nagel (1994) asserted that ethnic identity is somewhat fluid, a construction of the specific social context, and thus varies depending on the situation. People often choose which part of their identity to present or which label to ascribe themselves based on what seems most favorable to them in the particular moment, combined with the categories available in that particular moment (Nagel, 1994). It is interesting to consider from this perspective how ethnic identity might also be influenced by experiences of racial discrimination. Many scholars have suggested that ethnic identity functions as a protective factor that enables individuals to be resilient in response to discrimination (Brittian et al., 2015; Umaña-Taylor & Updegraff, 2007). In other words, due to the strong positive influence ethnic identity has on psychological well-being, it is likely that higher levels of ethnic identity might reduce the negative impacts of racial discrimination on psychological well-being. A substantial body of research has investigated the potential protective role of ethnic identity as a moderating, or buffering, influence on the relationship between discrimination and psychological well-being. The findings have been mixed. Pascoe and Smart Richman (2009) reported findings from a systematic analysis of studies on the impacts of racial discrimination on health showing that the buffering effect was not substantiated in 71% of the effects of this relationship. However, the study also revealed that 18% of the analyses found a positive buffering effect such that ethnic identity was related to a decreased impact of perceived discrimination on negative indicators of psychological well-being including depressive symptomatology (Jones, Cross, & DeFour, 2007; Lee, 2005; Mossakowski, 2003), well-being (Lee, 2003, 2005), self-esteem (Romero & Roberts, 2003), and perceived stress (Sellers, Caldwell, Schmeelk-Cone, & Zimmerman, 2003). To confuse matters even further, 12% of the analyses found that higher levels of ethnic identity led to more negative impacts in the relationship between racial discrimination and mental health including self-esteem (McCoy & Major, 2003), well-being (Sellers, Copeland-Linder, Martin, & Lewis, 2006), perceived stress (Sellers et al., 2006), and depression (McCoy & Major, 2003; Noh, Beiser, Kaspar, Hou, & Rummens, 1999; Sellers et al., 2006). These mixed findings point to the importance of continued efforts to better explain the relationships among these variables. A much smaller number of research investigations have modeled ethnic identity’s potential protective effect as a mediating variable instead and found that perceived discrimination had indirect positive relationships with favorable impacts to psychological indicators (negative emotions and self-esteem) through ethnic identity (Armenta & Hunt, 2009; Branscombe et al., 1999; Brittian et al., 2015; Umaña-Taylor & Updegraff, 2007). It may even be that when people from racial and ethnic minority groups experience discrimination, a heightened sense of ethnic identity is triggered and results in enhanced psychological well-being (Branscombe et al., 1999; Brittian et al., 2015). Branscombe and colleagues (1999) called this effect “rejection identification.” The mediating relationship of ethnic identity between discrimination and psychological well-being may help explain differential development of resilience to racial discrimination that the buffering hypothesis does not account for. Racial and Ethnic Group Differences Results from meta-analysis have strongly suggested that there are no differences in the extent to which racial discrimination negatively affects psychological well-being for various racial and ethnic groups including Asian, Hispanic, black, Native American, and white (Pascoe & Smart Richman, 2009). However, as Brittian and colleagues (2015) pointed out, the experiences of racial discrimination and ethnic identity may be very different for each racial and ethnic group. The mechanisms by which racial microaggression influences psychological well-being might also vary across racial and ethnic groups. The Current Study The current study aimed to contribute to what is understood about how racial discrimination influences psychological well-being by conducting a path analysis of a model that hypothesized that ethnic identity would mediate a relationship between racial microaggression and outcomes of psychological distress, self-esteem, academic self-efficacy, and substance abuse. Although the data are cross-sectional, causal relationships are hypothesized with the intent to see how well the findings support the model and inform further research investigations. We posited that individuals’ ethnic identity would be strengthened by experiencing microaggression. We also expected findings to support the hypothesis that racial microaggression has a detrimental relationship with psychological well-being (that is, psychological distress, self-esteem, academic efficacy, and substance abuse), whereas ethnic identity has a beneficial one. As a secondary objective of the study, we conducted a multigroup test of the path analysis to explore the potential for variation in the results across racial and ethnic groups. Method Sampling and Data Collection Cross-sectional data were collected using a Web-based survey from young adults enrolled in an urban public college in the western United States. Participants were 210 black, Latino/Hispanic, and Asian undergraduate students (ages 18 to 35 years). The college is publically funded. Approximately 40% of the students were recipients of Pell Grants or veterans benefits. The student body of the college was racially and ethnically diverse (white 63%, Latino/Hispanic 18%, black or African American 6%, Asian 4%, American Indian or Alaskan Native students 1%, two or more races 3%, and other/unknown 5%). Fifty-four percent of undergraduate students were female and 46% were male. The current study included all black, Hispanic/Latino, and Asian participants. An e-mail address dedicated to the study and a Listserv of all student e-mail addresses enabled the invitation to the anonymous survey to be sent to all prospective participants at once. Participants proceeded to the survey only after completing informed consent. All aspects of the protocol were approved through the institutional review board before proceeding with the data collection. Measures Self-report standardized scales were used to measure six distinct constructs: racial microaggression, ethnic identity, psychological distress, self-esteem, academic self-efficacy, and substance abuse. Racial Microaggression Racial microaggression was operationalized by 28 ordinal indicators (α = .88) from the Revised 28-Item Racial and Ethnic Microaggressions Scale (Forrest-Bank, Jenson, & Trecartin, 2015). Prior factor analysis has validated the use of the scale across racial groups (Forrest-Bank et al., 2015). The original instrument contained 45 items and was developed by Nadal (2011) based on the microaggression taxonomy developed by Sue (2010). Participants are asked to think about their experience with race and then respond to each item indicating how many times they had experienced the event in the past six months. There are six response choices ranging from not at all to five or more times. The 28 items were summed then divided by the number of items used to create it to establish a composite variable, with higher scores indicative of more microaggression experienced (M = 3.89). Ethnic Identity Ethnic identity was operationalized by the summation of 12 ordinal indicators (α = .91) from Phinney’s (1992) Multigroup Ethnic Identity Measure (MEIM). Respondents were asked to choose the responses that best fit how they felt about each of the 12 items on a four-point scale ranging from 1 = strongly agree to 5 = strongly disagree. The scale contains five questions representing both affective and cognitive–behavioral components of ethnic identity search. Reliability of the 12-item scale has been demonstrated previously with a wide range of age groups and ethnicities (Roberts et al., 1999). The 12 items were summed to create a summative variable, with higher scores indicating greater ethnic identity (M = 35.61). Substance Abuse Substance abuse was measured with the CRAFFT (Knight et al., 1999), a six-item instrument used to screen for potential alcohol or drug abuse in adolescents and young adults. CRAFFT is an acronym using the first letters of the key words in the six questions. For example, the letter “C” represents the question, “Have you ever ridden in a CAR driven by someone (including yourself) who was ‘high’ or had been using alcohol or drugs?" Substance abuse was represented by the summation of six dichotomous indicators representing whether or not the items were true for them (α = .76). Items were summed to create a single summative variable in which higher scores represent the participant endorsing more symptoms associated with substance use disorders (M = 1.61). The CRAFFT has demonstrated criterion validity in identifying adolescents with substance abuse and dependence (Knight, Sherritt, Shrier, Harris, & Chang, 2002). Based on recommendations from the instrument developers, endorsement of two or more indicators is likely to predict a substance use disorder (Knight, Sherritt, Harris, Gates, & Chang, 2003). Psychological Distress Psychological distress was operationalized by the summation of 10 ordinal items used in the National Survey of Black Americans (Brown et al., 2000). Items in the scale measured symptoms of depression and anxiety. For example, symptoms included “downhearted and blue” and “restless and upset.” Respondents were asked to respond to the prompt, “During the past month, how much of the time did you feel . . .” on a four-point scale with 1 = none of the time, 2 = some of the time, 3 = most of the time, and 4 = all of the time. This scale was derived from research done by renowned innovator and expert in health outcome measurement John Ware and the Rand Corporation on the Mental Health Inventory (Ware, Davies-Avery, & Donald, 1978; Ware, Johnston, Davies, & Brook, 1979). The scale demonstrated strong reliability in the current study (α = .92). Higher scores on this variable represent more symptoms of psychological distress (M = 21.03). Self-esteem The Rosenberg Self-Esteem Scale (Rosenberg, 1965) is an extensively validated 10-item scale consisting of both positive and negative feelings about the self-value and worth (α = .88). Items are answered using a four-point Likert scale ranging from 1 = strongly agree to 4 = strongly disagree. Higher scores on this variable are indicative of higher levels of self-esteem (M = 32.51). Academic Self-Efficacy Academic self-efficacy was measured by summing seven items from the College Self-Efficacy Instrument (Solberg, O’Brien, Villareal, Kennel, & Davis, 1993). The scale demonstrated strong reliability (α = .86). This instrument assesses college students’ sense of confidence in their ability to manage tasks related to course completion that are integral to college participation, such as “do well on your exams” and “manage time effectively.” For example, one question reads, “How confident are you that you could successfully complete the following tasks?” The response choices range from 0 = not at all confident to 10 = extremely confident. Higher scores indicate higher levels of academic self-efficacy (M = 51.96). Data Analysis SPSS (Version 22) was used to generate descriptive, frequency, and percentage information for the variables used in analyses. Mplus (Version 7) (Muthén & Muthén, 2012) was used to perform path analyses to determine the extent to which microaggression, directly and through ethnic identity, affects psychological distress, self-esteem, academic self-efficacy, and substance abuse. Path analysis was the analytic strategy used for the current study for several reasons. First, unlike standard ordinary least squares (OLS) regression, it allowed for us to assess whether ethnic identity mediates the relationships between microaggression and indicators of psychological well-being. Although structural equation modeling was considered, parameter estimation would have been inadequate given the sample size. That is, the number of parameters to be estimated via a structural model would have far exceeded that recommended by current practice (Kline, 2015). Therefore path analysis using summative scales of measures that have demonstrated strong psychometric properties in past research (as discussed in the Measures section) was used. Model fit statistics were not reported as only observed indicators were analyzed within the path model and thus perfect measurement of observed indicators is assumed (Muthén & Muthén, 2012). Total direct and indirect effects were also estimated to identify paths within the model that further explained the role ethnic identity might play in mediating the effects of microaggression on outcomes of interest. Unstandardized (B) and standardized (StdYX) estimates are reported. The StdYX output option in Mplus (Version 7) was used to produce standardized coefficients, with the objective of standardizing the parameter estimates within the model and their standard errors. This option uses the variances of all variables in the model for standardization (Muthén & Muthén, 2012). A total of 213 cases of data were included for analysis. Of these, three participants had missing data on all variables included in analyses and were therefore excluded using listwise deletion, leaving a final sample size of 210. Missing data analysis of the final sample revealed that approximately 95% had available data on all variables of interest. Mean comparison methods (t test and analysis of variance) were used to compare scores on outcome variables across age, gender, ethnicity, and survey completion (that is, whether the participant had data on all items of the survey). These analyses revealed no patterns of missingness in the data. Therefore, it was assumed that data were missing at random (Little, 1988; Little & Rubin, 1989) and estimates reported in the analysis were generated using full information maximum likelihood imputation for missing values (Muthén & Muthén, 2012). Results The sample ranged in age from 18 to 35 years (M = 23.74) and was primarily female (63.8%). Moreover, 34.7% of the sample reported their race or ethnicity as being Asian, 31.5% Latino or Hispanic, 33.5% black, and 0.5% Native American. The mean time (in years) participants had spent in college was 3.22. Descriptive information for the sample can be found in Table 1. Table 1: Sample Demographics (N = 213)   M (SD)  n  (%)  Age  23.74 (4.11)       18–23    114  53.5   24–29    68  31.9   30–35    27  12.6  Gender         Male    77  36.2   Female    136  63.8  Race or ethnicity         Asian    74  34.7   Latino/Hispanic    67  31.5   Black    71  33.5   Native American    1  .5    M (SD)  n  (%)  Age  23.74 (4.11)       18–23    114  53.5   24–29    68  31.9   30–35    27  12.6  Gender         Male    77  36.2   Female    136  63.8  Race or ethnicity         Asian    74  34.7   Latino/Hispanic    67  31.5   Black    71  33.5   Native American    1  .5  Path Analysis with the Whole Sample The findings of the a priori path analysis model with the whole study sample are reported in Table 2. As expected, we found that microaggression was positively associated with ethnic identity. As predicted, microaggression had a significant positive association with psychological distress but no other direct relationships with the outcome variables. As expected, ethnic identity was negatively associated with psychological distress, but positively associated with self-esteem and academic self-efficacy. We did not anticipate that the effect found between ethnic identity and substance abuse would be positive. Findings supported our central hypothesis that ethnic identity would mediate the relationship between racial microaggression and outcomes of psychological well-being. Ethnic identity significantly mediated the effect of microaggression on psychological distress. Moreover, including ethnic identity in the equation revealed that microaggression has a positive effect on self-esteem and academic self-efficacy via participants’ reported degree of ethnic identity. Table 2: Results of Path Analyses (N = 210)   B  95% CI  StdYX  95% CI  Microaggression → ethnic identity  .52***  .22, .81  .23***  .10, .36  Microaggression → substance abuse  –.04  –.11, .02  –.09  –.22, .04  Microaggression → psychological distress  .33**  .08, .58  .18**  .04, .32  Microaggression → self-esteem  –.12  –.34, .09  –.07  –.20, .05  Microaggression → academic self-efficacy  –.09  –.56, .37  –.02  –.17, .11  Ethnic identity → substance abuse  .03*  .00, .06  .14*  .00, .28  Ethnic identity → psychological distress  –.19**  –.30, –.08  –.23***  –.37, –.10  Ethnic identity → self-esteem  .26***  .16, .36  .35***  .22, .47  Ethnic identity → academic self-efficacy  .31***  .09, .52  .20**  .06, .34  Microaggression → ethnic identity → substance abuse  .01  –.02, .03  .03  –.04, .07  Microaggression → ethnic identity → psychological distress  –.10*  –.18, –.02  –.05*  –.10, –.01  Microaggression → ethnic identity → self-esteem  .13**  .04, .23  .08**  .02, .13  Microaggression → ethnic identity → academic self-efficacy  .16*  .01, .30  .04*  .00, .09    B  95% CI  StdYX  95% CI  Microaggression → ethnic identity  .52***  .22, .81  .23***  .10, .36  Microaggression → substance abuse  –.04  –.11, .02  –.09  –.22, .04  Microaggression → psychological distress  .33**  .08, .58  .18**  .04, .32  Microaggression → self-esteem  –.12  –.34, .09  –.07  –.20, .05  Microaggression → academic self-efficacy  –.09  –.56, .37  –.02  –.17, .11  Ethnic identity → substance abuse  .03*  .00, .06  .14*  .00, .28  Ethnic identity → psychological distress  –.19**  –.30, –.08  –.23***  –.37, –.10  Ethnic identity → self-esteem  .26***  .16, .36  .35***  .22, .47  Ethnic identity → academic self-efficacy  .31***  .09, .52  .20**  .06, .34  Microaggression → ethnic identity → substance abuse  .01  –.02, .03  .03  –.04, .07  Microaggression → ethnic identity → psychological distress  –.10*  –.18, –.02  –.05*  –.10, –.01  Microaggression → ethnic identity → self-esteem  .13**  .04, .23  .08**  .02, .13  Microaggression → ethnic identity → academic self-efficacy  .16*  .01, .30  .04*  .00, .09  Notes: CI = confidence interval, StdYX = standardized covariance using the variances of y and x. *p ≤ .05. **p ≤ .01. ***p ≤ .001. There are a number of additional notable correlations among the outcome variables. First, psychological distress was significantly negatively correlated with self-esteem (r = –.38; 95% confidence interval [CI] [–.50, –.26]; p < .001) and academic self-efficacy (r = –.26; 95% CI [–.39, –.12]; p < .001). In addition, self-esteem and academic self-efficacy were significantly positively correlated (r = .33; 95% CI [.21, .46]; p < .001). There were no significant correlations between substance abuse and psychological distress, self-esteem, or academic self-efficacy. Multigroup Path Analysis A multigroup test of the path analysis was conducted to explore the potential for variation in the effects across the Asian, Latino/Hispanic, and black groups while recognizing the limitations for interpretation given the subsample sizes (Kline, 2015). The significant results of the multigroup analysis are reported here. The single Native American participant was not included in this part of the analysis. See Figure 1 for the findings of the path analysis depicted for the whole sample and each of the subgroups. Figure 1: View largeDownload slide Results of Path Analysis for Whole Sample and Each Racial or Ethnic Group Note: *p ≤ .05. **p ≤ .01. ***p ≤ .001. Figure 1: View largeDownload slide Results of Path Analysis for Whole Sample and Each Racial or Ethnic Group Note: *p ≤ .05. **p ≤ .01. ***p ≤ .001. For the Asian subgroup (n = 74), ethnic identity was positively associated with self-esteem (StdYX = .35; 95% CI [.14, .55]; p < .001) and academic self-efficacy (StdYX = .29; 95% CI [.07, .51]; p < .01). Within this group, there was a significant negative correlation between psychological distress and self-esteem (r = –.31; 95% CI [–.52, –.10]; p < .01), a negative correlation between psychological distress and academic self-efficacy (r = –.27; 95% CI [–.48, –.05]; p < .05), and a positive correlation between self-esteem and academic self-efficacy (r = .36; 95% CI [.16, .57]; p < .001). For the Latino/Hispanic subgroup (n = 67), microaggression experienced was positively associated with ethnic identity (StdYX = .29; 95% CI [.07, .51]; p < .01). Among Latino/Hispanic participants, ethnic identity was negatively associated with psychological distress (StdYX = –.33; 95% CI [–.56, –.11]; p < .01) and positively associated with self-esteem (StdYX = .37; 95% CI [.15, .59]; p < .01). Microaggression was positively associated with psychological distress (StdYX = .26; 95% CI [.03, .49]; p < .05) and negatively associated with self-esteem (StdYX = –.22; 95% CI [–.45, –.00]; p < .05). Within this group, there was a negative correlation between psychological distress and self-esteem (r = –.58; 95% CI [–.74, –.42]; p < .001) and a negative correlation between psychological distress and academic self-efficacy (r = –.27; 95% CI [–.50, –.04]; p < .05). Ethnic identity had a significant mediating effect on the relationship between microaggression and self-esteem among Latino/Hispanic participants (StdYX = .11; 95% CI [.02, .21]; p < .05). For the black subgroup (n = 71), ethnic identity was negatively associated with psychological distress (StdYX = –.34; 95% CI [–.56, –.12]; p < .01) and positively associated with self-esteem (StdYX = .30; 95% CI [.06, .53]; p < .05). Microaggression was positively associated with psychological distress (StdYX = .29; 95% CI [.06, .52]; p < .05). Within this group, there was a negative correlation between psychological distress and self-esteem (r = –.31; 95% CI [–.54, –.09]; p < .01) and a positive correlation between self-esteem and academic self-efficacy (r = .26; 95% CI [.01, .50]; p < .05). Discussion The current study contributes to the literature that seeks to understand the relationship between racial discrimination and psychological well-being among young adult college students. Specifically, the study applied path analysis to observe whether ethnic identity has a protective effect on the negative influence of racial microaggression on substance abuse, psychological distress, self-esteem, and academic self-efficacy. Congruent with the research literature, our findings suggest that subtle forms of discrimination can have a damaging impact on the emotional health of racial and ethnic minority young adults. In addition, ethnic identity had a beneficial effect on the relationship between racial discrimination and all of the outcomes of psychological well-being except substance abuse. In fact, ethnic identity reversed the negative direct effect of racial microaggression on psychological well-being and resulted in positive associations instead. It is clear from these findings that ethnic identity has a critically positive role in emotional well-being and can serve as a protective factor to the negative effects of microaggression on psychological well-being. Said differently, those young adults who have more developed ethnic identity may experience fewer psychologically detrimental effects when exposed to racial microaggression. The positive effect of microaggression on ethnic identity that was found in this pathway supports the proposition of the rejection-identification model (Branscombe et al., 1999), which says that awareness and preparedness for racial discrimination can heighten individuals’ ethnic group identification. This effect cannot be observed in the context of the relationship between discrimination and psychological well-being through traditional OLS interaction analysis and appears to provide crucial insight into the dynamics involved in developing resilience to racial microaggression. Interestingly, substance abuse had opposite relationships than expected with both racial microaggression and ethnic identity. Ethnic identity was positively and significantly related to substance abuse, and racial microaggression found a nonsignificant negative relationship that was also reversed when the interaction with ethnic identity was tested. In addition, substance abuse was not correlated with the other outcome indicators of psychological well-being. The differential findings may lie in that substance abuse is a behavioral indicator, whereas the other indicators of psychological well-being measure more internal states. A possible explanation might be that substance abuse was measured using CRAFFT, an instrument designed as a screening tool to assess the likelihood of substance abuse, rather than as a summed measure of extent of substance abuse. Another possible explanation may concern social relationships that foster substance use among college students. Some of the dimensions of ethnic identity explicitly measured with MEIM (Phinney, 1992) have to do with spending time with people from one’s own ethnic group. It may be that substance use is increased in social contexts, and people with higher levels of ethnic identity are more likely to engage in social contexts. Future investigations of the relationships among racial microaggression, ethnic identity, and substance use and abuse might benefit from assessing for contextual information about substance use as well as for specific substances and frequency of use, in addition to using instruments that use diagnostic indication of abuse. Racial and Ethnic Group Differences A post hoc multigroup analysis was conducted to explore how the relationships in the model differed as a function of racial and ethnic group. No hypotheses were asserted about what specifically might be different between the groups, and path analysis is optimally conducted with larger sample sizes, so specific interpretation of the variation in the findings should be drawn cautiously. The results of the multigroup analysis suggest that there were substantial differences for each group, though the directionality of all of the relationships was similar for all of the groups (except that ethnic identity and substance abuse found zero effect in the Latino/Hispanic group). Figure 1 provides a helpful visual reference for how the findings for each of the groups differed from the analysis with the whole sample, as well as how they compare with each other. Interestingly, there was considerable variation in which relationships were significant for each group; however, only one relationship appeared in the multigroup analysis that was not found in the whole sample. This exception was that microaggression was significantly related to self-esteem in the Latino/Hispanic and not in either of the other subgroup findings. The relationship between microaggression and ethnic identity was not significant in the Asian and black subsamples, but was in the Latino/Hispanic subsample. Microaggression was not significantly related to psychological distress in the Asian group, but was in the Latino/Hispanic and black groups. The positive significant effect between ethnic identity and substance abuse in the whole sample was not found in any of the subgroups and was nonexistent in the Latino/Hispanic group. Ethnic identity was significantly related to self-esteem in all the subgroups, to psychological distress in both the Latino/Hispanic and black groups but not the Asian group, and to academic self-efficacy only in the Asian group. Also noteworthy is that the correlations among the outcome variables of psychological distress, self-esteem, and academic self-efficacy were all significantly related to each other in all of the subsamples, but no significant relationship was found between psychological distress and academic self-efficacy for the black group. Only one of the mediation effects that was significant in the subgroup analysis was observed in the multigroup analysis. That significant mediation was found for the self-esteem outcome for the Latino/Hispanic group. There appear to be some substantial differences among racial and ethnic subgroups that were obscured within the bigger study sample. These differences are much more difficult to compare across different research investigations, pointing to recommendations for future research involving large-scale multigroup comparison. Many researchers have suggested that it is important to study specific racial and ethnic groups separately because every group has unique experiences, and every group is composed of numerous ethnicities and wide variation in culture (Mossakowski, 2003). In addition, the social context is likely to influence variations in discriminatory experiences and opportunities for cultural participation, as are factors like how long people have been in the United States and how much they have acculturated (Yip, Gee, & Takeuchi, 2008). At least as important to consider is the unique historical oppression of each group in the United States and how that shapes the meaning for what race, ethnicity, and discrimination mean in their lives. Some researchers have also implicated different dimensions of ethnic identity to explain variations among these relationships (Brittian et al., 2013). Implications Although there are inherent limitations in the study’s use of cross-sectional data to test hypothesized causal relationships, and in sample sizes that are smaller than optimal for multigroup comparison, the study offers some important insights and points to a number of implications for social work practice, research, and education. Social workers aligned to eradicate health inequalities strive to reveal and combat forces of oppression that influence life trajectories (Walters et al., 2016). Racial and ethnic minority young adult college students enrolled in a public college, such as those in our study, may have faced and overcome considerable disadvantage earlier in their lives. For example, some may have experienced financial hardship, exposure to risk factors for behavioral and emotional problems, or racial discrimination. College should contribute to their pathways toward positive development, psychological well-being, and equal opportunity—not contribute further to disparities. Practitioners should include assessment of client experiences concerning racial discrimination, particularly in the form of racial microaggressions, and ethnic identity in developing case conceptualizations. These assessments can help practitioners work with clients to recognize the role racial discrimination may have in their compromised well-being as well as the strength of ethnic identity in fostering resilience to the harmful impacts of racial discrimination. It seems that effective intervention might lie in a balance between raising awareness of how to manage microaggressive experiences when they arise while creating opportunities for young adults to strengthen their ethnic identity, for example through participating in cultural activities germane to their ethnicities. It is also critical to recognize how microaggression may occur within clinical settings and even unintentionally from social workers themselves. Social workers’ unawareness of microaggressions that clients are exposed to in agencies and within therapeutic dyads can be harmful to clients and undermine the therapeutic alliance. Social workers are encouraged to intentionally assess and address common microaggressions that might be occurring within their practices. In addition, social work education should include instruction about microaggression to help students understand how racism is perpetuated and explicitly teach about the importance of discrimination and ethnic identity in influencing well-being among racial and ethnic minority groups. Prevention and intervention measures specifically aimed toward ameliorating the negative impacts of racial and ethnic discrimination among college students might include universal strategies that combat stereotypes and promote open discussions about race and the importance of ending subtle and overt forms of discrimination. In addition, the findings highlight the importance of providing opportunities like clubs and physical space for students to gather with other students with similar ethnic backgrounds to promote ethnic identity development. Furthermore, colleges and universities should be encouraged to strive for inclusive messages that do not marginalize racial and ethnic minority students and do promote and celebrate the range of diversity represented on campus. Social work researchers should further explore the relationships among the variables that were identified in this article. This might be done using a larger and more representative sample of young adults, and sampling and longitudinal methods that allow for inferring causality between discussed constructs, as well as studies focused on the role of ethnic identity as it mediates the effects of microaggression on psychological well-being over time. In any case, a better understanding of how ethnic identity is protective against the negative effects of microaggression on psychological well-being in young adults can help inform mental health care practice and education to better serve racial and ethnic minority young adults. Conclusion This study contributes to growing evidence that ethnic identity acts as a protective factor against the harmful impacts of racial discrimination on psychological well-being among racial and ethnic minority college students. Specifically, this study assessed the extent to which racial discrimination in the form of microaggression, and ethnic identity, predict participants’ psychological distress, self-esteem, academic self-efficacy, and substance abuse. The findings point to the value in measuring racial discrimination in the form of specific microaggressions in continued pursuit of understanding the relationships among these variables. The findings also highlight the critical importance of ethnic identity in the psychological well-being of racial and ethnic minority young adults. Differential findings between racial and ethnic groups suggest that research should be specifically targeted to inform practice with specific groups. As advocates for both social justice and mental wellness, social workers should strongly consider how racial microaggressions and ethnic identity can be integrated toward better understanding and eradicating inequalities in psychological well-being. Shandra S. Forrest-Bank, PhD, is assistant professor, College of Social Work, University of Tennessee, Knoxville, 1618 Cumberland Avenue, 410 Henson Hall, Knoxville, TN 37931; e-mail: [email protected]. Matthew J. Cuellar, PhD, is assistant professor and assistant director of PhD Program, Yeshiva University, New York. References Ahmed, A. T., Mohammed, S. A., & Williams, D. R. ( 2007). Racial discrimination & health: Pathways & evidence. Indian Journal of Medical Research,  126, 318– 327. Armenta, B. E., & Hunt, J. S. ( 2009). Responding to societal devaluation: Effects of perceived personal and group discrimination on the ethnic group identification and personal self-esteem of Latino/Latina adolescents. 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2018 Social Work Research

doi: 10.1093/swr/svy003

Correction: In the article titled “Psychological Self-Sufficiency: A Bottom-Up Theory of Change in Workforce Development” by Philip Young P. Hong, Sangmi Choi, and Whitney Key (March 2018, Vol. 42, No. 1, pp. 22–32), when the article first appeared online, Philip’s professional title was listed as “professor”; it has been changed to “Lucian and Carol Welch Matusak endowed professor and director, Center for Research on Self-Sufficiency.” The article should have appeared with the following information: This research was supported by the University Partnership Research Grants for the Health Profession Opportunity Grants Program under the Affordable Care Act, Grant #90PH0018, from the Office of Planning, Research and Evaluation of the Administration for Children and Families, U.S. Department of Health and Human Services, and the Korea Foundation of the Republic of Korea. © 2018 National Association of Social Workers.

Hawkins, Jaclynn M;Mitchell, Jamie

2018 Social Work Research

doi: 10.1093/swr/svx028

Patient-centered communication is a component of high-quality health care that foregrounds the patient’s orientation and experience during patient–provider interactions. Specifically, these medical interactions emphasize caring, trust, rapport building, shared decision making, and the social and emotional well-being of the patient (Mauksch, Dugdale, Dodson, & Epstein, 2008). In practice, effective communication between patients and their health providers involves and informs a number of actions consequential to short- and long-term health outcomes. These actions include shared medical decision making, facilitating positive patient activation and successful self-management of health conditions, helping patients to navigate complex health systems, managing uncertainty, and engaging in a meaningful information exchange (Epstein & Street, 2007; Maly, Stein, Umezawa, Leake, & Anglin, 2008; Ong, de Haes, Hoos, & Lammes, 1995). Studies affirm various pathways through which specific and well-defined communicative behaviors on the part of physicians directly and indirectly contribute to better health outcomes for patients (Street, Makoul, Arora, & Epstein, 2009). However, research suggests that, when interacting with physicians in general, African Americans report less satisfaction with patient–provider communication and less shared decision making when compared with white Americans (Maly et al., 2008; Manfredi, Kaiser, Matthews & Johnson, 2010; Peek et al., 2009). Furthermore, studies have reported that some primary care physicians hold negative perceptions of African American patients that affect their ability and willingness to engage in patient-centered communication and care, resulting in objectively different content, tone, and overall quality of communication with their African American versus white patients (Cooper et al., 2012; Johnson, Roter, Powe, & Cooper, 2004; Moore et al., 2012) and less time spent talking with African American patients (Cené, Roter, Carson, Miller, & Cooper, 2009). For instance, African American patients are often viewed as noncompliant, less-effective communicators, and less educated during medical encounters (Cooper & Roter, 2003; Moore et al., 2012). In line with these findings, studies show that some primary care physicians are more verbally dominant with African American patients than white patients; treat African American patients more contentiously; are less likely to use supportive talk; and misunderstand or misinterpret cultural values, beliefs, and relational needs that inform African American patients’ health decisions and behaviors (Hammond, 2010; Moore et al., 2012; Street, Gordon, & Haidet, 2007). These findings are particularly relevant for African American men who experience higher morbidity, poorer prognoses, and higher mortality across a range of chronic diseases (Thorpe et al., 2013). Multiple factors contribute to such disparities, and a lack of patient-centered patient–provider communication may play an important role. Specifically, when African American men perceive differential treatment attributed to their race during medical visits, they are distrustful of their physician and the health care system overall (Hammond, 2010), potentially leading to a reluctance to engage with the health care system for future needs and critical delays in the diagnosis and treatment of serious illnesses (Hammond, Matthews, Mohottige, Agyemang, & Corbie-Smith, 2010). Social workers can play a key role in facilitating higher-quality patient–provider communication for African American men (Gehlert, 2012; National Association of Social Workers [NASW], 2017). According to NASW, social workers in health care settings offer a range of services aligned with strengthening the patient–provider relationship, including facilitating teamwork and collaboration, promoting quality improvement in health care processes, providing support with system navigation, soliciting the involvement of supportive networks, and advocating for a patient’s personal autonomy while working from a strengths-based perspective (NASW, 2017). Social workers are also trained to facilitate health behavior change, deliver individual and group psychosocial interventions that are consequential to health outcomes, help patients become more informed consumers of health care services, provide crisis intervention, and work within limited time and fiscal constraints to find medical or community-based accommodations for patients with complex health conditions, among a range of other skills (Mitchell, 2012). Prior research has often failed to consider how social workers in health care settings can uniquely support older adults and underserved racial and ethnic minority groups in improving the quality of patient–provider communication. In addition, research on African American men and their experiences with patient–provider communication remains limited. The current study highlights the sociodemographic variation among African American men who report experiencing suboptimal patient–provider communication with regard to one specific measure: feeling listened to during medical visits. We also consider the ways in which social workers embedded in clinical settings might serve as cultural brokers to facilitate improved health communication in support of African American male patients. Method Sample Between 2006 and 2010, a large hospital system in the Midwest conducted a demonstration project to examine whether a longitudinal patient navigation intervention could increase the utilization of cancer screening tests among older African American adults enrolled in Medicare (Parts A and B). This study was funded by the Centers for Medicare and Medicaid Services. The original study recruited a large sample of 5,783 African American adults age 60 and older from more than 25 senior residences, nearly 100 senior activity centers, and approximately 50 church groups within and closely representative of the catchment area of the health care system. Participants completed a baseline questionnaire that contained adapted items from existing instruments and measures assessing mental and physical health history and status, access to and perceptions of health care, health behaviors and beliefs, and demographic items. A trained research assistant or nurse collected the baseline data by phone or in person. The current study obtained the deidentified data for all African American men in the original study (n = 1,666), with the approval of institutional review boards of both the parent hospital and the affiliated university, to conduct a secondary analysis focused on their responses to items referencing health communication. Data underlying the findings described here are not yet publicly available, and additional details about this study and findings from this sample are published elsewhere (Mitchell, Manning, Shires, Chapman, & Burnett, 2015; Mitchell, Watkins, Shires, Chapman, & Burnett, 2017) Measures Outcome Variable A single item measured patient–provider communication. Participants were asked, “In the past 12 months how often did you feel your doctors or other health care professionals listened carefully to you?” This variable was coded as 0 for always/usually/sometimes and 1 for never. Independent Variables Independent variables found in studies to be related to patient physician communication and included here were age, education, income, marital status, self-care, pain/discomfort, emotional well-being, and trouble with mobility. Age was coded as 0 for under 75 years and 1 for 75 years and older. Education was categorized as high school or less (0) or some college or more (1). Income was coded as 0 for greater than $20,000 per year and 1 for less than or equal to $20,000 per year. Marital status was categorized as single (0); widowed, divorced, or separated (1); and married or partnered (2). Self-care and pain/discomfort were measured by asking participants to indicate which statement best described their own health state today: Self-care was coded as 0 for “I have no problems with self-care” and 1 for “I have some problems washing or dressing myself” or “I am unable to wash or dress myself.” Pain/discomfort was coded as 0 for “I have no pain or discomfort” and 1 for “I have moderate or extreme pain or discomfort.” Emotional well-being was measured by asking, “How much of the time during the past four weeks have you felt downhearted and blue?” and coded as 0 for none of the time and 1 for most of the time. Last, trouble with mobility was measured by asking participants, “For each moderate activity, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf, how limited are you in performing the activity?” and was coded as 0 for not limited at all and 1 for limited moderate activity. Analysis For descriptive analysis, frequencies were calculated for all variables. Key independent variables were cross-tabulated with the outcome (patient–provider communication), and differences across groups were calculated using t tests for continuous variables and Pearson’s chi-square tests for categorical variables. A logistic regression model was used to examine the association between predictors and patient–provider communication. Logistic regression was used for patient–provider communication because the measure was dichotomous. Analyses were conducted using SPSS (Version 22), and a missing value analysis determined that missing values were randomly distributed across all cases such that no individual variable had more than 20 percent of missing cases. Results Demographic and other characteristics for the analytic sample for African American men who reported problems and those who did not report problems with patient–provider communication are presented in Table 1. A majority of African American men who reported that their physician never listened to them in the last 12 months were under the age of 75 (51%); earned more than $20 K (70%); and were more likely to be widowed, separated, or divorced (52%). In addition, 70% of men in this category reported their highest level of education as high school or less. A majority of men who perceived that their physicians did not listen to them also reported no problems with self-care (91%), experienced no pain (53%), were more likely to be downhearted (79%), and had no trouble with mobility (69%). Table 1: Main Variable Comparisons, by Patient–Physician Communication (N = 1,666) Variable  Doctor Never Listens (n = 1,336) n (%)  Doctor Always/Usually/ Sometimes Listens (n = 330) n (%)  p  Education      .03   High school or less  938 (70)  214 (65)     Some college or more  396 (30)  116 (35)    Age      .00   Under 75  682 (51)  214 (65)     75 and older  654 (49)  116 (35)    Annual income ($)      .00   Greater than 20,000  931 (70)  180 (55)     20,000 or less  405 (30)  150 (45)    Marital status      .00   Single  149 (11)  52 (16)     Widowed/separated/divorced  700 (52)  101 (31)     Married/partnered  486 (36)  167 (52)    Self-care      .01   No problems with self-care  1,219 (91)  287 (87)     Some problems/unable to care for self  117 (9)  43 (13)    Pain/discomfort      .00   No pain  705 (53)  141 (43)     Moderate/extreme pain or discomfort  631 (47)  189(57)    Emotional well-being      .00   Not downhearted/blue  281 (21)  138 (42)     Downhearted/blue some/most of the time  1,055 (79)  192 (58)    Trouble with mobility      .00   Not limited at all  927 (69)  186 (56)     Limited moderate activity  409 (31)  144 (44)    Variable  Doctor Never Listens (n = 1,336) n (%)  Doctor Always/Usually/ Sometimes Listens (n = 330) n (%)  p  Education      .03   High school or less  938 (70)  214 (65)     Some college or more  396 (30)  116 (35)    Age      .00   Under 75  682 (51)  214 (65)     75 and older  654 (49)  116 (35)    Annual income ($)      .00   Greater than 20,000  931 (70)  180 (55)     20,000 or less  405 (30)  150 (45)    Marital status      .00   Single  149 (11)  52 (16)     Widowed/separated/divorced  700 (52)  101 (31)     Married/partnered  486 (36)  167 (52)    Self-care      .01   No problems with self-care  1,219 (91)  287 (87)     Some problems/unable to care for self  117 (9)  43 (13)    Pain/discomfort      .00   No pain  705 (53)  141 (43)     Moderate/extreme pain or discomfort  631 (47)  189(57)    Emotional well-being      .00   Not downhearted/blue  281 (21)  138 (42)     Downhearted/blue some/most of the time  1,055 (79)  192 (58)    Trouble with mobility      .00   Not limited at all  927 (69)  186 (56)     Limited moderate activity  409 (31)  144 (44)    Using logistic regression, Table 2 shows odds ratios (ORs) and confidence intervals for a model predicting African American men’s likelihood of having a doctor who never listens. The model tested the role of sociodemographic and other factors relevant to patient–provider communication. In the present sample, African American men who were older than 75 years were more likely to report that their doctor never listens (OR = 1.540, p < .05). Men who were separated, widowed, or divorced (OR = 1.635, p < .05) and those who reported feeling downhearted or blue some or most of the time (OR = 2.143, p < .001) were also more likely to report problems with patient–provider communication. Men who reported limited moderate activity (OR = 0.603, p < .001) and those who had an income of less than $20,000 per year (OR = 0.745, p < .05) were less likely to encounter a doctor who they perceived as never listening to them. Table 2: Predictors of Having a Doctor Who Never Listens   OR  95% CI  Education       High school or less       Some college or morea  0.779  [0.594, 1.022]  Age (years)       Under 75a   75 and older  1.540**  [1.170, 2.027]  Annual income ($)       20,000 or less   More than 20,000a  0.745**  [0.561, 0.989]  Marital status       Single       Married/partnereda  0.899  [0.616, 1.313]   Separated/widowed/divorced  1.635**  [1.078, 2.481]  Self-care       Not a problema       Some problems/unable to care for self  1.052  [0.688, 1.610]  Pain/discomfort       No paina       Moderate/extreme pain or discomfort  0.870  [0.6622, 1.142]  Emotional well-being       Not downhearted/bluea       Downhearted/blue most of the time  2.143***  [1.631, 2.802]  Trouble with mobility       Not limited at alla       Limited moderate activity  0.603***  [0.451, 0.807]    OR  95% CI  Education       High school or less       Some college or morea  0.779  [0.594, 1.022]  Age (years)       Under 75a   75 and older  1.540**  [1.170, 2.027]  Annual income ($)       20,000 or less   More than 20,000a  0.745**  [0.561, 0.989]  Marital status       Single       Married/partnereda  0.899  [0.616, 1.313]   Separated/widowed/divorced  1.635**  [1.078, 2.481]  Self-care       Not a problema       Some problems/unable to care for self  1.052  [0.688, 1.610]  Pain/discomfort       No paina       Moderate/extreme pain or discomfort  0.870  [0.6622, 1.142]  Emotional well-being       Not downhearted/bluea       Downhearted/blue most of the time  2.143***  [1.631, 2.802]  Trouble with mobility       Not limited at alla       Limited moderate activity  0.603***  [0.451, 0.807]  Notes: OR = odds ratio; CI = confidence interval. aReference category. *p < .10. **p < .05. ***p < .001. Discussion These results reflect one important dimension of the patient–physician relationship, feeling heard. If reflective of actual practice, our study raises the concern that certain sociodemographic subgroups among African American men may be afforded fewer opportunities to communicate their needs and concerns during already brief medical visits. In the current study, men who were age 75 or older, single, separated, widowed, or divorced, and men with depressive symptoms were the same participants who reported that their doctor never listened to them. African American men over age 75 were significantly more likely than younger men to report this issue with patient–provider communication. These findings are consistent with a body of literature documenting a range of barriers older adults may face to accessing high-quality patient-centered health care. For example, older adults may have difficulty due to a lack of reliable transportation, inadequate social support at home, trouble navigating an ever-changing and complex health care system, encountering health care professionals undereducated in geriatrics, and cultural barriers to care (Horton & Johnson, 2010). Our findings suggest a need for increased clinical attention to the specific and potentially unique communicative needs of older African American men. Marital status was also significant in predicting communication issues in this study, and older African American men may also benefit from increased research on how the presence of spouses or partners during medical interactions influences the quality and dynamic of patient–provider communication. In line with extant research (Mitchell et al., 2017; Watkins, Hawkins, & Mitchell, 2015), our findings suggest a link between mental and emotional health, and communication with a health provider in health care settings. In the current study, men who reported feeling downhearted or blue were more likely to report that their doctor did not listen to them. Literature documents that African American men may not identify with the label of depression, but instead use more culturally acceptable language for depressive symptoms, such as “downhearted” or “blue” (Mitchell et al., 2017). Research has also shown that health providers may often misidentify or misdiagnose depressive symptoms in African American male patients (Watkins, 2012; Watkins et al., 2015). Earlier work has shown that individuals who experience mental health issues, such as depression, are more likely to engage in poor adherence to medical regimens, experience worse health outcomes, and are more likely to report feeling disrespected by physicians in medical settings (Jonassaint et al., 2013); moreover, physicians are more likely to negatively rate and have lower levels of positive regard for patients who present with depressive symptoms (Jonassaint et al., 2013). Together, then, this research suggests that among older African American male patients, experiencing mental or emotional health issues may both compound and contribute to the effects of poor patient–provider communication. Research on the specific mechanisms through which mental and emotional health affect patient–provider communication is extremely limited for this population and in need of attention. Interestingly, men who had an income of less than $20,000 per year and men who had limited mobility were less likely to report having a doctor who never listens. We speculate that men with a lower income may have less access to health care and thus may have fewer interactions with physicians, in addition to being less informed about effective communication practices in patient–provider interactions. In addition, men with limited mobility may have more interactions with health care due to their physical conditions, which may increase their level of communication with providers. More research is needed to gain a deeper understanding of these findings. Role of Social Work in Health Care Communication Positive communication with providers can result in more active participation in medical care and improvement in perceived quality of communication by patients (Gourlay, Lewis, Preisser, Mitchell, & Sloane, 2010). Successful patient–provider communication can be achieved through the utilization of an interdisciplinary team of health care professionals (Gehlert, 2012; Peterson, 2012). Specifically, social workers are in a unique position to help improve the quality of communication between physicians and patients (Peterson, 2012). As our results indicate, clinicians working within integrated and community health settings might identify and provide additional support and resources for older, single African American men with higher incomes and those with depressive symptoms in an effort to facilitate higher quality doctor–patient communication. A review of interventions geared at treating older adults found that a team-based approach to health care provision in addition to enhanced communication between care providers across the continuum was essential for achieving optimal health outcomes for older adults (Hickman, Newton, Halcomb, Chang, & Davidson, 2007). Social workers are particularly well trained to identify and assess the needs of older African American men experiencing emotional issues, who may be at higher risk for having suboptimal medical interactions with physicians. Social work clinicians are critical gatekeepers who may be able to assess a patient’s understanding of and ability to navigate the health care system, connect them with resources, and work as a cultural broker to aid physicians and other health providers in understanding how age, culture, gender, and possible prior poor health care experiences may shape how individual patients respond to medical advice, express concerns, or involve family members in decision making. Social work clinicians in health care settings have a particularly important role in supporting the personal autonomy of older African American men and advocating that physicians empower them in medical decision-making processes. Research has shown that primary care providers are often more verbally dominant and engage in less patient-centered communication with African American patients than with white patients (Johnson et al., 2004) and may be less culturally competent regarding how African Americans perceive and manage health conditions (Street & Haidet, 2011). Social workers can help to inform health care providers of the importance of respecting and promoting the right of patients to self-determination, while assisting patients in their efforts to identify and communicate their own goals for their care. Additional studies that test the effectiveness of social work interventions for promoting improved patient–provider communication and patient’s active participation would support social work clinician’s increased involvement in this aspect of patient-centered care. Limitations and Conclusions A relatively large sample size was used for an otherwise understudied population. However, the baseline questionnaire analyzed for this study was cross-sectional and lacked random sampling, limiting the ability to infer causality or generalize these findings to other patient populations. Furthermore, due to the secondary nature of existing data, we were limited in our ability to use more comprehensive measures of patient–provider communication. Also, the geographic homogeneity of participants limited the ability to generalize findings to other populations. Future studies seeking to overcome such limitations might use a more representative longitudinal study with a more comprehensive set of validated patient–provider communication measures. Despite these limitations, this study has advanced the discussion on important determinants of patient–provider communication for a population experiencing marked health care disparities and provided a nuanced examination of older African American men’s experiences with this important aspect of health care. Furthermore, we advocate for social work’s increased role in engaging patients and providers around communication quality. This recommendation is tempered by the reality that not all social work clinicians are in a position to directly influence this dynamic. Undoubtedly, social work clinicians and researchers will be critical to improving the quality of patient-centered health care and communication for older African American men. Our hope is that this line of inquiry will move us one step closer. Jaclynn M. Hawkins, PhD, MSW, is assistant professor, School of Social Work, Michigan State University, 655 Auditorium Road, Baker Hall, Room 122, East Lansing, MI 48824; e-mail: [email protected]. Jamie Mitchell, PhD, MSW, is assistant professor, School of Social Work, University of Michigan, Ann Arbor. References Cené, C. W., Roter, D., Carson, K. A., Miller, E. R.III, & Cooper, L. A. ( 2009). The effect of patient race and blood pressure control on patient-physician communication. 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