Barriers to adopting EBPs - Community

Journal of Substance Abuse Treatment 42 (2012) 231 – 238
Regular article
Barriers to implementation of evidence-based addiction treatment:
A national study
Lena Lundgren, (Ph.D.) a , Deborah Chassler, (M.S.W.) a,⁎, Maryann Amodeo, (Ph.D., M.S.W.) a ,
Melinda D'Ippolito, (M.S.W.) a , Lisa Sullivan, (Ph.D.) b
a
Center for Addictions Research and Services, Boston University School of Social Work, Boston, MA 02215, USA
b
Boston University School of Public Health, Boston, MA 02118, USA
Received 11 February 2011; received in revised form 12 August 2011; accepted 15 August 2011
Abstract
Prior studies have identified that working in an addiction treatment unit with higher levels of organizational capacity is a factor associated
with positive staff attitudes about evidence-based addiction treatment practices (EBPs). The study presented here explored whether staff
perceptions about the organizational capacity of their treatment unit are also associated with staff experience of barriers to implementing
EBPs. Multivariate regression methods examined the relationship between the clinical staff (n = 510) and director (n = 296) perceptions of
organizational capacity (Texas Christian University Organizational Readiness for Change [TCU ORC]-staff and TCU ORC-director) and
level of barriers experienced when implementing a new EBP controlling for a range of treatment unit characteristics, staff characteristics, and
type of EBP implemented. For both samples, reporting higher levels of stress in their organizations was significantly associated with
reporting higher levels of barriers when implementing a new EBP. For clinical staff only, experiencing lower levels of program needs in their
organization, working in a program that had been in existence for a shorter period, and implementing motivational interviewing techniques
compared with other EBPs were all factors significantly associated with experiencing lower levels of barriers with EBP implementation.
© 2012 Elsevier Inc. All rights reserved.
Keywords: Implementation of evidence-based addiction treatment practices; Barriers to implementation of evidence-based addiction treatment; Organizational
capacity of addiction treatment organizations; Community-based addiction treatment organizations
1. Introduction
An increasing number of empirically valid, efficacious
behavioral and pharmacological therapies are available for
the treatment of alcohol and drug use disorders (McCarty,
McConnell, & Schmidt, 2010). Clinical trials of addiction
treatment therapies conducted within the National Drug
Abuse Treatment Clinical Trials Network suggest that
evidence-based treatments can be effective in real-world
clinical environments and with heterogeneous clinical
populations (Amass et al., 2004; Carroll et al., 2006; Ling
et al., 2005; Peirce et al., 2006; Petry et al., 2005), and in the
past decade, the federal government, through its various
⁎ Corresponding author. Center for Addictions Research and Services,
Boston University School of Social Work, 264 Bay State Road, Boston, MA
02215, USA. Tel.: +1 617 353 1748; fax: +1 617 353 5612.
E-mail address: [email protected] (D. Chassler).
0740-5472/11/$ – see front matter © 2012 Elsevier Inc. All rights reserved.
doi:10.1016/j.jsat.2011.08.003
institutions including the Substance Abuse and Mental
Health Services Administration (SAMHSA) and Centers
for Disease Control, has actively encouraged communitybased addiction treatment organizations to implement
evidence-based practices (EBPs).
The study presented here adds to the body of literature on
EBP implementation in community-based addiction treatment organizations; it examined the association between
clinical staff and program director ratings of the organizational capacity of their treatment unit and the level of barriers
experienced when implementing a new EBP.
1.1. Organizational capacity and implementation of
evidence-based addiction treatment
The Simpson and Flynn (2007) Organizational Readiness
for Change model, further refined in the Lehman, Simpson,
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L. Lundgren et al. / Journal of Substance Abuse Treatment 42 (2012) 231–238
Knight, and Flynn (2011) article, is the underlying
framework for the study presented here. These authors
offer a four-stage model of organizational change related to
the adoption of EBPs in addiction treatment: exposure to the
new practice (i.e., training); adoption of the practice—a
decision to try it out; implementation of the practice; and
standardization and routine use of the practice. Implementation, the third stage of the model and the focus of our study,
requires allocation of sufficient resources and institutional
support for the change effort. Implementation serves as the
crucial stage that connects adoption decisions with routine
practice (Simpson & Flynn, 2007, p. 112).
The Simpson and Flynn (2007) Organizational Readiness
for Change model and the Texas Christian University
Organizational Readiness for Change scales (TCU ORCstaff, TCU ORC-director; Lehman, Greener, & Simpson,
2002) have been used in prior addictions treatment research
efforts to understand staff attitudes toward EBPs (see, for
example, Fuller et al., 2007; Lundgren et al., 2011;
Lundgren, Krull, Zerden, & McCarty, 2011). These articles
indicate that specific aspects of organizational capacity are
associated with more positive staff attitudes toward EBPs.
For example, both the Fuller et al. (2007) and Lundgren,
Amodeo, et al. (2011) studies identified that staff in
organizations with better technology (e.g., Internet access)
have more positive attitudes toward EBPs.
These research studies on barriers to EBP implementation
have primarily used qualitative methods to describe the types
of barriers experienced by addiction treatment personnel. To
complement this body of research, quantitative studies are
needed so that a greater range of variables and their
associations can be considered when studying barriers to
EBP implementation. In the study presented here, the TCU
ORC measures are used to explore the association between
organizational capacity and clinical staff and the program
director ratings of levels of barriers experienced when
implementing a new EBP.
Specifically, the study examines, through separate
multivariate regression models for addiction treatment
clinical staff and for directors, whether there was a
significant association between their ratings of the capacity
of the organizations within which they worked and their
perception of the level of barriers encountered when
implementing a new EBP controlling for (a) staff and
director characteristics (demographic characteristics, i.e., age
and gender, level of education, and years of experience in
drug abuse counseling); (b) treatment unit characteristics
(type of treatment unit, whether the organization was
affiliated with a research institution, the organization's
primary service area [urban, rural and suburban], and the
length of time the program had been in operation); and (c)
type of EBP implemented.
1.2. Barriers to EBP implementation
1.3. Control factors
To date, research on barriers to the implementation of
EBPs has largely focused on describing the type of barriers
experienced by practitioners. For example, the following
researchers examined whether EBPs were user-friendly
and/or whether they presented particular challenges in
implementation: Bartholomew, Joe, Rowan-Szal, and Simpson (2007); Brown (2004); Godley, White, Diamond,
Passetti, and Titus (2001); Nelson and Steele (2007); and
Thomas, Wallack, Lee, McCarty, and Swift (2003). Other
studies identified that lack of knowledge and lack of time to
apply training materials are key barriers to implementation
(Bartholomew et al., 2007; Mark, Kranzler, & Song, 2003;
Thomas et al., 2003). A third set of qualitative studies
identified that barriers to EBP implementation differed by
the characteristics and the types of EBP implemented. For
example, in a small qualitative study (Nelson, Steele, &
Mize, 2006), challenges to EBP implementation were the
following: (a) characteristics of EBPs (e.g., long treatment
duration, for example, 12 sessions, specialized staff
competence required); (b) characteristics of practitioners
and settings (e.g. , limited practitioner time, lack of training
and supervision, economic restrictions); and (c) characteristics of clients (complex client presentation, client resistance,
and client inconsistency in therapy). In addition, in the
findings from the qualitative component of our mixedmethods study, Amodeo et al. (2011) found that barriers
reported by staff varied by the type of EBP implemented.
Education levels of staff/years of professional experience. The treatment unit and staff characteristic factors
selected as control factors have been identified in a number
of research efforts as associated with attitudes about EBPs
and EBP implementation. For example, several studies
have identified that addiction treatment staff (both directors
and clinical staff) with higher levels of education and with
more professional knowledge have more positive attitudes
about evidence-based addiction treatment practices, science-based staff training, and usefulness of EBPs (Lundgren, Amodeo, et al., 2011; Lundgren, Krull, et al., 2011;
McCarty et al., 2007; Rieckmann, Daley, Fuller, Thomas,
& McCarty, 2007).
Type of treatment unit. Findings on whether supervisor or
staff attitudes about EBP implementation vary by the type of
treatment unit within which the respondent works are mixed.
McCarty et al. (2007) and Fuller et al. (2007) did not find
treatment unit to be associated with differences in staff
attitudes regarding EBPs. However, in a study of 376
counselors and 1,083 clients involved in methadone, residential and outpatient substance abuse programs in Oregon and
Massachusetts, Rieckmann et al. (2007) reported that the most
consistent support for pharmacological therapies was from
staff in outpatient settings. Furthermore, in a comparison of
over 750 public and private substance abuse treatment
organizations, Roman, Ducharme, & Knudsen (2006) found
L. Lundgren et al. / Journal of Substance Abuse Treatment 42 (2012) 231–238
that pharmacotherapy was more likely to be adopted in private
centers compared with public centers.
Research affiliation. Staff in organizations affiliated with
research institutions, such as universities and hospitals, have
been found to have more positive attitudes regarding
science-based addiction treatment (Lundgren et al., 2011;
McCarty et al., 2007; Pinto, Yu, Spector, Gorroochurn, &
McCarty, 2010). Our study controlled for whether the
principal investigator (PI) or the evaluator of the program
was affiliated with a university or a hospital. It also
controlled for whether the organization was located in an
urban, rural, or suburban area.
Program duration. The study also controlled for how
long the program in which the EBP was implemented had
been in existence. Program staff in organizations with a
longer history may experience a lower level of barriers
because of organizational stability. Conversely, they may
experience a higher level of barriers if organizational
attitudes and functioning have changed little over the years
or decades. Finally, the qualitative studies discussed above
indicate that the type of EBP implemented has to be
controlled for when exploring barriers to EBP implementation (Amodeo et al., 2011; Nelson et al., 2006).
2. Methods
2.1. Data collection and samples
Telephone interviews and Web surveys were conducted
with two samples: 510 staff and 296 program directors (see
Lundgren, Amodeo, et al., 2011 for further details on data
collection methods). Potential participants were sampled
from a publicly available listing of agencies receiving awards
from the Center for Substance Abuse Treatment (CSAT)/SAMHSA between 2003 and 2008; this list included 495
grantee organizations, of which 330 were selected. The two
samples of staff and directors working in treatment centers
funded by CSAT/SAMHSA to implement EBPs were
selected because (a) these treatment centers had included
in their CSAT proposals descriptions of the specific EBPs
they would implement; (b) funding was not a key barrier to
EBP implementation because the CSAT awards were
substantial so a lack of economic capacity to implement
EBPs should not be a factor influencing staff attitudes or
experiences; and (c) a range of geographic areas and program
types from around the country were included.
Of the 330 selected organizations, 10 program directors
declined to participate. Further, 24 cases were excluded from
the data analyses presented below because of missing data,
yielding an analysis sample of 296 program directors. Staff
were identified by their program directors as directly
involved in the implementation of EBPs. Of the 524 staff
who were contacted, only 5 individuals declined to
participate. Further, 9 staff cases were eliminated because
of missing data, resulting in an analysis sample of 510 cases
233
(see Missing data section below for details). Study protocols
were approved by the Boston University Institutional
Review Board.
2.2. Measures
2.2.1. Independent variables
Age was calculated by subtracting the date of birth from
the date the online survey was completed. Gender was
measured as a two-category variable. Level of education was
measured both by identifying highest degree status (no high
school, high school or equivalent, some college, associate's
degree, bachelor's degree, master's degree, doctoral degree,
or other professional degree) and through a two-category
recoded variable (below a master's degree education, and
master's degree or above). Years of experience in drug abuse
counseling was measured by using both a scale (0–5 years)
and a recoded two-category variable (four or fewer years of
experience and five or more years of experience).
Treatment program affiliated with a research institution
was a recode of four variables: whether the PI for the
project was affiliated with a university, whether the PI was
affiliated with a hospital, whether the evaluator was
affiliated with a university, and whether the evaluator
was affiliated with a hospital.
Type of treatment unit was measured through a threecategory variable: (a) outpatient unit, (b) inpatient/therapeutic community unit; and (c) other. Primary service area was
measured through a three-category variable, which identified
whether the organization was situated in a rural, suburban, or
urban location. Program duration was measured by
subtracting the year the program was first funded from the
year of the study interview. Type of EBP implemented: for
the two samples, more than 60 different EBPs were named as
having been implemented during the project period. The four
most common were selected: motivational interviewing
(MI), adolescent community reinforcement approach (ACRA), assertive community treatment (ACT), and cognitive–behavioral therapy (CBT). For each of these, at least 45
staff had been involved in implementation. Four dummy
variables were created: (a) MI versus other EBP, (b) A-CRA
versus other EBP, (c) ACT versus other EBP, and (d) CBT
versus other EBP.
2.2.1.1. TCU ORC-staff and TCU ORC-director. To assess
organizational readiness for change, the study instrumentation included the scales from the TCU ORC (TCU ORCstaff, TCU ORC-director) assessment (Lehman et al., 2002).
Directors and staff completed 115 items (5-point agree–
disagree Likert scales) hypothesized to form 18 scales
reflecting attributes of organizations in the following areas:
motivation for change, adequacy of resources, staff attributes, and organizational climate. Formal tests of multicollinearity were conducted using the variance inflation
factors test, and none were unacceptably high. A higher
order principal components analysis confirmed the internal
234
L. Lundgren et al. / Journal of Substance Abuse Treatment 42 (2012) 231–238
structure of the variables, and reliability was tested using
Cronbach's alpha (Lundgren, Amodeo, et al., 2011). The
Cronbach's alpha scores indicate high reliability for about
half of the 18 subscales (see Table 1).
2.2.2. Dependent variable: Level of barriers experienced
when implementing EBP
In the telephone interview, the interviewer first asked
each program director and each staff person to “describe
barriers that your project encountered in providing this
EBP.” Then, the respondent was asked: “Using a scale from
1 to 10 where number 1 means that barriers did not interfere
with providing [the EBP] and number 10 means that barriers
totally interfered with providing [the EBP], what number
best represents how much these barriers interfered with your
project's ability to provide [the EBP]?” It is the responses to
this ordinal scale that are used to measure director and staff
level of barriers experienced when implementing an EBP.
The distribution of responses to the above question was
unimodal, and responses covered the full range. 1
2.3. Missing data
A detailed analysis of the missing data was conducted.
For each variable that was missing data, barrier scale scores
(the dependent variable) were compared for the missing data
cases and the complete data cases. To evaluate the impact of
missing data on the final results, mean imputation was used,
and all bivariate and multivariable analyses were repeated
and compared with bivariate and multivariable results based
on complete cases. Results for each of these analyses
showed that the analysis with imputed data was highly
comparable to the analysis based on complete cases. Results
using complete cases are presented in both the narrative and
the tables.
2.4. Data analysis
Two separate sets of analyses were conducted using the
two samples, staff and program directors. As a first step,
bivariate analysis (one-way analysis of variance and
correlation analysis for categorical and continuous variables,
respectively) for the two separate samples examined the
statistical relationship between all independent variables
(age, gender, level of education, years of experience in drug
abuse counseling, organizational affiliation with a research
institution, type of treatment unit, primary service area,
program duration, type of EBP implemented, and the 18
1
Because of the ordinal nature of the scale, we also explored a
dichotomous outcome using multivariable logistic regression analysis. For
both program director and staff samples, independent variables in the logistic
regression analyses reached statistical significance, and the direction of the
association between independent variables and the dependent variable was
the same in the logistic regression analyses and the linear regression models
(logistic regression models not shown in the tables).
Table 1
TCU ORC Cronbach's alpha scores
Variable
Motivation for change
Program needs for improvement
Immediate training needs
Pressures for change
Adequacy of resources
Offices
Staffing
Training
Equipment (computer
access; 2002)
Internet (e-communications; 2002)
Staff attributes
Growth
Efficacy
Influence
Adaptability
Organizational climate
Mission
Cohesion
Autonomy
Communication
Stress
Change
TCU ORC-director TCU ORC-staff
(n = 296)
(n = 510)
.83
.88
.75
.89
.87
.77
.78
.64
.37
.59
.73
.68
.60
.58
.48
.42
.66
.66
.69
.55
.60
.62
.80
.58
.65
.83
.43
.70
.72
.51
.71
.87
.48
.79
.77
.62
TCU ORC subscales) and the dependent variable. Second,
for the two respondent samples, separate linear regression
models were developed with all variables significant at the
bivariate level (p b .05).
3. Results
3.1. Staff results
Five hundred ten clinical staff members were identified
by program directors as directly involved with the
implementation of EBPs in their organization. Respondents
were primarily women (72%) with a mean age of 42 years.
Approximately half (52%) held a graduate degree, and 50%
had 5 years or more of experience as drug abuse counselors.
Bivariate analyses (see Table 2) indicate that 11 TCU
ORC scores and three other variables were associated with
staff ratings of level of barriers experienced when implementing a new EBP. Specifically, staff who worked in
programs that had been funded by CSAT/SAMHSA for a
longer period, who were working in a treatment organization
in an urban area, who were not implementing MI compared
with any other EBP, and who rated their organization as
having greater program needs, more immediate training
needs, less adequate office resources, less adequate staffing
resources, less adequate training resources, an unclear
mission, less cohesion, higher levels of stress, and less
support for change reported higher levels of barriers
experienced with implementing a new EBP. There were no
significant relationships with staff level of education,
L. Lundgren et al. / Journal of Substance Abuse Treatment 42 (2012) 231–238
Table 2
Bivariate program director and staff ratings of barriers to implementing
EBPs by independent variables
Variable
Age of respondent a
Gender b
Male
Female
No. of years of education b
Below master's level
of education
Master's level of education
or higher
No. of years of experience in the field b
Four or fewer years
Five or more years
Program duration a
Primary service area of treatment unit b
Rural
Suburban
Urban
Type of treatment unit b
Inpatient (includes halfway
house and work
release programs)
Outpatient (including methadone
maintenance, outpatient)
Other
Motivational Interviewing b
Yes
No
Cognitive Behavioral Treatment b
Yes
No
Assertive Community Treatment b
Yes
No
Adolescent Community
Reinforcement Approach b
Yes
No
Research affiliation (PI or evaluator
affiliated with hospital or university) b
Yes
No
Motivation for change
Program needs a
Immediate training needs a
Pressures for change a
Adequacy of resources
Offices a
Staffing a
Training a
Equipment a
Internet a
Staff attributes
Growth a
Efficacy a
Influence a
Adaptability a
Organizational climate
Mission a
Cohesion a
Autonomy a
Program director
barrier scale
Staff barrier
scale
−0.03
−0.02
4.09
3.85
3.82
3.83
4.10
3.75
3.90
3.91
235
Table 2 (continued)
Variable
Program director
barrier scale
Staff barrier
scale
Communication a
Stress a
Change a
−0.07
0.22 ⁎⁎⁎
0.08
−0.18 ⁎⁎⁎
0.21 ⁎⁎⁎
−0.16 ⁎⁎⁎
a
4.25
3.82
0.05
3.79
3.85
0.11 ⁎
4.00
3.13
4.07
3.82 ⁎
3.18
3.93
3.49
3.95
4.09
3.58
4.09
3.83
4.10
3.94
3.36 ⁎
3.92
3.13
4.04
3.62
3.85
4.06
3.96
4.36
3.81
4.12
3.96
4.13
3.81
3.89
4.17
3.70
4.06
0.22 ⁎⁎⁎
0.23 ⁎⁎⁎
0.02
0.19 ⁎⁎⁎
0.15 ⁎⁎
0.07
−0.14 ⁎
−0.09
−0.11
−0.05
−0.02
−0.09 ⁎
−0.12 ⁎⁎
−0.16 ⁎⁎⁎
−0.07
−0.07
−0.07
0.04
−0.04
0.06
−0.05
−0.04
−0.11 ⁎
−0.03
−0.15 ⁎
−0.07
−0.01
−0.18 ⁎⁎⁎
−0.21 ⁎⁎⁎
−0.08
Correlation coefficient.
Mean.
⁎ p b .05.
⁎⁎ p b .01.
⁎⁎⁎ p b .001.
b
working in a research-affiliated organization, age, gender,
years of experience in drug abuse counseling, and type of
treatment unit and staff level of barriers experienced
implementing a new EBP. Notably, variables from all four
TCU ORC areas (organizational motivation for change,
organizational resources, staff attributes, and organizational
climate) were associated at the bivariate level with staff
perceptions of level of barriers experienced when implementing an EBP.
3.1.1. Regression results—Staff
The linear regression model presented in Table 3
includes all variables significant at the bivariate level. In
this model, program duration, implementing MI compared
with any other EBP, program needs, and experiencing
stress in the organization remained significantly associated
with level of barriers in implementing an EBP. Specifically, staff who rated their organization as having greater
program needs, who experienced greater stress in their
organization, who worked in programs that had been in
existence for a longer period, and who did not implement
MI but other EBPs were significantly more likely to
Table 3
Multivariate regression model of clinical staff ratings of barriers to
implementing EBPs (n = 461)
Variable
Program duration⁎
Primary service area of
treatment unit
MI⁎
Program needs⁎
Immediate training needs
Offices
Staffing
Training
Influence
Mission
Cohesion
Communication
Stress⁎
Change
Standardized
β coefficient
p
CI.95 for B
.115
.047
.012
.303
.054
−.090
.441
.290
−.104
.148
−.002
−.021
.032
−.034
−.045
.005
−.132
.046
.123
−.014
.023
.029
.973
.680
.581
.542
.382
.947
.059
.540
.041
.829
−.403
.032
−.278
−.260
−.180
−.306
−.320
−.285
−.571
−.215
.012
−.304
−.030
.605
.269
.170
.321
.161
.123
.305
.010
.409
.522
.244
Note. R2 = .117, adjusted R2 = .089, p b .000.
⁎p b .05.
236
L. Lundgren et al. / Journal of Substance Abuse Treatment 42 (2012) 231–238
experience greater barriers with implementing a new EBP
compared with their counterparts.
3.2. Program director results
Program directors were primarily women (66%), with an
average age of 48 years. Nearly 90 percent (89%) of the
program directors had a master's or professional degree.
Most directors (76%) reported five or more years of
experience as drug abuse counselors. Further, program
directors reported that the organizations within which they
worked were 49% outpatient, 24% inpatient, and 28%
“other,” and most (77%) of the units were located in urban
areas. In approximately half the organizations (52%), the
program directors reported that the PI or the evaluator was
affiliated with a university or hospital.
Bivariate analyses show that five variables were
associated with director ratings of the level of barriers
experienced in implementing a new EBP (see Table 2).
These variables were all measures of organizational
readiness for change. Directors who reported that their
program had greater program needs, higher levels of
immediate training needs, fewer office resources, less clear
organizational mission, and higher levels of stress also
reported higher level of barriers implementing a new EBP.
There were no significant relationships with age, gender,
years of experience in drug abuse counseling, type of
treatment unit, primary service area, working in an
organization with a research affiliation through the PI or
hospital, and the TCU ORC scores on staff attributes and
level of barriers experienced.
As the next step, a linear regression model was created,
which included all variables significant at the bivariate level.
This model is presented in Table 4.
3.2.1. Regression—Program director results
The results from the linear regression model indicate
that for program directors, only one factor was significantly associated with experiencing more difficulty (higher
level of barriers reported) in implementing an EBP.
Specifically, directors who reported higher levels of stress
within their organization also reported higher level of
barriers to EBP implementation.
Table 4
Multivariate linear regression model of program director ratings of barriers
to implementing EBPs (n = 291)
Variable
Standardized
β coefficient
p
CI.95 for B
Program needs
Immediate training needs
Office
Mission
Stress⁎
.092
.103
−.051
.003
.129
.209
.179
.422
.970
.045
−.118
−.109
−.408
−.298
.007
Note. R2 = .078, adjusted R2 = .062, p b .000.
⁎p b .05.
.537
.582
.171
.310
.579
4. Summary/discussion
For both staff and program directors, the regression
models indicate that the primary factors associated with level
of barriers were aspects of organizational capacity. Specifically, for both samples, reporting higher levels of stress
within their organization was significantly associated with
experiencing greater level of barriers with implementing a
new EBP. However, for program directors, only level of
stress in the organization was highly associated with level of
barriers, whereas for staff, another element of organizational
capacity proved to be significant. Specifically, for staff,
working in a program with greater program needs was also
significantly associated with experiencing greater levels of
barriers with EBP implementation. Because staff are
involved in the hands-on, day-to-day implementation of
EBPs in a way that program directors are often not, staff are
more likely to have personal exposure to the program needs
of an organization, which in the TCU ORC were defined as
the need for more guidance in client assessment and
performance, matching client needs with services and
increasing client participation, and the need for improving
overall program quality. Program staff who perceive a high
level of program needs may be more likely to experience
greater burden in EBP implementation.
In addition to our findings regarding the relationship
between organizational capacity and barriers to EBP
implementation, the study identified that staff who implemented an EBP in a program that had been in existence for a
shorter period and staff who implemented MI techniques
compared with other EBPs experienced lower level of
barriers when implementing an EBP.
4.1. Future theory and research
The findings indicate that the Simpson and Flynn
(2007) theoretical framework and their measures of
organizational readiness for change (Lehman et al., 2002)
are important to incorporate in any study of addiction
treatment staff, particularly studies of frontline staff and
their attitudes and experiences of EBP implementation.
Prior research has identified significant relationships
between organizational capacity using the TCU ORC
measures and director and staff attitudes about EBPs. Our
study provides an important new step in the study of EBP
implementation in addiction treatment.
Finally, the substance abuse field could benefit from
knowing whether a similar study of the mental health system
would yield comparable results regarding the influence of
organizational capacity and the extent of reported barriers
related to certain types of EBPs. To what extent is the
influence of organizational capacity specific to the substance
abuse treatment system or common to publicly funded
service systems? We might assume the former because the
substance abuse treatment system historically has been
underfunded and seriously lacking in other aspects of its
L. Lundgren et al. / Journal of Substance Abuse Treatment 42 (2012) 231–238
infrastructure. However, because the mental health system
has had some similar infrastructure problems (e.g., unstable
funding, high staff turnover), there is reason to believe that
the latter may be true. Adapting the TCU ORC measures to
the mental health field, given the stability of findings across
studies and treatment organizations, could be valuable.
4.2. Limitations
This project only sampled community-based CSAT/SAMHSA-funded substance abuse treatment organizations.
It did not include treatment organizations solely funded by
states or by private insurance. Second, the study relied on
program directors to identify clinical staff directly involved
with implementing EBPs. Third, given that this is an
exploratory cross-sectional study, it is only able to identify
possible associations rather than causal connections among
selected study factors. Fourth, a possible concern is sample
bias; because organizations that are the least successful with
implementation of EBPs probably never apply for government funding such as these CSAT/SAMHSA funds, the
director and staff perspectives from those organizations are
not included in our study. Fifth, on a methodological note, a
number of the Cronbach's alpha scores for the TCU ORC
scales were below the conventional threshold of .70. Finally,
it should be noted that our study shows relatively low levels
of variation in staff and director levels of education, which
possibly could have contributed to the lack of significance
identified between education and level of barriers in
implementing an EBP. However, published results from
one of our studies (Lundgren, Krull, et al., 2011), focusing
not on barriers but on attitudes about EBPs, identified
through multivariate modeling that educational levels of staff
and directors were significantly associated with more
positive attitudes about EBPs, which is consistent with
prior research efforts (Ducharme, Knudsen, Roman, &
Johnson, 2007; Knudsen, Ducharme, & Roman, 2007;
Rieckmann et al., 2007; Roman et al., 2006). In addition,
as in our study, the samples of addiction treatment staff in
these studies all showed a similar range in education levels.
237
resources, it cannot be viewed as an EBP without barriers.
As Amodeo et al. (2011) found, lack of quality training on
MI and controversies among staff about the MI approach
served as barriers to EBP implementation for respondents in
some addiction program settings.
In conclusion, the results of our study suggest that if
federal funders of community addiction programs want to
see greater implementation of EBPs, they must take
organizational capacity into account. These factors influence
the extent to which treatment programs will be ready,
willing, and able to implement change. As findings here
suggest, addiction program staff are more likely to perceive
higher levels of barriers to EBP implementation when
aspects of organizational capacity are lacking. Organizational capacity represents the context in which EBPs are
provided and clinicians need to be able to rely on some
reservoir of organizational capability at times when EBP
implementation does not go smoothly. When making
financial decisions, funders must consider both the costs of
implementing EBPs and the costs related to ensuring that
programs have sufficient organizational capacity to deliver
them. If funds to enhance organizational capacity are
lacking, it may be that only a narrow set of addiction
treatment programs will be equipped to implement EBPs
without being deterred by barriers. Thus, a future research
goal would be to identify organizations with the highest and
lowest levels of organizational capacity. Examining them in
relation to EBP implementation would be the next step that
could inform policy makers and program managers.
Acknowledgments
Funding was provided by the Robert Wood Johnson
Foundation Substance Abuse Policy Research Project (Grant
65029). The authors would like to thank the many program
directors and staff who participated in our interviews and
completed online surveys. This research would not have
been possible without their willingness to give us their time
and valuable insights.
4.3. Conclusion: Practice and policy implications
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