Case Analysis – Treatment Format

Article

Benchmarks for Outpatient Dialectical Behavioral Therapy in Adults With Borderline Personality Disorder

Micki Washburn 1 , Allen Rubin

1 , and Shu Zhou

1

Abstract Purpose: This article provides benchmark data on within-group effect sizes from published randomized clinical trials supporting the efficacy of dialectical behavioral therapy (DBT) for borderline personality disorder (BPD) in adults aged 18–65 years. Method: Within-group effect sizes were calculated via the Glass approach and adjusted for sample size using Hedges’s g then aggregated to produce benchmarks for symptoms commonly associated with BPD, such as self-harm, depression, and anger. Results: Aggregate within-group effect sizes are presented separately for treatment (DBT) and control (treatment as usual) groups and for interviewer assessed and self-reported outcome measures. Discussion: Community-based practitioners can use these benchmarks as a comparison tool to evaluate the ways in which they are adopting or adapting the DBT intervention and to determine if the intervention should be modified or replaced, given their unique practice setting and client population.

Keywords dialectical behavioral therapy, benchmarking, borderline personality disorder, research supported interventions, implementation science

Dialectical behavioral therapy (DBT) is a multicomponent cog-

nitive–behavioral intervention for the treatment of borderline

personality disorder (BPD). BPD is a long-term condition usu-

ally developing in adolescence and has higher rate of occur-

rence in those experiencing childhood trauma (American

Psychiatric Association, 2013; Lieb, Zanarini, Schmahl, Line-

han, & Bohus, 2004; Nicki, 2016). BPD impacts multiple areas

of adaptive functioning and is characterized by a long-standing

pattern of emotional dysregulation and instability in one’s

sense of self and one’s interpersonal relationships (American

Psychiatric Association, 2013). Individuals struggling with

BPD often experience chronic suicidal ideation and engage

in self-harm behaviors (Linehan, 1999; Ost, 2008; Panos, Jack-

son, Hasan, & Panos, 2014; Stoffers et al., 2012), leading to

high levels of service utilization including inpatient treatment

(Chaput & Lebel, 2007; Gunderson, 2016; Tomko, Trull,

Wood, & Sher, 2014; Zanarini, Frankenburg, Reich, Conkey,

& Fitzmaurice, 2015). A diagnosis of BPD also impacts access

to services, as individuals with a diagnosis of BPD must deal

not only with the symptoms of the disorder itself but also with

the high levels of stigma often associated with this diagnosis

(Aviram, Brodsky, & Stanley, 2006; Knaak, Szeto, Fitch, Mod-

gill, & Patten, 2015; Nicki, 2016). Service access also can be

impeded by negative perceptions of many providers concerning

long-term prognosis, chances for recovery, and a return to

adaptive functioning (Aviram et al., 2006; Knaak et al.,

2015; Lieb et al., 2004; Markham, 2003).

Several aspects of DBT distinguish it from other forms of

cognitive–behaviorally based therapies that may be used for the

treatment of BPD. DBT incorporates the theory of dialectics

and the belief that two opposing premises can hold true simul-

taneously (Koerner, 2013; Peng & Nisbett, 1999). Clients are

taught to accept the self without judgment and self-criticism,

but at the same time acknowledge that change is necessary and

that active, adaptive coping strategies must be employed to

bring about that change (Dimeff & Koerner, 2007). The cog-

nitive component of DBT helps clients identify maladaptive

assumptions, thoughts, and beliefs grounded in all or nothing

thinking that keep the individual stuck in a state of emotional

dysregulation. The behavioral components of DBT are struc-

tured hierarchically, addressing first what are termed ‘‘therapy

interfering behaviors’’ (Dimeff & Linehan, 2001; Linehan,

1993a). These behaviors include suicide attempts, nonsuicidal

self-injury, missing appointments or coming to appointments

under the influence of drugs or alcohol. Once therapy-

interfering behaviors are adequately controlled, the focus of

therapy shifts to building concrete skills that can be translated

1 University of Houston, Houston, TX, USA

Corresponding Author:

Micki Washburn, University of Houston, 309 Social Work Building, Houston,

TX 77204, USA.

Email: [email protected]

Research on Social Work Practice 2018, Vol. 28(8) 895-906 ª The Author(s) 2016 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/1049731516659363 journals.sagepub.com/home/rsw

across multiple settings in which the client is expected to

function.

DBT is support oriented, strengths based, and collabora-

tive, in that it provides structured module-based training to

build key coping skills. These skills include: core mindful-

ness (reconnecting with one’s body and being aware and

fully present in the moment), distress tolerance (accepting

and managing distress rather than trying to eliminate it via

self-harm, substance abuse, or other maladaptive means),

emotional regulation (understanding emotional sensitivity and

reducing vulnerability to negative emotions), and interperso-

nal effectiveness (learning how to respectfully say ‘‘no’’ to

the demands of others while maintaining relationships and

asking for what one needs directly and in a way that others

will respond to (Dimeff & Koerner, 2007; Linehan, 1993b,

2014). Homework is assigned between sessions to continue to

build skills in real-world environments. Between-session

coaching provided by phone (Dimeff & Linehan, 2001; Line-

han, 1993a), or more recently via mobile device application

(Rizvi, Dimeff, Skutch, Carroll, & Linehan, 2011; Washburn

& Parrish, 2013), encourages clients to obtain timely support

in using their newly learned skills when attempting to cope

with difficult situations (Rizvi et al., 2011; Washburn &

Parrish, 2013).

DBT therapists operate from the premise that the client is

currently doing the best that they can and that maladaptive

coping is a ‘‘reasonable and functional response to dysfunc-

tional biological, psychological and environmental events’’

(Nejad & Wesseling, 2015, p. 5). Clients with a diagnosis of

BPD often exhibit high levels of complex needs that may lead

to provider compassion fatigue and burnout (Dimeff & Line-

han, 2001; Ditty, Landes, Doyle, & Beidas, 2015; Linehan,

1993a). Thus, DBT is usually provided as a team-based multi-

component intervention comprised of weekly individual ses-

sions and weekly skills groups, with consultation teams sharing

the responsibility for each client’s treatment (Chugani &

Landes, 2016; Dimeff & Koerner, 2007). These teams support

individual therapists and help them remain motivated to keep

doing their best with challenging clients.

Implementation of DBT can be challenging in outpatient

settings, due to the length of the intervention, typically

52 weeks, and the need for at least one intervention team

member to be ‘‘on call’’ at all times for phone coaching.

Issues such as staff cohesion and turnover may also impact

the team based structure of DBT (Aviram et al., 2006;

Comtois et al., 2007; Koerner, 2013). Despite these chal-

lenges, DBT is an attractive alternative for outpatient treat-

ment, especially when other less structured and intensive

types of community-based treatment have failed to produce

the desired results. In addition to the desirability of its team-

based, skills-focused approach to treatment, DBT has ample

research evidence to support its efficacy. A recent Cochrane

Collaboration review by Stoffers et al. (2012) indicated that

standard DBT was superior to treatment as usual (TAU) for

issues commonly associated with BPD, such as parasuicid-

ality, depression, and anger.

Based on its research support, community practitioners

engaged in the evidence-based practice process might assume

that when providing the complete DBT protocol to clients with

BPD they are being as effective as possible (Chorpita, Becker,

Dalieden, & Hamilton, 2007; Parrish & Rubin, 2012). How-

ever, various studies have provided empirical grounds for ques-

tioning that assumption due to the differences in the service

provision characteristics of research-based and community-

based settings (Brownson, Colditz, & Proctor, 2012; Embry

& Biglan, 2008; Hoagwood, Burns, Kiser, Ringeisen, &

Schownwald, 2001; Koerner, 2013; Weisz, Ugueto, Cheron,

& Herren, 2013).

Unlike those implemented in research-based settings,

empirically supported interventions implemented in

community-based settings, such as campus counseling centers

and outpatient mental health agencies, tend to have fewer

mechanisms in place for intervention-specific training, contin-

ual supervision, and fidelity monitoring (Brownson et al., 2012;

Comotis et al., 2007; Ditty et al., 2015). Community-based

caseloads are likely to be larger and more heterogeneous and

with more extensive comorbidity (Lieb et al., 2004). Moreover,

issues with client attendance, premature termination, and prac-

titioner turnover are far more likely in community-based set-

tings (Drake et al., 2001; Rubin, Parrish, & Washburn, 2016).

These concerns are even more acute when DBT is the inter-

vention being implemented due to high levels of client needs,

the length of the intervention, and number of providers who

serve as part of the treatment team (Chungai & Landes, 2016;

Comotis et al., 2007; Ditty et al., 2015).

In light of these differences, instead of simply assuming

that they are maximizing their effectiveness simply by pro-

viding an intervention that is deemed evidence-based,

community-based practitioners should consistently evaluate

the outcome of their efforts when providing such interven-

tions (Comotis et al., 2007; McHugh & Barlow, 2010). Recent

studies show that there is often reluctance on the part of pro-

viders and administrators in community settings to engage in

outcome evaluations with their clients (Parrish, Washburn, &

Torres, 2013; Roth & Fonagy, 2013). This reluctance can be

due to feasibility obstacles, such as a lack of time or access to

appropriate outcome measures, coupled with the assumption

that such an evaluation requires using a control group or con-

ducting a time series design (Rubin, 2014; Sperry, Brill,

Howard, & Grissom, 2013).

In recent years, however, a more suitable alternative for

community providers in social work, counseling, and psychol-

ogy has emerged which involves comparing the outcomes of

treated clients in community-based settings to within-group

effect size benchmarks derived from the randomized clinical

trials (RCTs) that have provided the research support for the

intervention that they are implementing (Minami, Wampold,

Serlin, Kircher, & Brown, 2007; Rubin, Parrish et al., 2016;

Rubin, Washburn, & Schieszler, 2016; Rubin & Yu, 2015).

These benchmarks can be calculated by employing meta-

analytic techniques to calculate aggregate within-group effect

sizes separately for experimental and control groups in the

896 Research on Social Work Practice 28(8)

RCTs that have provided the research support (Minami, Serlin,

Wampold, Kircher, & Brown, 2008; Spilka & Dobson, 2015).

It is not the purpose of benchmarking to add to the empirical

base regarding the efficacy of a given intervention. Neither is

its purpose to establish the efficacy of one intervention over

another (Rubin, Washburn, et al., 2016). Rather, comparing the

community-based within-group effect size of treated clients to

the RCT within-group effect sizes (benchmarks) provides

descriptive evidence that can inform decision-making about

how well the research-supported intervention is being imple-

mented and whether it needs to be modified or replaced (Rubin,

Parrish, et al., 2016). For example, if the community-based

effect size much more closely approximates the experimental

group effect size than the control group effect size, support

would be indicated for the adequacy of the way the intervention

is being implemented. The opposite would be implied if the

community-based effect size is much closer to the control

group effect size. For readers unfamiliar with the process of

benchmarking, a thoughtful description of the process of aggre-

gating within-group effect sizes and the differences between

this approach and traditional meta-analyses that aggregate

between group effect sizes can be found in Minami, Serlin,

Wampold, Kircher, and Brown (2008). Recent benchmarking

studies have been reported regarding the treatment of trau-

matic stress among adults (Rubin, Parrish, et al., 2016), the

treatment of depression (Rubin & Yu, 2015), and the treat-

ment traumatized children and youth (Rubin, Washburn,

et al., 2016). The current study provides benchmarks for the

use of DBT in an outpatient setting for treating adults experi-

encing symptoms of BPD.

Method

Study Identification and Selection

An initial Internet database search was conducted in the

spring of 2015 for meta-analyses and systemic reviews pub-

lished since 2000 on the efficacy of DBT for treating BPD.

This search included the following databases: Google Scho-

lar, Web of Science, Medline, Academic Search Complete,

ERIC, Social Work Abstracts, Soc INDEX with full text,

Social Sciences full text, PubMed, PsychARTICLES and Psy-

cINFO, Psychology and Behavioral Sciences Collection,

Health and Psychosocial Instruments, Health Source Nur-

sing/Academic Edition, Families Studies Abstracts, and the

Cochrane and Campbell libraries. Search terms included

‘‘DBT,’’ ‘‘dialectical behavior therapy,’’ ‘‘borderline person-

ality disorder,’’ ‘‘BPD,’’ and combinations of these terms with

the addition of ‘‘treatment,’’ ‘‘systematic review,’’ and ‘‘meta-

analysis.’’ Two Cochrane reviews, five meta-analyses, and nine

systematic reviews focusing on either DBT or the treatment of

BPD were found.

An initial screening of the title of each article regarding

DBT or BPD identified in those meta-analyses and systematic

reviews was conducted, yielding a total of 48 intervention

studies related to DBT. Next, a targeted search was executed

in spring 2016 using the same databases to locate additional

studies relevant to our inquiry that were published too late to be

included in the meta-analyses and reviews. It yielded two addi-

tional studies for a total of 50 studies that were reviewed in

their entirety to make preliminary determinations on if they met

inclusion criteria.

Studies were included if they (a) included only adults ages

18–64; (b) were in English or an English translation was avail-

able; (c) were conducted in a nonresidential setting;

(d) included participants with a diagnosis of BPD; (e) used

an RCT design to evaluate the efficacy of standard (individual

sessions plus skills group) DBT; (f) reported at least one stan-

dardized outcome measure of self-harm, depression, or anger;

and (g) reported pretest and posttest means and standard devia-

tions for each group for standardized measures. Participants

with co-occurring Axis I diagnoses were included in these

analyses, as the overwhelming number of individuals meeting

the criteria for a diagnosis of BPD also concurrently met the

criteria for at least one other Axis I diagnosis (American Psy-

chiatric Association, 2013; Lieb et al., 2004; Linehan, 1993a;

Nicki, 2016; Tomko et al., 2014). Studies that included parti-

cipants who were currently taking psychotropic medication

concurrently with DBT treatment or those comparing DBT

alone to DBT plus medication were also included, as the major-

ity of RCTs evaluating DBT did not require participants to

suspend use of psychotropic medication as a condition of par-

ticipation (Stoffers et al., 2012). The first two authors indepen-

dently judged whether each article met the inclusion criteria.

There was initial disagreement about inclusion of one article.

This discrepancy was resolved via a joint reexamination of the

full text article by both authors.

Fifteen of the 50 studies reviewed were deemed to meet the

inclusion criteria. As elimination criteria were not mutually

exclusive, some of the excluded studies failed to meet more

than one inclusion criterion. Five studies focusing on the use of

DBT with adolescents were eliminated. Two more were

excluded because their participants were older than 65.

Another seven were eliminated because they focused on the

treatment of eating disorders rather than BPD. Six studies con-

ducted in inpatient or residential settings were excluded. Six

more using the DBT skills group alone as an intervention were

eliminated. Nine studies did not report outcome measures

related to self-harm, depression, or anger and thus were

excluded. Two studies were excluded because they did not

present the means and standard deviations of their findings and

attempts to contact the authors for these data were met with no

response. Figure 1 depicts the article selection process.

Data Extraction Process

The lead author recorded the data from all of the included

articles and the second author independently recorded the data

from one third of the articles. Their interrater agreement rate

was 100%. The following information was recorded for each study: authors and year of study, sample size of each group,

type of control condition, percentage of participants who were

Washburn et al. 897

White/Caucasian, percentage of participants who were female,

percentage of attrition in the control and DBT groups, length of

intervention in weeks, if assessors blind to participants treat-

ment condition were used, and whether an intent-to-treat (ITT)

analysis was conducted.

All of the studies included in these benchmarks utilized

assessors who were blinded to the participants’ treatment con-

dition. A total of 743 participants were included in these bench-

mark calculations, 425 receiving DBT as the intervention and

318 receiving TAU. The large majority (93%) of participants met the full criteria for BPD, and all other participants met

partial diagnostic criteria, reporting on average five symptoms

consistent with that diagnosis out of the required seven (Amer-

ican Psychiatric Association, 2013). Most (77%) of the sample were Caucasian/White and 95% were female. Most (73%) par- ticipated in the DBT intervention for 52 weeks, while the rest

participated in a shorter version of DBT lasting between 12 and

36 weeks. Dropout rates ranged from 15% to 66% for the DBT

intervention and from 6% to 50% for TAU, with higher levels of dropout in both conditions being associated with a longer

intervention period. An ITT analysis was utilized for 80% of the studies. There were not sufficient studies reporting data

for treatment completers alone to establish separate bench-

marks comparing outcomes of completers to those found

using an ITT analysis.

Additional data extraction included baseline scores, baseline

standard deviations, and postintervention scores for target con-

structs. All calculations were conducted using IBM’s SPSS

Statistical Software Version 21. Glass’s (1976) D approach was used to calculate within-group effect sizes. This approach

divides the difference between the preintervention and postin-

tervention means by the preintervention standard deviation. As

was done in other benchmarking studies (Rubin, Parrish, et al.,

2016; Rubin, Washburn, et al., 2016; Rubin & Yu, 2015),

follow-up (after posttest) data were not included because these

benchmarks are intended to guide practice decision-making in

Literature Search N = 2

MA/SR Articles N= 48

Total Reviewed N = 50

Included Articles N = 15

Id en

tif ic

at io

n E

lig ib

ili ty

S cr

ee n

In cl

ud ed

Missing Outcomes N = 7

Teens N = 5

No Mean/SD N = 2

Inpatient N = 6

Eating Disorders N = 7

Elders N = 2

Skills Only N = 6

E xc

lu si

on

Figure 1. Article selection and review process.

898 Research on Social Work Practice 28(8)

nonresearch settings that typically do not conduct follow-up

assessments months after the termination of treatment. The

emphasis in this benchmarking approach is to provide an

agency-friendly way to make evidence-informed treatment

decisions. Based on our program evaluation experience with

such agencies, implying the need to conduct such follow-up

assessments would risk discouraging them from conducting the

pretests and posttests needed to calculate a within-group effect

size that they can compare to the benchmarks.

Separate calculations were performed for the experimental

group and for the control group so as to establish separate

within-group effect sizes for recipients of DBT and for those

receiving TAU (Feingold, 2009). The effect sizes, d (J. Cohen,

1992), were then converted to Hedge’s g to adjust for sample

size (Wilson, 2011). The Hedge’s g adjustment involves multi-

plying the effect size (d) by (1 � f3 over [4N � 9]g). This calculation yields a more conservative estimate of effect size.

The individual study within-group effect sizes were then aver-

aged across studies using a two-step calculation process. First,

the variance of the individual study within-group effect sizes

was estimated as follows: 2(1 � ri)/ni þ g2i =2ni. Next, the variance and effect size (gi) were used to estimate the fixed

benchmark effect size across studies using the following for-

mula as recommended by Minami and colleagues (2008):

gB ¼ h Sigi=d

2 gðiÞ

i over

h Si1=d

2 gðiÞ

i :

Outcome data were recorded separately for self-report mea-

sures and interviewer assessed measures. Outcome data were

also recorded separately for three categories of BPD sympto-

matology: self-harm behavior, depression, and anger. Not all

studies contained self-report and interview assessments for all

three of the target outcomes. Self-harm behavior was selected

as a target outcome due to its impact on overall functioning and

the fact that it is addressed first within the DBT protocol. The

most commonly used standardized self-report instrument used

in these studies to assess self-harm behaviors was the Suicidal

Behaviors Questionnaire-14 (Linehan, 1996). The most com-

monly used standardized self-harm assessment interview

instrument was the Suicide Attempt Self-Injury Interview

(Linehan, Comtois, Brown, Heard, Wagner, 2006).

Since chronic depression frequently co-occurs within the

course of BPD (Lieb et al., 2004; Linehan, 1993; Tomko

et al., 2014), depression was our second key outcome for which

benchmarks were constructed. The most commonly used self-

report instrument to assess depressive symptomatology was the

Beck Depression Inventory (Beck, Steer, & Carbin, 1988). For

interview-based assessment, it was the Hamilton Depression

Rating Scale (Hamilton, 1960). A third symptomatology cate-

gory was calculated for self-reported measures of anger, which

is also characteristic of BPD symptomology (American Psy-

chiatric Association, 2013). The most commonly used self-

report anger assessment measure was the Spielberger’s

(1999) State-Trait Anger Expression Inventory. There were not

enough studies using an interviewer assessed measure of anger

to construct a benchmark for those data. Although additional

symptoms are commonly associated with a diagnosis of BPD,

such as dissociation, aggression, and impulsivity, there were

not enough studies reporting those specific symptoms to be

included in our analysis. Articles on which these benchmarks

are based, along with demographics of each, effect sizes and

standard errors of target constructs are included in Appendix

Table A1.

Results

Table 1 displays the aggregate within-group effect sizes for

interviewer-assessed outcome measures. In addition, the mini-

mum and maximum effect sizes from individual studies are

presented to show the range of effect sizes due to the potential

for skewed distributions in which the confidence interval esti-

mates could exceed the minimum or maximum effect size. The

aggregated within-group DBT effect sizes were 0.63, 95% CI [0.50, 0.76] for self-harm and 1.02, 95% CI [0.84, 1.20] for depression. The aggregated within-group TAU interviewer–

assessed effect sizes were 0.43, 95% CI [0.29, 0.57] for self- harm and 0.55, 95% CI [0.35, 0.75] for depression.

Table 2 displays the aggregate within-group effect sizes for

self-reported outcome measures. The self-reported aggregate

within-group DBT effect sizes were 0.80, 95% CI [0.65, 0.96] for self-harm; 0.99, 95% CI [0.83, 1.15] for depression; and 0.44, 95% CI [0.29, 0.59] for anger. The aggregated within- group TAU effect sizes were 0.66, 95% CI [0.47, 0.85] for self- harm; 0.86, 95% CI [0.71, 1.01] for depression; and 0.13 95% CI [�0.03, 0.29] for anger.

Discussion and Application to Practice

This study calculated and reported aggregate within-group

effect sizes from published RCTs on DBT for the outpatient

treatment of adults with BPD. These results provide

Table 1. Within-Group Interviewer Assessed Aggregate Effect-Size by Treatment Condition and BPD Symptom Category.

Treatment Self-Harm Depression

DBT k 10 7 gHedges 0.63 1.13 SEg 0.07 0.10 95% CI [0.50, 0.76] [0.92, 1.34] Minimum 0.33 0.41 Maximum 4.80 2.82

TAU k 7 3 gHedges 0.43 0.70 SEg 0.07 0.10 95% CI [0.29, 0.57] [0.44, 0.96] Minimum �0.21 0.63 Maximum 2.95 1.01

Note. k ¼ number of studies; BPD ¼ borderline personality disorder; DBT ¼ dialectical behavioral therapy; TAU ¼ treatment as usual; SE ¼ standard error; CI ¼ confidence interval.

Washburn et al. 899

community-based providers with benchmarks that can assist

them with assessing if the results they are achieving with DBT

are similar to those seen in clinical trials. Comparing their

within-group DBT effect size to our benchmarks can facilitate

conversations in an agency concerning potential barriers or

challenges to providing DBT in that setting. Such conversa-

tions also could explore whether the way that they currently

provide DBT should be modified or potentially replaced by a

different intervention to better serve the needs of their clients.

One of the most useful ways in which these benchmarks

may be used is to evaluate dismantled versions of the DBT

intervention, such as use of the DBT skills group alone. For

example, if providing DBT via skills group alone is found to

have an effect size that compares favorably to our benchmarks,

it would provide descriptive empirical support for continuing

that approach. This comparison tool may be especially helpful

for the evaluation of BPD interventions of varying duration,

which can be more easily implemented in various outpatient

settings and may lead to lower levels of premature client drop-

out (Baer, 2014; D. J. Cohen et al. 2008; Comotis et al., 2007;

Koerner, 2013). A more time-limited approach to DBT may be

more typical of the course of community-based outpatient

treatment, especially in the era of managed care. As previously

mentioned, clients who struggle with BPD utilize a dispropor-

tionally high amount of outpatient mental health services,

consequently, having benchmarks against which to compare

time-limited DBT approaches may also assist in improving the

cost-effectiveness of these adapted approaches (Dimeff &

Koerner, 2007; Tomko et al., 2014).

When comparing their effect sizes to our benchmarks, we

encourage community-based practitioners to look not only at

the DBT effect sizes displayed in Tables 1 and 2 but also to

examine the TAU effect sizes as well as the confidence interval

data in those tables. A community-based DBT effect size need

not be nearly identical to our DBT benchmark to offer support

for the adequacy of the way DBT is being implemented in the

community setting. For example, suppose an interviewer-

assessed community-based DBT effect size for depression is

0.80. That would be less than our interviewer-assessed DBT

benchmark of 1.02 but would be closer to that benchmark than

to the corresponding 0.55 TAU benchmark. In that case, it is

recommended that the practitioner consult the corresponding

95% confidence intervals for each benchmark to determine in which distribution their results are more likely to fall. In this

case, the community sample value of 0.80 more likely would be

included in the distribution for the DBT group than the TAU

group, lending support to the adequacy of the way that DBT is

being delivered in that particular setting. However, if small

sample sizes are used for comparison, it may also be beneficial

to consult the minimum and maximum values that are included

in the tables as well as to consult the values of the pre–post

effect sizes reported for individual studies in Appendix Table

A1. For example, if the community setting is implementing

DBT for only 12 weeks, providers may also want to compare

their effect sizes with those for the constructs of interest listed

in Appendix Table A1 for studies using a 12-week intervention.

When considering the magnitude of within-group effect sizes,

things such as the passage of time and/or contemporaneous events

should be taken into account as factors that could have fostered

symptom improvement, especially in light of the lengthy amount

of time elapsing between pretests and posttests in studies imple-

menting the full 52-week DBT protocol. Consequently, within-

group effect sizes are likely to be larger than between-group effect

sizes, because the latter control for those factors via the rando-

mized experimental design. It follows that TAU within-group

effect sizes are irrelevant to questions about the effectiveness of

TAU. Likewise, the between-group effect sizes reported in RCTs

typically will be smaller than within-group effect sizes (and will

bear upon the effectiveness of DBT), because they pertain to the

difference in improvement between DBT and TAU groups and

thus cancel out the degree of improvement in both groups that

might have been fostered by the passage of time and/or contem-

poraneous events. For the same reason, J. Cohen’s (1992) stan-

dards regarding the magnitude of effect sizes do not pertain to

within-group effect size benchmarks.

As with any study, there are some limitations to our results.

A number of the studies included in our analysis, particularly

those conducted outside of the United States, did not report

the ethnicity of study participants. The majority of partici-

pants in the studies reporting race/ethnicity data were identi-

fied as Caucasian/White, thus potentially limiting the

generalizability of our results to ethnically diverse client

populations. Due to the small number of included studies not

using an ITT analysis, separate benchmarks could not be

established for comparing the benchmarks derived from stud-

ies using an ITT analysis and those that did not. However, the

total number of participants in those studies without an ITT

analysis was only approximately 10% of the total sample size on which these benchmarks were calculated, thus limiting the

potential for inflation of these benchmarks through inclusion

of studies not using ITT.

Table 2. Within-Group Self-Reported Aggregate Effect Size by Treatment Condition and BPD Symptom Category.

Self-Harm Depression Anger

DBT k 8 8 6 gHedges 0.80 0.99 0.44 SEg 0.08 0.08 0.08 95% CI [0.65, 0.96] [0.83, 1.15] [0.29, 0.59] Minimum 0.27 0.30 0.39 Maximum 5.01 2.91 0.77

TAU k 5 7 5 gHedges 0.66 0.86 0.13 SEg 0.10 0.08 0.08 95% CI [0.47, 0.85] [0.71, 1.01] [�0.03, 0.29] Minimum �0.47 0.51 �0.20 Maximum 3.30 1.19 0.56

Note. k ¼ number of studies; BPD ¼ borderline personality disorder; DBT ¼ dialectical behavioral therapy; TAU ¼ treatment as usual; SE ¼ standard error; CI ¼ confidence interval.

900 Research on Social Work Practice 28(8)

The majority of studies on which our benchmarks were

calculated contained samples that were 100% female. Thus, one should be cautious in using these benchmarks to evaluate

DBT with male clients. However, a small percentage of these

studies included males in their samples and the outcomes of

these studies were similar to those of studies having samples

that were 100% female. It stands to reason that RCTs for the treatment of DBT would have significantly more female parti-

cipants than male participants, as females comprise approxi-

mately 75% of those receiving a BPD diagnosis (American Psychiatric Association, 2013). Similarly, not all studies

included in these benchmarks utilized the full 52-week DBT

protocol. We felt that these shorter duration studies should also

be included, as the feasibility of a full 52 weeks of DBT treat-

ment in many outpatient settings is somewhat limited, and

community providers may already be providing DBT-based

interventions which are shorter than 1 year in duration.

Although our benchmarks are calculated from a relatively

small number of studies, other works using meta-analytic tech-

niques to aggregate effect sizes of DBT interventions have

been calculated including as few as 5 (Panos et al., 2014), 8

(Kliem, Kroger, & Kosfelder, 2010), or 13 studies (Ost, 2008).

Although meta-analytic techniques were used in this study, this

is not a traditional meta-analysis. We did not search for unpub-

lished papers for inclusion, as the goal of this work was not to

establish the efficacy of DBT as an intervention but rather to

construct tools for practitioners against which they can com-

pare their outcomes.

Despite these potential limitations, we believe that the

benchmarks provided by this study have significant potential

utility to providers in outpatient settings who are implementing

DBT-based interventions. Although we were unable to

calculate benchmarks for all symptoms that may be associated

with BPD due to the limited number of studies measuring and

reporting symptoms such as impulsivity, dissociation, and

aggression, we believe that these benchmarks are helpful tools

for providers in the community whose focus must be first on

client safety. As with previously published benchmark studies

(Rubin, Parrish, et al., 2016; Rubin, Washburn, et al., 2016;

Rubin &Yu, 2015), it is important to remind readers that our

findings are not meant to imply causality. That is, our study did

not aim to evaluate the effectiveness of DBT. Instead, it pro-

vides descriptive benchmarks to inform community-based

decision-making in light of the within-group effect sizes of the

RCTs that have already provided the empirical support for the

efficacy of DBT for BPD.

Conclusion

Given that providers often need flexibility to make research-

supported interventions fit their practice setting, agency and/or

the clients they serve, these benchmarks offer providers con-

venient tools through which to evaluate their use of DBT with-

out requiring a control group design. Moreover, our

benchmarks can be used to support innovation efforts in out-

patient treatment settings. In that the evidence-based practice

process encourages such innovation when practitioners believe

there is a need to adapt research supported treatments to their

clients or setting, or perhaps even try a completely different

intervention approach, practitioners can judge the adequacy of

their innovative approach without using a control group design

by evaluating if progress that their clients are making compares

favorably with our benchmarks.

Appendix

Table A1. Demographics and Effect Sizes for Studies Included in Benchmark Calculations.

Demographic Characteristics Treatment Conditions and Constructs Measured

N Female (%) White (%) Weeks Dropout (%) Treatment Effect Size Standard Error

Carter, Willcox, Lewin, Conrad, and Bendit (2010) 38 100 N/D 26 48 DBT Self-harm a a

Depression a a

SR—self-harm 0.27 0.03 SR—depression 0.72 0.03 SR—anger a a

35 11 TAU Self-harm a a

Depression a a

SR—self-harm 0.75 0.03 SR—depression 0.68 0.03 SR—anger

a a

Clarkin, Levy, Lenzenweger, and Kernbergb (2007) 30 92.2 68 52 43 DBT Self-harm 0.70 0.04

Depression a a

SR—self-harm a a

SR—depression 0.80 0.04 SR—anger 0.51 0.04

(continued)

Washburn et al. 901

Table A1. (continued)

Demographic Characteristics Treatment Conditions and Constructs Measured

N Female (%) White (%) Weeks Dropout (%) Treatment Effect Size Standard Error

30 27 TAU Self-harm 0.36 0.04 Depression a a

SR—self-harm a a

SR—depression 1.09 0.04 SR—anger 0.56 0.04

Feigenbaum et al. (2012) 25 83 N/D 52 66 DBT Self-harm 0.39 0.04

Depression a a

SR—self-harm 0.36 0.04 SR—depression 0.30 0.04 SR—anger 0.40 0.04

16 6 TAU Self-harm 0.95 0.08 Depression

a a

SR—self-harm �0.47 0.07 SR—depression 0.51 0.07 SR—anger 0.51 0.07

Harned, Korslund, and Linehan (2014) 9 100 81 52 29 DBT Self-harm 0.48 0.12

Depression 1.45 0.16 SR—self-harm

a a

SR—depression a a

SR—anger a a

Koons et al. b

(2001) 10 100 75 26 29 DBT Self-harm 0.33 0.10

Depression 0.83 0.11 SR—self-harm a a

SR—depression 0.77 0.11 SR—anger 0.77 0.11

10 29 TAU Self-harm �0.21 0.10 Depression 0.78 0.11 SR—self-harm

a a

SR—depression 0.33 0.10 SR—anger 0.07 0.10

Linehan, Tutek, Heard, and Armstrong (1994) 13 100 N/D 52 23 DBT Self-harm a a

Depression a a

SR—self-harm a a

SR—depression a a

SR—anger 0.53 0.08 13 8 TAU Self-harm

a a

Depression a a

SR—self-harm a a

SR—depression a a

SR—anger �0.20 0.08

Linehan, Comtois, Murray, et al. (2006) 52 100 87 52 25 DBT Self-harm 0.42 0.02

Depression 1.03 0.02 SR—self-harm 1.06 0.02 SR—depression a a

SR—anger a a

49 59 TAU Self-harm 0.29 0.02 Depression 0.63 0.02 SR—self-harm 1.23 0.03 SR—depression a a

SR—anger a a

(continued)

902 Research on Social Work Practice 28(8)

Table A1. (continued)

Demographic Characteristics Treatment Conditions and Constructs Measured

N Female (%) White (%) Weeks Dropout (%) Treatment Effect Size Standard Error

Linehan, McDavid, Brown, Sayrs, and Gallop (2008) 12 100 79 26 33 DBT Self-harm 1.35 0.13

Depression 0.41 0.09 SR—self-harm a a

SR—depression a a

SR—anger a a

Linehan et al. (2015) 33 100 71 52 24 DBT Self-harm 1.03 0.00

Depression 1.31 0.04 SR—self-harm 0.91 0.03 SR—depression a a

SR—anger a a

McMain et al. (2009) 90 92 N/D 52 39 DBT Self-harm 0.69 0.00

Depression a a

SR—self-harm a a

SR—depression 1.19 0.01 SR—anger 0.39 0.01

90 38 TAU Self-harm 0.57 0.01 Depression

a a

SR—self-harm a a

SR—depression 0.99 0.01 SR—anger 0.30 0.01

Pistorello, Fruzzetti, MacLane, Gallop, and Iverson (2012) 31 81 70 52 35 DBT Self-harm

a a

Depression a a

SR—self-harm 1.18 0.04 SR—depression 2.91 0.07 SR—anger

a a

32 52 47 TAU Self-harm a a

Depression a a

SR—self-harm 0.42 0.03 SR—depression 1.19 0.04 SR—anger a a

Simpson et al.b (2004) 12 100 69 12 15 DBT Self-harm

a a

Depression a a

SR—self-harm 0.60 0.08 SR—depression 1.44 0.11 SR—anger 0.40 0.08

Soler et al. (2005) 30 90 N/D 12 33 DBT Self-harm a a

Depression 1.49 0.05 SR—self-harm a a

SR—depression a a

SR—anger a a

TAU Self-harm a a

Depression a a

SR—self-harm 1.61 0.05 SR—depression a a

SR—anger a a

(continued)

Washburn et al. 903

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to

the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship,

and/or publication of this article.

References

American Psychiatric Association. (2013). Diagnostic and statistical

manual of mental disorders (5th ed.). Washington, DC: Author.

Aviram, R. B., Brodsky, B. S., & Stanley, B. (2006). Borderline per-

sonality disorder, stigma, and treatment implications. Harvard

Review of Psychiatry, 14, 249–256.

Baer, R. A. (2014). Mindfulness-based treatment approaches: Clini-

cian’s guide to evidence base and applications (2nd ed.). Cam-

bridge, MA: Academic Press.

Beck, A. T., Steer, R. A., & Carbin, M. G. (1988). Psychometric

properties of the Beck Depression Inventory: Twenty-five years

of evaluation. Clinical Psychology Review, 8, 77–110.

Brownson, R. C., Colditz, G. A., & Proctor, E. K. (2012). Dissemina-

tion and implementation research in health: Translating science to

practice. New York, NY: Oxford University Press.

Carter, G. L., Willcox, C. H., Lewin, T. J., Conrad, A. M., & Bendit,

N. (2010). Hunter DBT project: Randomized controlled trial of

dialectical behaviour therapy in women with borderline personality

disorder. Australian and New Zealand Journal of Psychiatry, 44,

162–173.

Chaput, Y. J., & Lebel, M. J. (2007). Demographic and clinical pro-

files of patients who make multiple visits to psychiatric emergency

services. Psychiatric Services, 58, 335–351.

Chorpita, B. F., Becker, K. D., Daleiden, E. L., & Hamilton, J. D.

(2007). Understanding the common elements of evidence-based

practice: Misconceptions and clinical examples. Journal of the

American Academy of Child & Adolescent Psychiatry, 46, 647–652.

Chugani, C. D., & Landes, S. J. (2016). Dialectical Behavior Therapy in

college counseling centers: Current trends and barriers to implemen-

tation. Journal of College Student Psychotherapy, 30, 176–186.

Clarkin, J. F., Levy, K. N., Lenzenweger, M. F., & Kernberg, O. F.

(2007). Evaluating three treatments for borderline personality dis-

order: A multiwave study. American Journal of Psychiatry, 166,

922–928.

Cohen, D. J., Crabtree, B. F., Etz, R. S., Balasubramanian, B. A.,

Donahue, K. E., Leviton, L. C., . . . Green, L. W. (2008). Fidelity

versus flexibility: Translating evidence-based research into prac-

tice. American Journal of Preventive Medicine, 35, S381–S389.

Cohen, J. (1992). A power primer. Psychological Bulletin, 112,

155–159.

Comtois, K. A., Koons, C. R., Kim, S. A., Manning, S. Y., Bellows, E.,

& Dimeff, L. A. (2007). Implementing standard dialectical beha-

vior therapy in an outpatient setting. In L. A. Dimmeff & K. Koer-

ner (Eds.), Dialectical behavior therapy in clinical practice:

Table A1. (continued)

Demographic Characteristics Treatment Conditions and Constructs Measured

N Female (%) White (%) Weeks Dropout (%) Treatment Effect Size Standard Error

Turner (2000) 12 79 76 52 33 DBT Self-harm 4.80 0.29

Depression 2.82 0.19 SR—self-harm 5.01 0.31 SR—depression 2.21 0.16 SR—anger a a

12 50 TAU Self-harm 2.95 0.19 Depression 1.01 0.10 SR—self-harm 3.30 0.21 SR—depression 0.55 0.09 SR—anger a a

van den Bosch, Koeter, Stijnen, Verheul, and van den Brink (2005) 27 100 N/D 52 23 DBT Self-harm 0.47 0.04

Depression a a

SR—self-harm a a

SR—depression a a

SR—anger a a

31 8 TAU Self-harm 0.18 0.03 Depression

a a

SR—self-harm a a

SR—depression a a

SR—anger a a

Note. N/D ¼ no data given; SR ¼ Self report measure. aConstruct was not measured in that study. bData for completers only.

904 Research on Social Work Practice 28(8)

Applications across disorders and settings (pp. 37–68). New York,

NY: Guilford Press.

Dimeff, L. A., & Koerner, K. E. (2007). Dialectical behavior therapy

in clinical practice: Applications across disorders and settings.

New York, NY: Guilford Press.

Dimeff, L. A., & Linehan, M. M. (2001). Dialectical behavior therapy

in a nutshell. The California Psychologist, 34, 10–13.

Ditty, M. S., Landes, S. J., Doyle, A., & Beidas, R. S. (2015). It takes a

village: A mixed methods analysis of inner setting variables and

dialectical behavior therapy implementation. Administration and

Policy in Mental Health and Mental Health Services Research, 42,

672–681.

Drake, R. E., Goldman, H. H., Leff, H. S., Lehman, A. F., Dixon, L.,

Mueser, K. T., & Torrey, W. C. (2001). Implementing evidence-

based practices in routine mental health service settings. Psychia-

tric Services, 52, 179–182.

Embry, D. D., & Biglan, A. (2008). Evidence-based kernels: Funda-

mental units of behavioral influence. Clinical Child and Family

Psychology Review, 11, 75–113.

Feigenbaum, J. D., Fonagy, P., Pilling, S., Jones, A., Wildgoose, A., &

Bebbington, P. E. (2012). A real-world study of the effectiveness

of DBT in the UK National Health Service. British Journal of

Clinical Psychology, 51, 121–141.

Feingold, A. (2009). Effect Sizes for growth-modeling analysis for

controlled clinical trials in the same metric as for classical analysis.

Psychological Methods, 14, 43–53.

Glass, G. V. (1976). Primary, secondary, and meta-analysis of

research. Educational Researcher, 5, 3–8.

Gunderson, J. G. (2016). The emergence of a generalist model to meet

public health needs for patients with borderline personality disor-

der. American Journal of Psychiatry, 173, 452–458.

Hamilton, M. (1960). A rating scale for depression. Journal of

Neurology, Neurosurgery, and Psychiatry, 23, 56–62.

Harned, M. S., Korslund, K. E., & Linehan, M. M. (2014). A pilot

randomized controlled trial of Dialectical Behavior Therapy with

and without the Dialectical Behavior Therapy Prolonged Expo-

sure protocol for suicidal and self-injuring women with border-

line personality disorder and PTSD. Behaviour Research and

Therapy, 55, 7–17.

Hoagwood, K., Burns, B. J., Kiser, L., Ringeisen, H., & Schoen-

wald, S. K. (2001). Evidence-based practice in child and ado-

lescent mental health services. Psychiatric Services, 52,

1179–1189.

Kliem, S., Kröger, C., & Kosfelder, J. (2010). Dialectical behavior

therapy for borderline personality disorder: A meta-analysis using

mixed-effects modeling. Journal of Consulting and Clinical Psy-

chology, 78, 936–951.

Knaak, S., Szeto, A. C., Fitch, K., Modgill, G., & Patten, S. (2015).

Stigma towards borderline personality disorder: Effectiveness and

generalizability of an anti-stigma program for healthcare providers

using a pre-post randomized design. Borderline Personality Dis-

order and Emotion Dysregulation, 2, 2–9.

Koerner, K. (2013). What must you know and do to get good outcomes

with DBT? Behavior Therapy, 44, 568–579.

Koons, C. R., Robins, C. J., Tweed, J. L., Lynch, T. R., Gonzalez, A.

M., Morse, J. Q., . . . Bastian, L. A. (2001). Efficacy of dialectical

behavior therapy in women veterans with borderline personality

disorder. Behavior Therapy, 32, 371–390.

Lieb, K., Zanarini, M. C., Schmahl, C., Linehan, M. M., & Bohus, M.

(2004). Borderline personality disorder. The Lancet, 364, 453–461.

Linehan, M. M. (1993a). Cognitive behavioral treatment of borderline

personality disorder. New York, NY: Guilford Press.

Linehan, M. M. (1993b). Skills training manual for treating border-

line personality disorder. New York, NY: Guilford Press.

Linehan, M. M. (1996). The suicidal behaviors questionnaire—14

(SBQ-14). Unpublished instrument, Department of Psychology,

University of Washington, Seattle.

Linehan, M. M. (1999). Borderline personality disorder: costs, course,

and treatment outcomes. In N. Miller & K. M. Magruder (Eds.),

The cost-effectiveness of psychotherapy: A guide for practitioners,

researchers, and policy-makers (pp. 291–305). New York, NY:

Oxford University Press.

Linehan, M. M. (2014). DBT skills training manual (2nd ed.). New

York, NY: The Guilford Press.

Linehan, M. M., Comtois, K. A., Brown, M. Z., Heard, H. L., &

Wagner, A. (2006). Suicide Attempt Self-Injury Interview

(SASII): Development, reliability, and validity of a scale to assess

suicide attempts and intentional self-injury. Psychological Assess-

ment, 18, 303–312.

Linehan, M. M., Comtois, K. A., Murray, A. M., Brown, M. Z., Gal-

lop, R. J., Heard, H. L., . . . Lindenboim, N. (2006). Two-year

randomized controlled trial and follow-up of dialectical behavior

therapy vs therapy by experts for suicidal behaviors and borderline

personality disorder. Archives of General Psychiatry, 63, 757–766.

Linehan, M. M., Korslund, K. E., Harned, M. S., Gallop, R. J., Lungu,

A., Neacsiu, A. D., . . . Murray-Gregory, A. M. (2015). Dialectical

behavior therapy for high suicide risk in individuals with border-

line personality disorder: A randomized clinical trial and compo-

nent analysis. JAMA Psychiatry, 72, 475–482.

Linehan, M. M., McDavid, J. D., Brown, M. Z., Sayrs, J. H., & Gallop,

R. J. (2008). Olanzapine plus dialectical behavior therapy for

women with high irritability who meet criteria for borderline per-

sonality disorder: A double-blind, placebo-controlled pilot study.

Journal of Clinical Psychiatry, 69, 999–1005.

Linehan, M. M., Tutek, D. A., Heard, H. L., & Armstrong, H. E.

(1994). Interpersonal outcome of cognitive behavioral treatment

for chronically suicidal borderline patients. American Journal of

Psychiatry, 151, 1771–1775.

Markham, D. (2003). Attitudes towards patients with a diagnosis of

‘borderline personality disorder’: Social rejection and dangerous-

ness. Journal of Mental Health, 12, 595–612.

McHugh, R. K., & Barlow, D. H. (2010). The dissemination and

implementation of evidence-based psychological treatments: A

review of current efforts. American Psychologist, 65, 73–84.

McMain, S. F., Links, P. S., Gnam, W. H., Guimond, T., Cardish, R. J.,

Korman, L., & Streiner, D. L. (2009). A randomized trial of dia-

lectical behavior therapy versus general psychiatric management

for borderline personality disorder. American Journal of Psychia-

try, 166, 1305–1374.

Minami, T., Serlin, R. C., Wampold, B. E., Kircher, J. C., & Brown, G.

S. (2008). Using clinical trials to benchmark effects produced in

clinical practice. Quality and Quantity, 42, 513–525.

Washburn et al. 905

Minami, T., Wampold, B. E., Serlin, R. C., Kircher, J. C., & Brown, G.

S. J. (2007). Benchmarks for psychotherapy efficacy in adult major

depression. Journal of Consulting and Clinical Psychology, 75,

232–243.

Nejad, L., & Wesseling, G. (2015). Clinical and cost-effectiveness of

dialectical behavior therapy (DBT) in a Melbourne adult public

health outpatient service. Retrieved from https://www.researchgate

.net/publication/281210292_clinical_and_cost-effectiveness_of_

dialectical_behaviour_therapy_dbt_in_a_melbourne_adult_public_

mental_health_outpatient_service

Nicki, A. (2016). Borderline personality disorder, discrimination, and

survivors of chronic childhood trauma. IJFAB: International Jour-

nal of Feminist Approaches to Bioethics, 9, 218–245.

Ost, L. G. (2008). Efficacy of the third wave of behavioral therapies: A

systematic review and meta-analysis. Behaviour Research and

Therapy, 46, 296–321.

Panos, P. T., Jackson, J. W., Hasan, O., & Panos, A. (2014). Meta-

analysis and systematic review assessing the efficacy of dialectical

behavior therapy (DBT). Research on Social Work Practice, 24,

213–223.

Parrish, D. E., & Rubin, A. (2012). Social workers’ orientations

toward the evidence-based practice process: A comparison with

psychologists and licensed marriage and family therapists. Social

Work, 57, 201–210.

Parrish, D. E., Washburn, M., & Torres, L. R. (2013). Bridging the

research and practice gap through enhancing factors facilitating

evidence based practice behaviors. Paper presented at the 59th

Annual Program Meeting of Council on Social Work Education,

Dallas, TX.

Peng, K., & Nisbett, R. E. (1999). Culture, dialectics, and reasoning

about contradiction. American Psychologist, 54, 741–754.

Pistorello, J., Fruzzetti, A. E., MacLane, C., Gallop, R., & Iverson, K.

M. (2012). Dialectical behavior therapy (DBT) applied to college

students: A randomized clinical trial. Journal of Consulting and

Clinical Psychology, 80, 982–994.

Rizvi, S. L., Dimeff, L. A., Skutch, J., Carroll, D., & Linehan, M. M.

(2011). A pilot study of the DBT coach: An interactive mobile

phone application for individuals with borderline personality dis-

order and substance use disorder. Behavior Therapy, 42, 589–600.

Roth, A., & Fonagy, P. (2013). What works for whom? A critical

review of psychotherapy research (2nd ed.). New York, NY:

Guilford.

Rubin, A. (2014). An alternative paradigm for social workers seeking

to do intervention research. Social Work Research, 38, 69–71.

Rubin, A., Parrish, D. E., & Washburn, M. (2016). Outcome bench-

marks for adaptations of research-supported treatments for adult

traumatic stress. Research on Social Work Practice, 26, 243–259.

Rubin, A., Washburn, M., & Schieszler, C. (2016). Within-group

effect-size benchmarks for trauma-focused cognitive behavioral

therapy with children and adolescents. Research on Social Work

Practice. doi:10.1177/1049731515620016

Rubin, A., & Yu, M. (2015). Within-group effect-size benchmarks for

problem-solving therapy for depression in adults. Research on

Social Work Practice. doi:10.1177/1049731515592477

Simpson, E. B., Yen, S., Costello, E., Rosen, K., Begin, A., Pistorello,

J., & Pearlstein, T. (2004). Combined dialectical behavior therapy

and fluoxetine in the treatment of borderline personality disorder.

The Journal of Clinical Psychiatry, 65, 379–385.

Soler, J., Pascual, J. C., Campins, J., Barrachina, J., Puigdemont, D.,

Alvarez, E., & Perez, V. (2005). Double-blind, placebo-controlled

study of dialectical behavior therapy plus olanzapine for borderline

personality disorder. American Journal of Psychiatry, 162,

1221–1224.

Sperry, L., Brill, P. L., Howard, K. I., & Grissom, G. R. (Eds.). (2013).

Treatment outcomes in psychotherapy and psychiatric interven-

tions (Vol. 6). New York, NY: Routledge.

Spielberger, C. D. (1999). STAXI-2: State-trait anger expression

inventory-2. Professional manual. Odessa, FL: Psychological

Assessment Resources.

Spilka, M. J., & Dobson, K. S. (2015). Promoting the internationaliza-

tion of evidence-based practice: Benchmarking as a strategy to

evaluate culturally transported psychological treatments. Clinical

Psychology: Science and Practice, 22, 58–75.

Stoffers, J. M., Völlm, B. A., Rücker, G., Timmer, A., Huband, N., &

Lieb, K. (2012). Psychological therapies for people with borderline

personality disorder. Cochrane Database of Systematic Reviews,

2012. doi:10.1002/14651858.CD005652.pub2

Tomko, R. L., Trull, T. J., Wood, P. K., & Sher, K. J. (2014). Char-

acteristics of borderline personality disorder in a community sam-

ple: Comorbidity, treatment utilization, and general functioning.

Journal of Personality Disorders, 28, 734–750.

Turner, R. M. (2000). Naturalistic evaluation of dialectical behavior

therapy-oriented treatment for borderline personality disorder.

Cognitive and Behavioral Practice, 7, 413–419.

van den Bosch, L. M., Koeter, M. W., Stijnen, T., Verheul, R., & van

den Brink, W. (2005). Sustained efficacy of dialectical behaviour

therapy for borderline personality disorder. Behaviour Research

and Therapy, 43, 1231–1241.

Washburn, M., & Parrish, D. E. (2013). DBT self-help application for

mobile devices. Journal of Technology in Human Services, 31,

175–183.

Weisz, J. R., Ugueto, A. M., Cheron, D. M., & Herren, J. (2013).

Evidence-based youth psychotherapy in the mental health ecosys-

tem. Journal of Clinical Child & Adolescent Psychology, 42,

274–286.

Wilson, D. B. (2011). Calculating effect-sizes [PowerPoint Slides].

Retrieved from http://www.campbellcollaboration.org/artman2/

uploads/1/2_D_Wilson__Calculating_ES.pdf

Zanarini, M. C., Frankenburg, F. R., Reich, D. B., Conkey, L. C., &

Fitzmaurice, G. M. (2015). Treatment rates for patients with bor-

derline personality disorder and other personality disorders: A

16-year study. Psychiatric Services, 66, 15–20.

906 Research on Social Work Practice 28(8)