Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Aug 8.
Published in final edited form as: Psychiatry Res. 2020 Feb 8;286:112855. doi: 10.1016/j.psychres.2020.112855

Effects of Perceived Social Status and Discrimination on Hope and Empowerment among Individuals with Serious Mental Illnesses

Stephanie Langlois a, Luca Pauselli b, Simone Anderson c, Oluwatoyin Ashekun a, Samantha Ellis c, JaShala Graves c, Adria Zern d, Ebony Gaffney c, Ruth S Shim e, Michael T Compton d,*
PMCID: PMC7775621  NIHMSID: NIHMS1565552  PMID: 32092596

Abstract

Hope and empowerment are key elements of recovery in the context of serious mental illnesses (SMI). We examined predictors of hope among individuals with SMI and tested a hypothesized path model in which perceived social status and perceived discrimination adversely impact hope, directly and through their impacts on depressive symptoms. Data from 232 individuals with SMI receiving care in public-sector settings were used in both a multiple linear regression (predicting Herth Hope Scale scores), and in path analyses examining both direct and indirect effects of perceived social status (Social Status Ladder) and perceived discrimination (Everyday Discrimination Scale). Depressive symptoms, perceived social status, and perceived discrimination were predictive of hope. Path analyses revealed that perceived social status has a direct effect on hope and empowerment but also impacts hope through its effects on depression. Similarly, perceived everyday discrimination affects hope and empowerment, though this effect is mediated through its effects on depression. Two alternative models and a trimmed hypothesized model did not fit the data or improve fit These social determinants of mental health should provoke program and policy change to improve mental health and enhance recovery among persons with SMI.

Keywords: Bipolar disorder, Depression, Empowerment Scale, Functional recovery, Herth Hope Scale, Mental health policy, Schizophrenia, Social determinants, Social Status Ladder

1. INTRODUCTION

Serious mental illnesses (SMI) are chronic conditions often requiring continuous treatment that is shaped both by advances in psychopharmacology and the ongoing restructuring of the mental health system (e.g., transitions from institutional to community-based care). Longitudinal studies show that individuals with SMI can show significant clinical and functional improvements over time (Ciompi, 1980; Gagné et al., 2007; Harding et al., 1987a, 1987b; Liberman et al., 2002; McGlashan, 1987, 1988; Pevalin and Goldberg, 2003; Rogers et al., 2005). The concept of recovery in the context of SMI remains a continually evolving paradigm now centered around being “in recovery,” as opposed to “recovery from” in the sense of cure. That is, while “recovery from” might suggest that the individual’s health and wellbeing will return to a pre-illness state, being “in recovery” describes the process toward a successful acclimatization to SMI, somewhat analogous to functional recovery (Davidson and Roe, 2007; Piat, 2009), even if some symptoms persist. This acclimatization embodies a range of individual goals and expectations that vary between those living with SMI and their clinicians.

Hope has been described as the “catalyst of the recovery process” (Substance Abuse and Mental Health Services Administration, 2006). Hope heavily influences functional recovery outcomes in persons with SMI (Corrigan et al., 2001; Coskun et al., 2018; Hoffman et al., 2000; Kern et al., 2009; Kylma et al., 2006; Yanos et al., 2007) and inspires people to reach their goals by serving as a positive coping mechanism that increases self-efficacy and empowerment (Herth, 1995; Lysaker et al., 2001, 2005). Additionally, the presence of hopelessness reduces functional recovery, decreases treatment adherence, and increases suicide risk in persons with SMI (Kavak and Yilmaz, 2018; Littrell et al., 1996; Lysaker et al., 2001, 2005; Lyu and Zhang, 2014). Consequently, recovery-oriented mental health professionals seek to promote hope and empowerment through a variety of strategies. To develop and strengthen such strategies, there must be more research on predictors of hope in persons with SMI.

Perceived social status and perceived discrimination could be determinants of hope, though research is lacking in this area. Persons with mental illness report lower subjective social status compared to people without mental illnesses (Scott et al., 2014; Singh-Manoux et al., 2005). The stigma/discrimination around SMI is extremely pervasive, with a majority of persons with SMI reporting discrimination due to mental illness (Dickerson et al., 2002; Koschorke et al., 2014; Mantovani et al., 2016; Oh et al., 2014; Thornicroft et al., 2009; Vass et al., 2015). Other studies have found that lower perceived social status and discrimination have many negative effects on a variety of physical and mental health conditions (Gabbidion et al., 2014; Kessler et al., 1999; Scott et al., 2014; Singh-Manoux et al., 2005; Todorova et al., 2010; Üçok et al., 2013; Vass et al., 2015). Consequently, perceptions of both social status and discrimination are of interest, but their effects as predictors of hope and empowerment among individuals with SMI have seldom been explored (Vass et al., 2015).

We sought to gain a better understanding of several constructs associated with hope among those with SMI, including perceived social status and discrimination. After examining the effects of such constructs through regression analysis (i.e., predicting variance in hope scores based on a number of independent variables), we then further explored associated variables through path analysis, which allows simultaneous analysis of all the variables in the model through a series of regressions applied sequentially to the data to allow for tests of theoretical propositions in nonexperimental data. Identifying such a model could enhance our understanding of the diverse ways in which functional recovery can be maximized by enhancing hope and empowerment.

2. METHODS

2.1. Setting, Sample, and Procedures

Participants were drawn from a parent study (n=240) in Southeast Georgia focused on the effectiveness of a new form of recovery-oriented case management and community navigation (Compton et al., 2011; Compton et al., 2016); those included in this analysis (n=232) had complete data on the variables of interest. All participants were adults with SMI recruited from three inpatient psychiatric facilities. Staff at each facility referred eligible patients to the research team; enrollment occurred as patients were about to be discharged from a stay of at least two nights in a state hospital or one of two crisis stabilization units. Research diagnoses were obtained using the mood disorders and psychotic disorders modules of the Structured Clinical Interview for DSM-5: Clinical Version (SCID5-CV) The SCID is considered the gold standard instrument to obtain psychiatric diagnoses in clinical research settings due to its high validity and reliability (First et al., 2015; Lobbesteal et al., 2011; Segal and Williams, 2014). Interviews lasted 2–4 hours, and covered the measures and rating scales described below, along with others that were part of the parent study. All procedures were reviewed and approved by the university’s and the State’s Institutional Review Boards. In this analysis, we make use of baseline data obtained prior to discharge and before randomization to either of the two arms of the overarching study, which then followed participants longitudinally.

Inclusion criteria consisted of the following: (1) 18–65 years of age; (2) English speaking; (3) a clinical diagnosis of a psychotic disorder or a mood disorder (confirmed with the SCID); (4) two separate inpatient psychiatric admissions for two or more days in the past 12 months; (5) being able and willing to provide written informed consent for research participation; and (6) being discharged to reside within the eight counties served by the Community Service Board (public mental health agency) hosting the research. Exclusion criteria included: (1) known or suspected developmental or intellectual disability, or dementia; and (2) presence of a serious medical condition that would interfere with study participation (e.g., shortness of breath, pain).

2.2. Materials

Patients were evaluated using the following scales. The assessments were presented verbally, and patients were provided with cue cards showing the response options.

The Herth Hope Scale (HHS) is a 30-item, 4-point scale with response options ranging from “Never applies to me” to “Often applies to me.” Total scores can range from 0 to 90 (observed range, 3–90), with a higher score denoting greater hope. Examples of items include: “I have plans for the future,” “I believe that good is always possible,” “I can seek and receive help,” and “I know my life has meaning and purpose.” High internal consistency reliability has been reported, with a Cronbach’s alpha of .94, remaining high at .91 after a 3-week re-test (Herth, 1991). The alpha value in the present sample was .93. The HHS has divergent validity of −.69 with the Beck Hopelessness Scale (Herth, 1991).

The Empowerment Scale is a 28-item scale with a 4-point Likert rating ranging from “strongly agree” to “strongly disagree” for statements such as “I see myself as a capable person” and “People working together can have an effect on their community,” “I generally accomplish what I set out to do,” and “Very often a problem can be solved by taking action.” A higher score indicates more empowerment. The scale has demonstrated good reliability and validity across a number of samples (Corrigan et al., 1999; Rogers et al., 1997; Rogers et al., 2010). The Cronbach’s alpha internal consistency reliability value in the present sample was .76.

The Socioeconomic Status Ladder (SSL) is a MacArthur scale pictorial ladder with 10 rungs (Adler and Stewart, 2007). The instructions read to participants are as follows: “Think of this ladder as representing where people stand in the United States. At the top of the ladder are the people who are the best off—those who have the most money, the most education, and the most respected jobs. At the bottom are the people who are the worst off—who have the least money, the least education, and the least respected jobs or no job. The higher up you are on this ladder, the closer you are to the people at the very top; the lower you are, the closer you are to the people at the very bottom. Where would you place yourself on this ladder? Please place a large “X” on the rung where you think you stand at this time in your life, relative to other people in the United States” (Adler and Stewart, 2007). As such, higher rungs (and thus scores) indicate greater perceived social status. This test has established test-retest reliability (r=.62, p<.01) (Giatti et al., 2012; Operario, 2004). It has consistent validity documented through correlations with other similar measures both in the U.S. and internationally (Adler et al., 2000; Miyakawa et al., 2011; Scott et al., 2014; Singh-Manoux et al., 2005).

The Everyday Discrimination Scale (EDS) is a 9-item measure that begins by asking, “In your day-to-day life, how often do any of the following things happen to you?” (Williams et al., 1997). Participants then rate statements such as “You are treated with less respect than other people are,” “People act as if they think you are not smart,” and “You are threatened or harassed,” with response options on a 6-point Likert-type scale ranging from “never” to “almost every day” Scores are proportional to levels of perceived discrimination. The EDS has been demonstrated to be reliable among diverse racial and ethnic populations, as well as patients with SMI (Gabbidon, 2014; Harangozo, 2013; Koschorke et al., 2014; Oh, 2014; Üçok et al., 2013). It also has high convergent and divergent validity in addition to internal consistency (Barnes et al., 2004; Gordon and Meyer, 2004; Shariff-Marco et al., 2011; Taylor et al., 2004). Studies have reported Cronbach’s alpha values of .85 (Gordon and Meyer, 2007) and .90 (Oh, 2014). The alpha value in the present sample was .86.

Given that the study included patients with both mood disorders and psychotic disorders, symptom severity was thoroughly measured with instruments covering depressive symptoms, manic symptoms, and the full array of psychotic and general psychopathology symptoms. First, the Patient Health Questionnaire-9 (PHQ-9) is a 9-item measure of depressive symptoms (Fann et al., 2005; Kroenke and Spitzer, 2002). Response options range from “not at all” (0) to “nearly every day” (3) to questions such as “Over the last month have you been bothered by feeling down, depressed, or hopeless?” and “Over the last month have you been bothered by feeling tired or having little energy?” The PHQ-9 has high sensitivity and specificity and can predict depression outcomes and changes over time (Lowe et al., 2003; Wittkampf et al., 2007). It strongly correlates with the Beck Depression Inventory (r=.77), General health Questionaire-12 (r=.59), Hospital Depression and Anxiety Scale, and WHO Well Being Index 5, showing high construct validity (Adewuya et al., 2006; Kung et al., 2012; Lowe et al., 2004; Martin et al., 2006). Second, the 11-item Young Mania Rating Scale (YMRS) was used to assess symptoms of mania. The YMRS has demonstrated reliability and validity in prior studies. (Jensen, 2007; Lukasiewicz et al., 2013; Pavuluri et al., 2006; Young et al., 1978; Youngstrom et al., 2003). Third, patients were also assessed using the Positive and Negative Syndrome Scale (Jerrell and Hrisko, 2013; Kay et al., 1987; PANSS), and for this analysis we used the grandiosity item, which defines grandiosity as “exaggerated self-opinion and unrealistic convictions of superiority, including delusions of extraordinary abilities, wealth, knowledge, fame, power, or moral righteousness.” Scores range from 1 (absent) to 7 (extreme).

2.3. Data Analysis

Distributional properties of all study variables were examined. A stepwise multiple linear regression (with an entry criterion of p≤0.05 and a removal criterion of p≥0.10) was calculated to predict hope scores, using IBM SPSS version 25. We then constructed a path model and tested its fit to the data using LISREL (Linear structural relations) 10.20. In addition to our hypothesized model, two alternative models were tested, as well as several trimmed models.

3. RESULTS

Table 1 shows the sociodemographic characteristics of study participants. The mean age was 35.9±11.7 years and the sample was 64.7% male. Race was split nearly evenly between African Americans and Caucasians (46.8% and 48.9%, respectively). Overall, the sample was socially and economically disadvantaged. About one-third (32.3%) of the sample had less education than a high school degree, and 28.8% were homeless. A large majority were uninsured (73.6%).

Table 1.

Sociodemographic Characteristics of the Study Sample (n=235)

Age, years (mean±SD) 35.9±11.7
Sex, male 152 (64.7%)
Ethnicity, non-Hispanic 224 (95.3%)
No health insurance 171 (73.6%)
Race
 African American 110 (46.8%)
 Caucasian 115 (48.9%)
 Other (self-report given below) 10 (4.3%)
  Mixed, biracial, or identified with more than one race 4 (1.7%)
  Hispanic or Latino 4 (1.7%)
  No race given 2 (0.9%)
Marital Status
 Single and never married 148 (61.7%)
 Divorced or separated or widowed 78 (32.6%)
 Married or living with a partner 14 (5.8%)
 Highest Level of Education
Junior high school or less 32 (13.65%)
 Some high school 446 (19.48.7%)
 General Educational Development (GED) diploma 42 (17.97%)
 High school graduate 43 (18.31%)
 Some college or trade/vocational school 612 (26.2%)
 College or professional school graduate 112 (5.14.7%)
 Living Situation
With parents, siblings, or other family members 803 (34.6%)
 Homeless, or staying in a homeless shelter 689 (28.98%)
 Alone 31 (13.2%)
 With friends 189 (7.79%)
 With boyfriend, girlfriend, spouse, or partner 24 (10.20%)
 Other 14 (5.98%)
 Currently working 31 (13.20%)
Open in a new tab

3.1. Multiple Linear Regression Model of Herth Hope Scale Scores

HHS scores ranged from 3 to 90 (possible range of 0–90), with a mean of 62.3±15.9. A stepwise multiple linear regression was conducted to predict HHS scores in patients with SMI based on depression severity (PHQ score), mania severity (YMRS score), grandiosity severity (PANSS grandiosity item score), perceived social status, perceived everyday discrimination, empowerment, diagnostic category (mood disorder versus psychotic disorder), age, and gender. A significant regression equation was found (F(5, 226)=43.767, p<0.001), with an R2 of 0.492. Details are given in Table 2. Variation in HHS scores was best explained by PHQ-9 scores (R2 change=0.334), followed by empowerment (R2 change=0.075), perceived social status (R2=0.047), perceived everyday discrimination (R2=0.021), and age (R2=0.015). The excluded variables were: YMRS score, grandiosity severity, diagnostic category, and gender. There were no significant collinearity problems between the remaining variables.

Table 2.

Means and Standard Deviations of Independent Variables, and Multiple Linear Regression Results among 232 Patients with Serious Mental Illnesses; Dependent Variable, Herth Hope Scale Scores

M SD B SE B β t p
Depression (PHQ-9) 12.8 7.2 −0.757 0.124 −0.340 −6.105 <0.001
Empowerment 2.9 0.5 8.180 1.635 0.254 5.002 <0.001
Perceived
Social Status		 4.0 2.4 1.371 0.343 0.205 3.994 <0.001
Discrimination
(EDS)		 20.7 11.7 −0.234 0.069 −0.170 −3.376 0.001
Age 35.8 11.6 −0.180 0.070 −0.130 −2.559 0.011
Open in a new tab

PHQ-9 = Patient Health Questionnaire-9; EDS = Everyday Discrimination Scale

Given that perceived social status and perceived everyday discrimination were significant predictors, we then computed independent samples t-tests to identify any differences in scores on these two variables by gender and race. There was a significant difference between social status ladder scores between females (3.6±2.2) and males (4.3±2.5); t(234)=1.97, p=0.050. However, there was no significant difference in everyday discrimination scores between females and males (19.8±11.4 and 21.4±12.2, respectively); t(237)=0.943, p=0.035. There was a significant difference in social status ladder scores between African Americans (4.5±2.4) and Caucasians (3.6±2.4); t(225)=2.70, p=0.008, though there was no difference in everyday discrimination scores (20.8±12.0 in both groups).

3.2. Path Analysis

Based on results from the MLR, we then sought to gain a deeper understanding of the ways in which the various independent variables drive HHS scores. In the path analysis we only included variables that the MLR had identified as having the greatest impact on HHS scores. We constructed a hypothesized model that might best describe the connections between the variables, based on our analysis of the literature and clinical experience. Our hypothesized model, depicted in Figure 1, shows that depression is associated with hope and empowerment, and that hope also has a direct link with empowerment. The model indicates that both perceived social status and perceived everyday discrimination are associated with hope; they also impact hope, however, through their link with depression. This hypothesized model fit the data well, as given by the fit indices in Table 3. For example, adjusted goodness of fit index (AGFI), non-normed fit index (NNFI), and comparative fit index (CFI ) were all >0.90.

Figure 1.

Figure 1.

Open in a new tab

The Original and Trimmed Hypothesized Model and Path Coefficients

Table 3.

Fit Indices for the Hypothesized Model, Two Alternative Models and Respective Trimmed Models, and Three Trimmed Versions of the Hypothesized Model

Model χ2 AGFI NNFI (TLI) CFI RMSEA AIC
Hypothesized Model 7.80 0.903 0.920 0.984 0.111 4061.348
Alternative Model #1 26.65 0.692 0.662 0.932 0.229 4080.195
Alternative Model #1, Trimmeda 56.62 0.606 0.509 0.853 0.276 4108.169
Alternative Model #2 89.46 0.157 0.761* 0.760 0.431 4143.003
Alternative Model #2, Trimmedb 115.53 0.328 0.691* 0.691 0.400 4167.077
Hypothesized model, Trimmed #1c 248.33 0.160 0.336* 0.326 0.590 4299.877
Hypothesized model, Trimmed #2d 22.08 0.827 0.825 0.948 0.165 4073.630
Hypothesized model, Trimmed #3e 30.06 0.812 0.821 0.928 0.167 4079.610
Open in a new tab
*

Value given is a Normed Fit Index, rather than Non-Normed Fit Index.

AGFI = Adjusted Goodness of Fit Index; NNFI = Non-Normed Fit Index; TLI = Tucker Lewis Index; CFI = Comparative Fit Index; RMSEA = Root Mean Square Error of Approximation; AIC = Akaike Information Criterion.

a

the path from depression to social status was eliminated

b

the path from perceived social status to hope was eliminated

c

the path from perceived everyday discrimination to hope was eliminated

d

the path from depression to empowerment was eliminated

e

both the path from perceived everyday discrimination to hope, and the path from depression to empowerment, were eliminated

To ensure that this model fits the data better than other potential models, we tested two alternative models, shown in Figure 2. The first alternative model swapped the predictors (perceived social status and everyday discrimination) and outcomes (hope and empowerment). As such, empowerment and hope are predictive of depression, which in turn is associated with perceptions of discrimination and social status. The second model used everyday discrimination and empowerment as predictors, and perceived social status and depression as mediators of our outcome of interest: hope. In the trimmed models, we removed the path from the mediator to one of the outcomes in accordance with suggested modification indices. As indicated by the fit indices (Table 3) both the original alternative models and two subsequent trimmed models (based on modification indices) did not fit the data well. Across the four alternative models, for example, AGFI dropped from 0.903 to ranging from 0.157 to 0.692, and TLI dropped from 0.920 to ranging from 0.509 to 0.761.

Figure 2.

Figure 2.

Open in a new tab

The Two Alternative Models and Path Coefficients

Having determined that the hypothesized model fit the data better than either of two alternative models (and a subsequent trimmed model for each), we then sought to determine whether the fit of the hypothesized model could be further improved through select trimming. We first removed the path with the lowest coefficient (−0.15) from perceived everyday discrimination to hope. This model no longer fit the data, as shown by the fit indices in Table 3. We then instead removed the path with the next lowest coefficient (−0.21) from depression to empowerment. This was a better fitting model than the previous trimmed model (e.g., AGFI, TLI, CFI of 0.827, 0.825, and 0.948, respectively), though, as expected, the fit was not as good as in the original hypothesized model. Finally, we removed both of the previously mentioned paths concurrently, and the fit remained roughly the same, with fit indices generally in the accepted ranges (also shown Figure 1).

4. DISCUSSION

With regard to hope and empowerment among individuals with SMI—two key constructs known to be associated with recovery—several findings were noteworthy, each of which will be discussed in turn. First, we identified several factors that were associated with hope, with regression results showing a prominent role of depressive symptoms, but also associations with socially relevant variables including perceived social status in relation to others and perceived experiences of everyday discrimination. Second, in our path analyses, perceived social status appears to have a direct connection with hope (and thus empowerment), and to also impact hope through its association with depression. Third, perceived everyday discrimination links to hope (and thus empowerment), but this effect is primarily one that is mediated through its association with depression.

Although we did not find gender or race differences in everyday discrimination scores, this is likely due to the broad nature of that measure. Specifically, the Everyday Discrimination Scale does not parse the source of the discrimination, whether it be racism, sexism, ageism, ableism, a combination of these, or stemming from something else entirely. It is well established that persons with SMI are discriminated against and perceive that discrimination (Dickerson et al., 2002; Koschorke et al., 2014; Mantovani et al., 2016; Oh et al., 2014; Thornicroft et al., 2009; Vass et al., 2015). It is possible that everyday discrimination scores did not vary by gender or race because of this overarching factor (the mental illness itself) that accounted for most of the discrimination perceived in the sample (thus over-riding the effects of felt sexism and racism. A study of community mental health clients found that the most prevalent attributions for everyday discrimination were mental illness at 39%, appearance at 17%, and race/ethnicity at 11% (Gabbidon et al., 2014), which could account for our results. Group differences regarding subjective social status were evident in that women and Caucasians reported lower social status. While the former is not surprising given gender-based discrimination and gender inequality, the fact that Caucasians reported a lower perceived social status in this sample was unexpected and requires further exploration.

Previous studies have found strong correlations between hope and several positive external support systems including strong interpersonal relationships, social support systems, and recovery outcomes (Hagerty et al., 1992; Rogers, 2010). Instead of focusing on positive external support systems, our study identified two negative external factors that affect hope: perceived social status and everyday discrimination. Unlike diagnostic category and gender, some of the most influential predictors of hope in our regression model were those most heavily influenced by one’s external environment (perceived social status and everyday discrimination). Relatedly, our best-fitting hypothesized path model indicates opportunities to intercept the development of depression and thus reduction of hope and poor functional recovery outcomes. These opportunities would include an integration of societal changes, policy reform, and individual psychotherapies targeting depression’s precursors, perceived social status and everyday discrimination. Our model also highlights the far-reaching impacts of discrimination and inequality, which should instigate future research and policy reform.

Previous studies have shown that subjective ideas about one’s social status are actually a better predictor of mental illness than objective measures like educational level, income, and occupation (Scott et al., 2014; Singh-Manoux et al., 2005). Our results help specify the mechanism through which this may occur. We were able to show that perceived social status can affect hope both directly and through depression as a mediator. Impacting both discrimination and socioeconomic standing require structural interventions at the policy level; this work once again demonstrates that the mental health profession has a vested interest in such policy. Within mental health settings, given that depressive symptoms are associated with hope and empowerment, psychotherapies like resiliency training and Cognitive-Behavioral Therapy (CBT) could help to mitigate the effects of perceived social status on hope and functional recovery in disadvantaged persons with SMI. Resiliency training teaches a patient to adapt in the face of negative experiences and has been shown to reduce hopelessness and suicidal ideation in persons with schizophrenia (Bozikas and Parlapani, 2016; Johnson et al., 2010). CBT aims to change reactions to a negative situation by working on problem solving skills and reducing cognitive biases. In addition, CBT has demonstrated some effectiveness after only one session (Depp et al., 2018; Turkington et al., 2002) which, although not preferred, is noteworthy for persons with SMI who face significant barriers to consistent, continuous treatment. As noted, however, clinical interventions targeting depression, hope, and empowerment must ultimately be accompanied by policy-level, societal interventions that also reduce and eliminate discrimination and that improve equality, especially among those who are most disadvantaged.

Other studies have found depression to be a mediator between discrimination and other health outcomes, both physical and mental (Kessler et al., 1999; Todovora et al., 2010). Our path analysis also confirmed depression as a mediator between everyday discrimination and hope and empowerment among persons with SMI. As with perceived social status, CBT and resilience therapy can potentially help individuals with SMI keep from internalizing experiences of discrimination and developing self-stigma (Beck, 1979; Beck and Kovacs, 1977; O’Connor et al., 2019) Clinicians can use strengths-based therapy to work with individuals with SMI to stop seeing their diagnosis as a limiting factor and increase empowerment. There should be more effort to introduce clients with SMI to others who may face similar types of discrimination, such as making sure treatment teams are racially and ethnically diverse, and include peer specialists. Although these suggestions focus on resiliency approaches for the individual with SMI, the more upstream conclusion is that stigma surrounding mental illnesses and racial discrimination must be addressed at a societal level. Our results, along with those from many others, show the detrimental effect of perceived discrimination, and thus reinforce the call for lawmakers and other policymakers to address these issues with system-wide reform.

These findings should be interpreted in light of several methodological limitations. First, the data were collected from individuals who were inpatients about to be discharged (because they were participating in a larger, longitudinal project). Findings might not be generalizable to a broader population of individuals with SMI, including those who have maintained stability, achieved a sense of recovery, and not needed to be admitted to inpatient settings. Similarly, because participants were enrolled from public-sector (as opposed to private) mental health settings, results might not be generalizable to those who are not socioeconomically disadvantaged. Second, all data are self-report; as such, social status and everyday discrimination were perceived social status and perceived everyday discrimination. Nonetheless, it is perceived social status and discrimination that are of interest and that have been measured in prior studies, so the self-report methodology is less of a concern. Third, the Everyday Discrimination Scale was not specific regarding the type of discrimination experienced. Racial discrimination is obviously a major, ongoing issue in American society, and about half of the sample was African American; yet, the intersectionality of other forms of discrimination (including the discrimination associated with having SMI or other types of discrimination due to being a member of other often-discriminated against population groups, such as those who are homeless) must be considered. Future research should study perceived racism, which is more specific than the measure of perceived everyday discrimination that we used. Fourth, although path analysis gives clue about directionality/causality, all data were cross-sectional. Although our testing and disproving of the select alternative models give further credence to our hypothesized model, only a longitudinal and controlled study could more definitively show that perceived social status, for example, leads to depression, which leads to hope and thus empowerment. Fifth, because hope has been shown to influence functional recovery (Coskun et al., 2018), future research should consider our models while measuring functional recovery (especially over time) for a more complete analysis. Finally, because monitoring depression and hopelessness are crucial in the ongoing screening and assessment of suicidality—and because substance use is a common factor underpinning suicidality—suicidality and substance use are additional variables of interest.

Overall, findings lend further support to the accumulating evidence that recovery among individuals with SMI hinges in part on social and societal factors commonly framed as social determinants of mental health (Compton and Shim, 2015a, 2015b) and that mental health professionals thus have a role in addressing those social and societal factors (Shim and Compton, 2018). To further improve the lives of those with mental illnesses (and indeed those at risk for, or even those likely not at risk for, mental illnesses) requires programs and policies that address inequality (and thus perceived social status) and discrimination and social exclusion (and thus perceived everyday discrimination). Addressing these complex, structural aspects of society is not easy—and solutions may not be readily apparent given their entrenched nature—but is necessary if we are to improve mental health, reduce risk for mental illnesses, and enhance recovery among those living with disabling mental illnesses.

Highlights.

  • Hope is decreased by depression, perceived social status, and discrimination.

  • Perceived social status directly impacts hope, and indirectly through depression.

  • Perceived everyday discrimination affects hope through effects on depression.

Acknowledgements

All persons who have made substantial contributions to the work reported in the manuscript (e.g., technical help, writing and editing assistance, general support), but who do not meet the criteria for authorship, are named in the Acknowledgements and have given us their written permission to be named. If we have not included an Acknowledgements, then that indicates that we have not received substantial contributions from non-authors.

Disclosures and Acknowledgments: The authors report no competing interests. Research reported in this publication was supported by National Institute of Mental Health grant R01 MH101307 (“A Trial of “Opening Doors to Recovery” for Persons with Serious Mental Illnesses”) to the last author. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Institute of Mental Health. The funding agency had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the manuscript for publication. The authors appreciate the collaboration from clinicians and staff at Gateway Behavioral Health Services, Georgia Regional Hospital at Savannah, and Coastal Harbor Health System.

Footnotes

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

REFERENCES

  1. Abdi N, & Asadi-Lari M, 2011. Standardization of three hope scales, as possible measures at the end of life, in Iranian population. Iran Journal of Cancer Prevention,2, 71–77. [Google Scholar]
  2. Adewuya AO, Ola BA, & Afolabi OO, 2006. Validity of the patient health questionnaire (PHQ-9) as a screening tool for depression amongst Nigerian university students. Journal of Affective Disorders,96(1–2), 89–93. [DOI] [PubMed] [Google Scholar]
  3. Adler NE, Epel ES, Castellazzo G, & Ickovics JR, 2000. Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women. Health Psychology,19(6), 586–592. [DOI] [PubMed] [Google Scholar]
  4. Adler N, & Stewart J, 2007. The MacArthur scale of subjective social status. from https://macses.ucsf.edu. [Google Scholar]
  5. Barnes LL, Leon CF, Wilson RS, Bienias JL, Bennett DA, & Evans DA, 2004. Racial differences in perceived discrimination in a community population of older blacks and whites. Journal of Aging and Health,16(3), 315–337. [DOI] [PubMed] [Google Scholar]
  6. Beck AT, Cognitive Therapy of Depression: Aaron T. Beck U.a. Guilford, 1979. [Google Scholar]
  7. Beck A, Kovacs M, 1977. A new, fast therapy for depression. Psychology Today. 94. [Google Scholar]
  8. Bozikas V, Parlapani E, 2016. Resilience in patients with psychotic disorder. Psychiatriki2016, 27 (1), 6–13. [PubMed] [Google Scholar]
  9. Ciompi L, Clemens S, 1980. Catamnestic long-term study on the course of life and aging of schizophrenics. Schizophrenia Bulletin. 6(4), 606–618. [DOI] [PubMed] [Google Scholar]
  10. Compton MT, Hankerson-Dyson D, Broussard B, Druss BG, Haynes N, Strode P, Grimes C, Li C, DiPolito JA, Thomas GV, 2011. Opening Doors to Recovery: A novel community navigation service for people with serious mental illnesses. Psychiatric Services, 62.1270–1272. [DOI] [PubMed] [Google Scholar]
  11. Compton MT, Kelley ME, Pope A, Smith K, Broussard B, Reed TA, DiPolito JA, Druss BG, Li C, Lott Haynes N, 2016. Opening Doors to Recovery: Recidivism and recovery among persons with serious mental illnesses and repeated hospitalizations. Psychiatric Services, 67. 169–175. [DOI] [PubMed] [Google Scholar]
  12. Compton MT, Shim RS, 2015. The social determinants of mental health. FOCUS, 13. 419–425. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Compton MT, Shim RS, 2015. The Social Determinants of Mental Health. Washington, DC: American Psychiatric Publishing, 270 pages, ISBN-13: 978–1-58562–477-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Corrigan PW, Faber D, Rashid F, Leary M, 1999. The construct validity of empowerment among consumers of mental health services. Schizophrenia Research. 38(1), 77–84. [DOI] [PubMed] [Google Scholar]
  15. Corrigan PW, McCraken SG, Holmes EP, 2001. Motivational interviews as goal assessment for persons with psychiatric disability. Community Mental Health Journal. 37(2), 113–122. [DOI] [PubMed] [Google Scholar]
  16. Coşkun E, Altun ÖŞ, 2018. The relationship between the hope levels of patients with schizophrenia and functional recovery. Archives of Psychiatric Nursing,32(1), 98–102. [DOI] [PubMed] [Google Scholar]
  17. Davidson L, Roe D, 2007. Recovery from versus recovery in serious mental illness: One strategy for lessening confusion plaguing recovery. Journal of Mental Health. 16(4), 459–470. [Google Scholar]
  18. Depp CA, Perivoliotis D, Holden J, Dor J, Granholm EL, 2018. Single-session mobileaugmented intervention in serious mental illness: A three-arm randomized controlled trial. Schizophrenia Bulletin. 45(4), 752–762. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Dickerson FB, Sommerville J, Origoni AE, Ringel NB, Parente F, 2002. Experiences of stigma among outpatients with schizophrenia. Schizophrenia Bulletin. 28(1), 143–155. [DOI] [PubMed] [Google Scholar]
  20. Fann JR, Bombardier CH, Dikmen S, Esselman P, Warms CA, Pelzer E, Temkin N, 2005. Validity of the Patient Health Questionnaire-9 in assessing depression following traumatic brain injury. Journal of Head Trauma Rehabilitation. 20(6), 501–511. [DOI] [PubMed] [Google Scholar]
  21. First MB, Williams JB, Karg RS, Spitzer RL, 2015. Structured clinical interview for DSM-5 disorders, clinician version (SCID-5-CV) Arlington, VA: American Psychiatric Association. [Google Scholar]
  22. Gabbidon J, Farrelly S, Hatch SL, Henderson C, Williams P, Bhugra D, Clement S, 2014. Discrimination attributed to mental illness or race-ethnicity by users of community psychiatric services. Psychiatric Services. 65(11), 1360–1366. [DOI] [PubMed] [Google Scholar]
  23. Gagne C, White W, Anthony WA, 2007. Recovery: A common vision for the fields of mental health and addictions. Psychiatric Rehabilitation Journal. 31(1), 32–37. [DOI] [PubMed] [Google Scholar]
  24. Giatti L, Camelo LD, Rodrigues JF, Barreto SM, 2012. Reliability of the MacArthur scale of subjective social status - Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). BMC Public Health. 12(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gordon AR, Meyer IH, 2007. Gender nonconformity as a target of prejudice, discrimination, and violence against LGB individuals. Journal of LGBT Health Research. 3(3), 55–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hagerty BM, Lynch-Sauer J, Patusky KL, Bouwsema M, Collier P, 1992. Sense of belonging: A vital mental health concept. Archives of Psychiatric Nursing. 6(3), 172–177. [DOI] [PubMed] [Google Scholar]
  27. Harangozo J, Reneses B, Brohan E, Sebes J, Csukly G, López-Ibor J, Sartorious J, Rose D, Thornicroft G, 2013. Stigma and discrimination against people with schizophrenia related to medical services. International Journal of Social Psychiatry. 60(4), 359–366. [DOI] [PubMed] [Google Scholar]
  28. Harding CM, Brooks GW, Ashikaga T, Strauss JS, Breier A, 1987. The Vermont longitudinal study of persons with severe mental illness, I: Methodology, study sample, and overall status 32 years later. American Journal of Psychiatry. 144(6), 718–726. [DOI] [PubMed] [Google Scholar]
  29. Herth K 1991. Development and refinement of an instrument to measure hope. Scholarly Inquiry for Nursing Practice. 5(1), 39–51. [PubMed] [Google Scholar]
  30. Herth K 1995. Engendering hope in the chronically and terminally ill: Nursing interventions. American Journal of Hospice and Palliative Medicine. 12(5), 31–39. [DOI] [PubMed] [Google Scholar]
  31. Hoffmann H, Kupper Z, Kunz B, 2000. Hopelessness and its impact on rehabilitation outcome in schizophrenia –an exploratory study. Schizophrenia Research. 43(2–3), 147–158. [DOI] [PubMed] [Google Scholar]
  32. Jensen P 2007. Child Mania Rating Scale: Development, reliability, and validity. Yearbook of Psychiatry and Applied Mental Health. 10–11. [Google Scholar]
  33. Jerrell JM, Hrisko S, 2013. Utility of two PANSS 5-factor models for assessing psychosocial outcomes in clinical programs for persons with schizophrenia. Schizophrenia Research and Treatment. 1–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Johnson J, Gooding PA, Wood AM, Taylor PJ, Pratt D, Tarrier N, 2010. Resilience to suicidal ideation in psychosis: Positive self-appraisals buffer the impact of hopelessness. Behaviour Research and Therapy. 48(9), 883–889. [DOI] [PubMed] [Google Scholar]
  35. Kavak F, Yilmaz E, 2018. The effect of hope on the treatment adherence of schizophrenia patients. Annals of Medical Research. 25(4), 416. [Google Scholar]
  36. Kay SR, Fizbein A, Opler LA, 1987. The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin. 13, 261–276. [DOI] [PubMed] [Google Scholar]
  37. Kern RS, Glynn SM, Horan WP, Marder SR, 2009. Psychosocial treatments to promote functional recovery in schizophrenia. Schizophrenia Bulletin. 35(2), 347–361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Kessler RC, Mickelson KD, Williams DR, 1999. The prevalence, distribution, and mental health correlates of perceived discrimination in the United States. Journal of Health and Social Behavior. 40(3), 208. [PubMed] [Google Scholar]
  39. Koschorke M, Padmavati R, Kumar S, Cohen A, Weiss HA, Chatterjee S, Pereira S, Naik S, Sujit J, Dabholkar H, Madhumitha B, Chavan A, Varghese M, Thara R, Thornicroft G, Patel V, 2014. Experiences of stigma and discrimination of people with schizophrenia in India. Social Science & Medicine. 123, 149–159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Kroenke K, Spitzer RL, 2002. The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals. 32(9), 509–515. [Google Scholar]
  41. Kung S, Alarcon RD, Williams MD, Poppe KA, Moore MJ, & Frye MA, 2013. Comparing the Beck Depression Inventory-II (BDI-II) and Patient Health Questionnaire (PHQ-9) depression measures in an integrated mood disorders practice. Journal of Affective Disorders. 145(3), 341–343. [DOI] [PubMed] [Google Scholar]
  42. Kylmä J, Juvakka T, Nikkonen M, Korhonen T, Isohanni M, 2006. Hope and schizophrenia: An integrative review. Journal of Psychiatric and Mental Health Nursing. 13(6), 651–664. [DOI] [PubMed] [Google Scholar]
  43. Liberman RP, Kopelowicz A, Ventura J, Gutkind D, 2002. Operational criteria and factors related to recovery from schizophrenia. International Review of Psychiatry. 14(4), 256–272. [Google Scholar]
  44. Littrell KH, Herth KA, & Hinte LE, 1996. The experience of hope in adults with schizophrenia. Psychiatric Rehabilitation Journal. 19(4), 61–65. [Google Scholar]
  45. Lobbestael J, Leurgans M, Arntz A, 2011. Inter-rater reliability of the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID I) and Axis II Disorders (SCID II). Clinical Psychology & Psychotherapy. 18(1), 75–79. [DOI] [PubMed] [Google Scholar]
  46. Löwe B, Gräfe K, Zipfel S, Witte S, Loerch B, Herzog W, 2004. Diagnosing ICD-10 depressive episodes: superior criterion validity of the patient health Questionnaire. Psychotherapy and Psychosomatics. 73(6), 386–390. [DOI] [PubMed] [Google Scholar]
  47. Löwe B, Kroenke K, Herzog W, Gräfe K, 2004. Measuring depression outcome with a brief self-report instrument: Sensitivity to change of the Patient Health Questionnaire (PHQ-9). Journal of Affective Disorders. 81(1), 61–66. [DOI] [PubMed] [Google Scholar]
  48. Lukasiewicz M, Gerard S, Besnard A, Falissard B, Perrin E, Sapin H, Tohen M, Reed C, Azorin J, 2013. Young Mania Rating Scale: How to interpret the numbers? Determination of a severity threshold and of the minimal clinically significant difference in the EMBLEM cohort. International Journal of Methods in Psychiatric Research. 22(1), 46–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Lysaker PH, Campbell K, Johannesen JK, 2005. Hope, awareness of illness, and coping in schizophrenia spectrum disorders. Journal of Nervous and Mental Disease 193(5), 287–292. [DOI] [PubMed] [Google Scholar]
  50. Lysaker PH, Clements CA, Wright DE, Evans J, Marks KA, 2001. Neurocognitive correlates of helplessness, hopelessness, and well-being in schizophrenia. Journal of Nervous and Mental Disease. 189(7), 457–462. [DOI] [PubMed] [Google Scholar]
  51. Lysaker PH, Bond G, Davis LW, Bryson GJ, Bell MD, 2005. Enhanced cognitive-behavioral therapy for vocational rehabilitation in schizophrenia: Effects on hope and work. Journal of Rehabilitation Research and Development. 42(5), 673–682. [DOI] [PubMed] [Google Scholar]
  52. Lyu J, Zhang J, 2014. Characteristics of schizophrenia suicides compared with suicides by other diagnosed psychiatric disorders and those without a psychiatric disorder. Schizophrenia Research. 155, 59–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Mantovani N, Pizzolati M, Edge D, 2016. Exploring the relationship between stigma and help-seeking for mental illness in African-descended faith communities in the UK. Health Expectations. 20(3), 373–384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Martin A, Rief W, Klaiberg A, Braehler E, 2006. Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ-9) in the general population. General Hospital Psychiatry. 28(1), 71–77. [DOI] [PubMed] [Google Scholar]
  55. McGlashan TH,1987. Borderline personality disorder and unipolar affective disorder long-term effects of comorbidity. Journal of Nervous and Mental Disease. 175(8), 467–473. [DOI] [PubMed] [Google Scholar]
  56. Miyakawa M, Hanson LL, Theorell T, Westerlund H, 2011. Subjective social status: Its determinants and association with health in the Swedish working population (the SLOSH study). European Journal of Public Health. 22(4), 593–597. [DOI] [PubMed] [Google Scholar]
  57. O’Connor LK, Yanos PT, Firmin RL, 2018. Correlates and moderators of stigma resistance among people with severe mental illness. Psychiatry Research. 270, 198–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Oh H, Yang LH, Anglin DM, & Devylder JE, 2014. Perceived discrimination and psychotic experiences across multiple ethnic groups in the United States. Schizophrenia Research. 157, 259–265. [DOI] [PubMed] [Google Scholar]
  59. Operario D, Adler NE, & Williams DR, 2004. Subjective social status: Reliability and predictive utility for global health. Psychology & Health. 19(2), 237–246. [Google Scholar]
  60. Pavuluri MN, Henry DB, Devineni B, Carbray JA, Birmaher B, 2006. Child Mania Rating Scale: Development, reliability, and validity. Journal of the American Academy of Child & Adolescent Psychiatry. 45(5), 550–560. [DOI] [PubMed] [Google Scholar]
  61. Pevalin DJ, Goldberg DP, 2003. Social precursors to onset and recovery from episodes of common mental illness. Psychological Medicine. 33(2), 299–306. [DOI] [PubMed] [Google Scholar]
  62. Piat M, Sabetti J, Couture A, Sylvestre J, Provencher H, Botschner J, Stayner D, 2009. What does recovery mean for me? Perspectives of Canadian mental health consumers. Psychiatric Rehabilitation Journal. 32(3), 199–207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Rogers ES, Chamberlin J, Ellison ML, Crean T, 1997. A consumer-constructed scale to measure empowerment among users of mental health services. Psychiatric Services. 48(8), 1042–1047. [DOI] [PubMed] [Google Scholar]
  64. Rogers ES, Farkas M, & Anthony WA, 2005. Recovery from severe mental illnesses and evidence-based practice research. In The evidence-based practice: Methods, models, and tools for mental health professionals. 199–219. Hoboken, NJ: John Wiley and Sons. [Google Scholar]
  65. Rogers ES, Ralph RO, Salzer MS, 2010. Validating the Empowerment scale with a multisite sample of consumers of mental health services. Psychiatric Services. 61(9), 933–936. [DOI] [PubMed] [Google Scholar]
  66. Scott KM, Al-Hamzawi AO, Andrade LH, Borges G, Caldas-de-Almeida JM, Fiestas F, Gureje O, Hu C, Karam EG, Kawakami N, Lee S, Levinson D, Lim CC, Navarro-Mateu F, Okoliyski M, Posada-Villa J, Torres Y, Williams DR, Zakhozha V, Kessler RC., 2014. Associations between subjective social status and DSM-IV mental disorders. JAMA Psychiatry. 71(12), 1400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Segal DL, Williams K, 2014. Structured and semistructured interviews for differential diagnosis: fundamental issues, applications, and features. In: Beidel DC, Frueh BC, Hersen M, editors. Adult Psychopathology and Diagnosis. Hoboken, NJ: John Wiley & Sons Inc; 2014. 103–129. [Google Scholar]
  68. Shariff-Marco S, Breen N, Landrine H, Reeve BB, Krieger N, Gee GC, Williams DR, Mays VM, Ponce NA, Alegría M, Liu B, Willis G, Johnson TP, 2011. Measuring everyday racial/ethnic discrimination in health surveys. Du Bois Review: Social Science Research on Race. 8(1), 159–177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Shim RS, Compton MT, 2018. Addressing the social determinants of mental health: If not now, when? If not us, who? Psychiatric Services. 69, 844–846. [DOI] [PubMed] [Google Scholar]
  70. Singh-Manoux A, Marmot MG, Adler NE, 2005. Does subjective social status predict health and change in health status better than objective status? Psychosomatic Medicine. 67(6), 855–861. [DOI] [PubMed] [Google Scholar]
  71. Abuse Substance and Mental health Administration. 2006, February. National consensus statement of mental health recovery. from http://www.cibhs.org
  72. Taylor TR, Kamarck TW,bShiffman S, 2004. Validation of the Detroit area study discrimination scale in a community sample of older African American adults: The Pittsburgh healthy heart project. International Journal of Behavioral Medicine. 11(2), 88–94. [DOI] [PubMed] [Google Scholar]
  73. Thornicroft G, Brohan E, Rose D, Sartorius N, Leese M, 2009. Global pattern of experienced and anticipated discrimination against people with schizophrenia: A cross-sectional survey. The Lancet. 373(9661), 408–415. [DOI] [PubMed] [Google Scholar]
  74. Todorova IL, Falcón LM, Lincoln AK, Price LL, 2010. Perceived discrimination, psychological distress and health. Sociology of Health & Illness. 32(6), 843–861. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Turkington D, Kingdon D, Turner T, 2002. Effectiveness of a brief cognitive–behavioural therapy intervention in the treatment of schizophrenia. British Journal of Psychiatry. 180(6), 523–527. [DOI] [PubMed] [Google Scholar]
  76. Üçok A, Karadayı G, Emiroğlu B, Sartorius N, 2013. Anticipated discrimination is related to symptom severity, functionality and quality of life in schizophrenia. Psychiatry Research. 209(3), 333–339. [DOI] [PubMed] [Google Scholar]
  77. Vass V, Morrison AP, Law H, Dudley J, Taylor P, Bennett KM, & Bentall RP, 2015. How stigma impacts on people with psychosis: The mediating effect of self-esteem and hopelessness on subjective recovery and psychotic experiences. Psychiatry Research. 230(2), 487–495. [DOI] [PubMed] [Google Scholar]
  78. Williams DR, Yu Y, Jackson JS, Anderson NB, 2007. Racial differences in physical and mental health: Socioeconomic status, stress, and discrimination. Journal of Health Psychology. 2(3), 335–351. [DOI] [PubMed] [Google Scholar]
  79. Wittkampf KA, Naeije L, Schene AH, Huyser J, Weert HC, 2007. Diagnostic accuracy of the mood module of the Patient Health Questionnaire: A systematic review. General Hospital Psychiatry 29(5), 388–395. [DOI] [PubMed] [Google Scholar]
  80. Yanos P, Moos R, 2007. Determinants of functioning and well-being among individuals with schizophrenia: An integrated model. Clinical Psychology Review. 27(1), 58–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Young RC, Biggs JT, Ziegler VE, Meyer DA, 1978. A rating scale for mania: Reliability, validity and sensitivity. The British Journal of Psychiatry. 133(5), 429–435. [DOI] [PubMed] [Google Scholar]
  82. Youngstrom EA, Gracious BL, Danielson CK, Findling RL, & Calabrese J, 2003. Toward an integration of parent and clinician report on the Young Mania Rating Scale. Journal of Affective Disorders. 77(2), 179–190. [DOI] [PubMed] [Google Scholar]

RESOURCES