Clergy Characteristics as Predictors of Mental Health Literacy

Clergy Characteristics as Predictors of Mental Health

Literacy

Chapter 1: Introduction to the Study

In a recent national survey, the Substance Abuse and Mental Health Services

Administration (SAMHSA, 2012) found that approximately 60% of individuals

diagnosed with any type of mental illness (AMI) and 40% diagnosed with a serious

mental illness (SMI) reported not receiving any formal mental health assistance.

Moreover, individuals of ethnic minority status reportedly received help with 50% less

frequency than their White/Caucasian counterparts (John & Williams, 2013; Mills, 2012;

SAMHSA, 2012). In seeking to reduce these disparities, researchers have noted that

informal helpers, especially clergy members, may provide conduits to the formal mental

health system (Lopez, Barrio, Kopelowicz, & Vega, 2012; Snowden, 2012; Sue, Cheng,

Saad, & Sue, 2012). In fact, over the last 50 years researchers have found that as many as

40% of U.S. residents look to clergy for their psychological needs (Chalfant et al., 1990;

Gurin, Veroff, & Feld, 1960; Polson & Rogers, 2007; Weaver, Flannelly, Flannelly, &

Oppenheimer, 2003). Furthermore, researchers also revealed a significant tendency to

seek out clergy instead of formal mental health care providers (MHPs) among cultural

groups that include African/Black, Asian, Hispanic/Latino, elderly, rural, veteran, and

female populations (John & Williams, 2013; Kirchner et al., 2011; Mills, 2012; Pickard

& Tang, 2009; Stanford, 2007).

In recognizing this demand for clergy assistance, clergy participants from other

studies acknowledged that their training for addressing psychological problems was

limited and claimed they would refer serious cases to MHPs (Moran et al., 2011; Payne,

2013). In actual practice, however, clergy referrals to MHPs remain uncommon, only

referring about 10% of their cases to formal providers (Farrell & Goebert, 2008; Pickard,

2012; Polson & Rogers, 2007; Standford & Philpott, 2011). Although researchers have

examined attitudinal (e.g., interprofessional distrust) and external reasons (e.g., lack of

access) for this discrepancy, the primary question of whether clergy recognize SMI in the

first place must also be investigated before other explanations for low referral rates can be

established (Farrell & Goebert, 2008; Ross & Standford, 2014; Sullivan et al., 2013).

In seeking to understand whether Christian clergy recognize SMI, I identified only

three studies conducted within the past decade (Chevalier et al., 2015; Pillion, Reed, &

Shetiman, 2012; Stansbury, Marshall, Harley, & Nelson, 2010). Those studies examined

the mental health literacy (MHL) abilities of less than 200 total clergy across the United

States and only sampled homogeneous populations in terms of denomination and

geography. Given the lack of data and limited generalizability of these previous findings,

potential collaborators and referral partners may not fully understand the MHL of clergy

in relation to their referral practices.

If clergy cannot identify SMI, it is likely they will not make referrals to

appropriate mental health providers (Farrell & Goebert, 2008; Pickard, 2012). With more

information about the MHL of clergy, trained mental health counselors may be able to

initiate interprofessional dialog, bidirectional trainings, and referral partnerships with

local clergy (Sullivan et al., 2013). Additionally, knowledge about clergy MHL may aid

in current social change efforts to utilize clergy in reducing current mental health care

disparities experienced by marginalized populations (Snowden, 2012). Information about

clergy’s MHL may also assist counselor educators in preparing counseling students to

collaborate more effectively with their local clergy and advance these social change

initiatives (Cashwell & Watts, 2010). From an ethical standpoint, research on clergy’s

MHL is warranted so that counselors and counselor educators may facilitate effective

clergy partnerships, which has been advocated in the American Counseling Association’s

(ACA) multicultural competencies (Sue, Arredondo, & McDavis, 1992), muliticultural

and social justice competencies for counselors (MCSJCC) (Ratts, Singh,

NassarMcMillan, Butler, & McCullough, 2015), and Association for Spiritual, Ethical,

and

Religious Values in Counseling’s (ASERVIC) spiritual competencies (Cashwell & Watts,

2010). This study will address the current gap in knowledge about clergy’s MHL so that

informed counselors and counselor educators can attend to the documented problem of

clergy members’ low rates of referral to MHPs (Farrell & Goebert, 2008; Openshaw &

Harr, 2009; Pickard, 2012; Polson & Rogers, 2007; Standford & Philpott, 2011).

In this chapter, I present the major components of this dissertation study. In the

background section, I provide an overview of current MHL studies, the key variables

found to predict MHL, and the MHL of clergy as related to their referral behaviors.

Following the background discussion, I introduce the problem statement, purpose, and

research questions and hypotheses of the study. In these sections, I focus attention on a

gap in the research with particular emphasis on mental health care disparities, social

justice concerns, and the use of clergy-MHP partnerships to reduce these disparities.

Background

Rooted in health literacy studies, MHL studies attend to the extent to which

individuals recognize mental illness and seek appropriate help (Jorm, 2012). Anthony

Jorm (2012) pioneered the study of MHL in Australia nearly 20 years ago. He and his

colleagues defined the construct of MHL as “knowledge and beliefs about mental

disorders which aid their recognition, management or prevention” (Jorm, Korten, Jacomb,

Rodgers, & Pollitt, 1997, p. 182). These seminal researchers created and utilized vignette

case studies to test MHL. The vignette method has since become commonplace in MHL

research across many countries (Burns & Rapee, 2006;

Cotton,Wright, Harris, Jorm, & McGorry, 2006; Jorm et al., 1997; Lauber, Nordt, Falato,

& Rossler, 2003; Reavely & Jorm, 2012; Yoshioka, Reavely, Hart, & Jorm, 2014). In

conjunction, researchers consistently showed that recognizing the seriousness of mental

health symptoms promoted the use of formal MHPs including psychiatrists,

psychologists, and professional counselors.

Until recently, researchers relied on the vignette case study method to measure

MHL (O’Connor, Casey, & Clough, 2014). O’Connor et al. (2014) critiqued the vignette

method by citing the need for a psychometrically sound, brief, and easily-administered

measure of the MHL construct. In their discussion, they argued the importance of MHL

for improving access to appropriate treatment, as well as the need to meausre MHL

among different population-types so that trainings may be implemented where learning

needs become apparent. Furthermore, O’Connor et al. (2014) questioned whether MHL

was a unified theoretical concept or a set of separate constructs. With several rounds of

pilot testing and statistical analyses, they produced and published a new parametric

instrument, the Mental Health Literacy Scale (MHLS), in order to provide data points for

robust quantiative statistical analysis (O’Connor & Casey, 2015).

In fitting with previous MHL research findings, O’Connor and Casey’s (2015)

testing of the new scale-based measure provided psychometric evidence supporting the

validity of MHL as a singular construct. More specifically, researchers showed that

MHL reflected the following combination of factors: “the ability to recognize disorders,”

“knowledge of where to seek information,” “knowledge of risk factors and causes,

“knowledge of self-treatment,” “knowledge of professional help available,” and

“attitudes that promote recognition or appropriate help-seeking behavior” (O’Connor &

Casey, 2015, p. 3). These six factors shape the construct of MHL and provide the basis

for testing links between various aspects of MHL as they relate to the overall MHL rates

of diverse populations. Data from scale-based measures of MHL may inform and justify

the use of educational programs for increasing MHL and related help-seeking behaviors

in areas currently manifesting mental health care disparities (O’Connor & Casey, 2015).

Regarding current MHL research, findings from epidemiological studies have

consistently shown that demographic variables (e.g., age, gender orientation,

geographical location, and race/ethnicity) and educational variables (e.g., years and type

of training) impacted MHL rates of participants from the general population (Druss et al.,

2011; Mojtabai et al., 2011; Wang et al., 2005). More specifically, these epidemiological

research results revealed correlations between the variables of older age, female gender

orientation, rural location, and minority ethnic/racial status with lower rates of MHL.

Such data informed the rationale for this study and the investigation of demographic and

educational variables as potential predictors of rates of MHL.

Researchers have identified low MHL as a key reason for the expansive mental

health care disparities in the United States (Kirchner, Farmer, Shue, Blevins, & Sullivan,

2011; Pescosolido, 2013; Snowden, 2012). Therefore, social justice researchers have

begun to investigate MHL among diverse populations (Lopez, Barrio, Kopelowicz, &

Vega, 2012; Snowden, 2012; Sue, Cheng, Saad, & Sue, 2012). Because of the studies on

MHL and concomitant disparities, policy-makers and researchers have discussed the need

for educating healthy individuals who can encourage those with SMI to seek appropriate

mental health services. To that end, clergy commonly emerge as vital providers of

mental health first aid in communities with low MHL (Lopez et al., 2012; Mills, 2012;

Snowden, 2012). In their meta-analysis research, Oppenheimer, Flannelly, and Weaver

(2004) described clergy as the “gatekeepers” between individuals experiencing mental

illness and the formal mental health care system (p. 155). Clergy are regularly the first

point of contact for those in the beginning stages of SMI (Stanford & Philpot, 2011).

Therefore, understanding clergy’s MHL remains a necessary first-step toward assessing

their capacities to assume the gatekeeping role. However, few researchers have examined

the MHL rates of Christian clergy in the United States who provide spiritual leadership

and support to the 71% of US residents who profess the Christian faith (Pew Research

Center, 2015).

Most studies addressing the MHL of clergy were conducted in countries other

than the United States (James, Igbinomwanhia, & Omoaregba, 2014; Leavey,

Loewenthal, & King, 2007; Noort, Braam, van Gool, & Beekman, 2012) or among non-

Christian clergy in the US, such as imams (Ali, Milstein, & Marzuk, 2005; Ali &

Milstein, 2012) and rabbis (Milstein, Maneire, Sussma, & Bruce, 2008). These

researchers have consistently shown that clergy can identify some of the more serious

mental illnesses, but often lacked the training and literacy to recognize the severity of

presenting symptoms and concomitant need for timely referral to MHPs. Clergy

participants in these studies also expressed the desire for additional training and referral

oppoortunities to ensure their parishioners received adequate help (Ali et al., 2005; Ali &

Milstein, 2012; James et al., 2014; Leavey et al., 2007; Milstein et al., 2008).

Regarding research specific to Christian clergy, data from other countries have

provided evidence of moderate MHL in developed nations. In their study of clergy and

MHPs, Noort et al. (2012) found that Christian clergy in the Netherlands showed

equivocal abilities in detecting spiritual problems when compared to formal providers.

However, clergy identified psychiatric illness as serious and requiring professional

assistance at significantly reduced odds. Compared to MHPs, clergy were 17% more

likely to label SMI (i.e., major depression and psychosis) as a religious problem (OR

= .170, x2 = 25.7, p < .001) and 8.9% less likely to identify the problem as requiring

formal mental health care (OR = .886, x2 = 27.4, p < .001). Potentially, this

underrecognition of the seriousness of mental illness can result in poor outcomes for

those relying on clergy assistance (De Hert et al., 2011; Noort et al., 2012).

Fitting with the results from earlier studies on non-Christian clergy’s MHL (Ali &

Mistein, 2005; Milstein et al., 2000), Noort et al. (2012) also found that clergy required

additional training to meet the mental health needs of their parishioners. Additionally,

they called for research targeting the following topic areas: (a) the constuction of

interprofessional collaborative models using mental health liaisons, (b) the clergy training

required for identification of referrals for symptoms of prolonged grieving, (c)

comparative impact of denominational affiliations (e.g., liberal, conservative beliefs) on

clergy responses to mental health needs, and (d) those issues most neglected by clergy

(e.g., issues of sexuality, abuse). Few robust quantiative studies like Noort et al.’s (2012)

have been identified for Christian clergy in the United States, showing both a current gap

in the literature base and a framework for future inquiries. Noort et al. (2012), with Ali

and Mistein (2005), and Milstein et al. (2000) provided a sound theoretical and

methodological basis for testing MHL among clergy participants across diverse

denominations.

Thus far in the current literature, I have found only three studies focused on

examining Christian clergy from the United States and their ability to recognize mental

illness (Chevalier et al., 2015; Pillion et al., 2012; Stansbury et al., 2010). Stansbury et

al. (2010) utilized qualitative methods to reflect on African American clergy’s

interactions with older parishioners presenting with symptoms of alzheimer’s desease

(AD) (Stansbury & Schumacher, 2008). The results of this study demonstrated that eight

out of nine adequately recognized AD and highlighted the need for increasing

investigation into MHL needs of rural African American clergy. Investigating clergy as

front-line mental health workers to veterans, Chevalier et al. (2015) found that clergy

members demonstrated moderate MHL with regard to suicide and depression, but limited

MHL with regard to posttraumatic stress disorder (PTSD) and traumatic brain injury

(TBI). Pillion et al. (2012) collected data from 48 Catholic priests in North Carolina who

reported willingness to refer case studies depicted in the given vignettes to formal

providers. In these three studies researchers examined the MHL of fewer than 200 clergy

across the United States and used primarily qualitative measures. Although the collective

results of these studies pointed to the need for additional MHL training for clergy, further

research must be pursued in order to understand how better to prepare clergy for their

work with parishioners who require clinical attention.

A review of these three identified studies demonstrated that clergy understand

general mental health issues but do not feel fully prepared to recognize or treat SMI

(Chevalier et al., 2015; Pillion et al., 2012; Stansbury et al., 2010). Furthermore,

researchers have cited that fewer than 10% of clergy participants recognized the

presented symptoms of SMI as serious enough to warrant referral to formal mental health

care services (Farrell & Goebert, 2008; Pickard, 2012; Polson & Rogers, 2007; Sullivan

et al., 2013). These low referral rates point to a need for increasing MHL, especially

those components of MHL related to knowledge about effective and appropriate

treatment options (Jorm, 2012). In response to this need, researchers challenged MHPs to

support clergy in their gatekeeping role via offering training and educational

opportunities (Farrell & Goebert, 2008; Pickard, 2012; Polson & Rogers, 2007; Sullivan

et al., 2013).

In regard to clergy referral patterns and interprofessional training and collaboration

efforts, Thomas (2012) tested the following five variables as predicting clergy referrals to

MHPs: academic education, interprofessional education, teamwork and communication

skills, interprofessional trust, and interprofessional collaborative practice. The multiple

regression model demonstrated that 22% of the total variance was predicted by the

variables (adjusted R2 = .25, SE = 2.16 , p < .05). However, stepwise regression further

showed that most of the variance was explained by the education-related variables

(adjusted R2 = .19, p < .05). The results of this study pointed to educational variables as

the strongest predictors of interprofessional collaboration and referral. This finding also

fits into the findings and recommendations of previous MHL researchers who conducted

studies among the general population (Reavely, Morgan, & Jorm, 2014) and divinity

students (Ross & Stanford, 2014). Collectively, these researchers have indicated a

significant relationship between educational variables and clergy MHL, a finding on

which I will propose variables and hypotheses for this dissertation research.

In regard to educational variables, Polson and Rogers (2007) found that

approximately 71% of clergy admitted to unpreparedness in the counseling role. Payne

(2013) concurred that over 70% of surveyed clergy desired additional MHL training. In

their qualitative exploration, Montesano, Layton, Johnson, and Kranke (2011) examined

clergy from a small sample of American clergy. Clergy participants described the need

for additional training needs with 60% asking for general mental health information and

best-practice treatment options (Montesano et al., 2011). Participants also nominated (a)

finding mental health professionals and (b) having shared beliefs systems with those

providers as key needs for increasing MHL and referral behaviors. Although the small

sample size and geographic area limited the generalizability of the findings, data showed

that these clergy desired additional trainings in MHL. These authors also found that

clergy more often seek training from MHPs rather than the reverse pattern. Thus,

bidirectional training and collaboration models emerged as potentially important aspects

of enhancing trust between professionals.

Given that clergy are not formal MHPs, their lack of training in MHL is not

unexpected. However, the current state of mental health care disparities among minority

populations has positioned clergy as key social justice partners for increasing access to

formal mental health treament (Snowden, 2012). Whether they embrace this role, clergy

will likely remain a first point of contact for for millions of Americans suffering with

mental health issues (Pickard, 2012; Pillion et al., 2012; Sullivan et al., 2013). Therefore,

clergy acting as conduits to the mental health care system have become a focal point for

policy makers and social justice researchers who highlight that ethnic and racial

minorities seek mental health assistance from clergy more often than from MHPs (Lopez

et al.., 2012; Snowden, 2012; Sue et al., 2012).

In relation to mental health care among marginalized groups, Mills (2012) used the

response data of 102,749 participants to measure the differences of mental health use

(traditional, uncoventional [e.g., alternative or parochial], or psychotopic) among

Caucasian/White, African/Black, and Hispanic/Latino populations. Overall, after

controlling for various factors (e.g., sex, age, severity of mental illness), African/Black

and Hispanic/Latino participants were both statistically less likely than Caucasian/White

participants to use any mental health care services, except for parochial. Data also

showed that both minority groups sought parochial care at 30% greater likelihood than

their Caucasian/White counterparts (OR = 1.30 [African/Black] and 1.29

[Hispanic/Latino], p < .001). These researchers amassed data from a large and diverse

cross-section of the population, thereby substantiating a need to address parochial care in

diverse areas of the United States. Furthermore, Mills (2012) challenged future

researchers to investigate reasons for this phenomenon and whether parochial care could

provide linkages to evidenced-based services in efforts to reduce disparities. This study

represented a scientifically-based argument for secular investigation of how to leverage

the influence of clergy providers as potential resources for reducing current mental health

care disparities in communities of color in the United States.

Regarding mental health disparities among ethnic minority populations, Chatters et

al. (2011) added important knowledge about the predictors of seeking assistance from

clergy providers. They found that African/Black participcants not only sought, but also

preferred, clergy assistance with serious mental health issues, with the variables of

Pentecostal denomination, female gender, and older age showing significant relationships

with this preference. In this study, Chatters et al. (2011) provided a comprehensive

review of mental health care disparities and discussed the significance of demographic

variables in predicting the use of clergy as mental health providers. Other ressearchers

have also shown demographic characteristics as predictors of MHL among ethnically and

racially diverse communities (Lopez et al., 2012; Mills, 2012; Snowden 2012; Sue et al.,

2012), but such studies have rarely focused on the characteristics and MHL of the clergy

serving as front-line mental health care providers in these communities.

Researchers have shown the need for clergy to partner with MHPs in order to bridge

the gap between communities of color and the formal mental health care system (Chatters

et al., 2011; Mills, 2012). Additional researchers concurred and called policy makers to

focus on supporting and preparing clergy in ethnically and racially diverse communities

to serve as conduits between help-seekers and the formal mental health care system

(Alegria et al., 2014; Lopez et al., 2012; Snowden et al., 2012; Sue et al., 2012). If clergy

do not recognize mental illness, however, they may not promote or refer parishioners to

formal providers (Farrell & Goebert, 2008; Pickard, 2012). Therefore, a need for the

investigation of clergy MHL as the precursor for making appropriate referrals has become

manifest (Farrell & Goebert, 2008; Polson & Rogers, 2007; Thomas, 2012). A review of

the recent literature, however, showed that MHL research involving clergy participants is

lacking.

Although clergy and MHPs have agreed on the need for increasing interprofessional

collaboration and referral partnerships (Farrell & Goebert, 2008; Polson & Rogers, 2007;

Thomas, 2012), few studies have examined whether clergy recognize when such referrals

become vitally necessary. Thus, I have identified a gap in the literature involving clergy

and MHL. A review of MHL research on nonclergy participants showed that

demographic and educational variables impacted MHL rates (Jorm, 2012; Mojtabai et al.,

2011; Montesano et al., 2011; Reavely et al., 2014; Thomas, 2012), yet clergy

participants remain largely underinvestigated. With this dissertation study, I will attend

to this gap in understanding by examining both demographic and educational variables in

relation to the MHL of clergy participants. The results may provide counselors and

counselor educators with the information they need to build collaborative relationships

and referral partnerships with clergy who serve as conduits to the mental health care

system for millions of help-seekers currently without formal care. Additionally, the

results of this study may support social justice research efforts aimed to reduce mental

health care disparities among minority communities.

Problem Statement

Researchers have shown that clergy might not refer their help-seeking

parishioners to formal MHPs as often as they claimed was necessary (Farrell & Goebert,

2008; Polson & Rogers, 2009; Sullivan et al., 2013). Proposed explanations for

nonreferring behaviors included (a) interprofessional distrust between clergy and MHPs,

(b) clergy’s low MHL, (c) existence of stigma, and (d) contextual barriers (e.g., lack of

resources) (Moran et al., 2011; Sullivan et al., 2013). Few researchers, however, have

explored clergy’s MHL in relation to their referral behaviors. This gap in knowledge is

problematic for millions of U.S. residents who rely on clergy for their mental health care

needs and may receive inadequate treatment and referral responses (Farrell & Goebert,

2008; Openshaw & Harr, 2009; Pickard, 2012; Sullivan et al., 2013).

Under the conceptual framework of MHL, investigation of clergy’s MHL rates

would be the first and most important step in determining why clergy do not refer cases

of SMI to formal MHPs (Jorm, 2012). Given the lack of information on clergy’s MHL,

however, researchers cannot hypothesize or establish the extent to which MHL affects

referral behaviors. This gap in the research remains problematic to the MHPs and

policymakers attempting to reduce mental health care disparities through clergy linkages

to the formal mental health care system (Snowden, 2012). Furthermore, negative mental

health outcomes may emerge for those individuals who rely on untrained clergy for

assistance and referral to appropriate mental health care (De Hert et al., 2011; Snowden,

2012).

Regarding social justice concerns, the lack of information about clergy’s MHL may

profoundly affect communities of color who prefer clergy providers rather than formal

MHPs (Lopez et al., 2012; Snowden, 2012; Sue et al., 2012). Therefore, this research

attends not only to a gap in the research but also to a vital area of social justice.

Understanding clergy’s MHL in relation to various demographic and educational

variables may provide counselors and counselor educators with the information they need

to improve interprofessional trainings and referral partnerships with local clergy (Sullivan

et al., 2013; Thomas, 2012). As indicated in ACA’s multicultural competencies (Sue et

al., 1992), MCSJCC (Ratts et al., 2015), and ASERVIC’s spiritual competencies

(Cashwell & Watts, 2010), counselors and counselor educators must learn to collaborate

with local clergy in order to provide culturally sound and spiritually-aware services.

Given the lack of research into clergy’s MHL, counselor educators may not have the tools

to adequately prepare students to form such collaborative relationships in their future

practices (Reiner, Dobmeier, & Hernandez, 2013; Robertson, 2010). This study attended

to the current gap in understanding clergy’s MHL so that informed counselors and

counselor educators may more effectively address the documented problem of clergy’s

low rates of referral to MHPs (Farrell & Goebert, 2008; Openshaw & Harr,

2009; Pickard, 2012; Sullivan et al., 2013).

Purpose

The purpose of this quantitative, cross-sectional survey research was to investigate

the MHL rates of Christian clergy across the United States and to help bridge the gap in

understanding clergy’s collaboration and referral behaviors. According to the theoretical

framework of the behavioral model (Andersen, 1968, 1995), certain predisposing

variables can affect MHL rates. Using the behavioral model as the framework for this

study, I explored whether extant demographic variables predicted rates of MHL. Using

the Mental Health Literacy Scales (MHLS) (O’Connor & Casey,

2015), I measured clergy’s ability to accurately (a) label various mental health disorders,

(b) identify where to find help, (c) recognize risk factors and causes, (d) understand

appropriate self-help methods, (e) recognize types of professional help, and (e) exhibit the

attitudes that promote help-seeking behaviors (O’Connor & Casey, 2015). The results

may inform counselors and counselor educators about the current MHL of clergy;

furthermore, by relating MHL with various predictor variables, counselors and counselor

educators might gain knowledge regarding how to establish and improve

interprofessional trainings and referral partnerships.

Guided by the behavioral model theory, I analyzed the following variables as

potential predictors of clergy MHL via a demographic questionnaire: denominational

affiliation, age, gender orientation, geographical location, and training and educational

factors. These clergy characteristics were the predictor variables and the rate of MHL

was the outcome variable for this quantitative investigation. Results of the study showed

the rates of clergy members’ MHL and may aid in defining the context in which

counselors and counselor educators may best establish referral partnerships in their

communities.

Research Questions

Research Question 1: Is there a significant difference in mental health literacy

scores, as measured by the MHLS (2015), among Christian clergy of different

denomination-types?

H01: There are no significant differences between the MHL scores of Christian

clergy from different denominations, as measured by the MHLS. H0: μ1 = μ2 = ... = μk

H11: There are significant differences between the MHL scores of Christian clergy

from different denominations, as measured by the MHLS. H0: μ1 ≠ μ2 ≠... ≠ μk

Research Question 2: To what extent, if at all, do educational variables, including post-

secondary years of schooling (in whole numbers), degree-type [divinity, mental health,

other], and number of clinical MH training courses (in whole numbers), predict

significantly higher scores of mental health literacy, as measured by the MHLS (2015),

for Christian clergy in the United States?

H02: Educational variables, including post-secondary years of schooling (in

whole numbers), degree-type [divinity, mental health, other], and number of clinical MH

training courses (in whole numbers), do not predict significantly higher scores of mental

health literacy, as measured by the MHLS (2015), for Christian clergy in the United

States? H0: β1 = β2 = β3 = 0

H12: At least one of the predictor variables, including post-secondary years of

schooling (in whole numbers), degree-type [divinity, mental health, other], and number of

clinical MH training courses (in whole numbers), predict significantly higher scores of

mental health literacy, as measured by the MHLS (2015), for Christian clergy in the

United States? H1: At least one β1 ≠ 0, p≤ .05

Research Question 3: To what extent, if at all, do demographic variables (age,

gender orientation, geographical location) predict significantly higher scores of mental

health literacy, as measured by the MHLS (2015), for Christian clergy in the United

States?

H03: Demographic variables (age, gender orientation, geographical location) do

not predict statistically significantly higher scores of mental health literacy, as measured

by the MHLS (2015), for Christian clergy in the United States. H0: β1 = β2 = β3 = 0

H13: At least one of the predictor variables (age, gender orientation, geographical

location) predict statistically significantly higher scores of mental health literacy, as

measured by the MHLS (2015), for Christian clergy in the United States. H1: At least

one β1 ≠ 0, p ≤ .05.

Framework

I positioned my study among the conceptual frameworks of the MHL research and

behavioral model theories. As first defined nearly 20 years ago, MHL is the ability to (a)

identify symptoms of mental illness, (b) seek information about mental illness and

understand the etiology of the concerns, (c) recognize appropriate help seeking behaviors

and professionals, and (d) seek treatment in a timely manner (Jorm, 2012). Since the

earliest MHL inquiries (Jorm et al., 1997), researchers have consistently found that the

recognition of SMI predicted positive help-seeking behaviors and resulted in improved

mental health outcomes (Jones, Cassidy, & Heflinger 2012; Lauber, Ajdacic-Gross,

Fritschi, Stulz, & Rössler, 2005; Reavley & Jorm, 2011a; Wright, Jorm, & Mackinnon,

2012). Therefore, MHL is not only a construct, but a conceptual foundation, by which

researchers have since tested and related various levels of MHL to (a) recognition rates of

mental illness, (b) help-seeking behaviors and attitudes, (c) symptom reduction, and (d)

stigma (Coles & Coleman, 2010; Farrer, Leach, Griffiths, Christensen, & Jorm, 2008;

Pickard, 2012; Wright et al., 2012; Yap, Reavley, & Jorm, 2012).

Previous researchers frequently relied on Reavley and Jorm’s (2011b)

vignettebased instrument as one of the few options for measuring MHL. Citing the

importance of

MHL to early detection, treatment, and healing of mental illness, O’Connor and Casey

(2015) created a scale-based measure of MHL that supports earlier conceptualizations of

the construct by incorporating six different elements of the construct. These six factors

include the (a) ability to recognize mental illness, (b) awareness of how to access accurate

information, (c) recognition of risk factors and causes (d) familiarity with appropriate

self-help measures, (e) knowledge of appropriate helpers, and (f) attitudes that promote

application of help-seeking strategies (O’Connor & Casey, 2015). When examining these

six elements, researchers found that the proportion of variance explained by different

factorial configurations was low (factor loads between .166-.239), thus supporting a

univariate structure as “statistically and theoretically appropriate”

(O’Connor & Casey, 2015, p.3). As a result, their validity analyses provided evidence of

MHL being a singular conceptual construct. To that end, I operationalized the outcome

variable, MHL rates, according to this conceptual definition and utilized the MHLS

(O’Connor & Casey, 2015) to collect the data.

To delineate the most relevant predictor variables to test in this study, I used

Andersen’s original behavioral model (1968) and revised behavioral model (1995), which

list predisposing, enabling, and need-based factors associated with health literacy. A

legacy of health literacy research showed that the following predisposing factors

significantly impacted health literacy rates: age, gender orientation, culture, educational

components, and geographical context (Anderson & Newman, 2005; Goodwin &

Andersen, 2002; Philips, Morrison Andersen, & Aday, 1998; Pescosolido et al., 1998).

Other researchers have utilized the behavioral model to hypothesize and examine

helpseeking behaviors concerning mental illness in the general population (Pescosolido et

al., 1998; Sharp, Hargrove, Johnson, & Deal, 2006; Pescosolido, 2013). In the few MHL

studies specifically involving clergy (Chevalier et al., 2015; Milstein et al., 2000; Noort et

al., 2011), denominational type has also been proposed as a predisposing factor impacting

MHL rates. This inclusion is appropriate due to the linkages found between culture and

denominational affiliation (Brunn, 2015; Caroll, 2002). In using the theoretical

framework of the behavioral model, I examined the MHL rates of clergy participants in

relation to the statistically significant factors identified in earlier research. I develop the

discussion about the conceptual framework of MHL and the behavioral model theories in

chapter two.

Nature of the Study

This study was a quantitative, cross-sectional survey research design. I used

analysis of variance (ANOVA) to determine whether significant differences in MHL rates

occur between clergy of different denominational groups. I also employed multiple linear

regression analyses to explore which, if any, predisposing characteristics (i.e., the

predictor variables) predicted higher MHL scores (i.e., the outcome variable). Threats to

internal validity related to the study’s lack of probability random sampling, control group,

and manipulation of the independent variable (Creswell, 2013; Campbell, Stanley, &

Gage, 1963). Therefore, I was not be able to determine causal relationships between the

variables (Campbell et al., 1963; Podskahoff, MacKenzie, & Podskahoff, 2012). Because

researchers cannot investigate predisposing demographic factors under true experimental

design, I relied on statistical analyses to offset some of the threats to validity

(FrankfortNachmias & Nachmias, 2008; Podsakoff et al., 2012). Given the potential

relevance of this study to actual clergy practices, an advantage of using the cross-

sectional design was the external validity resulting from an investigation of participants in

their natural settings, as found by methodologists (Chang & Krosnick, 2009; Evans et al.,

2015; Wright, 2005).

Since online surveys provide a more feasible means of collecting data from a large

cross-section of clergy, I utilized a computer-administered survey questionnaire

(CASQ) (Ward, Clark, Zabriskie, & Morris, 2012; Wright, 2005). Using Survey Monkey

(Survey Monkey, 2016), I collected data via web-based methods. In the first electronic

mail (email) invitation, I detailed informed consent processes, the general purposes of the

study, and additional consent policies (ACA, 2014; Millar & Dillman, 2011). I also

provided a direct link to the survey, a one-step clicking process proposed by Millar and

Dillman (2011) for significantly increasing survey response rates. After confirming

participation via the informed consent document, participants completed the demographic

questionnaire, followed by the MHLS (O’Connor & Casey, 2015).

Using an online database of Christian church directories with over 109,000 clergy

contacts to identify potential participants, I randomly selected Christian clergy from

across the US who have active email addresses. By using the CASQ method, I attempted

to give participants time to respond anonymously to the survey in order to reduce social

desirability biases and increase response rates, as were two cited advantages of using

CASQ methods in previous studies (Chang & Krosnick, 2009; Ward et al., 2012; Wright,

2005). However, researchers have also debated about the potential limitations of using

the CASQ method. Significant findings included concerns with self-selection and

coverage bias, as well as higher rates of non-response error (Millar & Dillman, 2011;

Ward et al., 2012). Therefore, I used additional strategies suggested by Millar and

Dillman (2011) in order to increase participation and reduce potential biases. These

strategies included (a) personalizing the web-based invitation, (b) using an .edu email

address, (c) sampling large numbers of potential respondents (Millar & Dillman, 2011).

Definitions

As I reviewed the literature, I found scholarly consensus regarding the definitions

of several constructs in my study. The following definitions provided the framework by

which I explored and investigated the MHL of clergy:

Mental health literacy: The ability to accurately (a) label various mental health

disorders, (b) recognize risk factors and causes, (c) seek information regarding mental

illness, (d) understand appropriate self-help methods, (e) understand appropriate

professional help, and (d) identify the attitudes that promote positive help-seeking

behaviors (Jorm, 2012; O’Connor & Casey, 2015). The outcome variable in this study

was the rates of MHL, as measured by the MHLS (O’Connor & Casey, 2015).

Denominational affiliation: Grouping categories according to religious beliefs

and behaviors made distinct by the group’s doctrinal adherence, religious texts used, and

historical traditions (Noort et al., 2012; Payne, 2009; Pickard, 2012). Depending on the

defining criterion, the range of denominational categories can be narrow (four categories)

to broad (over 1000) (Pew Research Center, 2015). For the purposes of this study, I

chose to delimit the type of denomination by using broad categories of the four most

populous Christian denominations, as indicated the epidemiological research base (Pew

Research Center, 2015). Each of the four categories separately represents at least 5% of

the US population and included the following named religious traditions: Evangelical

Protestant, Mainline Protestant, Historically Black Protestant (National Baptist

Convention), and Catholic.

Age: Number of years since biological birth, measured in whole number (Druss et

al., 2011; Jorm, 2012; Mojtabai et al., 2011; Wang et al., 2005).

Gender identity: “One’s sense of oneself” as impacted by biological sex (e.g.,

according to chromosomes and reproductive genitalia) and interpreted by the individual

(American Psychiatric Association [APA], 2011, p.1).

Geographical location: Physical area of residence defined according to the

number of inhabitants residing in a given locality (Druss et al., 2011; Jorm, 2012;

Mojtabai et al., 2011; Wang et al., 2005). Urban areas are those localities with greater

than 50,000 residents and rural areas are those localities with less than 50,000 (US

Census Bureau, 2015).

Educational status: The completed years (in whole number) of post-secondary

school and degree-level (less than high school/GED, high school/GED, associates,

bachelors, masters, doctoral) (Pescosolido, 2013; Philips et al., 1998; Noort et al., 2002;

Payne, 2012; Pickard, 2012).

Degree-type: The type of learning capacities obtained and whether schooling

involved predominantly psychological studies (mental health), religious studies (divinity),

or neither (other) (Ross & Stanford, 2014; Thomas, 2012; Yamada et al.,

2012).

Clinical mental health (MH) training course: any course, seminar, or

instructional program where the primary topic of study addressed knowledge, assessment,

or treatment of mental health disorders (Ross & Stanford, 2014; Thomas, 2012; Yamada

et al., 2012).

Christian clergy: Vocational category of professionals who serve churches in

“leadership roles, often serving concurrently as religious visionary, authoritative

spokesperson for their tradition, professional pastor, and organizational administrator

overseeing the demographic growth and viability of the religious community” (para. 1)

(Hartford Institute for Religion Research, 2015). Christian clergy further describes the

individual’s use of the canonized Bible and faith Jesus Christ as the foundation for

providing leadership, guidance, and oversight to a group of spiritual seekers (McMinn et

al., 2010; McMinn, Ruiz, Marx, Wright, & Gilbert, 2006; Tanner, Wherry, & Zvonkovic,

2013).

Assumptions

For this dissertation study, I assumed accuracy in some aspects that I could not

demonstrate to be true due to the web-based administration of the survey. I assumed that

the clergy participants willingly volunteered for the study and that their willingness did

not reflect any bias, impairment, or agenda on their part (Ward et al., 2012). I assumed

that the participants answered questions truthfully and to the best of their ability given the

online administration. Additionally, I assumed that the demographic questionnaire and

MHLS (O’Connor & Casey, 2015) offered accurate measures of the predictor and

outcome variables under unseen testing environments.

Due to the heterogeneity of the credentials and roles of Christian clergy and

inability to demonstrate actual clergy membership, I also assumed that participants were

active clergy members in Christian churches in the US. With the ethical mandate to

maintain the anonymity of the participants (ACA, 2014), I could not verify participants’

credentials, roles, or attitudes. In the context of studying a sample of Christian clergy

from diverse locations via web-based administration, I could not avoid these assumptions.

After thoroughly researching the suitability of the instruments and demographic items, I

posited that these assumptions were not haphazard and satisfied the demands of social

science research standards (Creswell, 2013). Furthermore, the use of a large sample size

offset any potentially purposeful inaccuracies (Field, 2013).

Because I analyzed the data using ANOVA and multiple linear regressions, I

briefly detail how I tested the statistical assumptions related to the general linear model. .

First, I checked for missing data points and the linearity of the relationship between the

variables (Field, 2013). Although prior MHL research showed linearity, I tested for this

assumption by analyzing a graph of the data points (Jorm, 2012). I also assumed that the

residuals demonstrated independence, constant variance (homogeneity of variance), and

normal distribution. To test for normality, I used histograms, Probability-Probability (PP)

plots, quartile-quartile (Q-Q) plots; furthermore, I divided skewness/kurtosis by the

standard errors to ensure that no values were large than the absolute value of 2 and within

the three standard deviations range (Field, 2013). For the ANOVA analysis, I conducted

Levene’s test to check the null hypothesis that the differences between the variances are

statistically equal (Field, 2013). For the multiple linear regression, I also tested for

homogeneity of variance by conducting a Levene’s test for equality and Durbin-Watson

to test for independence of errors (Field, 2013). Additionally, I conducted correlation

analyses to ensure that no multicollinearity emerged in the data (Field, 2013). I describe

the results of these tests and limitations in the results section and final discussion.

Scope and Delimitations

I delimited the sample for this dissertation study to include only Christian clergy

in the US. Although the term clergy also includes Imams, rabbis, gurus, and other

religious leaders, this study focused on Christian clergy due to their large influence in the

US and comparative lack of data regarding their MHL (Hartford Institute of Religion,

2015; Pickard, 2012). Surprisingly, researchers have investigated the MHL of imams and

rabbis, even though these non-Christian clergy groups represent less than 1% of the

populations’ religious preferences (Pew Research Center, 2015). As previously

discussed, I identified only a few studies on the MHL of Christian clergy, and most of

these other researchers examined clergy data from outside the US (Lauber et al., 2005;

Noort et al., 2012; Yoshioka, Reavley, Hart, & Jorm, 2014). Thus, the choice to delimit

the population to only Christian clergy in the US was deliberate and intended to fill a

current research gap. In terms of generalizability, the results of this study remain limited

to Christian clergy in the US with internet access.

Limitations

After comparing the design and methods of this current study with standards of

research (Campbell et al., 1963), I identified several limitations related to internal

validity. These limitations involved the methods for sampling, testing and administration,

and instrumentation. Regarding participant selection, the use of convenience sampling

reduced the level of control required for true experimental design

(Campbell et al., 1963). Although convenience sampling increases external validity

(Creswell, 2013), the lack of random selection in the total clergy population was a

significant limitation. However, due to the lack of uniformity in defining Christian clergy

in the US, as highlighted by the Hartford Institute of Research (2015), the current

convenience sample remained the most feasible solution to gathering data from a national

population of clergy

In regard to testing and survey administration, the results of this study are not

comparable to prior studies that utilized in-person or telephone interviews (Campbell et

al., 1963). Furthermore, web-based surveys often result in poor response rates and

nonresponses bias due to sections of the population not having access or knowledge of

webbased communications (Millar & Dillman, 2011). While web-based research has the

advantage of efficiently and affordably reaching diverse samples of participants (Meho,

2006; Ward et al., 2012), I identified the testing context and lack of uniformity of the

testing environment as threats to the internal validity of this study. Because a goal of this

study was to reach a large cross-section of clergy participants, however, I proposed that

web-based methods were feasible and appropriate for this study (Millar & Dillman, 2011;

Ward et al., 2012). Furthermore, I offset potential biases regarding participant selection

by using evidenced-based strategies shown to improve the response rates and accuracy of

web-based surveys (Millar & Dillman, 2011; Ward et al., 2012). These strategies

included utilizing repeat mailings, personalizing the invitation to participate, and

simplifying the length and language of the survey.

Finally, in addressing limitations with instrumentation, the choice of the new

scale-based measure, the MHLS (O’Connor & Casey, 2015), presented some limitations.

The vignette method is the standard for MHL research and has been found to provide

more accurate data than checklist and multiple-choice methods (Peabody et al., 2004).

Additionally, the MHLS is a new instrument with a limited number of testing

administrations (O’Connor & Casey, 2015). However, the MHLS has thus far

demonstrated strong validity and reliability and has the significant advantage of providing

parametric data (O’Connor & Casey, 2015). In comparison with using the vignette

method, I was able to conduct more robust statistical analysis by using data from

O’Connor and Casey’s (2015) scale-based measure. Additionally, researchers can use the

results of this dissertation as a point of comparison for future studies utilizing the

MHLS (O’Connor & Casey, 2015). In order to address limitations with instrumentation,

I examined statistical assumptions and cautiously examined data for potential outliers and

inaccuracies (Field, 2013).

Significance

Since Weaver et al. (1996) described clergy as “front-line community mental

health workers” 20 years ago, few researchers have examined the MHL rates of clergy (p.

846). This gap in understanding impacts the millions of Americans who rely on clergy

for their serious mental health challenges (Chatters et al., 2011; Cho et al., 2009; Jones,

Cassidy, & Heflinger, 2012; Kane & Green, 2009). Therefore, the results of this study

may inform social justice researchers and counselors as to the viability of enlisting clergy

to identify mental illness in their communities, promote appropriate help-seeking, and, in

doing so, reduce current mental health care disparities. With insight into clergy MHL

rates, counselors and counselor educators can more adequately understand how to (a)

support clergy as they work with parishioners, (b) provide interprofessional training

opportunities, and (c) improve collaborative and referral partnerships between clergy and

MHPs. Overall, the results of this study may inform clergy’s MHL training needs and

potential opportunities for establishing interprofessional training and referral relationships

between clergy and MHPs.

In terms of the significance of this study to the counselor education field, the

results of this study may inform current multicultural training curricula regarding

interdisciplinary collaboration and the importance of cooperating with informal helping

networks. According to the Council for Accreditation of Counseling and Related

Educational Programs (CACREP) 2016 Standards (CACREP, 2015), ACA’s

Multicultural Competencies (Sue et al., 1992) and MCSJCC (Ratts et al., 2015), and

ASERVIC’s Spiritual Competencies (Cashwell & Watts, 2010), the responsibility to

cooperate and collaborate with indigenous helpers is an ethical mandate for mental health

counselors when working among diverse populations. To that end, research has shown

that modeling collaborative relationships with clergy partners and providing experiential

learning opportunities increased cultural competence in student counselors (Dobmeier &

Reiner, 2012; Vogel, McMinn, Peterson, & Gathercoal, 2013). However, these learning

strategies are largely absent from the counselor education curricula and remain

understudied in the counselor education research (Dobmeier & Reiner, 2012; Shaw,

Bayne, & Lorelle, 2012). The results of this study may inform the current multicultural

curricula on training approaches for improving clergy-MHP referral partnerships. With

increased interprofessional dialog, training, and referrals, these partnerships may have the

potential for creating positive social change in reducing the current state of mental health

care disparities (Aten et al., 2013; Snowden, 2012).

Summary

Substantial numbers of researchers prescribe the use of clergy to bridge the gap

between currently underserved minority populations and the formal mental health care

system (Alegria et al., 2014; Aten et al., 2012; Lopez et al., 2012; Snowden et al., 2012;

Sue et al., 2012). Clergy members’ capacities to fulfill this role, however, requires their

ability to recognize when such referrals are necessary (Jorm, 2012). Currently, targeted

investigations of the MHL rates of Christian clergy in the US are lacking, with only three

identified studies from the past 10 years (Chevalier et al., 2015; Pillion et al., 2012;

Stansbury & Schumacher, 2008). Therefore, not enough information is available to

discuss the potential benefits and opportunities for collaborating with clergy as partners in

mental health care. Furthermore, the related research into clergy referral patterns

suggested that clergy referrals to MHPs are rare and may be due to low rates of MHL

(Farrell & Goebert, 2008; Sullivan et al., 2013).

To attend to this gap in understanding clergy MHL as related to their referral

patterns, I investigated the MHL rates of a large cross-section of clergy in the US.

Relying on the conceptual framework of MHL (Jorm, 2012) and the behavioral model

theories (Andersen, 1998), I explored several predictor variables in relation to the MHL

rates of clergy participants. These predictor variables included demographic and

educational variables that have been found to significantly affect MHL (Pescosolido,

2013). The results may inform counselors and counselor educators how to increase and

improve interprofessional training and referral partnerships between clergy and MHPs.

Furthermore, the results may fill the gap in understanding clergy MHL so that counselor

educators may be prepared to train student counselors to collaborate more effectively

with local clergy and, potentially, to reduce mental health care disparities. Next, I present

a brief overview of the next chapters of this dissertation study.

In chapter two of this dissertation, I review labeling theory as the precursor to the

concept of MHL, which provided the conceptual framework for this dissertation. With a

review of the behavioral model theories, I discuss various demographic and educational

variables found to impact MHL rates. Fitting within these theoretical foundations, I

review and describe previous MHL studies and their instruments, methods, and findings.

Then, I introduce the current research attending to clergy as informal mental health

providers alongside pertinent data demonstrating minority populations’ preferences for

clergy providers. Due to mental health disparities currently found in the formal mental

health care system in the US, I argue the significance of this dissertation study in the

context of the current literature base. I describe, examine, and assess research attending

to the (a) MHL rates of clergy, (b) need for clergy training in MHL, and (c) lacking

interprofessional collaboration and referral partnerships between clergy and MHPs.

Then, I evaluate the few studies attending to clergy MHL and describe current gaps in the

literature. Finally, in chapter two, I review the problem related to this gap in the literature

alongside the methodologies of previous studies that I will use to frame this dissertation

study.

Chapter 2: Literature Review

In the following literature review, I articulate the need for research attending to

clergy’s ability to recognize serious mental illness (SMI) and make appropriate referrals.

Examining clergy characteristics in relation to their mental health literacy (MHL) rates

may have important consequences for large numbers of Americans who rely on clergy for

psychological support and counseling (Pargament & Lomax, 2013; Thomas, 2012). For

nearly 20 years, researchers have focused on MHL as a key predictor of seeking and

receiving effective mental health care (Jorm et al., 1997; Reavley, Morgan, & Jorm,

2014; Wang et al., 2005). For millions of Americans currently not receiving needed

mental health services, MHL as a precursor to seeking appropriate services continues to

be a vital area of study (Druss et al., 2011). Additionally, mental health care disparities

become especially manifest among populations of minority status, findings which raise

questions of the differences in MHL perspectives according to diverse populations (Mills,

2012; Snowden, 2012). To that end, minority populations have shown preference for

clergy helpers over formal mental healthcare professionals (MHPs) for their

psychological needs (Mills, 2012; Snowden, 2012). Therefore, clergy continue to serve

as gatekeepers to the formal mental health care system for non-majority groups, making

their MHL rates a primary factor in their ability to collaborate with and refer to MHPs

(Snowden, 2012).

The concept of MHL inspired the conceptual basis for this dissertation. Mental

health literacy researchers position the ability to recognize accurately the symptoms of

mental illness as the key predictor variable for identifying and seeking appropriate

helpseeking measures (Jorm, 2012). Findings from MHL research demonstrated

significant relationships among types of mental health disorders, help-seeking behaviors,

and various cultural phenomena (Jorm, 2012). Previous studies have examined age

(Pickard, 2012;

Wright et al., 2012; Yap & Jorm, 2012), gender (Cotton et al., 2006; Lauber, et al.,

2003), race/ethnicity (Angermeyer, Holzinger, & Matschinger 2009; Bener & Ghuloum,

2011; Yoshioka, Reavley, & Rosetto, & Jorm, 2014), geographical characteristics

(Kirchner, Farmer, Shue, Blevins, & Sullivan, 2011), and socioeconomic status (Wang et

al., 2005) in relation to MHL. Additional studies have examined the impact of stigma,

educational levels, and awareness campaigns on rates of MHL (Jorm, Christensen, &

Griffiths, 2006; Wright et al., 2012). The MHL rates of populations most at-risk for

living with untreated SMI, as well as those who can promote MHL and referral to MHPs,

have also been studied (US Department of Health and Human Services [DHHS], 2001;

Snowden, 2012; Wang et al., 2005).

In this literature review, I first review studies establishing the conceptual

framework of mental health literacy and address the research attending to relationships

between MHL and participant characteristics. After discussing the secondary theory for

this study, the behavioral models theory, I establish the importance of exploring

predisposing variables as predictors of MHL. Then, I review the substantial history and

relevance of clergy’s serving as informal MHPs, especially for marginalized populations.

Additionally, I discuss the literature concerning the potential risks resulting from clergy’s

inability to recognize and refer cases of SMI to appropriate MHPs, findings that I used to

frame the problem statement. Taken together, these studies provided a rationale for the

purpose of the current study, which was to explore and examine the MHL of Christian

clergy in the US. After the literature review of MHL and clergy providers, I used the

current MHL research to discuss a methodological basis for the current inquiry, as well as

the potential limitations involved in studying the construct of MHL.

Literature Search Strategy

For this literature review, I conducted a search through PsychARTICLES,

PsychINFO, PsychTESTS, SAGE premiere, socINDEX, Mental Measurements

Yearbook, Health and Psychological Instruments, and Academic Search Complete

databases, accessed from Google Scholar and Walden University’s library via electronic

delivery. The key words included in the database search included clergy, mental health

literacy, interprofessional collaboration, mental health diagnosis, mental health illness,

mental health disparities, recognize/recognition, religion and spirituality, and counselor

education.

Before doing a limited search of the recent literature, I attempted to gain a broad

understanding of clergy MHL by using an open date setting in the databases. The results

of this inquiry led to a more current search, which I integrate with the prior historical

findings. After narrowing the search from the past 15, 10, and then five years, overall

trends in the research became apparent. By using weekly update alerts from the Google

Scholar search engine, I continued to identify and review newly published research and

articles until the final drafting of this study.

Mental Health Literacy: The Conceptual Framework

Before the construct of MHL emerged as a viable and important area of study,

other researchers had been investigating the relationships among mental illness, labeling,

stigma, and socialization (Link, Cullen, Streuning, Shrout, & Dohrenwend, 1989; Gove,

1982; Perry & Pescosolido, 1998; Scheff, 1974). William Scheff explained the

relationships among mental illness, stigma, and labeling in his seminal work on labeling

theory (Scheff, 1974). He postulated that labeling and classifying abnormal behaviors as

formal diagnoses increased their severity and caused social rejection (Scheff, 1974). The

tenets of labeling theory prompted an impassioned debate that centered on the etiology of

mental illness. In response to labeling theory, Gove (1982) rejected the causal effects of

labeling and posited that the act of labeling mental illness did not instigate sustained

negative perceptions. He argued that, regardless of the nominated label, the abnormal

behaviors involved in mental illness resulted in stigmatized responses and social rejection

(Gove, 1982).

Emerging between these opposing views, other researchers (Link et al., 1989) set

forth the proposition that labeling mental illness increased stigmatized responses through

the modifying effects involved in various socialization processes. According to modified

labeling theory, researchers linked the labeling of mental illness to stigmatization via

other pathways, such as negative self-esteem or isolating behaviors (Link et al., 1989).

However, since they advanced this modified theory, continued research has demonstrated

that labeling theories may not adequately explain the etiology of stigma or define the

complexities of stigmatized experiences and processes (Jones, Cassidy, & Heflinger,

2012; Lauber, Ajdacic-Gross, Fritschi, Stulz, & Rossler, 2005; Pescosolido et al., 2010).

For example, Link and Phelan (2013) posited that labeling mental illness could have both

positive and negative effects, with the key being whether the illness or the person became

the object of the label.

Mental Health Literacy

In the midst of this debate regarding the labeling theories, Anthony Jorm and

colleagues proposed the basis for the current concept of mental health literacy (MHL)

(Jorm et al., 1997). Nearly 20 years ago, these researchers first coined the term mental

health literacy and reasoned its importance to the overall health literacy research (Jorm et

al., 1997, p. 182). The seminal researchers defined MHL as “knowledge and beliefs

about mental disorders which aid their recognition, management or prevention” (Jorm et

al., 1997, p. 182). Defined more broadly than labeling, MHL represents the abilities to

accurately recognize and understand mental illness, choose positive help-seeking

behaviors, and successfully pursue appropriate treatment options (Jorm et al., 1997;

Reavley & Jorm, 2011a). In recent MHL research, the accuracy of the label (i.e., linking

of a specific grouping of symptoms to their respective diagnostic category), rather than

the act of labeling, has become the key experimental variable (Merritt, Tharp, &

Furnham, 2014; Reavley, McCann, & Jorm, 2012; Taylor, Lopez, Martinez & Velasco,

2012).

The concept of MHL not only depicts a knowledge of mental illness, but also the

mindfulness and understanding of symptoms and treatment options that lead to positive

help-seeking behaviors (Jorm, 2012). These behaviors involve the recognition of mental

health problems in conjunction with pursuing or promoting effective help-seeking

behaviors from appropriate providers, when necessary (Jacka et al., 2013; Reavley &

Jorm, 2012). For nearly 20 years now, MHL researchers have steadily provided evidence

that accurate recognition of mental health problems is the necessary precursor to seeking

appropriate and helpful treatment. As such, MHL is a dynamic construct rooted in the

theoretical tradition of labeling theory, and researchers now use MHL as the conceptual

foundation for investigating mental health care disparities (Jorm, 2012).

Measuring Mental Health Literacy

Until recently, the Mental Health Literacy and Stigma Questionnaire, now in the

8th updated version (MHLSQ-8) (Reavely & Jorm, 2011b), has been the main instrument

by which to examine MHL. Researchers use the MHLSQ-8 to examine participants’

knowledge and beliefs about six mental health disorders, as described in hypothetical

vignette case studies. With regard to the vignettes, the instrument includes questions

regarding diagnostic accuracy, treatment-seeking preferences, and beliefs about stigma

and the helpfulness of various interventions. To measure MHL, instrument creators

depicted the symptoms of mental disorders in fictitious case studies based on the criteria

taken from the Diagnsotic and Statistical Manual of Mental Health Disorders, Fourth

Edition– Text Revision (DSM-IV-TR) (American Psychological Association, 2000),

including depression, depression with suicidal thoughts, early schizophrenia, chronic

schizophrenia, social phobia, and post-traumatic stress disorder (PTSD). In the vignettes,

the symptoms of each of these disorders become manifest via hypothetical case studies of

an individual experiencing mental health challenges.

Dozens of studies from around the world have replicated, refined, and utlized this

instrument (Burns & Rapee, 2006; Cotton et al., 2006; Jorm, Kelly, et al., 2006; Jorm,

Kitchener, Sawyer, Scales, & Cetkoski, 2010; Reavley & Jorm, 2011a; Yap et al., 2011;

Yamada, Lee, & Kim, 2011; Yoshioka et al., 2014). The results of these studies

supported the conceptualization of the positive links between MHL, help-seeking

behaviors, and attitudes. Furthermore, researchers continue to study new and different

populations in order to extend current findings and theoretical support for the concept of

MHL (Jorm, 2012).

From the conceptual perspective, a strength of the MHLSQ-8 (Reavley & Jorm,

2011b) is its capacity to measure the accuracy of the label as related to treatment seeking,

beliefs, and stigma. Using the MHLSQ, researchers from the last 20 years have steadily

demonstrated robust links between accurate labels and increased nomination of

appropriate help-seeking measures (Jorm, 2012) and reduced stigma (Wright et al., 2012).

In the next section, I review these findings using the terms mental illness and SMI when

addressing mental health disorders. Because MHL researchers used medical definitions

to describe and measure mental health disorders, I also adopted these terms for the

dissertation study. The use of these medically-based terms does not negate the

philosophical foundation of mental health counseling professionals, who hold to a

developmental, preventitive, and strengths-based perspective (Kaplan, Tarvydas, &

Gladding, 2014).

Mental Health Litearcy and Treatment Seeking

Only in the past 20 years have epidemiological researchers investigated MHL

rates, the results of which show that only a minority of those with mental illness seek

treatment, even in developed nations (Dementtenaere et al., 2004; Kessler et al., 2010;

Wang et al., 2007). Even in the US, only half of the respondents with SMI had sought

treatment, and only half of those had sought evidenced-based treatment (Druss et al.,

2011). In another large-scale survey conducted in the US, researchers compared two

National Comorbidity Studies (NCS; 1993; 2003) and also found low rates of

treatmentseeking at 12.2% and 20.1%, respectively (Kessler et al., 2005). Replication

studies of these surveys (NCS-R) further found that “low perceived need” was the main

barrier to seeking treatment, even over contextual (e.g., cost, access) obstacles (Mojtabai

et al., 2011, para. 6). Furthermore, researchers have found that respondents, on average,

delayed treatment seeking for eight years from the onset of symptoms (Drancourst et al.,

2011; Marshall et al., 2005; Thompson, Issakidis, & Hunt, 2008). Importantly, the delays

in treatment resulted in poorer mental health outcomes (Marshall et al., 2005) and, in

another study, longer duration of the illness (Drancourt et al., 2013).

According the conceptual framework of MHL research, recognizing mental illness

and knowing where and from whom to seek help should lead to help-seeking behaviors

(Jorm, 2012; Mojtabai et al., 2011). The reason individuals do not seek or encourage

others to seek treatment was found to be largely due to their inability to recognize the

seriousness of the presenting mental health problems (Gulliver, Griffiths, &

Christensen, 2010; Mojtabai et al., 2011). For example, in one study, participants

reported an average of 9.6 years of “duration of untreated illness” (DUI), taking nearly a

decade to recognize that their bi-polar symptoms were serious enough to warrant

intervention (Drancourt et al., 2011, p. 1). Researchers posited that increasing the ability

to recognize mental illness should, conceptually, lead to improved help-promoting and

help-seeking behaviors (Drancourt et al., 2013; Jorm, 2012; Mojtabai et al., 2011;

Rosetto, Jorm, & Reavley, 2014; Thompson et al., 2008).

Though knowledge about mental illness has been linked to improved helpseeking

behaviors and health outcomes, results from international research have consistently

shown that many individuals do not accurately recognize and label mental illness (Coles

& Coleman, 2010; Farrer et al. , 2008; James et al., 2014; Swami, 2012;

Yoshioka et al., 2014). Low rates of MHL are found in Western cultures as well as in the

US. In their study on MHL for anxiety, Coles and Coleman (2010) found that less than

half of the US respondents correctly identified panic disorder or generalized anxiety

disorder in their respective vignettes. In another MHL study, Sai and Furnman (2013)

showed that just 46.4% of US respondents correctly identified schizophrenia from the

three presented vignette scenarios. In comparison to schizophrenia, depression was found

to be recognized more often (72.7%), a finding which researchers linked to increasingly

publicized awareness campaigns for depression literacy (Sai & Furnman, 2013). These

findings on MHL campaigns and depression literacy are encouraging; however, other

researchers showed that when individuals incorrectly labeled depression as merely a life

problem or stress, they were less likely to seek help (Jorm, Kelly et al.,

2006). Therefore, accurate labeling of mental illness, and not just the vague notion of a

potential problem, may be a vitally important to increasing treatment-seeking behaviors

and improving subsequent mental health outcomes (Jorm, Kell, et al., 2006; Wright et al.,

2012).

Researches have demonstrated that the recognition mental illness significantly

relates to improved mental health outcomes (Angermeyer et al., 2009; de Diego-Adelin˜o

et al., 2010, Gulliver et al., 2010; Reavley & Jorm, 2011a). Across many studies,

researchers have shown that knowledge of appropriate help-seeking behaviors may

moderate the relationship between recognition of mental illness and improved outcomes.

In the MHL research, mental health clinicians described these effective help-seeking

behaviors as seeking timely assistance from mental health counselors, psychiatrists,

psychologists, psychotherapists, general practitioners, and social workers via counseling

and pharmacotherapy strategies (Reavley et al., 2014; Wright et al., 2012). Ongoing

MHL research has consistently shown that accurately recognizing mental illness predicts

these positive help-seeking behaviors, which then leads to a reduction of symptom

severity and enhanced functionality (Jones et al., 2012; Lauber et al., 2005; Reavley &

Jorm, 2011a; Wright et al., 2012).

Because of the the investigated linkages between accurately recognizing mental

illness and seeking help, MHL campaigns have begun to increase in number across Asia,

Australia, Europe, and the US (Angermeyer et al., 2009; Coles & Coleman, 2010; Farrer

et al., 2008, Swami, 2012; Yap et al., 2012). Effective campaigns have involved

dissemination of educational information about the risks and warning signs of mental

illness as well as appropriate treatment options (Reavley & Jorm, 2011c; Wright, Jorm,

Harris, & McGrorry, 2007). The most rigorously tested campaigns occurred in Australia,

Germany, and Norway. In Australia, Jorm et al., (2006) found that a MHL training

program increased participants’ abilities to recognize depression and nominate

evidenced-based treatments as helpful. In a later Australian study, researchers also

boasted that 1% of their population had received MHL training, which resulted in

trainees’ abilities to recognize mental illness, reduce their own mental health symptoms,

and recommend mental health care treatment to others (Jorm & Kitchener, 2011).

Germany’s campaigns saw a reduction in suicidal acts and more accurate understanding

of the etiology of depression (Helergl & Wittenburg, 2009). In Norway, researchers

identified the MHL campaign as the defining factor in reducing untreated schizophrenia

(Joa et al., 2008). These findings are preliminary and limited, but also encouraging.

Importantly, one limitation of these findings is the apparent discrepancy that can

occur between help-seeking attitudes and help-seeking behaviors (Gulliver, Griffiths,

Christensen, & Brewer, 2012). Meta-analysis research on six randomized-controlled

trials of MHL interventions and outcomes showed that interventions improved attitudes

about mental illness, but did not see significant changes in participants’ willingness to

seek help (Gulliver et al., 2012). Therefore, even though accurate knowledge about

mental illness improved attitudes toward seeking help and reducing stigma, knowledge

did not, alone, coincide with actual seeking help-seeking behaviors. This phenomenon

suggested that additional mediating factors may be necessary to encourage actual

helpseeking behaviors. These findings fit within the framework of earlier researchers

who found evidence that those with SMI are more likely to seek help from trained MHPs

when others encouraged them to do so (Gulliver et al., 2010; Yap et al., 2011; Yap et al.,

2012). To that end, recognizing the problem may not be enough to prompt help-seeking

behaviors; the encouragement and referral of trusted others might also be required.

Mental Health Litearcy and Appropriate Treatment and Provider-Type

In addition to recognizing symptoms of mental illness as the precursor to

helpseeking behaviors, MHL also involves knowing where to seek evidenced-base care

and most appropriate forms of treatment (Jorm, 2012). Studies have shown that

individuals overwhelmingly nominated informal help-givers, such as family and friends,

as helpful for reducing mental health problems (Burns & Rapee, 2006; Jorm, Kelly et al.,

2006; Yap et al., 2012). Common forms of informal help include assistance from clergy

and teachers, who are preferred service-providers for many populations (Jorm et al.,

2010; Mills, 2012). While informal helping networks certainly support the healing

process (Pescosolido, 2013; Snowden, 2012), issues of SMI require adequate attention by

trained professionals (Golomb et al., 2014). Therefore, MHL research not only involves

recognition of mental illness as leading to help-seeking behaviors, but also the

investigation of the relationship between accurately recognizing SMI and the choice of

treatment and provider (Jorm, 2012).

Researchers found that accurate labeling of mental illness relates to the

recognition of the most helpful, evidenced-based treatments, treatment providers, and

services (Wright et al., 2012; Wright et al., 2007). Furthermore, these studies have

consistently shown that labeling mental illness accurately, when coupled with knowledge

about effective help-seeking behaviors, improved outcomes and reduced stigma (Wright

et al., 2007; Wright et al., 2012). In a study analyzing the relationship between MHL and

help-seeking behaviors, Wright et al. (2012) found that accurately recognizing and

labeling depression and schizophrenia from a given vignette case study was significantly

associated with the nomination of effective MHPs, which included trained counselors and

general practitioners, respectively.

These findings show increasingly significance when symptoms grow more severe.

The vignettes depicting SMI with bizarre symptom expression (e.g., delusions)

demonstrated higher MHL rates and appropriate help-seeking behaviors when compared

to other vignettes depicting symptoms with less obvious presentations (Reavley et al.,

2014). Researchers found that major depression, schizophrenia, and suicidal ideation are

recognized and treated more often than other mental illnesses with subtler manifestations

(e.g., social anxiety disorder, general anxiety disorder) (Burns & Rapee, 2006; Jorm,

Kelly, et al., 2006; Wright et al., 2007). Given that the more bizarre and outward

presentations of mental illness garner the worry and attention of others, help-seeking

behaviors may partly be the result of encouragement by others to seek help. In fact,

mental health promotion, or the act of recommending another individual to seek mental

health assistance, has become a key goal of mental health first-aid trainings carried out in

various communities (Kelly et al., 2011; Kingston et al., 2011; Yap & Jorm, 2011). As

such, trainings equip community leaders with MHL skills to identify mental illness and

subsequently, recommend and promote best-practice treatment options.

Additional Measures of Mental Health Literacy

In response to a flourish of MHL studies, O’Connor et al., (2014) critiqued the

vignette method of inquiry and cited the need for a psychometrically sound, brief, and

easily-administered measure of the MHL construct. In their discussion, they emphasized

the importance of MHL for improving access to appropriate treatment, as well as the need

to measure MHL among different population-types so that trainings could be

implemented where learning needs became most apparent. Because the MHL conceptual

framework rested primarily on data from vignette case studies, O’Connor et al. (2014)

also questioned the validity of the unified structure of the MHL construct.

In response to the identified limitations of previous MHL studies, O’Connor and

Casey (2015) conducted a study to review the main elements of MHL in order to craft a

valid scale-based measure. Based on the previous two decades of MHL research, they

began by defining MHL as the ability to (a) recognize mental illness, risk factors and

causes; (b) understand appropriate help-seeking measures; and (c) acknowledge attitudes

that promote such recognition and understanding (O’Connor & Casey, 2015). Their

Mental Health Literacy Scale-Pilot-Revised (MHLS-P-R) consisted of 51 items that

inquired about the following: (a) the “ability to recognize disorders” (21 items), (b) the

knowledge of “where to seek information” (4 items), (c) “risk factors and causes” (2

items), (d) “self-treatment” (2 items), and (e) “professional help available,” (5 items),

and (f) the “attitudes that promote recognition or appropriate help-seeking behavior” (17

items), (O’Connor & Casey, 2015, p. 3). More items were dedicated to testing

components of MHL deemed most vital (e.g., ability to recognize problems and promote

help-seeking behaviors), as found in previous studies (Jorm, 2012). After rigorous testing

via clinical panels and comparative analysis with other scale-based measures of the

components of MHL, O’Connor and Casey (2015) published the final 35-item scale, the

Mental Health Literacy Scale (MHLS).

The itemization of this instrument and resulting psychometric evidence

demonstrated the multi-faceted, yet unified, nature of MHL. The results of O’Connor and

Casey’s (2015) study led to the first scale-based measure of MHL, which I will use for

this dissertation study. Their research provided psychometric evidence for how the (a)

recognition of mental illness, (b) knowledge of where and from whom to seek help, and

Their research also provided evidence of the link between recognition and effective

treatment seeking from appropriate providers. In using the MHLS (O’Connor & Casey,

2015), I explored and examined the full construct of MHL, which is not only the ability to

recognize mental illness, but also the acknowledgement of where and how to receive the

most appropriate help.

O’Connor and Casey’s (2015) research further established the importance and

viability of researching the conceptual framework of MHL. With the introduction of the

MHLS, they invited other researchers to test the new instrument among diverse

populations, as such research is currently lacking. Therefore, the results of this

dissertation study inform the reliability and validity data of the new MHLS (O’Connor &

Casey, 2015), in addition to providing lacking data about clergy MHL.

Summary of Mental Health Literacy Research

For nearly 20 years, researchers have defined, challenged, refined, and tested the

concept of MHL among various populations and contexts. Not only do MHL researchers

position recognition accuracy as the precursor to help-seeking behaviors and reduction of

stigma, but also propose that correctly labeling mental illness can lead to seeking help

from appropriate mental health providers (Wright, et al., 2012; Yoshioka et al., 2014).

With the results of these studies, social justice advocates described the importance of

MHL among the general population and promoted research efforts to address the problem

of mental health disparities and the concomitant need for conduits between formal MHPs

and marginalized populations (Lopez et al., 2012; Snowden, 2012; Sue et al., 2012).

Furthermore, they have maintained the importance of examining the construct of MHL

among diverse populations and various demographic characteristics (Lopez et al., 2012;

Snowden, 2012; Sue et al., 2012).

In summary, MHL researchers have promoted and tested the proposition that high

rates of MHL lead to positive help-seeking behaviors, including the identification of the

most appropriate mental health treatment professionals. For the purposes of the current

dissertation study, this proposition provided a conceptual framework useful for

examining whether clergy demonstrated levels of MHL commensurate with their current

roles and obligations as gatekeepers to the formal mental health care system.

Furthermore, exploration of clergy MHL rates according to various demographic

characteristics may inform counselors and counselor educators how to build collaborative

relationships and referral partnerships with their local clergy, an interprofessional process

which has been described as an important strategy for reducing current disparities (Lopez

et al., 2012; Snowden, 2012; Sue et al., 2012). Additionally, the results of this research

may extend the theoretical foundations of MHL by exploring the MHL of clergy

populations with a new scale-based measure.

Predictors of Mental Health Literacy: The Behavioral Model

Frequently employed by social psychology researchers to study health literacy, the

“medical utilization models” draw upon individual, societal, and systemic factors to

measure determinants of health care use (Pescosolido, 2013; Pescosolido et al., 1998, p.

275). Of these models, researchers have used Ronald Andersen’s original behavioral

model (1968) and revised behavioral model (1995) to study three components of health

care use, including predisposing, enabling, and need-based factors. Researchers have

also relied on the behavioral model theories to investigate these factors in relation to help-

seeking behaviors in order to address mental health disparities (Pescosolido et al., 1998;

Pickard & Guo, 2008; Pescosolido, 2013; Sharp et al., 2006). For this study, I chose

utilize the behavioral model theories to hypothesize about potential predictors of MHL in

order to gain contextual understanding of clergy members’ ability to serve as front-line

mental health workers.

According to the original behavioral model (Andersen, 1968), predisposing

variables of health care usage included demographic variables (e.g., age, gender identity,

and geographical location), social factors (e.g., socioeconomic status, culture), and health

beliefs and attitudes (e.g., stigma, symptom severity). In the revised behavioral model,

these predisposing factors became part of the larger category, “primary determinants,”

into which Andersen (1995) also incorporated systemic variables related to physical,

financial, and psychological access to care (p. 7). Actual health-seeking behaviors (e.g.,

personal health habits and use of health care system) and concomitant treatment outcomes

(e.g., both perceived by clients and assessed by professionals) formed the second and

third components of the revised model, respectively (Philips et al., 1998). Since this

study will not explore these latter two categories (i.e., actual behaviors and outcomes), I

will use the original behavioral model to provide the contextual basis for the exploration

of demographic variables in relation to MHL.

Predictors of Health Care Literacy and Usage

Using the theoretical foundation of the behavioral models, researchers have

consistently found that identified need, older age, female gender, Western cultural

heritage, and elevated socioeconomic status predicted higher rates of health care usage

(Anderson & Newman, 2005; Goodwin & Andersen, 2002; Philips et al., 1998;;

Pescosolido, 2013; Pescosolido et al., 1998). Findings from MHL researchers concurred

(Farrer et al., 2008; Kirchner et al., 2011; Mojtabai et al., 2011; Pescosolido, 2013;

Pickard, 2012). Results of MHL studies pointed to various demographic characteristics

as predictors of MHL through participants’ recognition of the need for formal treatment.

For this dissertation, I used these findings to form hypotheses about the relationships

between clergy’s predisposing demographic variables and rates of MHL.

Previous researchers found that contextual, demographic variables did

significantly influence health literacy. Studies showed that differences in geographical

location (e.g., rural versus urban) resulted in differences in help-seeking behaviors for

both health (Philips et al. 1998) and mental health care, with rural communities showing

reduced rates of MHL and use of formal services (Jones et al., 2012; Kirchner et al.,

2011). In other studies, higher levels of educational and occupational attainment

predicted higher rates of health literacy and help-seeking behaviors (Andersen &

Newman, 2005; Goodwin & Andersen, 2002; Reavley et al., 2012). Although

researchers did not investigate potential moderating variables in these studies, they

demonstrated the importance of investigating geographical, educational, and occupational

variables in relation to MHL.

Given the findings from studies utilizing the behavioral model theories, I

hypothesized that certain predisposing demographic factors would show significant

relationships with the MHL rates of clergy. Drawing from the aforementioned studies, I

explored and examined these variables (e.g., age, gender identity, geographical location,

level and type of educational attainment) in relation to the MHL rates of clergy

participants. Findings may inform current understanding of where and how to support

clergy in their role as front-line mental health care workers. Additionally, I tested the

underlying propositions of the behavioral model, thereby extending the knowledge base

of this theory.

Variables Open to Intervention

As understanding of the determinants of health care use increased, researchers

also noted which variables provided ideal opportunities for reducing health and mental

health care disparities (Andersen, 1995; Andersen & Newman, 2005; Sharp et al., 2006;

Pescosolido, 2013). Researchers posited that many demographic characteristics (e.g.,

age, gender identity, geographical location) are not flexible or easily modified (Sharp et

al., 2006; Pescosolido, 2013). Some educational factors, however, do seem amenable to

intervention. Researchers identified these more flexible factors as the (a) knowledge to

recognize of mental illness symptoms (i.e., MHL) (Reavley & Jorm, 2012; Sharp et al.,

2006) and (b) understanding to reduce social stigma (Wright et al., 2012). Researchers

have shown that MHL educational programs were associated with improved recognition

rates of mental illness and the reduction of stigma (Reavley & Jorm, 2012; Sharp et al.,

2006, Wright et al., 2012). While these findings are encouraging, researchers have also

documented that increasing MHL may not be enough to increase actual use of formal

mental health care services (Griffiths et al., 2012). In fact, researchers showed that many

individuals with mental illness will only seek formal help via the encouragement,

assistance, and promotion provided by informal helpers and acquaintances (Alegria et al.,

2014; Jorm, 2012; Jorm & Kitchener, 2011; Snowden, 2012).

Mental Health Promotion

Mental health literacy campaigns among healthy individuals have been associated

with reduced stigmatized responses to mental illness (Wright et al., 2012) and increased

“mental health promotion” (Sharp et al., 2006, p. 422). Mental health promotion is the

nonprofessional practice of encouraging those with mental illness to seek help from

formal MHPs (Sharp et al., 2006, p. 422). Increasing the frequency of mental health

promotion among underserved populations may reduce current disparities and, as such,

remains the goal of many mental health first-aid campaigns (Jorm & Kitchener, 2011). In

the aftermath of Hurricane Katrina, for example, MHPs trained local clergy in rural parts

of Louisiana to offer mental health first aid and promotion to residents in crisis (Aten et

al., 2013). The success of this program illustrated how informal clergy providers can

become helpers and referral partners among populations whose predisposing

demographic characteristics negatively impacted their help-seeking behaviors.

To reduce disparities at the national level, policy makers have begun to identify

informal helping providers who can accurately recognize mental illness, promote

helpseeking behaviors, and refer those in need to appropriate treatment providers (Alegria

et al., 2014; Aten et al., 2013; DHHS, 2001; Lopez et al., 2012; Snowden, 2012). Using

the theoretical basis of the behavioral models, however, I hypothesize that these potential

referral partners residing in areas with low rates of MHL may likewise show inadequate

rates of MHL to become liaisons to formal providers. Therefore, research attending to

the MHL rates and training needs of these potential referral sources remains important for

effectively applying mental health promotion strategies to the current problem of

disparities (Bitanihirwe, 2014).

Summary of the Behavioral Model Theories

The behavioral model provided a theoretical framework by which I assessed the

MHL rates of clergy who often serve as conduits to the formal mental health care system.

By investigating the relationship between clergy members’ predisposing demographic

and educational characteristics and MHL rates, I may inform the interprofessional

training needs of clergy partners of various ages, gender identities, geographical

locations, denominational affiliations, and educational levels and types most in need of

intervention. As shown in the research, increasing the MHL rates of the referral partners

in these communities may provide additional layers of social support for those in need of

services (Bitanihirwe, 2014). In this dissertation study, I will use the behavioral model to

substantiate the use of demographic variables as potential predictors of MHL to

contextualize the discussion regarding clergy’s MHL rates.

The History of Clergy as Informal Providers of Mental Health Care Services

Prior to the Enlightenment period and toward the end of the 17th century, scholars

discussed the fields of religion, medicine, and psychology as overlapping and

complementary pedagogies (Bristow, 2011; Sullivan et al., 2013). With the advent of the

scientific method, however, increasing tension arose between matters of the physical

world and those belonging to the spirit (Sullivan et al., 2013). The eventual

medicalization of the mental health field gradually alienated the spiritual leaders (e.g.,

pastors, priests, imams, monks, gurus), who had traditionally been viewed as the trusted

healers, exorcists, and counselors of their local communities (McMinn, Staley, Webb, &

Seegobin, 2010).

Due to growing differences in perspective and authority, both clergy and MHPs

began to speak out against one another with suspicion and distrust. Most notably, clergy

have tended to disagree with psychological theories about various concepts dealing with

morality and sin (McMinn, Ruiz, Marx, Wright, & Golbert, 2006). In response to this

view, MHPs have traditionally responded aggressively to religious beliefs that they

considered narrow and irrational (Sullivan et al., 2013). As a famed example from 1907,

Sigmund Freud called religion a “universal obsessional neurosis” (Freud Reader, 1995, p.

435). Over the past century, the relationship between religion, spirituality, and

psychology gradually fragmented further; however, religious and spiritual integration in

mental health has recently been gaining renewed attention in the research (Weber &

Pargament, 2014).

Notwithstanding the complex relationship between psychology and religion,

individuals with emergent mental health issues still pursue mental health assistance from

their trusted clergy (Thomas, 2012). In fact, research efforts spanning five decades

consistently showed that approximately 40% of US residents seek out their clergy for

psychological help and counseling services (Chalfant et al., 1990; Gurin et al., 1960;

Hedman, 2014; Oppenheimer et al., 2004; Pillion et al., 2012). These findings fit within

current epidemiological studies showing that 83% and 78% of US residents (a) believe in

God and (b) find religion to be important aspects of their lives, respectively (Pew

Research Center, 2015). For the majority of people who value spiriutal belief systems,

their choice to seek clergy during times of distress is not surprising. Furthermore, the

availability, confidentiality, and low or no cost of the clergy services remain important

advantages of seeking clergy assistance (Bonner et al., 2013; Hall & Gjesfjeld, 2013).

The majority of polled clergy have reported assisting parishioners with some form

of mental illness (Stanford & Philpot, 2011). In view of this phenomenon, researchers

have called clergy “front-line community mental health workers” (Koenig, & Ochberg,

1996, p. 848), “de facto mental health” providers (Kirchner et al., 2011, p. 417), and

“gatekeepers” to the formal mental health system (Oppenheimer et al., 2004, p. 155). In

many communities, seeking spiritual guidance remains the normative response to

emotional, psychological, and familial problems (VanderWaal, Fernando, & Handsman,

2012). Additionally, clergy are often the first point of contact when individuals begin to

recognize mental illness (Stanford & Philpot, 2011).

Every week millions of US residents seek out trusted clergy to address their

mental health problems (Chevalier et al., 2015; McMinn et al., 2010; Polson & Rogers,

2007). Researchers also found that clergy provide counseling services to parishioners for

approximately 15% of their weekly working hours (Oppenheimer et al., 2004). Although

many motives for this phenomena have manifested over the past two decades (Kirchner et

al., 2011; Oppenheimer et al., 2004), more recent explanations include the current

financial crises and heightened fear of stigma among certain populations (Payne, 2013;

Runnels & Stauber, 2011). Furthermore, social justice activists and emergency

responders continue to ask clergy to step into the roles of de facto counseling service

providers during large-scale crisis events after the formal mental health system has

become saturated (Aten et al., 2013; Kelly et al., 2011).

Seeking clergy for mental health support may not merely be a matter of access,

cost, or convenience, however. Researchers have shown that religious and spiritual

matters are central pathways of healing for large numbers of help-seekers (Meyers &

Sweeney, 2008; Pargament & Lomax, 2013). From the scientific standpoint, religious

and spiritual involvement and strategies have gained empirical support as positive and

effective interventions (Koenig et al., 2015). Scholars from across disciplines have

boasted of the benefits of religious and spiritual interventions for prevention and wellness

in clinical mental health (Abu-Raiya, Pargament, Krause, & Ironson, 2015; Goncalves,

Lucchetti, Menezes, & Vallada, 2015; Koenig et al., 2015). In just the past few years,

findings showed that positive religious and spiritual beliefs, practices, and interventions

improved mental health. Data showed a reduction of symptoms of anxiety (Goncalves et

al., 2015), depression (Bonelli & Koenig, 2013; Koenig et al., 2015), substance abuse

(Bonelli & Koenig, 2013; Giordano et al., 2015), and suicidal ideation (Kyle, 2013;

Rasic, Robinson, Bolton, Bienvenu, & Sareen, 2011). Furthermore, researchers found

that the experience of being religious or spiritual related to a reduction in contextual

distress related to physical illness (Pedersen, Pargament, Pedersen, & Zachariae, 2013)

and interpartner violence (Mahoney, Abadi, & Pargament, 2015). In terms of wellness,

research also demonstrated that religion and spirituality correlated with improved

physical health outcomes (Koenig, 2014).

Overall, these findings indicated that religious and spiritual practices and

interventions increased client wellness, thereby illuminating some of the reasons why

individuals with mental illness continue to seek clergy as informal mental health

providers. That religion and spirituality provide important resources and interventions for

a majority of US residents suggests that clergy will continue in their role of informal

helpers. Therefore, MHPs maintain a responsibility to develop and improve collaborative

relationships and referral partnerships with faith leaders in their communities

(Breuninger, Dolan, Padilla, & Stanford, 2014; Cashwell & Watts, 2010; Stanford &

Philpot, 2011).

Clergy as Informal Providers to Marginalized Populations

Research revealed that as many as 60% of US residents did not receive treatment

for mental illness (SAMSHA, 2012). Furthermore, populations with minority statuses

(i.e., ethnic/racial, elderly, female, rural, veteran) sought formal mental health care

services half as often as populations with dominant racial, gender orientation, and

socioeconomic positions (John & Williams, 2013; Mills, 2012; Snowden, 2012). Of

those who did seek treatment, as few as 10% received evidenced-based services (Druss et

al., 2011; Torrey et al., 2014). Social justice researchers have related mental health

disparities (i.e., lack of appropriate services) with the deterioration of physical and mental

health, as well as loss of employment, quality of life, and interpersonal relationships

(Snowden, 2012; Unutzer, Schoenbaum, Druss, & Katon, 2012). These alarming

statistics become increasingly serious in the context of more severe mental illness.

Labeled the “mortality gap,” the death rates for those with untreated SMI (i.e.,

schizophrenia, bipolar, and major depression) reduced their life expectancy from between

13 and 30 years (De Hert et al., 2011). Therefore, the problem of mental health care

disparities among minority populations remains an urgent issue for investigation and

subsequent intervention.

Since the call for research by the US Surgeon General (DHHS, 2001) to examine

and remediate the problem of mental health care disparities among communities of color,

actual positive change remains limited (Lopez et al., 2012; Snowden, 2012; Sue et al.,

2012). Minority populations continue to receive fewer formal services than their majority

counterparts and subsequently experience negative outcomes in their physical, mental,

relational, and vocational lives (Lopez, et al., 2012; Snowden, 2012; Sue et al., 2012).

However, researchers also noted how these underserved populations relied on and, often,

preferred informal clergy providers to formal MHPs (Chatters et al., 2011).

Therefore, social justice researchers have called upon researchers to explore the use of

clergy as conduits between minority communities and the formal mental health care

system (Lopez et al., 2012; Snowden, 2012; Sue et al., 2012). In the next section of this

literature review, I provide a review of research regarding the use of clergy by specific

minority populations and discuss the findings in relation to the current dissertation study.

African/Black populations and informal clergy providers. Researchers found

that African/Black individuals sought clergy first and primarily for their mental health

issues (Chatters et al., 2011). In reviewing a national survey spanning 10 years of data,

researchers reported that African/Black participants most often choose clergy (21%) for

“serious personal problems,” followed by general practitioners (16.1%), psychiatrists

(9.4%), and other MHPs (8.7%) (Chatters et al., 2011, p. 123). In another study,

African/Black participants not only sought clergy assistance 60% more often than

Caucasian/White participants, but also preferred clergy over MHPs, seeking formal

treatment 53% less often than their Caucasian/White counterparts (Mills, 2012). In

another recent survey, researchers found that African/Black clergy spend about 13% of

their weekly time counseling parishioners for mental health challenges involving suicide,

substance abuse, familial strife, and grieving (Young, Griffith, & Williams, 2014).

Throughout the past 20 years, researchers have consistently shown African/Black

participants’ preferences for clergy providers over formal MHPs (Aten et al., 2013;

Neighbors et al., 1998; Neighbors et al., 2007; Stansbury & Schumacher, 2008; Taylor,

Ellison, Chatters et al., 2011). Under examination, these researchers have postulated

various reasons for this phenomenon, which I discuss next (Snowden, 2012).

Chatters et al. (2011) found that demographic variables were significantly

associated with African/Black participants’ choice of clergy assistance, with problem

type (x2 = 8.06, df = 3, p < .001), gender orientation (x2 = 16.23, df = 1, p < .001), and

denomination-type (x2 = 8.06, df = 5, p < .001) showing significant associations. In this

study, Pentecostal denominational members were two times more likely than Baptist

participants to seek clergy assistance. Death/grieving was the most often sighted reason

for reliance on clergy in the counseling capacity, with a 40% increase in odds of seeking

clergy assistance for each one unit increase of this predictor variable (Exp(B)¹ = .40, p

< .001) (Chatters et al., 2011). In a related study, older African/Black participants sought

out clergy providers more often than their younger counterparts (Stansbury &

Schumacher, 2008). Additionally, other researchers found that African/Black

participants’ use of clergy did not relate to income or educational levels, suggesting a

cultural component to their preference for clergy providers (Neighbors et al., 2007;

Snowden, 2012).

Other findings indicated that barriers, rather than preference, did influence the

African/Black American’s choice of clergy providers. Some found that accessing,

acquiring, and affording services remained significant barriers to seeking formal mental

health care (Unutzer et al., 2013). Even though insurance options have become more

readily available due to recent legislative changes, many do not understand the complex

process of acquiring insurance and, once obtained, leveraging their coverage options

toward obtaining appropriate mental health services (Protection & Act, 2010; Snowden,

2012). For example, general practitioners, as the first point of contact, may not refer

help-seeking individuals for mental health treatment, leaving them with fractionalized

care, at best (Glover et al., 2014). Furthermore, general practitioners may misdiagnose

African/Black help-seekers due to “diagnostic bias” (Baker & Bell, 2014, p. 363). In

fact, when African/Black populations do seek assistance, they are significantly

disproportionately diagnosed with SMI, which suggests a s pattern of misdiagnosis and

discrimination (Gara et al., 2012). As a result, African/Black populations experience

greater risk of not connecting with effective, formal mental health care services, even

when they do seek help from appropriate providers.

In addition to systemic barriers, cultural barriers may impede help-seeking

behaviors as well as the quality of the received services (Snowden, 2012). More

specifically, cultural factors involving MHL (i.e., stigma, fear, lack of trust in treatment

providers) have been identified as key points for intervention for many nonmajority

populations, including African/Black (Alegria et al., 2014; Chatters et al., 2011). In

addition, patriarchal interventions provided by Caucasian/White providers have created

environments that do not fit within the cultural framework of clients of color (Whittaker,

Whitaker, & Jackson, 2014). For example, minority clients may perceive MHPs who

utilize the medical model of intervention as power agents who reinforce patterns of

distrust (Young et al., 2014). Together, the demographic context, socioeconomic

variables, and cultural misunderstandings have created a complex environment in which

the majority of African/Black populations rely on clergy assistance for mental health

challenges (Chatters et al., 2011).

Hispanic/Latino populations and clergy providers. With respect to culture and

help-seeking preferences, Mills (2012) investigated Hispanic/Latino populations and

found that they, when compared to White/Caucasian populations, had 41% lower odds of

seeking formal mental health treatment and 36% greater odds of using parochial care for

their mental health concerns. Researchers suggested reasons for not accessing formal

services, which included low rates of MHL and the need for culturally-adapted models of

mental health care with language and translation considerations (Alegria et al., 2014;

Lopez et al., 2012). Cultural factors, such as language and stigma, have shown to be key

determinants of accessing formal mental health care, with shorter length in the US and

Spanish-speaking status predicting reduced service usage (Keyes et al., 2012; Rastogi,

Massey-Hastings, & Wieling, 2012). As such, clergy may be culturally preferred and

available mental health resources for Hispanic/Latino populations (Keyes et al., 2012).

In another study, 61% of Hispanic/Latino participants contacted clergy for their

mental health problems, further demonstrating the potential role of clergy as conduits to

the formal mental health care system (Villatoro, Morales, & Mays, 2014). Religion,

especially Catholicism, has traditionally been an important part of Hispanic/Latino lives

and a significant source of psychological assistance (McField & Belliard, 2009; Taylor,

Lopez, Martinez, & Velasco, 2012). For example, the Catholic sacrament of Confession

and counsel from priests in the confessional provide important resources for relieving

emotional and psychological stress (McField & Belliard, 2009). In a recent study, the

cultural mandate of familismo, or loyalty and bonding to family, predicted the use of

clergy for mental health care provision (Villatoro et al., 2014). Therefore, even though

financial barriers to health care are cited reasons for seeking clergy providers (Alegria et

al., 2014), cultural factors also strongly influence the help-seeking preferences of

Hispanic/Latino communities (Villatoro et al., 2014).

Asian populations and informal clergy providers. Researchers found that

Asian participants also sought informal providers during times of psychological distress,

showing 60% greater odds of using clergy providers when compared to Caucasian//White

individuals (Cho et al., 2009; John & Williams, 2013; Kane & Williams, 2009). In

contrast to Hispanic/Latino populations, Asian Americans born in the US preferred clergy

providers more than their immigrant counterparts (John & Williams, 2013). In another

study, over one-third of Asian participants disclosed their use of clergy for mental health

problems, 70% of whom indicated clinical mental health concerns and were never

referred for formal treatment (John & Williams, 2013). Although currently

underexamined in the MHL research, certain Asian sub-cultures (e.g., Korean, Indian)

who prefer interactions within their cultural communities have demonstrated strong ties

with their religious traditions and reliance on clergy for support (Huang, Calzada, Cheng,

& Brotman, 2012; Lee, Hanner, Cho, Han, & Kim, 2008).

Because Asian cultures tend to prioritize family loyalty and privacy, fear of

stigma is a frequently cited reason for preferring informal mental health care providers

(Masuda & Boone, 2011; Sue et al., 2012). In addition, researchers have identified low

rates of MHL among Asian populations, specifically low levels of knowledge about when

and where to receive appropriate assistance, as a significant barrier to seeking formal

mental health care (Masuda & Boone, 2011). Therefore, researchers have nominated

clergy in Asian communities to serve as promoters of MHL and conduits between their

faith communities and the formal mental health care system (John & Williams, 2013; Lee

et al., 2008).

Age and preference clergy providers. Researchers have also found that older

age predicted the use of clergy helpers for mental health issues. Using the behavioral

model of health services utilization (Andersen, 1995), Pickard and Guo (2008) found that

two predisposing characteristics of older participants (i.e., over 65 years of age) predicted

the choice of clergy assistance for their mental health needs. More specifically, poor

social support and increased frequency of religious attendance prompted older

participants to seek clergy first for mental health assistance (Pickard & Guo, 2008). In a

similar study, older participants with increased stress levels and frequency of religious

attendance showed preference for clergy as the first point of contact for mental health

issues (Pickard, 2012). In response to this phenomena of the link betwewn older age and

use of clergy providers, Hedman (2014) investigated clergy’s confidence about treating

older populations. Under comparison, participants reported less confidence in

recognizing depression in their older, as compared to younger, parishioners (Hedman,

2014).

Also observing of the relationship between age and preference for clergy

providers, Kim Stansbury has lead inquiries via the social work perspective and reported

the results of several qualitative studies investigating rural African American clergy

members’ perceptions (Stansbury, 2011) and perspectives (Stansbury, Beecher, & Clute,

2011) of counseling older parishioners and MHL for recognizing Alzheimer’s Disease

(AD; Stansbury et al.,, 2012). Overall, the findings from these studies described the

reliance of African elders on their trusted clergy and the need for increasing MHL

training and support for clery providing services to older parishioners, especially in rural

areas.

Furthermore, Stansbury et al. (2011) highlighted how individuals holding double

and triple minority statuses (e.g., African/Black and elder and African/Black, elder, and

rural, respectively) may seek clergy assistance more often than their younger

counterparts, citing poor MHL, stigma, and distrust as the key factors relating to this

choice. Therefore, multiple minority statuses may predict help-seeking behaviors and

encourage the choice of clergy providers, whom parishioners already know and trust. In

an earlier study, Stansbury & Shumacher (2008) also provided information regarding

clergy’s preference for the religious-community model, thereby stressing the need for

enhanced interprofessional collaboration between clergy and social workers to meet the

needs of older parishioners.

Female, veteran, and rural populations. Although under-investigated in the

current MHL research, additional factors may predict the use of clergy as informal mental

health care providers. Researchers found that female gender was associated with seeking

clergy assistance (Standford, 2007; Wang, Berlund, & Kessler, 2003), with some

suggesting that this phenomenon may be due to females’ tendency toward greater

emotionality (Pescosolido, Boyer, & Medina, 2013). In a recent study of older

participants’ choice of clergy for mental health assistance, older-aged females were

shown to nominate clergy providers significantly more often than their male counterparts

(Atkins, Naismith, Luscombe, & Hickie, 2015), again showing the preference for clergy

providers by those holding dual minority designations.

With elevated concerns regarding stigma and confidentiality, veteran and rural

participants also pursued clergy assistance more often than formal mental health care

services (Jones et al., 2012; Kirchner et al., 2011). Fearing negative stigma and

professional retribution, returning service members may seek out spiritual helpers, such

as chaplains, to protect their reputations and military positions and statuses (Kim, Britt,

Klocko, Riviere, & Adler, 2011). This need for confidentiality prompted service

members to seek chaplains and clergy for assistance, even though such services may not

adequately meet their psychological needs (Nieuwsma et al., 2013). In response,

researchers have introduced models of interprofessional collaboration between chaplains

and MHPs to ensure adequate treatment for returning service members (Nieuwsma et al.,

(2013).

Also with the goal to maintaining confidentiality, rural populations have shown

reliance on clergy as informal providers of mental health services (Smalley et al., 2010).

In rural areas, lack of access to formal mental health care due to geographical distance

and poor transportation are additional reasons for this pattern (Jones et al., 2012; Smalley

et al., 2010). Researchers have highlighted the reliance on clergy for both adult and

adolescent rural populations (Jones et al., 2012). Furthermore, data showed that the

tendency to rely exclusively on clergy for assistance increases when sharing dual

minority statuses, such as being both African/Black and rural (Murry, Heflinger, Suiter,

& Brody, 2011) or veteran and rural (Kirchner et al., 2011). Therefore, researchers

posited that embedded forms of mental health care (e.g., family, clergy, and teachers) are

important providers and promoters of mental health care among rural populations (Murry

et al., 2011).

Cultural Variables as Predictors of Clergy Providers: Summary

Clergy are an important part of the mental health system for populations of

minority status. Not only do clergy provide necessary services, they also can become

advocates and promoters of formal mental health care services when SMI becomes

apparent (Jones et al., 2011; Snowden, 2012). This review of the literature demonstrated

the importance of investigating the MHL rates of clergy, who may be powerful allies in

the struggle to reduce mental health care disparities among populations with minority

status or multiple minority statuses due to racial/ethnic, age-related, gender-affiliated,

geographical, and veteran-based factors.

Clergy Training

As previously discussed, many people look first and primarily to clergy for informal

counseling services and assistance with mental illness (Stanford & Philpot, 2011).

Although not all religions and denominations provide training in spiritual counseling

activities, research showed that approximately two-thirds of Christian, one-third of

Jewish clergy (Moran et al., 2005; Payne, 2013), and 12% of imams (Ali & Milstein,

2012) had taken at least one course in clinical pastoral education. Schools of pastoral

counseling use different theological perspectives, yet retain similar models of care,

integrating parishioners’ spiritual realities and mental health concerns (Ali et al., 2005;

McMinn et al, 2010). In addition, the theoretical base for pastoral counseling practices

involves the presupposition of clerical authority in working with parishioners during

times of distress (Ali & Mistein, 2012; Gonsiorek, Richards, Pargament, & McMinn,

2009; McMinn & Dominguez, 2005; McMinn et al., 2006; McMinn et al., 2010). In all

religious affiliations, clergy represent the authority figures on liturgical texts (e.g.,

Biblical Old and New Testament, Islamic Qur’an, Buddhist sutras, and Hindu Upanishads

and Vedas), which address various psychological, cognitive, emotional, familial, and

relational concerns (Ali et al., 2005; Ali & Mistein, 2012; McMinn et al., 2010; Pearce et

al., 2014). As mandated by their respective religious texts, clergy provide spiritual

assistance for secular problems in any of these domains.

In nuanced and subjective conceptualizations, clergy shape their responses to

helpseeking parishioners through the lens of the sacred texts and instructions, liturgical

requirements, and moral and religious traditions (McMinn et al., 2010; Pearce et al.,

2014; Thomas, 2012). In describing their counseling activities, clergy have used the

terms spiritual counseling, pastoral counseling, Biblical counseling, and Christian

counseling; however, these terms often become blended and interchangeable with each

other and other state-licensed professional counseling services (McMinn et al., 2010;

Pearce et al., 2014). In keeping with their sacred perspectives, the majority of clergy

have asserted their authority to counsel parishioners in matters involving death and

grieving, forgiveness, marital and parenting relationships, anger, sorrow, and worry (Ali

et al., 2005; Farrell & Goebert, 2008; McMinn et al., 2006; Moran et al., 2005; Pearce et

al., 2014). It is expected and natural, then, for clergy to provide informal counsel to their

help-seeking parishioners for such concerns. Indeed, variations of these psychological

issues manifest in the stories and standards of the religious texts as interpreted through

the healing traditions of confession, prayer, ritual, worship, fellowship, meditation,

mindfulness and yoga (Pearce et al., 2014).

However, researchers have found that clergy prefer for parishioners to seek help

from formal MHPs when the presenting problems surpass the clerical scope of

professional authority (Farrell & Goebert, 2008; Moran et al., 2005; Payne, 2013;

Stanford & Philpott, 2011; Weaver, Flannelly, Flannelly, & Oppenheimer, 2003). The

extent to which clergy actually refer out parishioners with SMI may not coincide with this

determination (Payne, 2013; Ross & Standford, 2011; Thomas, 2012). In fact, diagnostic

categories with subclinical presentations (e.g., pervasive sadness, shock, trauma

responses) may challenge clergy’s abilities to recognize when such formal mental health

care is required (Moran et al., 2005; Openshaw & Harr, 2009; Polson & Rogers, 2007).

Therefore, it becomes important for clergy to recognize those symptoms of mental illness

that outpace their levels of counseling training and competence.

Clergy reportedly saw parishioners most often for issues involving grief and loss,

marital distress, divorce, crises, and depression (Ross & Standford, 2011), all which

emerge along a spectrum of emotionality and functionality (APA, 2013). Therefore,

researchers have called clergy to the “gatekeeping model,” indicating the importance of

their recognizing when subclinical issues become more severe, requiring referral and

promotion to MHPs (Ross & Stanford, 2011, p. 177). However, the gatekeeping role

demands a level of training that researchers have found lacking (Stanford & Philpott,

2011).

Although research is scant on the topic of clergy training in clinical mental health

issues, a few studies offer some insights. Stanford and Philpott (2011) found that 71% of

Baptist senior ministers claimed an inability to recognize mental illness. Similarly, 71%

of California Protestant clergy claimed they received inadequate training for meeting the

current mental health needs of their parishioners (Payne, 2013). In fact, three-fourths of

them asserted that they would benefit from targeted counseling training (Payne, 2013).

Concerning training, only 25% of pastors from Hawaii reported that their seminary

adequately trained them in counseling services and 56% reported inability to deal with

SMI (Farrell & Goebert, 2008).

Early research revealed similar findings, with less than half of New York City

clergy reporting having at least one counseling course during seminary. Not surprisingly,

current levels of clergy training in formal mental health care practices (e.g., courses in

assessment, diagnosis, and evidenced-based treatment modalities) fall below those of

MHPs (Montesano et al., 2011). With a larger scope of investigation, Ross and Stanford

(2014) evaluated the accredited masters of divinity programs in North America and found

that only 27% of seminaries were attempting to increase effective trainings in mental

health, with most of them only giving perfunctory trainings for effective response to SMI.

Most seminary curricula neglected comprehensive coverage of clinical mental health

issues (Ross & Stanford, 2014). Although some seminary trainings included pastoral

counseling as part of the curricula, most clergy have admitted a lack of targeted training

in mental health counseling issues (Payne, 2013; Thomas, 2012).

The training programs of other non-Christian clergy are largely uninvestigated.

However, in one study imams reported receiving less training than did their Christian

clergy counterparts (Ali et al., 2005; Ali & Milstein, 2012). Although not recently

evaluated, rabbis also reported inadequate training for mental health issues (Milstein,

Midlarsky, Bruce, Raue, & Bruce, 2000). Overall, the identified studies on Christian,

Jewish, and Islamic leaders training and curricula suggest a risk for those populations

with SMI who rely on clergy assistance for their mental health challenges due to a

shortage of training.

Clergy as Referral Partners

Clergy have asserted professional competence for responding to subclinical

mental health issues, such as death and loss, family problems, and situational anxiety

(Moran et al., 2005; Openshaw & Harr, 2009; Payne, 2009; Polson & Rogers, 2007; Ross

& Standford, 2014). With regard to clinical and serious mental illnesses, however, clergy

would rather refer parishioners to MHPs (Aten et al., 2013; Aten, Mangis, & Campbell,

2010; Farrell & Goebert, 2008). More specifically, clergy have claimed that issues

involving suicide, major depression, schizophrenia, violence, and serious substance abuse

warranted professional intervention (Aten et al., 2013; Moran et al., 2005; Polson &

Rogers, 2007).

Surprisingly, the research showed that clergy would not refer the majority of their

parishioners to MHPs, even when the case vignettes depicted parishioners with serious

clinical problems (Farrell & Goebert, 2008; Moran et al., 2005; Polson & Rogers, 2007;

Stanford & Philopott, 2011). In fact, clergy rarely chose to refer their parishioners to

MHPs, with specific findings showing referral rates lower than 10% (Farrell & Goebert,

2008; Polson & Rogers, 2007; Stanford, 2007; Stanford & Philpott, 2011). Overall, these

data revealed inconsistencies between clergy’s acknowledged lack of counseling

preparation and the decision to persist in counseling those with clinical mental health

issues. This discrepancy leads researchers to question reasons for these patterns of

nonreferral.

Reasons for Nonreferral

Basing their hypotheses on the historical distrust between religion and

psychology, researchers investigated clergy-MHP referral partnerships and confirmed

that clergy prefer to work with MHPs with whom they have developed trusting and

longterm relationships (McMinn, Runner, Fairchild, Lefler, & Suntay, 2005; Nieuwsma

et al., 2013; Openshaw & Harr, 2009; Thomas, 2012). Other findings showed that clergy

insisted on having some shared values or beliefs with the MHPs from whom they will

seek consultation and referral connections (Breuninger et al., 2014; Pillion et al., 2012;

VanderWaal et al., 2012). With these findings, researchers have suggested models for

clergy-MHP collaboration and referral pathways with varying degrees of success (Aten et

al., 2013; Breuninger et al., 2014; Nieuwsma et al., 2013).

Additionally, practical considerations, such as lack of access to services, high cost

of formal treatment, and informational gaps about MHPs may pose significant barriers to

clergy referrals to MHPs (Alegria et al., 2014; Openshaw & Harr, 2009; Oppenheimer et

al., 2004). Again, these barriers manifest more often in populations of minority statuses,

where culturally sensitive services and funding remained largely unavailable (Lopez et

al., 2012; Stansbury & Schumacher, 2008; Snowden, 2012; Sue et al., 2012). In these

marginalized communities, clergy were busy filling the gap in services rather than

promoting formal mental health care provision. I must note, however, that no one

explanation for poor referral partnerships has emerged in the literature. This fact remains

problematic for those social justice agents attempting to use clergy to build bridges

between underserved populations and the formal mental health care system (Nieuwsma et

al., 2013; McMinn et al., 2005; Payne, 2013; Thomas, 2012).

Clergy Mental Health Literacy

Before investigating clergy-MHP interprofessional collaboration and referral

partnerships, clergy’s MHL rates must first be understood (Jorm, 2012). According to the

MHL model (Jorm, 2012), researchers must explore and examine the extent to which

clergy recognize mental illness in need of referral to MHPs before they can identify other

barriers (Farrell & Goebert, 2008; Pillion et al., 2012). In my review of MHL studies

conducted between 2000 and 2015, I found only five studies regarding US clergy’s ability

to recognize mental illness. Although other researchers inquired broadly into clergy

perceptions, perspectives, and experiences with counseling parishioners as informal

mental health providers (Pickard, 2012; Stansbury, 2011; Stansbury et al., 2012;

Stansbury & Schumacher, 2008), they did not measure the actual MHL rates of clergy,

which remains vital for understanding and improving training and referral activities via

interprofessional collaborative efforts with MHPs.

Of the five identified studies, two surveyed non-Christian clergy and three studied

Christian clergy. Milstein et al. (2000) and Ali et al. (2012) investigated non-Christian

clergy, including rabbis and imams, respectively. In a comparison between psychologists

and rabbis, Milstein et al. (2000) found that clergy were largely able to differentiate

between a schizophrenia vignette and ones depicting subclinical concerns, including

spiritual problems and mourning. However, the results also showed that rabbis

underestimated the severity of the clinical mental health symptoms. In their national

study, Ali et al. (2012) found imams could largely recognize depression; however, imam

participants preferred to refer their parishioners out to MHPs only when remaining in

close collaboration. The outcomes of these two studies provided an informative view of

the diagnostic abilities of non-Christian clergy members, but both studies were limited in

the types of disorders presented and the types of clergy populations examined.

Only three of the identified studies examined Christian clergy’s MHL. These

researchers limited the scope of their investigations to either particular denominationtypes

or specific populations. In their qualitative study, researchers addressed nine African

American clergy on their understanding of parishioners’ experiences of depression,

offering quantitative data on their ability to recognize depression (Stansbury et al., 2012).

Pillion et al. (2012) investigated 48 Catholic priests in North Carolina and their diagnostic

perspectives on a series of mental health symptoms presented in vignette case studies.

Most recently, researchers examined 61 clergy and their ability to recognize

SMI in returning service members (Chevalier et al., 2015).

Although the results of these three studies showed moderate clergy diagnostic

abilities, all three studies documented concerns with Christian clergy’s abilities to

recognize fully the symptoms of SMI. Furthermore, even though clergy participants “felt

comfortable” in their ability to identify mental health problems, participants also

expressed openness to increasing training opportunities in MHL (Pillion et al., 2012, p.1;

Stansbury et al., 2011). Training seemed especially important for less obvious mental

health illnesses, with depression and schizophrenia being largely identified while other

concerns (PTSD, traumatic brain injury) were found less recognizable (Chevalier et al.,

2015).

Overall, these three studies with US-based Christian clergy represented less than

200 of the total Christian clergy population in the US. Therefore, the majority of the

geographical locations, denominations, and illness-types remain largely unexplored in the

current literature base. Given that most residents of the US (71%) belong to Christian

denominations (i.e., with 23% Unaffiliated, 2% Jewish, and <1% Other), the current

scope of identified research does not represent the religious landscape of the country

(Pew Research Center, 2015) or the needs of the millions of US residents seeking

assistance from Christian clergy. A gap in the research regarding Christian clergy’s

ability to recognize mental illness remains problematic for the vast numbers of US

residents seeking clergy assistance who may receive inadequate treatment. Therefore, the

intent of this research study was to extend the current MHL research base to include a

diverse sample of Christian clergy from across the US. The results may inform

counselors and counselor educators how to increase interprofessional trainings,

collaboration, and referral partnerships with those clergy members currently serving on

the front-lines of mental health provision without adequate training or resources.

Methodological Review

Over the past 20 years, researchers have frequently investigated the construct of

MHL using the vignette case study method (Burns & Rapee, 2006; Coles & Coleman,

2010; Jorm, 2012; Jorm et al., 1997; Reavley et al., 2012). As an example, I identified

over 20 studies that used some version of the Mental Health Literacy and Stigma

Questionnaire- Version 8 (MHLSQ-8) (Reavley & Jorm, 2011b). This instrument

presents the symptoms of one of six mental health disorders in a vignette case study to

randomly selected participants to determine the accuracy of their diagnosis, choice of

treatment and provider, beliefs and stigma about etiology of mental illness, and opinions

about the helpfulness of various decision-making processes concerning the diagnoses.

The six disorders utilized in the survey include depression, depression with suicidal

thoughts, PTSD, social phobia, early onset psychosis/schizophrenia, and chronic

psychosis/schizophrenia (APA, 2000).

Researchers have relied on the vignette case study case method in studies with

both qualitative and quantitative designs, on diverse samples, across populations, and

even among clergy (Ali & Milstein, 2012; Coles & Coleman, 2010; Milstein et al., 2000;

Moran et al., 2005; Noort et al., 2012; Pickard, 2012). In these MHL studies, researchers

collected large amounts of demographic data in order to conduct various statistical tests

in relation to the MHL conceptual model (Pickard, 2012). Since the predictor variables in

most MHL studies involved predisposing demographic characteristics, researchers did not

use experimental and quasi-experimental designs, as classically defined by Campbell et

al. (1963). Rather, these MHL researchers relied on statistical controls to offset threats to

internal validity with regard to missing control groups and the nominal or dichotomous

nature of the variables. Therefore, the observed limitations of the majority of MHL

research involved the level of measurement utilized and concomitant lack of parametric

data collected (Campbell et al., 1963; O’Connor & Casey, 2014).

As is standard in MHL research, statistical data typically included descriptive

statistics, including numbers and percentages compared across predictor variables (Jorm,

2012). In addition, researchers conducted logistic regression (Reavley et al., 2014),

chisquare analysis (Reavley & Jorm, 2011a), and principle components analysis (Jorm et

al., 1997) to interpret the various qualitative data points. In one study, Neighbors,

Musick, and Williams (1998) investigated clergy as service providers to African

American participants and conducted chi-square analysis, cross-tabulation tables, and

regression to provide detailed perspective of the collected data. Other researchers have

interpreted

MHL rates according to demographic variables by utilizing multiple logistic regression

(Lauber et al., 2003; Pickard, 2012). Simple correlational analyses have also been useful

to interpret various data patterns (Farrer et al., 2008).

For the few identified studies that examined clergy’s MHL, researchers also

collected demographic data in order to explore potential areas for intervention.

Demographic information typically included forced choice responses about gender

(male/female), age (in whole years), highest level of education (high school, associates,

bachelors, masters, and doctoral), and degree type (divinity or other) (Ali et al, 2012;

Milstein et al., 2000; Noort et al., 2012; Pillion et al., 2012; Stansbury et al., 2011;

Stansbury & Schumacher, 2008). When examining diverse religious samples,

researchers also noted denominational affiliation types, usually narrowing choices by

either four main categories (i.e., Catholic, Traditional Protestant, Evangelical Protestant,

Historically Black Protestant) or according to the top ten most populous faith

communities (Catholic, Greek Orthodox, Southern Baptist Convention, United Methodist

Church, Evangelical Lutheran Church in America, National Baptist Convention

(Historically Black Tradition), Church of Christ, Assemblies of God, American Baptist

Churches (Mainline Tradition), and Presbyterian Church) (Pew Research Center, 2015;

Pickard, 2012). In addition, many studies inquired about the years in ministry (in whole

number), geographical location (by state), number of parishioners (by number), and how

much, if any, pastoral counseling training had been received (in courses) (James et al.,

2014; Noort et al., 2012; Sullivan et al., 2013). Using the examples of these previous

studies, I established the framework for exploring and examining clergy’s MHL rates

according to the demographic variables typically found in the MHL and religious studies

research.

Of the few previous studies on clergy’s MHL or referral rates, researchers also

used the vignette case study format (Ali et al., 2005; Farrell & Goebert, 2008; Pillion et

al., 2012) or qualitative designs (Stansbury & Schumacher, 2008). Only this past year

have researchers developed a valid and reliable MHL scale by which to conduct

parametric statistical analyses (O’Connor & Casey, 2015). With a parametric instrument,

such as the MHLS (O’Connor & Casey, 2015), data exploration could include statistical

measures, such as analysis of variance (ANOVA), to assess if there are any significant

differences between groups on the dependent variable, MHL. In addition, multiple linear

regression could be used to test various demographic variables (i.e., predisposing factors),

training methods (i.e., manipulated variables), or environmental changes (i.e., contextual

factors) to test which factors affect MHL. The development of the MHLS (O’Connor &

Casey, 2015) presents an important opportunity to collect parametric data on the MHL of

clergy. Researchers may then initiate additional follow-up studies as well as MHL

trainings and campaigns. Using the MHLS (O’Connor & Casey, 2015), I conducted the

first investigation of clergy’s MHL rates utilizing this valid and reliable scale to collect

parametric data and conduct quantitative analysis.

Summary of Literature Review

Millions of US residents currently go without evidenced-based mental health care

(SAMSHA, 2012). These statistics escalate among populations of minority status who

often seek and prefer clergy providers (Lopez et al., 2012; Snowden, 2012; Sue et al.,

2012; SAMSHA, 2012). In response, policy-makers have postulated that understanding

MHL rates in regard to help-seeking may inform programs aiming to increase

participation in the formal mental health care system and reduce the currently high

burden of disease caused by SMI, especially experienced by marginalized populations

(Alegria et al., 2014; Snowden, 2012; Unutzer et al., 2014; Wang et al., 2005). Clergy

providers have traditionally provided parishioners with emotional, familial, and

psychological support, and often act as front-line mental health care workers and

gatekeepers to the formal mental health care system (Oppenheimer et al., 2004; Payne,

2013; Sullivan et al., 2013; Thomas, 2012). As such, they may be the ideal conduits

between underserved populations and formal providers. This role, however, demands

that clergy recognize mental illness in need of referral to MHPs. In recent years, clergy’s

MHL has become a concern due to patterns of non-referrals (Farrell & Goebert, 2008;

Sullivan et al., 2013; Stanford & Philpott, 2011). If clergy do not recognize when

parishioners’ mental health needs cross from subclinical pastoral issues to more severe

symptomologies, the safety and wellness of these parishioners and their families and

contacts may be in jeopardy (Farrell & Goebert, 2008; Stanford & Philpott, 2011;

Sullivan et al., 2013).

In the past 20 years, researchers have conducted numerous MHL studies in the

general population (Angermeyer et al., 2009; Cho et al., 2009; Coles & Coleman, 2010;

Demyttenaere et al., 2004; Jorm, 2012). However, few researchers have investigated

clergy’s MHL rates though such literacy is the precursor to identifying mental illness and

making appropriate referral decisions (Jorm, 2012; O’Connor & Casey, 2014). Although

MHPs have called for research into improving clergy-MHP collaboration (McMinn et al.,

2010; Payne, 2013; Sullivan et al., 2013), current interprofessional training efforts by

both divinity instructors and counselor educators remain scant (Dobmeier & Reiner,

2012; Ross & Stanford, 2014; Singh, Shah, Gupta, Coverdale, & Harris, 2012). The

results of this dissertation may inform counselors and counselor educators how to

implement necessary interprofessional trainings and referral partnerships with those

clergy currently serving on the front lines of mental health. In the next chapter, I describe

the research design and methods appropriate for this line of study.

Chapter 3: Research Method

In the previous literature review, I described the need for targeted investigation of

clergy members’ abilities to recognize parishioners with mental illness in need of referral

to formal mental health resources. In this quantitative study, I explored and examined

clergy’s MHL rates according to demographic and educational variables. The results

may inform needed interprofessional trainings, referral partnerships, and collaborative

efforts between clergy and MHPs so that both professional groups may respond

effectively to those populations currently underserved by the formal mental health care

community.

In this chapter, I review the overall purpose, design, and methodology for this

dissertation study. I also explain the ethical and practical reasons for choosing specific

methods and approaches. With attention to ethical research standards of the counselor

education field (ACA, 2014), I describe the population, sampling and sampling

procedures, and methods of recruitment, participation, instrumentation, data collection,

and analysis chosen for this study. Throughout the chapter, I also explain the scientific,

ethical, and practical justification for the selected methodological choices.

Purpose of Study

Since the onset and proliferation of high-profile crisis events, health care debates,

and growing mental health care disparities, researchers have focused on the need for

interprofessional collaborative efforts between clergy and MHPs (Aten et al., 2013;

Milstein, Manierre, Susman, & Bruce, 2008; Singh et al., 2012; Snowden, 2012; Weaver,

1993). As the first-responders to those in crisis, clergy remain on the front lines of the

mental health care system, with reportedly low referral rates to MHPs (Farrell & Goebert,

2008; Singh et al., 2012; Stanford & Philpot, 2011; Sullivan et al., 2013). Researchers

posited that a legacy of distrust between clergy and MHPs is a main reason for the lack of

effective referral partnerships (McMinn et al., 2005; Oppenheimer et al., 2004; Stanford

& Philpot, 2011; Sullivan et al., 2013). Other researchers also found specific barriers to

referral and collaboration practices, including issues of access, cost, and stigma (McMinn

et al., 2005; Mojtabai et al., 2011; Openshaw & Harr, 2009; Young, Griffiths, &

Williams, 2015). Additionally, researchers hypothesized that low MHL rates potentially

reduced the extent to which clergy referred parishioners to MHPs (Farrell & Goebert,

2008; Moran et al., 2005; Openshaw & Harr, 2009; Sullivan et al., 2013; Weaver &

Koenig, 1996). They postulated that clergy who cannot discriminate between various

types and severity levels of mental health problems may not know when and how to refer

parishioners to MHPs.

In a review of literature, I identified only three studies in the last 15 years that

specifically examined the MHL rates of Christian clergy in the US (Chevalier et al., 2015;

Pillion et al., 2012; Stansbury et al., 2011). Therefore, the purpose of this quantitative,

cross-sectional survey research was to investigate the MHL rates of Christian clergy

across the US and to what extent demographic and educational variables predicted higher

levels of MHL. I measured clergy’s ability to (a) correctly label various mental health

disorders, (b) recognize risk factors and causes, (c) understand where to find accurate

mental health information, (d) recognize appropriate self-help methods (e) identify

effective professional help, and (d) demonstrate attitudes that promote helpseeking

behaviors, as shown by their MHL score on the MHLS (O’Connor & Casey, 2015). I

compared the findings of the MHLS (O’Connor & Casey, 2015) according to

denominational affiliation type and further analyzed which, if any, demographic and

educational variables impacted levels of MHL. The overall purpose was to provide the

first scale-based measure of the MHL rates of a national sample of Christian clergy in the

US and introduce potential predictors of MHL. The long-term purpose of the research

continues to reflect my intent to disseminate data in order to inform interprofessional

collaboration and referral processes between clergy and MHPs, potentially reducing

current mental health care disparities.

Research Design and Rationale

The nature of this study was a quantitative, cross-sectional survey research design.

The independent/predictor variables included the following demographic variables: age

(in whole years), gender identity (male, female, and other), geographical location

(rural/urban), and denominational affiliation, according to the four largest

denominational categories (Catholic, Mainline Protestant, Evangelical Protestant, and

Historically Black Protestant) (Pew Research Center, 2015). To be clear, several

Protestant denominations overlap, but reflect the following general categories: Mainline

Protestants typically include Methodist, Lutheran, American Bapstist, Episcopal,

Presbyterian, and Reformed movements. Evangelical Protestants typically include

Southern Baptist, Restorationist movements, Holiness, Pentecostal, Seventh Day

Adventist, Evangelical Lutheran, and non-denominational groups. Historically Black

Protestant groups include African Methodist-Episcopal (AME), National Baptist,

Missionary Baptist, Independent Baptist, and some Pentecostal and charistmatic groups

(Pew Research Center (2015).

In addition to demographic variables, I also explored various educational factors,

which resulted in significant findings in previous clerical studies (Hedman, 2014; Payne,

2013; Pickard, 2012). These independent/predictor variables included the following

educational variables: educational level in years of postsecondary schooling (in whole

years), degree-type (divinity, mental health, other), and number of clinical mental health

(MH) training courses (in whole numbers). For all the predictor variables, I relied on

previous religious and MHL studies to create an impartial and original demographic form

(Appendix D).

The dependent/outcome variable was the rate of MHL, and I administered the 35-

item MHLS to measure this variable. The MHLS is a scale-based measure of the

knowledge, beliefs, and attitudes regarding mental illness which produces a total MHL

score between 35-160 for each participant (O’Connor & Casey, 2015). I used analysis of

variance (ANOVA) to determine whether differences in MHL rates occur between clergy

of different denominational affiliations. I also employed multiple linear regression

analyses to explore which, if any, predisposing demographic characteristics predicted

significant changes in MHL scores.

Research Design With Regard to Research Questions

In this dissertation study, I used a quantitative cross-sectional survey design.

Researchers choose designs for their studies that fit both the question-type as well as

practical and ethical constraints (Campbell et al., 1963). Many social science researchers

in the counseling field utilize cross-sectional or quasi-experimental designs, which rely

on statistical analyses as a method of control (Podsakoff, MacKenzie, & Podsakoff,

2012). Cross-sectional designs measure aspects of a particular population at one moment

in time according to the property-disposition framework (Podsakoff et al., 2012). Using

this framework, researchers compare or relate participants’ characteristics or experiences

(i.e., the predictor variables) with their attitudes, beliefs, or preferences (i.e., the outcome

variable) using some formal measure or survey instrument (Groves et al., 2009). The

lack of randomization and control (i.e., of the type and timing of a predictor variable) in

the cross-sectional design can result in weaker internal validity when compared to the

quasi-experimental and true experimental design; however, the cross-sectional design

remains a feasible and ethical option for measuring property-disposition relationships

(Frankfort-Nachmias & Nachmias, 2008).

In this quantitative, cross-sectional survey research, I explored and examined the

property-disposition relationships among clergy in the US. Using this design, I examined

the following three questions:

Research Question 1: Is there a significant difference in mental health literacy

scores, as measured by the MHLS (2015), among Christian clergy of different

denomination-types?

H01: There are no significant differences between the MHL scores of Christian

clergy from different denominations, as measured by the MHLS. H0: μ1 = μ2 = ... = μk

H11: There are significant differences between the MHL scores of Christian clergy

from different denominations, as measured by the MHLS. H0: μ1 ≠ μ2 ≠... ≠ μk

Research Question 2:To what extent, if at all, do educational variables, including post-

secondary years of schooling (in whole numbers), degree-type [divinity, mental health,

other], and number of clinical MH training courses (in whole numbers), predict

significantly higher scores of mental health literacy, as measured by the MHLS (2015),

for Christian clergy in the United States?

H02: Educational variables, including post-secondary years of schooling (in

whole numbers), degree-type [divinity, mental health, other], and number of clinical MH

training courses (in whole numbers), do not predict significantly higher scores of mental

health literacy, as measured by the MHLS (2015), for Christian clergy in the United

States? H0: β1 = β2 = β3 = 0

H12: At least one of the predictor variables, including post-secondary years of

schooling (in whole numbers), degree-type [divinity, mental health, other], and number of

clinical MH training courses (in whole numbers), predict significantly higher scores of

mental health literacy, as measured by the MHLS (2015), for Christian clergy in the

United States? H1: At least one β1 ≠ 0, p≤ .05

Research Question 3: To what extent, if at all, do demographic variables (age,

gender orientation, geographical location) predict significantly higher scores of mental

health literacy, as measured by the MHLS (2015), for Christian clergy in the United

States?

H03: Demographic variables (age, gender orientation, geographical location) do

not predict statistically significantly higher scores of mental health literacy, as measured

by the MHLS (2015), for Christian clergy in the United States. H0: β1 = β2 = β3 = 0

H13: At least one of the predictor variables (age, gender orientation, geographical

location) predict statistically significantly higher scores of mental health literacy, as

measured by the MHLS (2015), for Christian clergy in the United States. H1: At least

one β1 ≠ 0, p ≤ .05.

Research Design with Regard to Time and Resource Constraints

For the current study, I collected data from, rather than conducted an intervention

for, a national sample of clergy from various denominations in the US. Previous

researchers found that electronic (i.e., web-based) designs could garner a diverse pool of

willing participants (Meho, 2006; Ward et al., 2012). I also found that the cross-sectional

survey design with a web-based administration enabled a large and diverse clergy

population to be included in the study. Since time and resources are generally limited in

doctoral studies, researchers often prefer web-based administrations of quantitative

surveys to face-to-face or telephone methods (Meho, 2006; Ward et al., 2012). However,

the cross-sectional survey design also has inherent threats to its internal validity due to

the researcher’s inability to manipulate the independent variable, conduct non-probability

sampling, and use a control group (Campbell et al., 1963; Creswell, 2013). Due to these

inherent design weaknesses, I was not able to ascribe causal meanings to the data for this

dissertation study (Campbell et al., 1963; Podskahoff et al., 2012). However, I relied on

statistical analyses to balance some of the threats to validity and find meaningful results,

as will discussed in the results section (Field, 2013).

Research Design with Regard to Advancing Knowledge

Since researchers cannot examine predisposing variables under true experimental

design, the cross-sectional survey design was appropriate for this dissertation study

(Frankfort-Nachmias & Nachmias, 2008; Podsakoff et al., 2012). Given the potential

relevance of this study to actual clergy practices, a strength of the cross-sectional survey

design was the strong external validity resulting from an investigation of participants in

their natural settings (Chang & Krosnick, 2009; Evans et al., 2015; Trochim & Donnelly,

2006). This design choice was also consistent with the designs of previous MHL

research (Jorm, 2012; Pillion et al., 2011). Using the results, I may advance, support, and

challenge both practical and evidenced-based knowledge involving MHL education for

clergy (Aten et al., 2013) and current policies for reducing disparities (Snowden, 2012). I

may also inform counselor training and education concerning interprofessional trainings

and referral partnerships with clergy (CACREP, 2015; Cashwell & Watts, 2010).

Design Summary

For this dissertation study, I investigated several predisposing demographic and

educational variables in relation to clergy’s MHL rates. The cross-sectional survey

design provided an appropriate and feasible framework for the examination of these

predisposing variables. Furthermore, I utilized evidenced-based strategies for increasing

the objectivity and generalizability of the study (Groves et al., 2009; Trochim &

Donnelly, 2006). In using the cross-sectional design, I employed current survey methods

to maximize the reliability and accuracy of the collected data (Groves et al, 2009;

Trochim & Donnelly, 2006). Because of feasibility concerns involved in the collection

of data from a national sample of diverse clergy, I used non-probability sampling

strategies and web-based methods, which I describe next in the methodology section

(Meho, 2006; Ward et al., 2012).

Methodology

The research method details the exact instructions regarding when, how, and from

whom the researcher collects data (Creswell, 2013). The objectives of this study guided

my choice of methods and included the following considerations: the variables; the

number of participants; the number, level, and type of measurements; the setting of the

study; sampling procedures; and potential controls for increasing the internal validity of

the study (Groves et al., 2009). Since online surveys provide a more feasible means of

collecting data from a large cross-section of clergy, I utilized a computer-administered

survey questionnaire (CASQ) to collect data (Ward et al., 2012). Using this method, I

attempted to maximize the accuracy of data collection processes and minimize

measurement error through the application of evidenced-based data collection strategies

utilized by prior researchers (Groves et al., 2009; Millar & Dillman, 2011; Ward et al.,

2012). In the next section, I discuss how I addressed concerns regarding (a) defining the

population, (b) choosing appropriate sampling and sampling techniques, and (c) applying

recruitment, participation, and data collection strategies related to instrumentation and the

CASQ process.

Population and Target Population

The target population was Christian clergy in the US as of March 1, 2016.

Because no uniform definition exists for Christian clergy, I chose the parameters for

defining this population by analyzing previous religious and social science research

(Adams, McMinn, & Thurston, 2014; Hartford Institute for Religious Research, 2015a;

Hedman, 2014; Payne, 2013; Pickard, 2012; Weaver et al., 1996). These religious

researchers described Christian clergy as including paid or unpaid, schooled or

unschooled, and licensed or unlicensed individuals who assume leadership roles in

churches that rely on faith in Jesus and the traditional Biblical text. Using this broad

definition, researchers have used any of the following titles to describe these leaders:

deacons, elders, leaders, ministers, pastors, priests, and/or teachers (Hartford Institute

for Religious Research, 2015a; Hedman, 2014; McMinn et al., 2006; Pickard, 2012). For

the purposes of this study, I chose the following parameters for inclusion in this study:

Christian clergy are any church deacons, elders, leaders, ministers, pastors, priests, and/or

teachers who interact with church attendees using the Bible and Jesus Christ as the

foundational belief system. Therefore, any individual listed on the Christian church

leadership or staff web page, regardless of their title, could participate in this study. I

explained these parameters on the participation invitation so that those not meeting these

standards could remove themselves from the study (Appendix B). I used electronic mail

(email) contacts taken directly from church staff registries to identify potential

participants, which may have also added a layer of protection ensuring proper participant

selection and participation. I outline the details regarding identifying church registries

below.

Target population size. The US Bureau of Labor and Statistics (2011) identified

48,020 American clergy members. Showing noteworthy difference, the Hartford Institute

for Religious Research (2015b) stated the number of North American clergy as

approximately 600,000. The divergence stemmed from differing definitions of clergy.

Dr. Jackson Carroll, divinity expert at Duke Divinity School, posited that using 600,000

would be most appropriate for religious studies research (Hartford Institute for Religious

Research (2015b). Because the exact population size is unknown, I had to use

nonprobability and convenience sampling methods with a specified population frame

(Groves et al., 2009; Hartford Institute for Religious Research, 2015b). The population

frame for the current study included Christian clergy, as defined above, from any

denomination in the US with known email addresses drawn from an extensive church

database, as discussed in the next section.

Sampling Strategy

In this dissertation study, I gathered information from a large and heterogeneous

clergy population. Given the challenges involved in defining, identifying, and contacting

all Christian clergy in the US (the target population), I applied non-probability sampling

strategies using a specified sampling frame. Non-probability sampling procedures

include convenience, purposive, or quota sampling (Frankfort-Nachmias & Nachmias,

2012). Although non-probability sampling strategies are not the gold standard for

research (Campbell et al., 1963), researchers can take appropriate precautions to preserve

the reliability and validity of the design (Creswell, 2013). Because a uniform definition

of Christian clergy does not exist, I could not use probability sampling (Campbell et al.,

1963). Therefore, non-probability convenience sampling procedures were appropriate

and feasible for the current investigation.

In order to gather data from clergy across the US and among various religious

denominations, I administered CASQ questionnaires delivered via the World Wide Web

(Web). As commonly used by other social science researchers (Chevalier et al., 2015;

Hedman, 2014; Pickard, 2012; Pillion et al., 2012; Rogers, Stanford, & Garland, 2012), I

used convenience sampling methods to gather data from clergy. The use of

nonprobability, convenience samples enabled data collection to be efficient in terms of

time, cost, and accessibility of respondents (Krosnick, 1999). Because the CASQ method

allowed participants time to respond to the survey, I may have increased participation by

using this method of data collection, as posited by Chang & Krosnick, 2009).

Additionally, CASQ methods may have mitigated potential interviewer biases that

sometime manifest during face-to-face data collection strategies (Groves et al., 2009).

With the strengths of using the CASQ method, however, were also several risks.

Compared to face-to-face and telephone methods, researchers have found that

selfselection bias, coverage bias, and non-response error rates increased in some CASQ

studies (Millar & Dillman, 2011; Ward et al., 2012). Researchers posited that reasons for

these risks cold reflect a lack of access to and familiarity with technology (Groves et al.,

2009). For example, researchers related older age of participants with reduced response

rates due to their lack of technology savvy (Klovning, Sandvik, & Hunskaar, 2009).

Since age was a predictor variable in my study, I considered this concern during the data

analysis process, which I present in the next chapter.

Further regarding response rate, other researchers compared web-based,

telephone, and in-person surveys and found that web-based methods showed the lowest

response rates (Heerwegh & Loosveldt, 2006). Other researchers reported response rates

for Web-based surveys between 25-35% (Millar & Dillman, 2011) Of particular concern

for this dissertation study was the documented response rates for the software being

utilized for survey delivery, Survey Monkey. Survey Monkey researchers showed an

even lower average response rate (as low as 5%) for their surveys, as compared to typical

web-based administrations (Survey Monkey, 2016). Therefore, I examined previous

research to discover potential strategies to increase participation and provide suggestions

for improving survey administration, which I discuss next (Millar & Dillman, 2011).

To reduce non-response error in online studies, Heerwegh and Loosveldt (2006)

found that personalizing invitation emails increased response rates. Researchers who had

academic email addresses (i.e., .edu) and utilized them to send out surveys also found

increased response rates (Porter & Whitcomb, 2007). Although addressing each

respondent personally was not possible, I did utilize my university email address and

addressed participants as fellow colleagues in the caring profession. I also included

transparent explanations for the purposes and motivation for the research, which

comprised both spiritual and mental health implications. In web-based research, such

transparency increased the response rates and participation in web-based surveys (Millar

& Dillman, 2011). Participants who found interest in the survey topic also demonstrated

increased response rates (Groves, Presser, and Dipko, 2004).

In consideration of the data from these collective studies regarding response rates

in web-based research, I included detailed information about the current project in the

initial email invitation (Appendix B) and utilized my .edu email address to increase

response rates. Although repeat emails also may have increased the response rates, I

chose to only send out one email to each potential participant in order to maintain the

anonymous nature of the study (i.e., avoiding the tracking individual participants) (Millar

& Dillman, 2011). Knowing the choice to not remind and repeat invitation emails likely

would reduce response rates (Millar & Dillman, 2011), I increased my potential

participant pool to a random selection of 6000 in order to reach the sample size.

Drawing the Sample

To fill a gap in the current research about the MHL of diverse clergy members, I

examined data from a heterogeneous sample of Christian clergy from across the US.

Using personal pastoral contacts, church registries advertised on the Web, and individual

church registries publicly provided by individual denominational groups, I researched and

performed an extensive search for a large compilation of Christian clergy across the US

who provided email contact. In my literature review of clerical research, I identified the

use of online church databases as a frequent form of recruitment for clergy participants

(Hedman, 2014; Openheimer et al., 2004; Pickard, 2012). To simplify the online

searches, researchers have either located church directories with attached clergy email

addresses or purchased pre-compiled lists of clergy participants (Hedman, 2014; Noort et

al., 2012; Pickard, 2012). In previous studies, researchers began their recruitment by

inviting participation, thereby highlighting the non-commercialized intent for their study

and volunteer nature of participation (Hedman, 2014; Noort et al., 2012; Pickard, 2012).

These strategies maintained ethical and legal compliance with current anti-spamming

regulations (i.e., CAN-SPAM) regarding the use of pre-compiled lists for drawing samples

(US Federal Trade Commission, 2008; Wiles, Heath, Crow, & Charles, 2005).

Using the strategies maintained by other researchers, I identified a pre-compiled

list of 109,000 clergy contacts drawn from church databases and public directories by

APC Services Limited (APC Services, Ltd., 2016). This research and management

organization consistently compiles and checks the list to maintain compliance with

antispamming regulations. They also publish the Directory of Churches, continually

monitoring the accuracy of contact information and compliance with opt-out (i.e.,

unsubscribe) requests (APC Services, Ltd., 2016). Regarding the ethical and legal use of

pre-compiled lists, I reviewed current legislation and found that researchers are exempt

from anti-spamming laws and may send participation invitations to large groups of

unknown individuals (US Federal Trade Commission, 2008). Prior to obtaining this list, I

also inquired of Walden University’s Institutional Review Board (IRB) to ensure the

ethical requirements when purchasing pre-compiled samplings lists. In response, I

received confirmation that such practices remain ethically acceptable and feasible options

for data collection (L. Munson, personal communication, January 11, 2016). See

Appendix A for a copy of this email exchange. Using these 109,000 clergy contacts as

the sampling frame, I collected data from clergy participants to conduct the described

data analyses.

In consideration of the above statistical, ethical, and methodological

considerations, the final sampling frame for this current study was a compilation of

109,000 church contacts throughout the US and representative of all major

denominations, as provided by APC Services, Ltd. (2016). This list included Christian

clergy from all major denominations across the US with email addresses and physical

addresses, the latter certified by the US Postal Service to ensure accuracy (APC Services,

Ltd., 2016). From this list, I randomly selected 6000 contacts for potential participation

using Microsoft Excel’s RAND function (Microsoft Corporation, 2016).

Sample Size

To determine appropriate sample sizes, researchers must first define the

population and population size. As previously discussed, the population size for

Christian clergy in the US varies according to polling methods (Hartford Institute for

Religious Research, 2015b). Survey estimates included rates as low as 48,020 (US

Bureau of Labor and Statistics, 2011) to 600,000 (Hartford Institute for Religious

Research, 2015b). Statistically, both poll numbers are large and require a similarly large

sample size in order to achieve representativeness, as discussed next (Bearden, Sharma,

& Teel, 1982; Groves et al., 2009).

A review of literature showed that most MHL researchers employed a 95%

confidence interval and .80 power to determine appropriate sample sizes (Jorm, 2012;

Reavley & Jorm, 2011a; Yoshioka et al., 2014). Examination of previous results of MHL

studies consistently showed outcomes of medium effect sizes (Mathews, 2008; Noort et

al., 2011; Payne, 2009; Reavley & Jorm, 2011a; Yoshioka et al., 2014). Other

researchers investigating clergy participants and using 95% confidence levels, and .80

power also found medium effect sizes (Hedman, 2014; Noort et al., 2012; Pickard, 2011).

Fitting with the current literature base, I decided to use similar figures for the power

analysis. I used the statistical software, G-Power (Faul, Erdfelder, Buchner & Lang,

2009), to conduct the power analysis for the current dissertation study. I entered into

GPower a 95% significance level (α = .05), .80 statistical power (1- β = .80), and medium

effect sizes (f = .25 or f = .15) for the two different statistical methods (i.e., one-way

ANOVA and multiple linear regression, respectively).

For Question 1 and subsequent ANOVA, a 95% significance level (α = .05), .80

statistical power (1-β =.80), and medium effect size (f = .25) for one-way comparison of

four groups required about 180 respondents, according to G-Power (Faul et al., 2009).

For Questions 2 and 3, I ran sample size analysis for conducting multiple regression

analyses on the collected data. By incorporating 95%, significance level (α=.05), .80

statistical power (1 - β = .80), and medium effect size (f = .15), the study’s sample size

required at least 77 respondents, according to G-Power (Faul et al., 2009).

Further evidence for setting these approximate sample sizes emerged from the

empirical data from previous MHL research. In exploring sample sizes from religious

studies, sample sizes between 48 and 150 clergy participants were common (Ali et al.,

2005; Chevalier et al., 2015; Pillion et al., 2012; Noort et al., 2012). Specific examples

included James et al. (2014) who used 103 clergy participants and Kane and Green

(2009) who surveyed a sample of 143. Pillion et al. (2012) and Noort et al. (2012)

conducted regression analyses with clergy participants and used between 100 and 150

participants. In fitting with the sample sizes from these studies, this dissertation research

utilized ANOVA and multiple regression data analysis and, respectively, at least 180 and

77 participants, according to power analysis. To further understanding of the collected

data, I also analyzed the data with descriptive statistics (i.e., percentages, means, and

standard deviation) to show MHL rates according to demographic and educational

variables, as has been conducted on data from other MHL studies (Jorm, 2012; Wright et

al., 2012).

For the ANOVA (i.e., Question 1), 180 participants was the suggested sample size

for achieving satisfactory power among four groups, according to power analysis. For

Question 1, I also analyzed the data with descriptive statistics to show MHL rates (in

percentages) according to denominational affiliation, as has been conducted by other

researchers investigating clergy participants and MHL (Milstein et al., 2000; Noort et al.,

2012; Pickard, 2012). I did not identify any other MHL studies employing ANOVA for

denominational comparisons. Given that this study was the first to examine MHL rates

of a national sample of clergy and compare denominational groups via ANOVA, I also

examined additional clergy studies that employed ANOVA to justify the sample size, as

discussed below.

Investigating clergy perceptions of depression and referrals, Hedman (2014)

employed one-way ANOVAs to investigate mean differences for provision of counseling

hours among six denominations. Hedman (2014) surveyed 270 participants to achieve

necessary power to make significant comparisons. Administering four different email

and physical mailings, Pickard (2012) surveyed 524 clergy participants to satisfy power

analysis for conducting ANOVAs on data from six different denominational groups.

These two examples provided additional support for choosing a larger sample size, as

compared to typical religious studies, for this dissertation study. For conducting an

ANOVA with four groups, the power analysis results drawn from G-Power (Faul et al.,

2009) further supported the choice of the larger sample size (n = 180).

Because response rates to email surveys can be as low as 15%, I first invited 2000

clergy to participate in order to achieve the sample size goal (Baruch & Holtom, 2008).

Due to my not repeating email invitations to participants, I had to administer further

email invitations to new sets of potential participants, as I had anticipated via previously

researched web-based research strategies (Millar & Dillman, 2011). After I sent the first

round of surveys to the first 2000 potential participants, I received responses and

recognized the need for a second and, then, third round of mailings. After two weeks of

data collection, the final response number satisfied the recommended sample size for both

the one-way ANOVA and multiple linear regression analyses in this study.

Procedures for Recruitment, Participation, and Data Collection

After receiving approval by International Review Board (IRB; Approval # 04-

0716-0260783), I randomly selected 6000 potential participants from the precompiled

and verified list of church contacts (APC Services, Ltd., 2016). I used the Microsoft

excel spreadsheet and the RAND function to randomly select the email addresses of

unnamed clergy members (Microsoft Excel, 2016). I sent an email to the first 2000 of the

randomly selected list members inviting them to participate in the study (See Appendix

B). In the invitation, I detailed the general purposes of the study, informed consent

processes, and additional consent policies, as well as potential limits to confidentiality

with descriptions of how their information would be protected (ACA, 2014; Millar &

Dillman, 2011). For ethical and practical reasons, I described these procedures and

policies in clear and simple language (ACA, 2014; Dillman, 2011). Since I did not

receive the sample size of 180 after the first mailing, I was required to send invitations for

a second and, later, third round of randomized, web-based mailings.

Those participants willing to participate in the study clicked on the link to the

informed consent document, which was read and box-checked yes to confirm assent.

Informed consent included the following descriptions: confidentiality safeguards and

risks, purposes/procedures of the study, potential risks and benefits of participation,

limitations of the study, plans for dissemination of findings, and the voluntary nature of

participation and completion of the survey (Appendix C). To honor ethical expectations,

I communicated with potential participants the goals of the research project. As both a

licensed MHP and licensed pastoral counselor/clergy member, I disclosed my

professional memberships and demonstrated transparency to assure clergy participants of

my unequivocal respect for both professions (ACA, 2014). To encourage

interprofessional collaboration and training resources, I also provided at the end of the

study a Web address link for participants to read the final results of the study, which they

will be able to access anonymously.

Once participants signed the informed consent, instructions on the web-based

survey directed participants to the demographic questionnaire created for this study

(Appendix D). In the demographic questionnaire, I explained the delimiters of the

sample population and provided additional information regarding participants. The

survey began with nine demographic and educational questions asking participants’ age,

gender identity, geographical location, denominational affiliation, number of years of

post-secondary schooling, highest level of education, degree type, number of years in

ministry, and how many courses, if any, of MH training they had completed. I included

these specific questions in the demographic questionnaire because behavioral model

theorists have shown these predisposing demographic variables consistenly impacted

MHL rates (Andersen, 1995; Pescosolido, 2013) and conceptual foundations of MHL

(Jorm, 2012). Slight differences in the demographic questionnaires from various MHL

studies reflected differences in the research questions. Overall, the choice of these

demographic and educational variables as potential predictors for my dissertation study

fit with previous religious studies and remained grounded in the literature base (James et

al. 2014; Noort et al., 2012; Sullivan et al., 2013). After completing the demographic

questionnaire, the survey instructions directed participants to the MHLS (Appendix F).

The MHLS instrument assesses clergy rates of MHL and presents several

depictions of SMI and resulting attitudes, beliefs, and behavioral choices. Although this

study involved minimal risk, I considered participants’ unidentified psychological issues,

potential issues which survey researchers have posited might prompt emotional or

psychological discomfort or reactions (American Association for Public Opinion

Research [AAPOR], 2010). Therefore, I included follow-up resources at the end of the

survey questionnaire (Appendix G). In case questions or feelings of distress emerged, I

also provided web links to the Association of Spiritual, Ethical, and Religious Values in

Counseling (ASERVIC) website and ACA website for training opportunities and

wellness resources. In addition, the researcher’s email address remained available for

questions and concerns. Finally, in order to maintain privacy and confidentiality, I

included a link at the end of the survey by which participants can access the results of the

study anonymously.

At the completion of the data collection, I inputted the anonymous data from

Survey Monkey into statistical software for analyses. According to the IRB application

(2015), “anonymous data contains absolutely zero identifiers and makes it impossible to

determine who participated and who did not” (p. 11). When data returned via Survey

Monkey, the email addresses were not attached to the data so that I could not know who

responded to the survey or completed the questionnaires. At my procedural request,

Survey Monkey reported the data with no identifying information and did not track

nonresponders for follow-up, ensuring that the collected data remained anonymous.

Instrumentation

The MHLS (O’Connor & Casey, 2015) is a 35-question scale-based measure that

examines MHL with one total score and may be useful for identifying communities with

low MHL so that improved education and support can be provided (O’Connor & Casey,

2015). O’Connor and Casey (2015) also described how researchers could use the

measure assess the helpfulness of various educational campaigns. The instrument

creators administered the survey via web-based formats, thereby validating the survey for

web-based administration (O’Connor & Casey, 2015). Additionally, the MHLS has good

internal and test-re-test reliability and validity, which I discuss later in the chapter. Int he

pilot studies for the MHLS, mental health professionals and those with previous

experiences with mental illness demonstrated greater MHL, which reflected consistency

with MHL prior research (Caldwell & Jorm, 2001; Lauber et al., 2005; Merritt et al.,

2013; O’Conner & Casey, 2015). Furthermore, help-seeking behaviors positively

correlated and psychological distress did not correlate with higher rates of MHL,

divergent findings that fit the current conceptual framework of MHL (O’Conner &

Casey, 2015; Smith & Shochet, 2011).

To create the MHLS, O’Connor and Casey (2015) used a panel of psychologists

to operationalize the definition of MHL, which led to a reduction from seven to six

attributes due to insufficient evidence for differentiating two of the attributes. The

research team and clinical panel generated items for each of the remaining attributes and

piloted the resulting 79 questions to a community sample of 202 individuals. Due to the

results of a discriminatory analysis, researchers excluded 28 items and reversed other

items for accuracy. O’Connor and Casey (2015) also employed a Likert-response format

for ease of administration.

Following the Phase I pilot-testing, the MHLS-Pilot (MHLS-P) consisted of 51

items that inquired about the following: (a) the ability “to recognize disorders” (21 items),

(b) the knowledge “of where to seek information” (4 items), “risk factors and causes” (2

items), “self-treatment” (2 items), and “professional help available,” (5 items), and (c) the

attitudes “that promote recognition or appropriate help-seeking behavior” (17 items),

(O’Connor & Casey, 2015, p. 3). Larger numbers of items per category reflected the

more comprehensive attributes. The MHLS-P was administered to 372 community

members and 43 health professionals along with demographic questionnaires, mental

health history inquiries, the General Help-Seeking Questionnaire (GHSQ) (Wilson et al.,

2007), and the Kessler Psychological Distress Scale (K10) (Kessler et al., 2002). The

results of two separate factor analyses demonstrated that the proportion of variance

explained by other factors was low (factor loadings between .16-.24), thus supporting the

current univariate structure as “statistically and theoretically appropriate” (O’Connor &

Casey, 2015, p.3). To reduce the number of items, researchers removed any items that,

when included in the analysis, resulted in unacceptable Cronbach’s alpha. The final

alpha level of the instrument was .873. Test-re-test reliability showed good reliability (r

(60) = .797, p < .001), with the final version of the MHLS including 35 items.

During Phase 3, O’Connor and Casey (2015) examined the descriptive data and

validity of the MHLS. The scale was “somewhat normally distributed” (skewness - .115,

kurtosis -.231) and demonstrated readability (i.e., grade level 7.6). Discriminant validity

was shown by the significantly higher MHL scores by the professional group (M = 145,

SD = 7.19) than the community sample (M = 127.38, SD = 12.63). Construct validity

was demonstrated by significant positive correlation between the GHSQ (i.e., testing

help-seeking behaviors) and the MHLS (r (370) = .234, p < .001). The finding of no

significant correlation between the K10 and MHLS (r (370) = -.087) showed that the

MHLS did not significantly relate to levels of psychological distress, which fits with the

conceptual framework of MHL.

Using the COSMIN quality rubric, O’Connor and Casey (2015) examined the

methodological integrity of the MHLS (Mokkink et al., 2010). As a result, six of the nine

domains demonstrated adequacy, including internal consistency, reliability, measurement

error, content validity, structural validity, and hypothesis testing. Due to the lack of other

parametric MHL measures, researchers did not assess the criterion validity of the MHLS.

Furthermore, researchers are currently assessing the instrument’s cross-cultural validity

and responsiveness (O’Connor & Casey, 2015). The brevity of the scales resulted in

some limitations in terms of the comprehensiveness of the questions to test the full

construct of MHL; however, the researchers addressed some of these concerns by using

multiple sources to guide item development. In a personal communication (January, 7,

2016), L. Casey granted me permission to use the MHLS via a web-based format, which

remains the intended mode of administration (O’Connor & Casey, 2015). A copy of this

communication and the author’s authorization to change references of Australia to the

United States is included in Appendix E.

Data Analysis

In this study, I used the general linear model (GLM) as the statistical basis for

analyzing the collected data. To conduct analyses, I used Individual Business

Managements (IBM) SPSS software version 23 (IBM, 2016). Administration of the

MHLS (O’Connor & Casey, 2015) provided parametric data that allowed for ANOVA

and linear regression analysis. The GLM was the appropriate model for conducting

analyses on data at the interval level, which corresponded with the level of the survey

data and hypotheses from this study (Field, 2013). Below, I list the research questions

and hypotheses for review.

Review of Research Questions and Hypotheses

Research Question 1: Is there a significant difference in mental health literacy

scores, as measured by the MHLS (2015), among Christian clergy of different

denomination-types?