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A Conceptual Framework for Transferring Research to Practice

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Abstract

Systematic evaluations of efforts to transfer research-based interventions and procedures into general practice at community drug treatment programs have been limited. However, practical experiences as well as results from studies of technology transfer and organizational behavior in related fields provide a basis for proposing a heuristic model of key factors that influence this process. The successful completion of four stages of activity typically involved in program change (exposure, adoption, implementation, and practice of new interventions) appears to be influenced by several organizational considerations (e.g., institutional readiness for change, resources, and climate) as well as staff attributes. Assessment instruments for measuring organizational functioning (based on ratings aggregated for staff and patients in a program) are introduced, along with preliminary evidence for their validity. A better conceptual understanding of the process of program change and common barriers that may be encountered is needed for effectively transferring research to practice.
Regular article
A conceptual framework for drug treatment process and outcomes
D. Dwayne Simpson, (Ph.D.)*
Institute of Behavioral Research, Texas Christian University, TCU Box 298740, Fort Worth, TX 76129, USA
Received 18 February 2004; received in revised form 2 May 2004; accepted 15 June 2004
Abstract
Evidence from specialized treatment evaluations and large-scale natural studies of treatment effectiveness is organized conceptually into a
btreatment modelQfor summarizing how drug treatment works. Sequential relationships between patient and treatment program attributes,
early patient engagement, recovery stages, retention, and favorable outcomes are discussed—along with behavioral, cognitive, and skills
training interventions that have been shown to be effective for enhancing specific stages of the patient recovery process. Applications of the
treatment model for incorporating science-based innovations into clinical practice for improving early engagement and retention,
performance measurements of patient progress, program monitoring and management using aggregated patient records, and organizational
functioning and systems change also are addressed. D2004 Elsevier Inc. All rights reserved.
Keywords: Treatment model; Process; Performance; Outcomes; Recovery; Interventions; Program monitoring
1. Introduction
A series of visionary research papers were published in
1979 for what was then a bnewQfield involving community-
based treatment for illegal drug use. Early evaluations and
issues from outpatient drug free (Kleber & Slobetz, 1979),
therapeutic communities (De Leon & Rosenthal, 1979), and
national multimodality treatment settings (Sells, 1979)
pointed to the importance of motivation, during treatment
process, retention, and evaluation designs. Citing this work,
Jaffe (1979, p. 9) concluded, bThe evidence is over-
whelming that while in treatment in a variety of programs,
and for varying periods thereafter, a significant proportion
of drug users exhibit substantial improvement in a number
of areas.QHe added, bWhat is still at issue is not that change
occurs, but rather the degree of change which can be
attributed to the treatment process.QEqually important
papers addressed the roles of information management
and organizational issues (Deitch, 1979; Sells & Simpson,
1979), transitional aftercare treatment systems (B. S. Brown
& Ashery, 1979), and mandated correctional treatment
systems (McGlothlin, 1979).
Over 20 years later, Prendergast, Podus, Chang, and
Urada (2002) concluded from their meta-analysis of
comparison group studies that drug treatment was effective.
More importantly, they recommended that less future
attention be paid to outcome evaluations and more to
questions of process—how treatment works and how it can
be improved. Indeed, the need for systematic process studies
of drug treatment has continued to be widely recognized
(Lamb, Greenlick, & McCarty, 1998; McLellan, Woody
et al., 1997; Moos, 2003). Attention has been given to the
concepts of drug treatment engagement and recovery
progress (Allison & Hubbard, 1985; Joe, Simpson, & Sells,
1994; Melnick, De Leon, Thomas, Kressel, & Wexler, 2001;
Sells, Demaree, Simpson, Joe, & Gorsuch, 1977), but
development of empirical measurement systems and inte-
grative approaches focused on relationships of patient and
program factors with outcomes has been more challenging.
Much of our evidence about treatment outcomes in
typical community-based settings comes from large-scale
national evaluations funded by the National Institute on
Drug Abuse (NIDA). Beginning in the early 1970s with the
Drug Abuse Reporting Program (DARP), followed by the
0740-5472/04/$ – see front matter D2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.jsat.2004.06.001
* Tel.: +1 817 257 7226; fax: +1 817 257 7290.
E-mail address: ibr@tcu.edu. (D.D. Simpson).
Journal of Substance Abuse Treatment 27 (2004) 99 – 121
Treatment Outcome Prospective Study (TOPS) a decade
later, and continuing through the 1990s with the Drug
Abuse Treatment Outcome Studies (DATOS), national
evaluations of effectiveness have examined over 65,000
admissions to 272 treatment programs using multi-modality
and multi-site followup sampling plans that allow the study
of treatment in natural settings (Hubbard et al., 1989;
Simpson & Brown, 1999; Simpson & Curry, 1997;
Simpson & Sells, 1982). Group-level improvements in
drug use and social functioning in the first year following
treatment were generally sustained in long-term followup
evaluations, ranging up to 12 years after treatment
(Hubbard, Craddock, & Anderson, 2003; Simpson, Joe, &
Bracy, 1982; Simpson, Joe, & Broome, 2002; Simpson &
Sells, 1990). These national projects comprise only part of
the large body of evidence from natural and experimental
studies accumulated over the past 30 years that supports the
general effectiveness of drug treatment (Gerstein & Har-
wood, 1990; Institute of Medicine, 1996; Lamb et al., 1998;
National Institute on Drug Abuse, 1999). Similar results
from the National Treatment Outcome Research Studies
(NTORS) in England add further support to this evidence
base (Gossop, Marsden, Stewart, & Kidd, 2003; Gossop,
Marsden, Stewart, & Rolfe, 1999).
Length of stay in drug treatment has been one of the
most consistent predictors of followup outcomes, with the
general relationship between treatment retention and out-
comes being replicated across major types of residential and
outpatient programs in all four of the previously mentioned
national evaluation studies—DARP, TOPS, DATOS, and
NTORS. Early studies of retention effects documented the
high prevalence of treatment dropouts in the first 90 days
following admission, which was also the point at which
beneficial therapeutic effects begin to materialize (De Leon,
Holland, & Rosenthal, 1972; De Leon, Jainchill, & Wexler,
1982; Simpson, 1979, 1981). Although treatment outcomes
tend to improve in a generally linear fashion as retention
increases from 3 months up to 12–24 months or more,
which is targeted as the goal for many treatment programs
(Etheridge, Hubbard, Anderson, Craddock, & Flynn, 1997),
a rigid bmore is betterQcriterion faces practical limitations
from managed care and other cost-containment pressures.
Studies from DATOS (Simpson, Joe, Broome et al., 1997;
Simpson, Joe, & Brown, 1997) replicated these retention
findings but began shifting attention to the concept of
achieving bminimum retention thresholdsQfor effective
treatment—that is, approximately 90 days for residential
and outpatient care, and a year for methadone (agonist
maintenance) treatment programs (National Institute on
Drug Abuse, 1999). Note that these thresholds are defined
bstatistically,Qmeaning that patients with treatment retention
below the threshold had low probability of showing
improved outcomes (comparable to very early dropout
comparison groups). As time in treatment increases beyond
these thresholds, therapeutic benefits begin to accrue—
although patients with more serious problem severity at
intake require longer and more intensive treatment. How-
ever, retention represents a cumulative index for a mixture
of patient, therapeutic, and environmental factors that
contribute to treatment progress and effectiveness. The
influences on a person to remain in treatment include
interactions among individual needs, motivation factors,
and social pressures with treatment attributes, such as policy
and practices, accessibility, services offered, counselor
assignment, therapeutic relations, and patient satisfaction.
In general, these represent aspects of the bblack boxQof
treatment and how they impact stages of patient recovery.
1.1. Background for treatment process research
Studies of drug treatment process have extensive back-
ground and foundations, especially from psychotherapy and
counseling psychology. The similarities in findings across
these areas reflect on the generalizability of therapeutic
process. Chapters on treatment process and outcomes in the
Handbook of Psychotherapy and Behavior Change (Orlin-
sky & Howard, 1978, 1986; Orlinsky, Rbnnestad, &
Willutzki, 2004) have been major resources for promoting
better understanding of constructs involved in therapeutic
interventions. In the latest iteration of these reviews on
process-to-outcome research findings, Orlinsky et al. (2004)
stress the importance of considering the broader context of
social institutions and cultural patterns as influences on the
outcomes of patient and therapist interactions. Namely,
treatment outcomes are impacted by social institutions
(including organizational attributes of the treatment agency),
role-related interactions with family and friends, and
normative pressures from society and culture. This type
of systems perspective helps emphasize that therapeutic
process represents more than just a bclinical intervention.QIt
directs attention to (1) the importance of bpatient suitabilityQ
in relation to early therapeutic engagement, which corre-
sponds to the notion of motivation and readiness at
treatment intake, (2) the overwhelming support based on
over 1,000 studies for the critical role of therapeutic bonding
between therapist and patient, (3) cognitive and behavioral
change processes during treatment, (4) the duration of
treatment as a major predictor of outcomes, (5) influences of
organizational and contextual factors on treatment, and (6) a
need for further development of treatment monitoring
systems to address clinical feedback and performance
evaluation needs. These are the same areas given priority
in drug treatment process and outcome research.
As summarized by Whiston and Sexton (1993), over
50 years of psychotherapy research have illuminated the
roles of therapeutic relationships, session factors, patient
attributes, and how they interact in conjunction with spe-
cial interventions and approaches. Using a systems and
developmental perspective for focusing on counseling
psychology, Hill and Corbett (1993) also provide a useful
historical overview with recommendations for the future.
They discuss psychotherapy, skills training, behavioral and
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121100
cognitive strategies, and social influence models—inter-
woven with advances that have occurred in research
methodologies. Steps they see as still needed for improve-
ments include practical and analytic issues, especially using
research designs that balance rigor with relevance as well as
identifying important therapeutic events embedded within a
longitudinal context. McLellan (2002) argues that post-
treatment outcome evaluation designs for drug treatment
have been overvalued and often misapplied. For instance,
treatment benefits for other chronic health conditions (like
asthma, diabetes, and hypertension) are judged primarily on
the basis of interim, in-treatment performance criteria.
Hill and Corbett (1993, p. 16) emphasize that bThe
overall goals of process and outcome studies should be to
develop new theories of therapy, to provide information
for practitioners about how to intervene with patients at
different points in therapy, and to develop training programs
based on empirical results of what works in therapy.QThey
go on to suggest this includes the need to test an entire
(longitudinal) model that incorporates patient pretreatment
characteristics, process factors, interim outcomes, external
influences, and long-term outcomes.
These suggestions resonate with methodological cautions
about efforts to impose controlled clinical trials as the sole
legitimate design for establishing efficacy of interventions
and causality (De Leon, Inciardi, & Martin, 1995). Krause
and Howard (2003; p. 754) state that bAll clinical trials are
quasiexperiments for the foreseeable future, so long as our
causal models are not fully specified and all the causal
variables are not precisely controlled or accurately mea-
sured.QThey go on to demonstrate additional limitations of
randomized designs in controlling interactions between
treatment and patient variables. Ablon and Jones (2002)
compared manualized treatment regimens and found overlap
between therapeutic process and technique that likewise
questions basic assumptions about using controlled experi-
mental designs for establishing the cause of patient
improvements. They conclude that the clinical trials model,
though appropriate for the medical science field to study
medications, fails when applied to psychological treatments
because therapeutic process and patient-counselor engage-
ment dynamics cannot be fully controlled. In particular, this
approach focuses more on outcomes and less on the linkage
of process with outcomes. bPsychotherapy research would
profit from the study of change processes as they occur
naturalistically, rather than focusing on the empirical
validation of brand names of therapyQ(p. 782). Others
agree with Ablon and Jones about the need for a shift in
treatment evaluation research towards more emphasis on
change processes (Goldfried & Wolfe, 1996; Howard,
Moras, Brill, Martinovich, & Lutz, 1996). It is longitudinal
effectiveness studies, as opposed to highly restricted
efficacy designs, that emphasize external validity and the
interactions of clinical protocol with patient dynamics in
natural settings. Furthermore, providers of behavioral health
services and policymakers need evidence based on real-
world applications of treatment in field studies (Messer,
2002; Moyer & Finney, 2002; Sturm, 2002).
1.2. Practical applications of treatment models
Connors, Donovan, and DiClemente (2001, p. 223) state
bresearch to date appears to support a process of change for
substance abusers that has a series of steps or phases that
require different strategies and address different issues.Q
They stress the role of cognitive functioning (decisional
balance, self-efficacy, and discrete stage perspectives) of
patients. This follows work by Rogers (1959) long ago
that focused on the relationship between counselor and
therapist as a way to improve patient changes, and the
notions of Erikson (1963) about stage-based personality
changes. These represent phases of the recovery process in
treatment settings.
A treatment model needs to be more than a description of
patient change, however, in order for interventions and other
influences to be integrated into it as exemplified by stepped
or staged care treatment approaches (Brooner & Kidorf,
2002; Sobell & Sobell, 2000; Weissberg & Greenberg,
1998). Although the NIDA publication Principles of Drug
Addiction Treatment: A Research Based Guide (1999)
provides an introduction and listing of prominent interven-
tions found to be effective, it is lacking in practical clinical
guidelines for when and why each one should be used. By
becoming more organized in assembling these components
conceptually, we could become more strategic in making
applications of evidence-based techniques as well as more
strategic in filling the voids. Indeed, there is great heuristic
potential for an evidence-based treatment model that
summarizes bwhen and whereQto use interventions for
maximum effect.
Can we therefore assemble a treatment model to serve as
a clinical guide for how to determine when various
interventions are needed and if they are working? Towards
these lofty goals, general features of the stage-based TCU
Treatment Model are summarized below, along with a
review of related drug treatment, psychological counseling,
and psychotherapy literature. The purpose of treatment
process and outcome research captured in the model is
four-fold. First, it should promote the use of patient
performance and monitoring indicators that serve as interim
criteria related to treatment planning and effectiveness.
Second, it should demonstrate the stages of patient change
in treatment and how specific interventions can be used to
address particular needs throughout the recovery process.
Third, it should clarify the rationale for using individual-
level and aggregated patient records of engagement and
performance as indicators for feedback to counselors and
patients, program performance monitoring, and manage-
ment of services. Finally, it should be a foundation and
guide for studying treatment gaps and improving organiza-
tional functioning and change (i.e., technology transfer,
or moving science to services). These are the criteria
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 101
recommended for judging the value of a treatment process
and outcome model.
1.3. Developing a research program on drug
treatment process
Research conducted at Texas Christian University
(TCU), especially during the past 15 years, has focused
on developing a conceptual framework for drug treatment
process and outcome research (see Simpson, 2001).
Psychotherapy, counseling psychology, and drug treatment
research has identified important therapeutic issues and
domains, but these findings have not been integrated
efficiently into a conceptual scheme to guide clinical
applications and improvements. This step is crucial for
communicating convincingly the notion that treatment is a
complex process rather than a singular beventQand to
capture dynamic aspects of its sequential nature. Our
research was therefore designed to be programmatic in its
conceptual and methodological approach to treatment
process (Chatham & Simpson, 1994; Simpson, Chatham,
& Joe, 1993; Simpson, Dansereau, & Joe, 1997), while
assimilating the contributions of many others in the
psychological and addiction treatment research fields.
Studies at TCU have spanned diverse settings and popu-
lations, but they share common assessment methodologies
and integrated strategies for obtaining longitudinal data in
natural and experimental research designs (more details on
scientific publications and related treatment intervention
manuals and assessment resources are available at www.
ibr.tcu.edu). This approach has allowed us to develop
sequentially a body of findings that could be assembled
into a general treatment model (Simpson, 2001; Simpson,
Joe, Dansereau, & Chatham, 1997).
A few landmark studies determined in large part the path
taken in pursuing this goal, leading up to the conceptual
model presented below. After selecting outpatient metha-
done programs as our initial focus—due to its stability and
slower pace of therapeutic change (in contrast to short-term
and highly diverse outpatient drug free treatment)—we
launched a comprehensive, prospective assessment system
for patient and program functioning as well as development
of intervention tools designed to improve services while we
studied the process involved (Simpson, Joe, Dansereau,
et al., 1997).Wereliedheavilyonexperiencefrom
numerous descriptive, process, methodological, and out-
come studies, including those conducted as part of our first
national evaluation of treatment effectiveness in the U.S.
(Sells, 1974; Sells & Simpson, 1976; Simpson & Sells,
1982, 1990). As a DATOS Research Center, our conceptual
models and measures were re-examined using the diversity
of treatment settings represented in DATOS (including long-
term residential, outpatient drug free, outpatient methadone,
and short-term residential programs), the multi-site repre-
sentation for each treatment (including over 10,000 patients
from 96 agencies), and its distinctly different data system
(Flynn, Craddock, Hubbard, Anderson, & Etheridge, 1997).
Psychometric calibrations of patient and program measures
and incorporation of new methodological techniques (e.g.,
hierarchical linear modeling) provided the basis for replicat-
ing and expanding the evidence for motivational influences
on treatment process and retention (Joe, Simpson, &
Broome, 1998; K. Knight, Hiller, Broome, & Simpson,
2000). It added broad multi-modality support for the TCU
Treatment Model (Joe, Simpson, & Broome, 1999), and
more evidence for these treatment process relationships
have come from a similar national treatment effectiveness
study in England (Gossop et al., 1999; Gossop, Marsden,
et al., 2003) as well as treatment evaluations in correctional
populations (Broome, Knight, Hiller, & Simpson, 1996).
Having a large-scale data system from 96 treatment
providers in DATOS also made it possible to examine
treatment process at both the patient and program level.
When patient-level records within agencies were aggre-
gated to represent program-level functioning, for instance,
they showed that programs with higher average patient
involvement successfully accessed more social and public
health services, maintained more consistent treatment
counseling patterns, and appeared to be more focused
on the particular needs of patients they served (Broome,
Simpson, & Joe, 1999). Thus, treatment process dynamics
operate at multiple levels (for patients and programs).
2. Overview of the TCU Treatment Model
Followup studies show drug treatment with adequate
intensity and duration can improve addiction recovery
rates. There are performance variations between programs
and patients within programs, however, which raise
questions about how to achieve improvements in treatment
effectiveness and efficiency. Therefore, growing attention
has been given in recent years to dynamic stages of
addiction treatment and recovery, along with support for
using a bchronic careQapproach to evaluating treatment
(McLellan, Lewis, O’Brien, & Kleber, 2000). At issue are
the goals of quality improvement and how treatment
systems might adopt bevidence-basedQpractices as well as
document their effectiveness based on patient performance
measures. Toward this end, the TCU Treatment Model
identifies key ingredients associated with effective process
and outcomes of specific treatment episodes. In particular,
it focuses attention on sequential phases of the recovery
process and how therapeutic interventions link together
over time to help sustain engagement and retention, thereby
improving patient functioning during treatment and after
discharge. Research findings will be summarized showing
the relationships between motivation, engagement, early
change, retention, family and social support networks, and
followup outcomes.
Each sequential facet of the TCU Treatment Model,
illustrated in Fig. 1, is described in more detail in the
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121102
following sections. Although portrayed parsimoniously as
an integrated treatment episode, there could be different
service providers in a continuum-of-care model, or multiple
episodes of treatment (e.g., detoxification, residential, and
outpatient) might in practice be chained together. The
significance of patient and program attributes for treatment
process and outcomes is discussed first, along with
examples of evidence-based interventions for increasing
patient motivation. Subsequent sections examine compo-
nents inside the box and interventions that amplify those
facets of treatment: early engagement, early recovery, and
retention-transition. The review concludes with an exami-
nation of bwrap-aroundQservices needed, but often difficult
to obtain, for social support and personal health care
of patients.
3. Patient attributes at intake
The left margin of Fig. 1 identifies contextual influences
on treatment outcomes involving patient background and
organizational functioning. Major patient attributes include
motivation for change, readiness for treatment, and
problem severity at intake—the types of measures believed
to be important for deciding treatment program placement
and planning the appropriate course of clinical care
(Gerstein & Harwood, 1990; Mee-Lee, 2001). In addition
to the setting and intensity levels that distinguish between
major drug treatment options (e.g., residential vs. out-
patient drug free programs, therapeutic communities, out-
patient agonist substitution programs), there are also
program attributes—resources, staff skills, climate, and
information systems for clinical and program manage-
ment—relevant to therapeutic effectiveness.
The positive relationships between treatment retention
and patient outcomes have been repeatedly affirmed across
different types of therapeutic settings, but closer study of
patient and program factors that mediate and influence
recovery stages is needed for bdecomposingQthe active
ingredients involved (Bell, Richard, & Feltz, 1996; De
Leon, 2000; Toumbourou, Hamilton, & Fallon, 1998).
Patient sociodemographic and other pretreatment character-
istics traditionally have not been strong predictors of post-
treatment outcomes. However, improved assessments of
patient functioning and better analytic techniques that distill
sequential relationships have modified this view. Addiction
severity (particularly involving multiple drug use), criminal
history, social resources, and psychological dysfunction at
treatment intake influence engagement and retention. Of
particular importance are patient motivation for treatment
and readiness to change (Baekeland & Lundwall, 1975; De
Leon & Jainchill, 1986; Simpson & Joe, 1993; Stark, 1992).
Compared to drug users entering outpatient methadone
treatment and probationers voluntarily entering residential
treatment, for instance, treatment readiness scores are much
lower among injection drug users in HIV/AIDS outreach
programs as well as probationers mandated to drug
education programs.
Among the most significant patient attributes is motiva-
tion for change, which gained much of its contemporary
prominence from work by Prochaska and DiClemente
(Connors et al., 2001; DiClemente & Prochaska, 1998;
Prochaska & DiClemente, 1986) on cognitive and behavioral
bstages of changeQas well as by Miller (1985, 1989, 1996)
on strategies to increase motivation. De Leon and Jainchill
(1986) have emphasized the role of intrinsic vs. extrinsic
motivation and readiness for treatment in their assessments
for therapeutic community settings, and discrete stages of
Post-treatment
Outcomes
Sufficient
Retention
Early
Engagement Early
Recovery
Drug
Use
Criminal
Activity
Social
Relations
Therapeutic
Relationship
Recovery
Support
Networks
Cognitive
Interventions
Behavioral
Interventions
Readiness
Interventions
Behavioral
Change
Patient
Attributes
Readiness
Severity
Social Support
Systems
Recovery Skills
Training
Program
Attributes
Resources
Staff
Climate
Mgmt Info
Systems
Interventions
Psycho-Social
Change
Stabilized
Recovery
Program
Participation
Social Skills
& Support
Personal Health
Services
Fig. 1. Overview of TCU Treatment Model, representing sequential influences of patient and program attributes, stages of treatment, and evidence-based
interventions on post-treatment outcomes.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 103
motivation also have been examined (Simpson & Joe, 1993).
Especially important is the growing evidence from path
analyses of longitudinal records showing programmatic
linkages of motivational stages with subsequent indicators
of therapeutic engagement and recovery of patients (Broome
et al., 1999; Gossop, Stewart, & Marsden, 2003; Joe et al.,
1998, 1999; Joe, Simpson, Greener, & Rowan-Szal, 1999; K.
Knight et al., 2000; Pantalon, Nich, Frankforter, & Carroll,
2002; Ryan, Plant, & O’Malley, 1995). De Leon and asso-
ciates (De Leon, Melnick, & Tims, 2001; Melnick et al.,
2001) point out the additional roles of circumstances and
resources in the recovery process.
Several of the most widely used assessment instruments
for motivational readiness for change—including eight
patient self-administered questionnaires and three measures
based on clinical ratings—were examined in terms of reli-
ability and validity by Carey, Purnine, Maisto, and
Carey (1999). One of the self-administered assessments
was the TCU scales (Simpson & Joe, 1993), which measure
problem recognition, desire for help, and treatment readi-
ness as discrete sequential stages. They have good
reliabilities in studies of African Americans (Longshore,
Grills, Anglin, & Annon, 1997), the homeless (Nwakeze,
Magura, & Rosenblum, 2002), and in cross-cultural settings
using a Dutch translation (De Weert-Van Oene, Schippers,
De Jong, & Schrijvers, 2002). The TCU scales of patient
motivation and readiness also have been used in correctional
settings (Farabee, Nelson, & Spence, 1993; Hiller, Knight,
Leukefeld, & Simpson, 2002; Hiller et al., 2003; K. Knight,
Simpson, Chatham, & Camacho, 1997) and included in
longitudinal process studies with results consistent with
those from community treatment programs (Broome,
Knight, Knight, et al., 1997; Broome, Knight, Hiller, &
Simpson, 1996; Broome, Knight, Joe, Simpson, & Cross,
1997; Hiller, Knight, Rao, & Simpson, 2002).
While significant advances have been made in the
theoretical and empirical role of btreatment motivation,Q
they are only a start. As discussed by De Leon (2000),
motivation and treatment readiness are often viewed as
global, undifferentiated constructs that can oversimplify
their dynamic and complicated role in treatment. Dansereau,
Evans, Czuchry, and Sia (2003) have therefore conceptual-
ized readiness in a two-dimensional framework. One
dimension represents three interdependent stages of readi-
ness, including readiness for personal change, for the
treatment program, and for specific intervention activities.
The second dimension represents important patient attrib-
utes, including motivation,skills/resources,andconfidence/
self efficacy. Because they can fluctuate, repeated measures
of these readiness dynamics are needed to examine
interactions with interventions and help maximize therapeu-
tic engagement over time.
Indicators of problem severity at intake also predict
levels of early engagement and retention. An oft-cited study
by Woody, McLellan, Luborsky, et al. (1984) demonstrated
the importance of psychiatric severity in relation to progress
of patients randomly assigned to treatment conditions
involving psychotherapy and drug counseling. Increasing
levels of severity generally required more intensive
psychotherapy. Similar findings were reported by Fals-
Stewart and Lucente (1994), based on comparisons of
outcomes related to patient retention in residential substance
abuse treatment for different antisocial personality and
cognitive impairment levels. And Simpson, Joe, Fletcher,
Hubbard, and Anglin (1999) found longer retention (over
90 days) in residential treatment for cocaine use was
associated with better post-treatment outcomes among high-
severity patients, whereas patients with lower problem
severity at intake were able to benefit from less intense,
outpatient care. bProblem severityQwas broadly defined,
based on seven indicators of psychological and social
functioning, legal status, and drug use history. Like severity
of psychiatric symptoms, however, higher pretreatment
drug use—especially cocaine and crack—is often a barrier
to favorable engagement and outcomes (Grella, Joshi, &
Hser, 2003; Patkar et al., 2002; Rowan-Szal, Joe, &
Simpson, 2000). In some instances, of course, heavy use
and dependence levels may require medical detoxification
to be part of the treatment readiness phase.
Because treatment motivation and problem severity
appear to interact as predictors, Carey, Maisto, Carey, and
Purnine (2001) have argued for assessing treatment moti-
vation even among high severity patients with mental
illness. Evidence suggests measures of motivation and
problem severity are positively correlated (Boyle, Polinsky,
& Hser, 2000), but their linkages to outcomes can be
complicated. For instance, using structural equation analysis
in a national multi-modality study of treatment effective-
ness, Joe, Simpson, and Broome (1999) identified motiva-
tion as the best predictor of engagement and retention (with
positive contributions from higher pretreatment depression,
alcohol problems, and legal pressures); on the other hand,
higher severity of cocaine use and hostility at intake
predicted early dropout. Low motivation, in turn, is linked
to client recollection of history of family dysfunction,
deviance of peer groups, and poor psychosocial adjustment
before treatment (Griffith, Knight, Joe, & Simpson, 1998).
3.1. Treatment settings and program attributes
Almost 14,000 specialized drug treatment facilities in
the U.S. currently provide services in a variety of settings
(Substance Abuse and Mental Health Services Adminis-
tration, 2003), mainly in residential, outpatient drug free,
and methadone (agonist maintenance) programs such as
those represented in DATOS (Etheridge et al., 1997).
Diagnosing drug dependence and abuse is a critical but
imperfect step to determining treatment needs and optimal
setting (Gerstein & Harwood, 1990). Assessment strategies,
treatment resources, and decision rules for program
admissions across state and local systems are highly
diverse, particularly for correctional populations (Farabee
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121104
et al., 1999; Hiller, Knight, Rao, et al., 2002). Even though
drug use histories and related problems of patients are
legitimate and appropriate considerations for selecting
treatment approaches and settings, there is growing senti-
ment that virtually all programs share some common
treatment process components (Connors et al., 2001;
Norcross & Goldfried, 1992).
This does not mean that all programs are alike or equally
effective. Indeed, those within a particular therapeutic
orientation—long-term residential, outpatient drug free,
and outpatient agonist substitution treatment—vary tremen-
dously in their ability to retain patients in treatment, and the
traits of their patients differ widely (Simpson, Joe, Broome,
et al., 1997). Since higher levels of addiction severity
(including drug injection frequency and alcohol use),
criminal history, and psychosocial dysfunction at treatment
intake are typically associated with poorer outcomes,
programs that draw more high-severity caseloads face more
difficult treatment challenges than others. Even after adjust-
ing for patient differences, however, programs within the
same type of treatment orientation show differential
effectiveness, demonstrating that both patient attributes
and program features have distinctive but complex influen-
ces on outcomes (Broome et al., 1999). Moos, King,
Burnett, and Andrassy (1997) found that in Veterans
Administration programs, high expectations for patients,
clear policies, structured programming, high proportion of
staff in recovery, and more emphasis on psychosocial
treatment were related to better participation in treatment
(and which independently predicted better outcomes at
discharge). Comparable findings from TOPS were reported
by Joe, Simpson, and Hubbard (1991).
A long-standing call for bmatching patients to treatmentQ
sometimes mistakenly assumes that centralized and com-
prehensive assessments are routinely conducted for large
numbers of treatment seekers, who then can be appropri-
ately matriculated into a rich diversity and clearly articulated
array of specialized treatment programs. More practical,
however, is the modest expectation that interventions and
services within each program should be tailored to acute
patient needs and stage of therapeutic progress (McLellan,
Grissom, et al., 1997). But even this limited application of
patient-to-treatment matching calls for a level of sophisti-
cation in assessments and availability of comprehensive (or
bwrap-aroundQ) services that are uncommon in the real
world. Programs often lack proficiency in customizing
services to progressively address distinct stages of patient
recovery, but evidence is growing in support of the
effectiveness and efficiency of reserving more intensive
services for patients with more severe problems (Gottheil,
Thornton, & Weinstein, 2002; Hser, Polinsky, Maglione, &
Anglin, 1999; Thornton, Gottheil, Weinstein, & Kerachsky,
1998). Similar support for matching patient problem
severity to treatment intensity comes from a national study
of cocaine users showing low-problem patients do about
equally well in virtually any type of program, but outcomes
plummet for high-problem cases treated in outpatient and
short-term programs. These higher severity patients do
much better in long-term, intensive residential services
(Simpson et al., 1999). Regardless of problem severity,
treatment setting, and post-treatment outcomes, however,
there are similarities in the therapeutic processes involved.
The weakest evidence represented in the TCU Treatment
Model involves these interactions between program effec-
tiveness and organizational dynamics (Simpson, 2002). In
particular, better assessments and conceptual models for
resources, staff functioning, organizational climate, and how
to use information for patient and program management are
crucial (Heinrich & Lynn, 2002; McCaughrin & Howard,
1996; Schneider, Salvaggio, & Subirats, 2002). However,
the need for this research is gaining attention in the growing
national agenda for translational studies on getting evi-
dence-based practices into broader field applications. Meta-
analytic results suggest organizational training can be
effective, depending on training methods used, the skill or
task being trained, and goals for the employee training
(Arthur, Bennett, Edens, & Bell, 2003), but organizational
readiness for change, climate for acceptance, and systems
infrastructure must also be considered in planning inter-
vention strategies for altering institutional functioning.
3.2. Evidence-based interventions for improving patient
readiness for treatment
Not everyone enters treatment with the same level of
motivation or problem severity, so it is not surprising that
some patients can benefit from special binductionQefforts
(Katz, Brown, Schwartz, Weintraub, Barksdale, & Robinson,
2004; Simpson & Joe, 1993). The use of systematic efforts
to improve treatment readiness and engagement of patients
reflects a fairly recent change in drug treatment practice.
Historically, patient motivation was not assessed compre-
hensively at intake, but induction strategies now are
increasingly viewed as part of the programTs public health
responsibility. Programs also recognize that high costs are
associated with early treatment dropouts. Gottheil, Sterling,
and Weinstein (1997) recommend the use of personal
(telephone) contacts to increase follow through on treat-
ment admissions, one of several social strategies to improve
engagement and retention. Motivational interviewing
(Miller, 1996; Miller & Rollnick, 1991, 2002) is among
the better-known approaches for raising patient commit-
ment, and it can be adapted to target special applications
such as for HIV/AIDS outreach efforts to increase the
effectiveness of treatment referrals (Booth, Crowley, &
Zhang, 1996). To reduce early dropout from therapeutic
communities, De Leon and colleagues (2000) employed
bsenior professor induction seminarsQas a motivational
strategy, while Foote, DeLuca, Magura, et al. (1999)
mounted a Group Motivational Intervention approach to
enhance and internalize the need for treatment. Other
social strategies include using bsignificant othersQ(family
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 105
or friends) as part of the induction plan for support of
treatment engagement (De Civita, Dobkin, & Robertson,
2000; Garrett, Landau-Stanton, Stanton, Stellato-Kabat, &
Stellato-Kabat, 1997; Landau et al., 2000). Another im-
portant approach focuses on reducing organizational bar-
riers to treatment, as illustrated by recent initiatives for
bPaths to RecoveryQbeing funded collaboratively by Robert
Woods Johnson Foundation and Center for Substance
Abuse Treatment.
Motivational induction is particularly beneficial in
settings such as correctional programs where low motivation
is a common problem (Farabee, Simpson, Dansereau, &
Knight, 1995), among adolescents (Battjes, Gordon,
O’Grady, Kinlock, & Carswell, 2003), and in outpatient
treatment for the mentally ill (Carey, Carey, Maisto, &
Purnine, 2002). Adaptations of cognitive-based enhance-
ment tools by Dansereau and associates (Blankenship,
Dansereau, & Simpson, 1999; Czuchry & Dansereau,
2000; Sia, Dansereau, & Czuchry, 2000) are effective as
the basis for treatment readiness training in small group
settings. These tools include a popular pedagogical board
game called bDownward SpiralQas a vicarious approach to
personalizing the multidimensional consequences of drug
abuse (Czuchry, Sia, Dansereau, & Dees, 1997), along with
cognitive exercises and associated homework applications
for exploring personal needs and strengths (Sia, Czuchry, &
Dansereau, 1999). Results from this series of experimental
studies show readiness training raises motivation and
program participation, as well as patient ratings of sessions,
peers, and counselors. Thus, motivation is viewed as a
dynamic bstateQthat must be sustained throughout treatment.
4. Early engagement
The first major step towards recovery in treatment
settings shown in Fig. 1 is early engagement, which refers
to the extent to which new admissions show up and actively
engage in their role as bpatient.QIt is measured primarily
by program participation and the formation of therapeutic
relationships in the initial weeks of treatment. Evidence
supports a sequential view of these components (Simpson
& Joe, in press), wherein more highly motivated patients at
intake are twice as likely to bparticipateQin treatment (e.g.,
attend sessions) in the first few months of treatment;
furthermore, patients achieving higher participation are then
twice as likely to develop a favorable therapeutic relation-
ship with their counselor. Although session attendance
logically precedes establishment of clinical relationships,
this is not a strictly linear process since interactive influences
accrue between participation and therapeutic relationships
that mutually strengthen these engagement components.
bParticipationQcan include session attendance (a more
appropriate behavioral indicator in outpatient than inpatient
settings) as well as assessments of psychological engage-
ment in these sessions (especially useful for group counseling
and residential settings where attendance is mandatory).
Session attendance has been examined as a corollary of
btreatment retention,Qleading to studies of dose-response
relationships in several types of treatment settings. In
general, higher session attendance predicts better out-
comes (Fiorentine & Anglin, 1997; Morral, Belding, &
Iguchi, 1999; Rosenblum et al., 1995; Rowan-Szal,
Chatham, et al., 2002; Toumbourou, Hamilton, U’Ren,
Stevens-Jones, & Storey, 2002). Rowan-Szal, Chatham, et
al. (2002) show higher total group and individual session
exposure in methadone treatment likewise is related to
stronger rapport or bonding with counselors; furthermore,
they found spending more time in sessions was related to
being female and having more alcohol use, childhood
problems, higher methadone dose, and more bstructuredQ
counseling sessions.
The literature in counseling psychology also focuses on
counseling session attendance, especially in the context of a
dose-response interpretation and the threshold required for
achieving clinically significant improvement. Lambert and
colleagues (Anderson & Lambert, 2001; Lambert, Hansen,
& Finch, 2001; Snell, Mallinckrodt, Hill, & Lambert, 2001)
find 10–20 sessions are usually required for at least 50%
of patients to show improvement, with further benefits ac-
cruing with additional sessions. Depending on their time
distributions and scheduling of sessions, therefore, these
findings suggest counseling of approximately 3 months may
be needed before reliable changes become detectable.
Refinements in this line of research focus on session-level
impact (Stiles, 1980; Stiles & Snow, 1984) and indicate that
session evaluations are positively associated with indices of
cognitive understanding, problem solving, and relationship
formation (Stiles et al., 1994). Kolden (1996) similarly
shows therapeutic openness and bonding are related to in-
session progress. This implies efforts to increase cognitive
engagement in each individual session may have promise as
micro-motivational strategies, paralleling motivational inter-
viewing and related techniques commonly used for treat-
ment induction (Czuchry & Dansereau, in press; Miller,
1985; Miller & Rollnick, 1991, 2002; Sia et al., 2000).
The other major component of early engagement is the
btherapeutic relationship,Qcommonly considered to be at
the very core of effective treatment. Its origin and assess-
ment philosophy come from the concept of bworking
allianceQin psychotherapy (Horvath & Greenberg, 1989;
Luborsky, McLellan, Woody, O’Brien, & Auerbach, 1985;
Tracey & Kokotovic, 1989), and earlier work by Rogers
(1959) using a patient-centered focus calling for therapist
empathy, warmth, and genuineness. The success of coun-
seling is consistently related to the quality of this relation-
ship, which is associated with participation in sessions that
patients consider to be effective, and there is general
similarity, or congruence, between patient and therapist
perceptions of its development (Al-Darmaki & Kivlighan,
1993; Horvath & Symonds, 1991; Mallinckrodt, 1993).
While both patient and counselor perceptions of their
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121106
working alliance are predictive of outcomes, those of
patients tend to be most discriminating.
Although patient satisfaction with services (reflecting
access, confidence in effectiveness, and commitment) is
related to drug treatment outcomes (Carlson & Gabriel,
2001), it appears to be secondary to the counseling
relationship which is variously referred to as rapport,
personal bonding, or therapeutic alliance (Joe, Simpson,
Dansereau, & Rowan-Szal, 2001). The association of
therapeutic relationship with outcomes is consistently
reported across drug use groups and treatment settings,
including alcohol outpatient and aftercare programs (Con-
nors, Carroll, DiClemente, Longabaugh, & Donovan,
1997), methadone treatment (Belding, Iguchi, Morral, &
McLellan, 1997), buprenorphine treatment (Petry & Bickel,
1999), family-based treatment for adolescents (Diamond,
Diamond, & Liddle, 2000), and drug free as well as
residential treatment settings (Kasarabada, Hser, Boles, &
Huang, 2002). A meta-analytic review by Martin, Garske,
and Davis (2000) further concludes that several formats for
assessing therapeutic relationship (that is, obtained from
patients, counselors, and observers) have adequate reliabil-
ity, they are similarly effective in predicting outcomes
across diverse settings, and the inclusion of moderator
variables does not diminish its predictive power.
Not surprisingly, the process involved in forming better
rapport with patients appears to depend in part on the
session format; group therapy calls for more attention to
social climate and interactions, while individual treatment
focuses more on gaining personal insight and problem
solving (Holmes & Kivlighan, 2000; Kivlighan & Schmitz,
1992). Session topics and counseling strategies also appear
to be relevant, with stronger rapport reported when drug use
problems are addressed by counselors using a positive
approach emphasizing relapse prevention and problem
solving, compared to using a punitive emphasis on program
rules and compliance requirements (Joe, Simpson, &
Rowan-Szal, in press). Better patient assessment systems
with counselor feedback for monitoring clinical progress,
however, are needed to guide this process.
4.1. Evidence-based interventions for improving
program participation
Behavioral intervention protocols that offer voucher-
based incentives for increasing treatment session attendance
and drug abstinence have been effective in various types of
drug treatment settings (Griffith, Rowan-Szal, Roark, &
Simpson, 2000; Higgins, Alessi, & Dantona, 2002). These
contingency management approaches originally were more
likely to focus on relapse indicators such as urinalysis
results, but over time have been expanded to other engage-
ment criteria. They have been particularly useful in out-
patient methadone treatment (Higgins, Budney, Bickel,
Foerg, Donham, & Badger, 1994; Petry & Simcic, 2002;
Robles, Stitzer, Strain, Bigelow, & Silverman, 2002;
Silverman, Higgins, et al., 1996; Silverman, Wong, et al.,
1996). Low-cost adaptations that emphasize social recog-
nition, small gifts, or treatment supportive items (e.g., bus
tokens or car fare) also have been effective for community-
based programs (Rowan-Szal, Joe, Chatham, & Simpson,
1994; Rowan-Szal, Joe, Hiller, & Simpson, 1997), as has a
procedure by Petry and colleagues using a bfish bowlQfor
drawing prizes contingent on negative urinalysis results
(Petry & Martin, 2002; Petry, Martin, Cooney, & Kranzler,
2000; Petry et al., 2001).
Improving the quality and structure of treatment
counseling has likewise shown benefits in raising partici-
pation levels and retention rates (Gottheil et al., 2002;
Hoffman et al., 1994; Rowan-Szal, Chatham et al., 2002).
Merging contingency management with cognitive-behavioral
therapy (usually a form or variant of relapse prevention
training) has been another method for effectively improving
treatment to achieve better attendance, engagement, and
retention (Epstein, Hawkins, Covi, Umbricht, & Preston,
2003; Farabee, Rawson, & McCann, 2002; Rawson, Huber,
et al., 2002; Rowan-Szal, Bartholomew, Chatham, &
Simpson, 2002).
4.2. Evidence-based interventions for improving
therapeutic relationships
Shifting focus from session participation to therapeutic
relationship calls for increasing emphasis on cognitive
tools, counselor skills, intervention strategies, and context.
Treatment effectiveness is not strictly aligned with any
particular treatment philosophy, orientation, or setting,
thereby prompting an interest in how much counselor skills
or strategies may interact with patient attributes to
determine outcomes. Indeed, there are between-counselor
outcome differences (Luborsky, McLellan, Diguer, Woody,
& Seligman, 1997) as well as between-program differences
(Broome et al., 1999; Joe et al., 1994) that are not explained
or accounted for by patient-level measures alone. So what
are the treatment program dynamics that could be involved?
Studies of general counselor attitudes or beliefs suggest
more flexible, eclectic, and abstinence orientations contrib-
ute to better outcomes (Caplehorn, Irwig, & Saunders,
1996; Caplehorn, Lumley, & Irwig, 1998; Humphreys,
Noke, & Moos, 1996).
In terms of specific skills, training, or experience, results
sometimes have been obtuse and inconsistent. An early
study by McLellan, Woody, Luborsky, and Goehl (1988)
compared four counselors on the basis of outcomes for their
patients. Their background and education were not related to
patient success, but counseling content and process pro-
vided a few clues by suggesting that being well organized,
systematic, and comprehensive were favorable traits. This
implies having more ready access to clinical records that are
user-friendly and relevant to treatment needs, as well as
being properly trained in their use, would enhance treat-
ment. Joe, Simpson, and Sells (1994) similarly found that
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 107
methadone programs with better patient retention and
outcome rates reported higher professional quality in
assessing patient needs and planning treatment. Attempts
to quantify effective counselor traits point to interpersonal
skills and empathy as being important qualities (Miller,
2000; Valle, 1981). When broken down into more explicit
dimensions, factors such as expertness, trustworthiness, and
attractiveness emerge (Corrigan & Schmidt, 1983; Heppner,
Rosenberg, & Hedgespeth, 1992). However, assessing,
training, and retaining effective counselors in the drug
treatment field continue to be a significant challenge (B. S.
Brown, 1997; Gallon, Gabriel, & Knudsen, 2003; Kasar-
abada et al., 2001).
Comprehensive bfull-courseQmanualized treatment inter-
ventions like the Matrix Model (Obert, London, & Rawson,
2002; Rawson et al., 1995) include a prescribed sequence
of behavioral and cognitive approaches, tailored initially
for stimulant users in outpatient programs. More specialized
cognitive strategies show evidence of having special
benefits for improving therapeutic relationships (Ahmed &
Boisvert, 2002; Dansereau, Dees, Greener, & Simpson,
1995; Magura, Rosenblum, Fong, Villano, & Richman,
2002), and similar combinations of cognitive-behavioral
social skills and cognitive skills training programs are
reportedly the most effective for prison settings and correc-
tional populations (Pearson, Lipton, Cleland, & Yee, 2002).
Ideally, use of these focused interventions should be bneeds-
driven,Qbased on appropriate assessments of patient
functioning and progress (Graham & Fleming, 1998).
Studies of counseling based on a cognitive visual
representation and communication technique illustrate
how engagement, progress during treatment, and followup
outcomes can be improved (Dansereau, Joe, & Simpson,
1993; Joe, Dansereau, Pitre, & Simpson, 1997). Simpson &
Joe (in press) found it raised by two-fold the odds that
methadone treatment patients would have higher engage-
ment scores. This technique, derived from basic psycho-
logical research on problem-solving (e.g., Larkin & Simon,
1987) and in educational psychology (e.g., Dansereau &
Newbern, 1997), uses cognitive (node-link) maps that allow
counselors and patients to display issues and solution plans
in a form similar to that of flow charts and organizational
diagrams (see Czuchry & Dansereau, 2003,foran
integrative overview of this research). Nodes (drawn as
boxes or circles) contain ideas, facts, and feelings while
links (usually drawn as labeled lines) express relationships
between the nodes. Several types of maps are used to serve
different needs and functions of counseling. Unstructured,
free form maps can be drawn on newsprint or a chalkboard
as a session progresses to maintain focus and record
discussions about issues, especially in a group setting.
Guide maps are pre-formed, bfill-in-the-nodeQmaps that
address special topics requiring problem solving or personal
insights, such as emotional distress, relapse, and decision
making. Nodes or boxes in these maps typically contain
questions (e.g., bHow have you tried to deal with this in the
past?Q) that are to be answered, either as part of a homework
assignment or during a counseling session. For didactic or
knowledge-based applications, information maps are used
to present details on important topics such as relapse,
communication, HIV/AIDS, depression, or the physiologi-
cal impact of certain drugs.
Results indicate that this type of conceptual visualiza-
tion technique reduces reliance on purely verbal communi-
cation (Dansereau et al., 1993), increases attentional focus
(Czuchry, Dansereau, Dees, & Simpson, 1995), and im-
proves memory for session content (K. Knight, Simpson, &
Dansereau, 1994). Further, the use of mapping has been
shown to be effective in a variety of settings and with a
variety of drug treatment outcome measures (Collier,
Czuchry, Dansereau, & Pitre, 2001; Czuchry & Dansereau,
1999; Dansereau et al., 1995; Dansereau, Joe, Dees, &
Simpson, 1996; Newbern, Dansereau, & Dees, 1997; Pitre,
Dansereau, & Joe, 1996; Pitre, Dansereau, Newbern, & Simp-
son, 1998), including treatment for gambling (Melville,
Davis, Matzenbacher, & Clayborne, 2004) and HIV/AIDS
risk reduction in prison populations (S. S. Martin, O’Con-
nell, Inciardi, Surratt, & Beard, 2003). Workshop, manual,
and Web-based methods for transferring mapping have been
developed and disseminated (see Dansereau & Dees, 2002).
5. Early recovery
The second major stage of treatment process in Fig. 1 is
characterized as early recovery, reflecting a series of
psychosocial and behavioral changes. Early stages of patient
recovery are signified by changes in thinking and acting,
comparable is some ways to the transition from cognitive-
based bcontemplationQto decision-based bpreparationQand
bactionQstages of the transtheoretical model (Connors et al.,
2001). It is this bchange in thinking and actingQthat
builds on successes from the previous engagement stage
and sustains retention in treatment for a long enough time to
see evidence of enduring change in drug use and related
problem behaviors (Joe, Simpson, & Broome, 1999; Simp-
son, Joe, Rowan-Szal, & Greener, 1997). Evidence for
sequential linkages of components in the TCU Treatment
Model (Simpson & Joe, in press) indicates that methadone
treatment patients who achieved stronger therapeutic rela-
tionships with counselors are 2.3 times more likely to report
positive change in psychosocial functioning (based on scales
for self-esteem, depression, anxiety, risk-taking, social con-
formity, and decision-making). More favorable levels of
psychosocial functioning, in turn, are related by almost a
two-fold increase in the likelihood of favorable behavioral
change (defined by urinalysis and self-reported use of
opiates and cocaine in Month 3 of treatment). And finally,
favorable behavioral measures of drug use in this sample
were associated with better chances of staying in treatment
beyond the minimum threshold (that is, 1 year for outpatient
methadone patients).
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121108
5.1. Evidence-based interventions for improving
early recovery
Relapse prevention (Marlatt & Gordon, 1985) is a classic
technique used in substance abuse treatments to enhance
behavioral self-control in preventing relapse to drug use and
building cognitive vigilance for high-risk situations that
represent btriggers.QThe intent is to establish new habit
patterns for thinking and acting that can be stabilized and
maintained over time. The extent to which a patient has
already become engaged in treatment in terms of partici-
pation and therapeutic relationship will favorably influence
the deployment of relapse prevention and related strategies
for strengthening recovery.
More systematic use of social support systems and
networks also has become a focal concern since families
often have been omitted from patient treatment plans.
Miller (2003) argues that families can be part of the
problem as well as the solution; they may themselves need
psychosocial treatment to deal with drug use problems of a
loved one, but they also can give effective support to
recovery of the patient. Family history, childhood back-
ground, parental support, and conflict influence psychoso-
cial adjustment in adulthood as well as engagement and
progress in drug treatment (Broome, Knight, Knight, et al.,
1997; De Civita et al., 2000; D. K. Knight, Cross, Giles-
Sims, & Simpson, 1995; D. K. Knight & Simpson, 1996;
Mallinckrodt, 1991). The focus of family-based interven-
tions takes into account the existing social structure and
resources because as patient age increases, family contacts
and investments can be diminished (Lemke & Moos, 2002).
After defining an appropriate network of bsignificant
others,Qthere is a variety of strategies that can help
strengthen social adjustment and coping skills. Twelve-
step programs are examples (Apodaca & Miller, 2003), but
other more structured and proactive interventions also are
available. The Community Reinforcement and Family Train-
ing approach (Meyers, Miller, Smith, & Tonigan, 2002;
Miller, Meyers, & Tonigan, 1999) and A Relational
Intervention Sequence for Engagement intervention (Landau
et al., 2000) follow manualized guides for recruiting and
engaging patients in treatment. Similarly, Brief Strategic
Family Therapy (Robbins, Bachrach, & Szapocznik, 2002;
Szapocznik & Kurtines, 1993), Multidimensional Family
Therapy (Liddle et al., 2000, 2002), and Multisystemic
Therapy (Henggeler, Schoenwald, Borduin, Rowland, &
Cunningham, 1998) address special developmental needs
of adolescents.
The core objective of these interventions, of course, is to
build social skills that link to support systems. These needs
are especially important in drug treatment programs for
women who have lost custody of children and have poor
economic prospects unless family connections and support
can be re-established. Knight, Joe, and Simpson (2003)
focused specifically on the intersection of social relation-
ships and treatment process for women in residential treat-
ment and found that level of social support was directly
associated with engagement indicators and treatment
completion. Specialized group education materials—often
delivered in female-only or male-only group settings for
sexual health and communication skills training, parenting
skills training, or transition to aftercare training—can
improve knowledge and psychosocial functioning (Bartho-
lomew, Hiller, Knight, Nucatola, & Simpson, 2000;
Bartholomew, Rowan-Szal, Chatham, & Simpson, 1994;
Gainey, Catalano, Haggerty, & Hoppe, 1995; Hiller,
Rowan-Szal, Bartholomew, & Simpson, 1996). The secon-
dary effect of these six- to eight-session training modules
(for residential and outpatient settings, as well as in cor-
rectional populations) has been to increase treatment reten-
tion and completion.
Findings from a recent review of 38 studies on womenTs
treatment by Ashley, Marsden, and Brady (2003) add
support to these conclusions. They found six treatment
components to be significantly related to longer treatment
retention and completion, reduced drug use and HIV risk
behaviors, and physical/mental health; these included (1)
child care services for mothers in treatment, (2) prenatal care
and parenting skill training, (3) use of women-only treatment
groups, (4) educational sessions on health care and social
skills, (5) access to mental health care, and (6) use of more
comprehensive or multi-service combinations of treatment.
6. Retention and transition
The third stage of treatment process, retention and
transition, helps stabilize recovery by building on progress
in the two previous stages and focuses on the need for
retaining patients beyond minimum beffectiveness thresh-
oldsQto allow optimal preparation for transition out of
primary treatment. This is comparable to the bmaintenanceQ
stage of the transtheoretical model, which Connors et al.
(2001, p. 117) suggest is meant bto sustain change over
time to integrate that change into the lifestyle of the
individual so that the new behavior, abstinence from drugs,
becomes the preferred habitual behavior.QIn recognizing
the high rate of relapse and return to treatment (e.g., Grella,
Hser, & Hsieh, 2003), Dennis, Scott, and Funk (2003) have
shown the effectiveness of a brecovery management
checkupQprotocol for improving this transitional phase by
re-engaging relapsers in treatment sooner, keeping them
there longer, and subsequently reducing treatment needs at
24 months followup.
Within the context of the TCU Treatment Model, this
stage reflects the expectation that patients remain in treat-
ment long enough to stabilize recovery habits and support
networks, especially before treatment discharge and social
re-entry. One of our studies showed patients who stayed
in outpatient methadone treatment for at least a year were
five times more likely to have favorable followup outcomes
on drug use and criminality measures (Simpson, Joe, &
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 109
Rowan-Szal, 1997). These findings made it clear that
gaining a better understanding of the sequential dynamics
involving patient attributes, engagement, interim perform-
ance changes, and retention was needed. Since then, a
variety of multivariate analytic models have proven useful
in examining data from diverse community and correctional
settings to establish converging relationships between
patient motivation and problem severity, treatment process
stages (i.e., program participation, therapeutic rapport,
psychosocial/cognitive improvements, and behavioral
change), retention, and followup outcomes (Broome,
Knight, Knight et al., 1997; Joe, Simpson, & Broome,
1999; Simpson, Joe, Greener, & Rowan-Szal, 2000;
Simpson, Joe, Rowan-Szal, & Greener, 1997).
Interventions for this stage of treatment include
several of the ones discussed above for early recovery,
but the emphasis shifts to bself-managementQof addiction
as a chronic condition (Bodenheimer, Lorig, Holman, &
Grumbach, 2002) by teaching problem-solving and social
functioning skills. Twelve-step programs are popular for
this stage (Fiorentine & Hillhouse, 2000; Hillhouse &
Fiorentine, 2001; Weiss et al., 2000), along with expanded
efforts to make favorable changes in the family and social
support networks of patients (Broome, Simpson, & Joe,
2002; D. K. Knight & Simpson, 1996). A training manual
entitled Straight Ahead:Transition Skills for Recovery
(Bartholomew, Simpson, & Chatham, 1993) provides a
counseling guide to meet some of these specific needs,
and a companion series of Time Out manuals address
communication and sexuality in gender-specific groups
(Bartholomew, Chatham, & Simpson, 1994; Bartholomew
& Simpson, 1996). Probably the most popular are relapse
prevention strategies (Marlatt & Gordon, 1985)that
focus on relapse triggers, dangerous situations, and cog-
nitive restructuring.
7. Community wrap-around and transitional services
Successful transitions back into the community and
social networks following drug treatment, whether coming
from community-based or prison-based settings, require a
variety of health and social support services that address
persistent mental health and social deficits of patients
(Moos, Finney, & Moos, 2000; Moos, Pettit, & Gruber,
1995). There are two important but distinct components
involved. The first component has been referred to variously
as ancillary, comprehensive, or wrap-around services, which
are recognized as part of the extended care system that
patients need during treatment as well as afterwards. The
second component is commonly referred to as transitional,
re-entry, or aftercare services, which may include a step-
down stage of continuum-of-care drug treatment or less
formal social support networks. Although conceptually
distinct, these services typically are procedurally intertwined
in the real world.
Several studies by McLellan and associates document
the positive role played by accessing a set of compre-
hensive, wrap-around services for medical, psychiatric,
family, and employment problems (McLellan et al., 1994,
1998; McLellan, Arndt, Metzger, Woody, & O’Brien, 1993;
McLellan, Grissom, et al., 1993), and Friedmann,
Alexander, and DTAunno (1999) add to the evidence
suggesting that some programs (with differences in resour-
ces and staffing patterns) appear to be more focused and
proficient than others in obtaining these services. The
general availability of health and social services to drug
treatment programs tended to diminish between the 1980s
and early 1990s (D’Aunno & Vaughn, 1995; Etheridge,
Craddock, Dunteman, & Hubbard, 1995), but with better
stability from 1990 to 1995 (Friedmann, Lemon, Durkin, &
D’Aunno, 2003). Case management techniques (McLellan
et al., 1999; Siegal, Rapp, Li, Saha, & Kirk, 1997) are
sometime needed to secure, guide, and link together needs
and resources in this complicated environment. Epstein,
Nordness, et al. (2003) stress the further importance of
engaging family and social support networks in this process.
Transitional care following primary treatment is a
challenging but crucial element of a comprehensive treat-
ment system. Nowhere is the importance of transitional
services treatment more evident than for correctional
populations, especially community re-entry programs that
follow prison-based treatment (K. Knight, Simpson, &
Hiller, 1999; S. S. Martin, Butzin, Saum, & Inciardi, 1999;
Wexler, Melnick, Lowe, & Peters, 1999). This process
requires careful planning prior to release (Farabee et al.,
1999; Wolff, Plemmons, Veysey, & Brandli, 2002) and
completion of aftercare services by offenders (Butzin,
Martin, & Inciardi, 2002; Wexler, 2003). Because transi-
tional services treatment usually requires coordination of
different bsystemsQof authority and responsibility, however,
it tends to be overlooked or ignored due to costs, complex-
ity, or lack of understanding. Cost-effectiveness analysis of
treatment in correctional settings gives further evidence of
its benefits, particularly to the value of completing transi-
tional care phases and for high-risk cases (Griffith, Hiller,
Knight, & Simpson, 1999). Criminal Justice Drug Abuse
Treatment Studies is a major NIDA-funded project focused
on these issues (see www.cjdats.org).
8. Conclusions
It was suggested at the outset of this paper that the value
of the TCU Treatment Model should be weighed against
how well it contributes to the four goals discussed below.
8.1. Defining practical patient performance and treatment
process indicators
Drug treatment services are delivered primarily in
facilities that are specialized in their treatment approaches.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121110
Regardless of the source of funding, all programs are under
increasing pressures to document effectiveness. Records for
delivery of medications and units of services typically are
routinized for billing requirements, but information on
bquality and process of careQis more elusive. This is the
type of information sought by various certification boards
and funding sources, and if accumulated on a system-wide
scale would be extremely valuable for policy decisions
about services and setting for treatment agencies.
The Addiction Severity Index has been the mainstay for
drug treatment intake assessments since 1980, going
through numerous revisions (McLellan et al., 1992), checks
for internal consistency and validity (Leonhard, Mulvey,
Gastfriend, & Shwartz, 2000), efforts to establish clinical
norms (Weisner, McLellan, & Hunkeler, 2000), conversions
to computer-assisted applications (Butler et al., 1998, 2001),
dissemination within new technologies (Carise, Cornely, &
Gurel, 2002), and comparisons with alternative assessments
(Joe, Simpson, Greener, & Rowan-Szal, in press). Continu-
ing growth in technology makes Internet-based or on-line
assessments a high priority for development and informa-
tion management (Buchanan, 2002), but more work and
resources clearly are needed.
Some of the most critical assessment needs for comple-
menting an evidence-based practice paradigm, however, are
for during-treatment performance indicators of treatment
progress and quality control (Barkham et al., 2001). Patient
and provider perspectives on services and progress are not
necessarily the same, of course, so these have been the focus
of several evaluations (Kressel, De Leon, Palij, & Rubin,
2000; Zanis, McLellan, Belding, & Moyer, 1997). As a
result, a multi-disciplinary group of providers, researchers,
managed care representatives, and public policy representa-
tives have recommended that exhaustive lists for perform-
ance indicators be reduced to focus on three domains: (1)
identification of treatment needs, (2) initiation of treatment
admission process, and (3) engagement in treatment services
(Garnick et al., 2002).
The TCU Treatment Model supports the rationale for
these assessments in terms of how they link to one another
over time, as well as how they can serve as dynamic prog-
ress indicators for intervention effectiveness and patient
change relevant to treatment stages. The core treatment
process measurement instrument that evolved from our
work is the TCU Client Evaluation of Self and Treatment
(CEST) which yields indicators of patient functioning
across 16 scales representing four domains—motivation
and psychosocial functioning, treatment engagement, social
support, and ancillary services (see Joe, Broome, Rowan-
Szal, & Simpson, 2002). The CEST is self-administered and
includes brief patient self-evaluations of motivation (desire
for help, treatment readiness, and external pressures),
psychological functioning (self-esteem, depression, anxiety,
decision making, and self-efficacy), social functioning
(hostility, risk taking, and social conformity), specific
services needed and received, treatment satisfaction, level
of rapport with their counselor, their participation in
treatment, peer support (from other patients), and social
support (from family).
These scales have provided the basis for clinical tracking
of patient functioning and engagement throughout the
course of treatment, and when aggregated across represen-
tative samples of patients, they depict program profiles for
problem severity characteristics of the clientele served, level
of therapeutic participation and engagement, service needs,
etc. These records also are sensitive to and diagnostic of
program differences in retention and post-treatment out-
comes, and are being integrated into state-wide networks for
patient and program performance monitoring systems (e.g.,
T. G. Brown, Topp, & Ross, 2003).
8.2. Using patient performance indicators to guide
clinical interventions
Recognizing and implementing evidence-based interven-
tions appropriately staged to patient needs at each con-
ceptual phase of treatment can improve effectiveness. This
is the goal of treatment planning. However, treatment
counselors need a practical navigation system with stream-
lined patient assessments and easy-to-use clinical interpre-
tations of needs and progress that address diagnostic and
treatment planning goals.
The TCU Treatment Model offers a graphic framework
for communicating how these elements fit together for
improving efficiency and effectiveness. It also demonstrates
areas in which treatment developers, evaluation scientists,
and federal agencies have some important work to do. This
includes formulating a structure for recognizing bevidence-
basedQinterventions and assessments, as well as the
promotion of effective dissemination strategies. Intervention
manuals and strategies must be well organized, user-
friendly, prescriptive in procedures and purpose, easily
accessible, and packaged for efficient training and adoption.
In addition, they need to be categorized according to type of
application and purpose, clinical skills required, appropriate
treatment settings, and philosophical assumptions. Assess-
ments to orchestrate this process must be brief, focused,
practical in clinical value, readily interpretable, packaged in
an efficient and user-friendly format, and available for easy
access on demand. It is especially important that assessment
guidelines and patient information systems eliminate mas-
sive redundancies and irrelevancies that now characterize
most states, and that assessment components be linked for
logical applications and automated for common report
generation. And counselors must be trained to use them
efficiently and effectively.
Assessment systems and treatment intervention manuals
(or selected sessions of interest) have become widely
available free of charge via the Internet, and the popular
response to these resources points to the need for their
further development. However, a wider array and better
guides for using Web-based assessment and information
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 111
management tools need to be created, tested, demonstrated,
and made available to programs and their staff. As noted by
Brown and Flynn (2002) as well as Rawson, Marinelli-
Casey, and Ling (2002), federal agencies have unique and
still unmet obligations in these applications. One of the
solutions may be to use Substance Abuse and Mental Health
Services Administration Model Programs for this purpose,
which relies on a standardized review process involving the
National Registry of Effective Program to identify and
disseminate bevidence-basedQtreatment interventions.
8.3. Applying patient and program assessments to
management needs
Improving drug treatment effectiveness requires an
understanding of the dynamic components of therapeutic
process, including patient strengths and deficits, program
participation, therapeutic relationships, psychosocial func-
tioning, and behavioral compliance. As reviewed in this
paper, research has identified several measurable domains
with direct connections to better treatment retention and
outcomes. These findings imply that patient-level reports for
summarizing needs and progress throughout treatment as
well as program-level reports based on aggregated patient
records could improve both clinical care and program
management (Westermeyer, 1989). More specifically, each
patientTs cognitive and behavioral responses to services
can be used to evaluate performance and progress through
successive stages of engagement and recovery (Beutler,
2001; Leon, Kopta, Howard, & Lutz, 1999). At the agency
level, efficient assessment systems that include routine
monitoring of aggregated patient retention (or dropout)
rates, services delivered, drug use (via bioassays), and
therapeutic interactions are feasible for better account-
ability of program functioning, especially with continuing
improvements in information technology in recent years. In
the long run, this can facilitate efforts to match patient needs
with appropriate interventions and manage clinical care
(Simpson, 2002), and it is encouraging to see performance
and outcome monitoring systems now beginning to mature
into reality (see T. G. Brown et al., 2003; Cre`vecoeur,
Finnerty, & Rawson, 2002; Kordy, Hannfver, & Richard,
2001; Schippers, Schramade, & Walburg, 2002; Soldz,
Panas, & Rodriguez-Howard, 2002; Unqtzer, Choi, Cook, &
Oishi, 2002).
Organizational-level assessments are perhaps the most
challenging because they require data to be taken from
individuals within an organization (e.g., leaders, staff,
patients) and then aggregated in ways that represent bthe
organization.QSelection of appropriate scales, data collec-
tion format, reliability and validity of measures, selection or
sampling of individuals to properly represent the organiza-
tion, and methodological alternatives for aggregating data
are issues that require more attention (Hermann & Provost,
2003). These needs are illustrated by the growing number of
studies addressing the relationship of organizational char-
acteristics with access to health services (Alexander et al.,
2003; Timko et al., 2003), and how service delivery and
quality are tied to cost effectiveness and efficiency (Hilton
et al., 2003; Lemak et al., 2003). Long-range implications
involve public accountability and further development of
breport cardsQfor performance comparisons between health
service facilities (Marshall et al., 2003).
At TCU, assessments of organizational needs and func-
tioning have been developed with these applications in
mind. The TCU Organizational Readiness for Change
focuses on organizational traits that predict program
change (Lehman, Greener, & Simpson, 2002). It includes
18 scales from four major domains—motivation, resour-
ces, staff attributes, and climate. Motivational factors in-
clude program needs, training needs, and pressures for
change, while program resources are evaluated in regard
to office facilities, staffing, training, computer equipment,
and e-communications. Organizational dynamics include
scales on staff attributes (growth, efficacy, influence,
adaptability, and clinical orientation) and program climate
(mission, cohesion, autonomy, communication, stress, and
flexibility for change). The TCU Program Training Needs
survey is used for identifying and prioritizing treatment
issues that program staff believe need attention. Its items
are organized into six domains focused on Facilities and
Climate, Satisfaction with Training, Preferences for Training
Content, Preferences for Training Strategy, Barriers to
Training, and Computer Resources. Collectively, this type
of information is intended to help guide overall training
efforts as well as predict the types of innovations that
participating programs are most likely to seek out and adopt.
8.4. Developing organizational strategies for
program improvement
The literature identifies numerous factors involved in
transferring drug treatment research to practice, but
improvement is needed in understanding how to do it
effectively. Therefore, incorporating these factors as ele-
ments into an integrated framework describing how
organizations change could help advance the scientific
progress and practical contributions in this field. Having
an integrated set of assessments for patient, staff, and orga-
nizational functioning dimensions is particularly important
for conducting systematic studies of efforts to disseminate
feasible and effective treatment innovations. By establishing
a general bmodel of program changeQrepresenting major
stages of change and factors that promote or inhibit success,
the process involved can be more readily communicated,
studied, and refined.
Although bchangeQroutinely occurs at both the personal
and organizational levels, making it intentional and positive
requires attention. This is especially true at the organiza-
tional level, which incorporates the collective attitudes,
actions, and relationships of a group of individuals. There is
growing consensus that problems in transferring research
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121112
to practice are more likely to be due to organizational
factors (e.g., leadership attitudes, staff resources, organiza-
tional stress, regulatory and financial pressures, manage-
ment style, tolerance for change) than to how materials
are disseminated.
At the core of this type of heuristic framework are four
action steps typically involved in the process of technology
transfer (Simpson, 2002). Exposure is the first stage, usually
involving training through lecture, self-study, workshops, or
expert consultants. The second stage, adoption, represents
an explicit intention to try an innovation. While this might
be a bformal decisionQmade by program leadership, it also
includes subtle levels of commitments made by individual
staff members at a more personal level about whether an
innovation is appropriate and should be tried. Implementa-
tion comes next, implying that there is a period of trial
usage of the new innovation to allow testing of its feasibility
and potential. Finally, the fourth stage moves to practice,
reflecting the action of incorporating an innovation into
regular use and sustaining it (even if it is in some modified
form). Each stage is subject to barriers and stimulants to
progressive change. Real-world examples of efforts to
transfer innovative treatments into new settings demonstrate
the types of challenges that face adoption of new
medications (Roman & Johnson, 2002; Thomas, Wallack,
Lee, McCarty, & Swift, 2003), comprehensive services for
adolescents (Liddle et al., 2002), and cognitive-based
counseling tools (Dansereau & Dees, 2002).
8.5. Concluding comments
Considerable progress has been made in cracking open
the bblack boxQof substance abuse treatment by partition-
ing the delivery process into dynamic phases of patient
recovery, identifying points of impact for specialized
interventions, and refining assessments for measuring
patient and program functioning. This information can
help operationalize efforts to increase therapeutic engage-
ment and retention, thereby improving patient outcomes.
We must now find ways to enhance the delivery of services
to patients by putting the next generation of clinical
technologies into practice. Since treatment programs are
not equally receptive or responsive to new innovations,
organizational functioning and related barriers should be
examined in terms of the climate for change. Improved
training models must be implemented, including a technical
infrastructure that makes evidence-based materials easily
identified, accessible, user-friendly, and inexpensive (pref-
erably created under the initiative of a federal agency, and
eventually using Internet-based data collection technology).
Simultaneously, program information and management
systems must be improved for better documentation of
patient care and performance.
Anyone who might think these are novel or unrealistic
recommendations could consult new treatment guidelines
that state bOnce a diagnosis has been established, it is
critical to identify the targets of each treatment, to have
outcome measures that gauge the effect of treatment, and
to have realistic expectations about the degrees of improve-
ment that constitute successful treatment.QEmphasis is
placed on developing a treatment plan to reduce or
eliminate symptoms, maximize quality of life and function-
ing, and promote recovery. The focus of these guidelines
next moves to the crucial role of establishing a therapeutic
relationship required for patients to progress successfully
through an acute stage of treatment into phases of
stabilization. Other issues addressed involve co-occurring
disorders, the possibility of multiple treatment episodes, and
various options to consider in regard to treatment strategies
and settings. Interestingly, these excerpts come from the
Executive Summary of Practice Guidelines from the
American Journal of Psychiatry Supplement—not for
substance abuse treatment but on Treatment Recommenda-
tions for Patients with Schizophrenia (American Journal of
Psychiatry, 2004,p.3).
Acknowledgments
The author thanks his senior colleagues (Lois Chatham,
Don Dansereau, and Pat Flynn) at the TCU Institute of
Behavioral Research for their contributions to this con-
ceptualization of drug treatment process, and especially
George Joe who translated concepts about process into
analytic models. Barry Brown, George De Leon, Bennett
Fletcher, Dennis McCarty, and Tom McLellan also provided
insightful editorial and organizational advice. The National
Institute of Drug Abuse (Grant No. R37 DA13093) funded
the work, but interpretations and conclusions do not
necessarily represent the position of NIDA or the U.S.
Department of Health and Human Services. Correspondence
concerning this paper should be addressed to Institute of
Behavioral Research, Texas Christian University, TCU Box
298740, Fort Worth, TX, 76129 U.S.A. More information
(including data collection instruments and intervention
manuals that can be downloaded) is available on the
Internet at www.ibr.tcu.edu and electronic mail can be sent
to ibr@tcu.edu.
References
Ablon, J. S., & Jones, E. E. (2002). Validity of controlled clinical trials of
psychotherapy: Findings from the NIMH Treatment of Depression
Collaborative Research Program. American Journal of Psychiatry,159,
775 – 783.
Ahmed, M., & Boisvert, C. M. (2002). Cognitive skills group treatment for
schizophrenia. Psychiatric Services,53, 1476 – 1477.
Al-Darmaki, F., & Kivlighan Jr., D. M. (1993). Congruence in client-
counselor expectations for relationship and the working alliance.
Journal of Counseling Psychology,40, 379 – 384.
Alexander, J. A., Nahra, T. A., & Wheeler, J. R. C. (2003). Managed care
and access to substance abuse treatment services. Journal of Behavioral
Health Services & Research,30, 161 – 175.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 113
Allison, M., & Hubbard, R. L. (1985). Drug abuse treatment process: A
review of the literature. International Journal of the Addictions,20,
1321 – 1345.
American Journal of Psychiatry. (2004). Practice guideline for the
treatment of patients with schizophrenia, (2nd ed.). Arlington, VA7
American Psychiatric Association.
Anderson, E. M., & Lambert, M. J. (2001). A survival analysis of clinically
significant change in outpatient psychotherapy. Journal of Clinical
Psychology,57, 875 – 888.
Apodaca, T. R., & Miller, W. R. (2003). A meta-analysis of the
effectiveness of bibliotherapy for alcohol problems. Journal of Clinical
Psychology,59, 289 – 304.
Arthur Jr., W., Bennett Jr., W., Edens, P. S., & Bell, S. T. (2003).
Effectiveness of training in organizations: A meta-analysis of design
and evaluation features. Journal of Applied Psychology,88, 234 – 245.
Ashley, O. S., Marsden, M. E., & Brady, T. M. (2003). Effectiveness of
substance abuse treatment programming for women: A review.
American Journal of Drug and Alcohol Abuse,29, 19– 53.
Baekeland, F., & Lundwall, L. (1975). Dropping out of treatment: A critical
review. Psychological Bulletin,82, 738 – 783.
Barkham, M., Margison, F., Leach, C., Lucock, M., Mellor-Clark, J.,
Evans, C., Benson, L., Connell, J., Audin, K., & McGrath, G. (2001).
Service profiling and outcomes benchmarking using the CORE-OM:
Toward practice-based evidence in the psychological therapies. Journal
of Consulting and Clinical Psychology,69, 184 – 196.
Bartholomew, N. G., Chatham, L. R., & Simpson, D. D. (1994). Time Out!
For Me: An assertiveness/sexuality workshop specially designed for
women. Bloomington, IL7Lighthouse Institute Publishing.
Bartholomew, N. G., Hiller, M. L., Knight, K., Nucatola, D. C., & Simpson,
D. D. (2000). Effectiveness of communication and relationship skills
training for men in substance abuse treatment. Journal of Substance
Abuse Treatment,18, 217– 225.
Bartholomew, N. G., Rowan-Szal, G. A., Chatham, L. R., & Simpson, D. D.
(1994). Effectiveness of a specialized intervention for women in a
methadone program. Journal of Psychoactive Drugs,26, 249 – 255.
Bartholomew, N. G., & Simpson, D. D. (1996). Time Out! For Men: A
communication skills and sexuality workshop for men. Bloomington,
IL7Lighthouse Institute Publishing.
Bartholomew, N. G., Simpson, D. D., & Chatham, L. R. (1993). Straight
ahead: Transition skills for recovery. Bloomington, IL7Lighthouse
Institute Publishing.
Battjes, R. J., Gordon, M. S., O’Grady, K. E., Kinlock, T. W., &
Carswell, M. A. (2003). Factors that predict adolescent motivation for
substance abuse treatment. Journal of Substance Abuse Treatment,24,
221 – 232.
Belding, M. A., Iguchi, M. Y., Morral, A. R., & McLellan, A. T. (1997).
Assessing the helping alliance and its impact in the treatment of opiate
dependence. Drug and Alcohol Dependence,48, 51 – 59.
Bell, D. C., Richard, A. J., & Feltz, L. C. (1996). Mediators of drug
treatment outcomes. Addictive Behaviors,21, 597 – 613.
Beutler, L. E. (2001). Comparisons among quality assurance systems: From
outcome assessment to clinical utility. Journal of Consulting and
Clinical Psychology,69, 197 – 204.
Blankenship, J., Dansereau, D. F., & Simpson, D. D. (1999). Cognitive
enhancements of readiness for corrections-based treatment for drug
abuse. The Prison Journal,79, 431 – 445.
Bodenheimer, T., Lorig, K., Holman, H., & Grumbach, K. (2002). Patient
self-management of chronic disease in primary care. Journal of
American Medical Association,288, 2469 – 2474.
Booth, R. E., Crowley, T. J., & Zhang, Y. (1996). Substance abuse
treatment entry, retention and effectiveness: Out-of-treatment opiate
injection drug users. Drug and Alcohol Dependence,42, 11– 20.
Boyle, K., Polinsky, M. L., & Hser, Y. (2000). Resistance to drug abuse
treatment: A comparison of drug users who accept or decline treatment
referral assessment. Journal of Drug Issues,30, 555 – 574.
Broome, K. M., Knight, D. K., Knight, K., Hiller, M. L., & Simpson, D. D.
(1997). Peer, family, and motivational influences on drug treatment
process and recidivism for probationers. Journal of Clinical Psychol-
ogy,53, 387 – 397.
Broome, K. M., Knight, K., Hiller, M. L., & Simpson, D. D. (1996). Drug
treatment process indicators for probationers and prediction of
recidivism. Journal of Substance Abuse Treatment,13, 487 – 491.
Broome, K. M., Knight, K., Joe, G. W., Simpson, D. D., & Cross, D.
(1997). Structural models of antisocial behavior and during-treatment
performance for probationers in a substance abuse treatment program.
Structural Equation Modeling,4, 37 – 51.
Broome, K. M., Simpson, D. D., & Joe, G. W. (1999). Patient and program
attributes related to treatment process indicators in DATOS. Drug and
Alcohol Dependence,57, 127 – 135.
Broome, K. M., Simpson, D. D., & Joe, G. W. (2002). The role of social
support following short-term inpatient treatment. The American Journal
on Addictions,11, 57 – 65.
Brooner, R. K., & Kidorf, M. (2002). Using behavioral reinforcement to
improve methadone treatment participation. Science & Practice
Perspectives,1, 38 – 46.
Brown, B. S. (1997). Staffing patterns and services for the war on drugs. In
J. A. Egertson, D. M. Fox, & A. I. Leshner (Eds.), Treating drug abusers
effectively (pp. 99 – 124). Cambridge, MA7Blackwell Publishers.
Brown, B. S., & Ashery, R. S. (1979). Aftercare in drug abuse programing.
In R. L. DuPont, A. Goldstein, & J. O’Donnell (Eds.), Handbook
on drug abuse (pp. 165 – 173). Washington, DC7U.S. Government
Printing Office.
Brown, B. S., & Flynn, P. M. (2002). The federal role in drug abuse
technology transfer: A history and perspective. Journal of Substance
Abuse Treatment,22, 245 – 257.
Brown, T. G., Topp, J., & Ross, D. (2003). Rationales, obstacles, and
strategies for local outcome monitoring systems in substance abuse
treatment settings. Journal of Substance Abuse Treatment,24, 31 – 42.
Buchanan, T. (2002). Online assessment: Desirable or dangerous? Profes-
sional Psychology: Research and Practice,33, 148 – 154.
Butler, S. F., Budman, S. H., Goldman, R. J., Newman, F. L., Beckly, K. E.,
Trottier, D., & Cacciola, J. S. (2001). Initial validation of a computer-
administered Addiction Severity Index: The ASI-MV. Psychology of
Addictive Behaviors,15, 4 – 12.
Butler, S. F., Newman, F. L., Cacciola, J. S., Frank, A., Budman, S. H.,
McLellan, A. T., Ford, S., Blaine, J., Gastfriend, D. R., Moras, K.,
Salloum, I. M., & Barber, J. P. (1998). Predicting Addiction Severity
Index (ASI) interviewer severity ratings for a computer-administered
ASI. Psychological Assessment,10, 399 – 407.
Butzin, C. A., Martin, S. S., & Inciardi, J. A. (2002). Evaluating component
effects of a prison-based treatment continuum. Journal of Substance
Abuse Treatment,22, 63 – 69.
Caplehorn, J. R. M., Irwig, L., & Saunders, J. B. (1996). Attitudes and
beliefs of staff working in methadone maintenance clinics. Substance
Use & Misuse,31, 437 – 452.
Caplehorn, J. R. M., Lumley, T. S., & Irwig, L. (1998). Staff attitudes and
retention of patients in methadone maintenance programs. Drug and
Alcohol Dependence,52, 57 – 61.
Carey, K. B., Carey, M. P., Maisto, S. A., & Purnine, D. M. (2002). The
feasibility of enhancing psychiatric outpatients’ readiness to change
their substance use. Psychiatric Services,53, 602– 608.
Carey, K. B., Maisto, S. A., Carey, M. P., & Purnine, D. M. (2001).
Measuring readiness-to-change substance misuse among psychiatric
outpatients: I. Reliability and validity of self-report measures. Journal
of Studies on Alcohol,62, 79– 88.
Carey, K. B., Purnine, D. M., Maisto, S. A., & Carey, M. P. (1999).
Assessing readiness to change substance abuse: A critical review of
instruments. Clinical Psychology: Science and Practice,6, 245– 266.
Carise, D., Cornely, W., & Gurel, O. (2002). A successful researcher-
practitioner collaboration in substance abuse treatment. Journal of
Substance Abuse Treatment,23, 157 – 162.
Carlson, M. J., & Gabriel, R. M. (2001). Patient satisfaction, use of
services, and one-year outcomes in publicly funded substance abuse
treatment. Psychiatric Services,52, 1230 – 1236.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121114
Chatham, L. R., & Simpson, D. D. (1994). Delivering and evaluating
outpatient treatment: Finding a practical balance. In B. W. Fletcher,
J. A. Inciardi, & A. M. Horton (Eds.), Drug abuse treatment: The
implementation of innovative approaches (pp. 181 – 193). Westport, CT7
Greenwood Press.
Collier, C. R., Czuchry, M., Dansereau, D. F., & Pitre, U. (2001). The use of
node-link mapping in the chemical dependency treatment of adoles-
cents. Journal of Drug Education,31, 305– 317.
Connors, G. J., Carroll, K. M., DiClemente, C. C., Longabaugh, R., &
Donovan, D. M. (1997). The therapeutic alliance and its relationship
to alcoholism treatment participation and outcome. Journal of Consult-
ing and Clinical Psychology,65, 588 – 598.
Connors, G. J., Donovan, D. M., & DiClemente, C. C. (2001). Substance
abuse treatment and the stages of change. New York7Guilford Press.
Corrigan, J. D., & Schmidt, L. D. (1983). Development and validation
of revisions in the counselor rating form. Journal of Counseling
Psychology,30, 64 – 75.
Cre`vecoeur, D., Finnerty, B., & Rawson, R. A. (2002). Los Angeles
County Evaluation System (LACES): Bringing accountability to alcohol
and drug abuse treatment through a collaboration between providers,
payers, and researchers. Journal of Drug Issues,32, 865 – 880.
Czuchry, M., & Dansereau, D. F. (1999). Node-link mapping and
psychological problems: Perceptions of a residential drug abuse
treatment program for probationers. Journal of Substance Abuse
Treatment,17, 321– 329.
Czuchry, M., & Dansereau, D. F. (2000). Drug abuse treatment in criminal
justice settings: Enhancing community engagement and helpfulness.
American Journal of Drug and Alcohol Abuse,26, 537 – 552.
Czuchry, M., & Dansereau, D. F. (2003). A model of the effects of node-
link mapping on drug abuse counseling. Addictive Behaviors,28,
537 – 549.
Czuchry, M., & Dansereau, D. F. (in press). Using motivational activities to
facilitate treatment involvement and reduce risk. Journal of Psycho-
active Drugs.
Czuchry, M., Dansereau, D. F., Dees, S. M., & Simpson, D. D. (1995). The
use of node-link mapping in drug abuse counseling: The role of
attentional factors. Journal of Psychoactive Drugs,27, 161 – 166.
Czuchry, M., Sia, T. L., Dansereau, D. F., & Dees, S. M. (1997). Downward
Spiral: A pedagogical game depicting the dangers of substance abuse.
Journal of Drug Education,27, 373– 387.
Dansereau, D. F., & Dees, S. M. (2002). Mapping training: The transfer of a
cognitive technology for improving counseling. Journal of Substance
Abuse Treatment,22, 219– 230.
Dansereau, D. F., Dees, S. M., Greener, J. M., & Simpson, D. D. (1995).
Node-link mapping and the evaluation of drug abuse counseling
sessions. Psychology of Addictive Behaviors,9, 195– 203.
Dansereau, D. F., Evans, S. H., Czuchry, M., & Sia, T. L. (2003). Readiness
and mandated treatment: Development and application of a functional
model. Offender Substance Abuse Report, 3, 1–2, 13 – 16.
Dansereau, D. F., Joe, G. W., Dees, S. M., & Simpson, D. D. (1996).
Ethnicity and the effects of mapping-enhanced drug abuse counseling.
Addictive Behaviors,21, 363 – 376.
Dansereau, D. F., Joe, G. W., & Simpson, D. D. (1993). Node-link
mapping: A visual representation strategy for enhancing drug abuse
counseling. Journal of Counseling Psychology,40, 385 – 395.
Dansereau, D. F., & Newbern, D. (1997). Using knowledge maps to
enhance teaching. In W. E. Campbell, & K. A. Smith (Eds.),
New paradigms for college teaching (pp. 127 – 147). Edina, MN7
Interaction Book Co.
D’Aunno, T. A., & Vaughn, T. E. (1995). An organizational analysis of
service patterns in outpatient drug abuse treatment units. Journal of
Substance Abuse,7, 27 – 42.
De Civita, M., Dobkin, P. L., & Robertson, E. (2000). A study of barriers to
the engagement of significant others in adult addiction treatment.
Journal of Substance Abuse Treatment,19, 135 – 144.
De Leon, G. (2000). The therapeutic community: Theory, model and
method. New York7Springer Publishing Company.
De Leon, G., Hawke, J., Jainchill, N., & Melnick, G. (2000). Therapeutic
communities: Enhancing retention in treatment using bsenior professorQ
staff. Journal of Substance Abuse Treatment,19, 375 – 382.
De Leon, G., Holland, S., & Rosenthal, M. S. (1972). Phoenix House:
Criminal activity of dropouts. Journal of the American Medical
Association,222, 686 – 689.
De Leon, G., Inciardi, J. A., & Martin, S. S. (1995). Residential drug abuse
treatment research: Are conventional control designs appropriate for
assessing treatment effectiveness? Journal of Psychoactive Drugs,27,
85 – 91.
De Leon, G., & Jainchill, N. (1986). Circumstance, motivation, readiness,
and suitability as correlates of treatment tenure. Journal of Psychoactive
Drugs,18, 203 – 208.
De Leon, G., Jainchill, N., & Wexler, H. K. (1982). Success and
improvement rates 5 years after treatment in a therapeutic community.
International Journal of the Addictions,17, 703– 747.
De Leon, G., Melnick, G., & Tims, F. M. (2001). The role of motivation
and readiness in treatment and recovery. In F. M. Tims, C. G. Leukefeld,
& J. J. Platt (Eds.), Relapse and recovery processes in the addictions
(pp. 143 – 171). New Haven, CT7Yale University Press.
De Leon, G., & Rosenthal, M. S. (1979). Therapeutic communities. In R.
L. DuPont, A. Goldstein, & J. O’Donnell (Eds.), Handbook on drug
abuse (pp. 39 47). Washington, DC7U. S. Government Printing
Office.
De Weert-Van Oene, G. H., Schippers, G. M., De Jong, C. A. J., &
Schrijvers, G. J. P. (2002). Motivation for treatment in substance-
dependent patients. European Addiction Research,8,29.
Deitch, D. A. (1979). Program management: Magical expectations and
harsh realities. In R. L. DuPont, A. Goldstein, & J. O’Donnell (Eds.),
Handbook on drug abuse (pp. 409 – 414). Washington, DC7U.S.
Government Printing Office.
Dennis, M., Scott, C. K., & Funk, R. (2003). An experimental
evaluation of recovery management checkups (RMC) for people
with chronic substance use disorders. Evaluation and Program
Planning,26, 339 – 352.
Diamond, G. M., Diamond, G. S., & Liddle, H. A. (2000). The therapist-
parent alliance in family-based therapy for adolescents. Journal of
Clinical Psychology,56, 1037 – 1050.
DiClemente, C. C., & Prochaska, J. O. (1998). Toward a comprehensive
transtheoretical model of change: Stages of change and addictive
behaviors. In W. R. Miller, & N. Heather (Eds.), Treating addictive
behaviors, (2nd ed., pp.3–24). New York7Plenum.
Epstein, D. H., Hawkins, W. E., Covi, L., Umbricht, A., & Preston, K. L.
(2003). Cognitive-behavioral therapy plus contingency management for
cocaine use: Findings during treatment and across 12-month follow-up.
Psychology of Addictive Behaviors,17, 73 – 82.
Epstein, M. H., Nordness, P. D., Kutash, K., Duchnowski, A., Schrepf, S.,
Benner, G. J., & Nelson, J. R. (2003). Assessing the wraparound
process during family planning meetings. Journal of Behavioral Health
Services & Research,30, 352 – 362.
Erikson, E. H. (1963). Childhood and society, (Rev. ed.). New York7
Norton.
Etheridge, R. M., Craddock, S. G., Dunteman, G. H., & Hubbard, R. L.
(1995). Treatment services in two national studies of community-
based drug abuse treatment programs. Journal of Substance Abuse,7,
9 – 26.
Etheridge, R. M., Hubbard, R. L., Anderson, J., Craddock, S. G., & Flynn,
P. M. (1997). Treatment structure and program services in the Drug
Abuse Treatment Outcome Study (DATOS). Psychology of Addictive
Behaviors,11, 244 – 260.
Fals-Stewart, W., & Lucente, S. (1994). Effect of neurocognitive status and
personality functioning on length of stay in residential substance abuse
treatment: An integrative study. Psychology of Addictive Behaviors,8,
179 – 190.
Farabee, D., Nelson, R., & Spence, R. (1993). Psychosocial profiles of
criminal justice- and noncriminal justice-referred substance abusers in
treatment. Criminal Justice and Behavior,20, 336 – 346.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 115
Farabee, D., Prendergast, M., Cartier, J., Wexler, H. K., Knight, K., &
Anglin, M. D. (1999). Barriers to implementing effective correctional
drug treatment programs. The Prison Journal,79, 150– 162.
Farabee, D., Rawson, R., & McCann, M. (2002). Adoption of drug
avoidance activities among patients in contingency management and
cognitive-behavioral treatments. Journal of Substance Abuse Treat-
ment,23, 343 – 350.
Farabee, D., Simpson, D. D., Dansereau, D. F., & Knight, K. (1995).
Cognitive inductions into treatment among drug users on probation.
Journal of Drug Issues,25, 669 – 682.
Fiorentine, R., & Anglin, M. D. (1997). Does increasing the opportunity for
counseling increase the effectiveness of outpatient drug treatment?
American Journal of Drug and Alcohol Abuse,23, 369 – 382.
Fiorentine, R., & Hillhouse, M. P. (2000). Drug treatment and 12-step
program participation: The additive effects of integrated recovery
activities. Journal of Substance Abuse Treatment,18, 65 – 74.
Flynn, P. M., Craddock, S. G., Hubbard, R. L., Anderson, J., & Etheridge,
R. M. (1997). Methodological overview and research design for the
Drug Abuse Treatment Outcome Study (DATOS). Psychology of
Addictive Behaviors,11, 230 – 243.
Foote, J., DeLuca, A., Magura, S., Warner, A., Grand, A., Rosenblum,
A., & Stahl, S. (1999). A group motivational treatment for
chemical dependency. Journal of Substance Abuse Treatment,17,
181 – 192.
Friedmann, P. D., Alexander, J. A., & D’Aunno, T. A. (1999).
Organizational correlates of access to primary care and mental health
services in drug abuse treatment units. Journal of Substance Abuse
Treatment,16, 71– 80.
Friedmann, P. D., Lemon, S. C., Durkin, E. M., & D’Aunno, T. A. (2003).
Trends in comprehensive service availability in outpatient drug abuse
treatment. Journal of Substance Abuse Treatment,24, 81 – 88.
Gainey, R. R., Catalano, R. F., Haggerty, K. P., & Hoppe, M. J. (1995).
Participation in a parent training program for methadone clients.
Addictive Behaviors,20, 117 – 125.
Gallon, S. L., Gabriel, R. M., & Knudsen, J. R. W. (2003). The toughest
job you’ll ever love: A Pacific Northwest Treatment Workforce survey.
Journal of Substance Abuse Treatment,24, 183 – 196.
Garnick, D. W., Lee, M. T., Chalk, M., Gastfriend, D., Horgan, C. M.,
McCorry, F., McLellan, A. T., & Merrick, E. L. (2002). Establishing the
feasibility of performance measures for alcohol and other drugs.
Journal of Substance Abuse Treatment,23, 375 – 385.
Garrett, J., Landau-Stanton, J., Stanton, M. D., Stellato-Kabat, J., &
Stellato-Kabat, D. (1997). ARISE: A method for engaging reluctant
alcohol- and drug-dependent individuals in treatment. Journal of
Substance Abuse Treatment,14, 235 – 248.
Gerstein, D. R., & Harwood, H. J. (Eds.) (1990). Treating drug problems.
Vol. 1. A study of evolution, effectiveness, and financing of public and
private drug treatment systems. Washington, DC7National Academy
Press.
Goldfried, M. R., & Wolfe, B. E. (1996). Psychotherapy practice and
research: Repairing a strained alliance. American Psychologist,51,
1007 – 1016.
Gossop, M., Marsden, J., Stewart, D., & Kidd, T. (2003). The National
Treatment Outcome Research Study (NTORS): 4–5 year follow-up
results. Addiction,98, 291 – 303.
Gossop, M., Marsden, J., Stewart, D., & Rolfe, A. (1999). Treatment
retention and 1 year outcomes for residential programmes in
England. Drug and Alcohol Dependence,57, 89 – 98.
Gossop, M., Stewart, D., & Marsden, J. (2003). Treatment process
components and heroin use outcome among methadone patients. Drug
and Alcohol Dependence,71, 93–102.
Gottheil, E., Sterling, R. C., & Weinstein, S. P. (1997). Outreach
engagement efforts: Are they worth the effort? American Journal of
Drug and Alcohol Abuse,23, 61 – 66.
Gottheil, E., Thornton, C., & Weinstein, S. P. (2002). Effectiveness of high
versus low structure individual counseling for substance abuse. The
American Journal on Addictions,11, 279 – 290.
Graham, A. W., & Fleming, M. S. (1998). Brief interventions: Practical
problems in clinical practice intervening in the continuum of use
integrating brief interventions into primary care. In A. W. Graham, &
T. K. Schultz (Eds.), Principles of addiction medicine (2nd ed., pp.
615–630). Chevy Chase, MD7American Society of Addiction
Medicine, Inc.
Grella, C. E., Hser, Y., & Hsieh, S. (2003). Predictors of drug treatment re-
entry following relapse to cocaine use in DATOS. Journal of Substance
Abuse Treatment,25, 145 – 154.
Grella, C. E., Joshi, V., & Hser, Y. (2003). Followup of cocaine-dependent
men and women with antisocial personality disorder. Journal of
Substance Abuse Treatment,25, 155 – 164.
Griffith, J. D., Hiller, M. L., Knight, K., & Simpson, D. D. (1999). A cost-
effectiveness analysis of in-prison therapeutic community treatment
and risk classification. The Prison Journal,79, 352 – 368.
Griffith, J. D., Knight, D. K., Joe, G. W., & Simpson, D. D. (1998).
Implications of family and peer relations for treatment engagement
and follow-up outcomes: An integrative model. Psychology of
Addictive Behaviors,12, 113 – 126.
Griffith, J. D., Rowan-Szal, G. A., Roark, R. R., & Simpson, D. D. (2000).
Contingency management in outpatient methadone treatment: A meta-
analysis. Drug and Alcohol Dependence,58, 55 – 66.
Heinrich, C. J., & Lynn Jr., L. E. (2002). Improving the organization,
management, and outcomes of substance abuse treatment programs.
American Journal of Drug and Alcohol Abuse,28, 601 – 622.
Henggeler, S. W., Schoenwald, S. K., Borduin, C. M., Rowland, M. D.,
& Cunningham, P. B. (1998). Multisystemic treatment of antisocial
behavior in children and adolescents. New York7Guilford.
Heppner, P. P., Rosenberg, J. I., & Hedgespeth, J. (1992). Three methods in
measuring the therapeutic process: Clients’ and counselors’ construc-
tions of the therapeutic process versus actual therapeutic events. Journal
of Counseling Psychology,39, 20 – 31.
Hermann, R. C., & Provost, S. (2003). Interpreting measurement data for
quality improvement: Standards, means, norms, and benchmarks.
Psychiatric Services,54, 655 – 657.
Higgins, S. T., Alessi, S. M., & Dantona, R. L. (2002). Voucher-based
incentives: A substance abuse treatment innovation. Addictive Behav-
iors,27, 887 – 910.
Higgins, S. T., Budney, A. J., Bickel, W. K., Foerg, F., Donham, R., &
Badger, G. (1994). Incentives improve outcome in outpatient
behavioral treatment of cocaine dependence. Archives of General
Psychiatry,51, 568 – 576.
Hill, C. E., & Corbett, M. M. (1993). A perspective on the history of
process and outcome research in counseling psychology. Journal of
Counseling Psychology,40, 3 – 24.
Hiller, M. L., Knight, K., Leukefeld, C., & Simpson, D. D. (2002).
Motivation as a predictor of therapeutic engagement in mandated
residential substance abuse treatment. Criminal Justice and Behavior,
29, 56 – 75.
Hiller, M. L., Knight, K., Rao, S. R., & Simpson, D. D. (2002). Assessing
and evaluating mandated correctional substance-abuse treatment. In
C. G. Leukefeld, F. M. Tims, & D. Farabee (Eds.), Treatment of drug
offenders: Policies and issues (pp. 41 56). New York7Springer.
Hiller, M. L., Narevic, E., Webster, J. M., Rosen, P., Staton, M., Leukefeld,
C., Garrity, T. F., & Kayo, R. (2003). Problem severity and motivation
for treatment in incarcerated substance abusers. Manuscript submitted
for publication.
Hiller, M. L., Rowan-Szal, G. A., Bartholomew, N. G., & Simpson,
D. D. (1996). Effectiveness of a specialized women’s intervention
in a residential treatment program. Substance Use & Misuse,31,
771 – 783.
Hillhouse, M. P., & Fiorentine, R. (2001). 12-Step program participation
and effectiveness: Do gender and ethnic differences exist? Journal of
Drug Issues,31, 767 – 780.
Hilton, M. E., Fleming, M., Glick, H., Gutman, M. A., Lu, Y., McKay,
J., McLellan, A. T., Manning, W., Meadows, J., Mertens, J. R.,
Moore, C., Mullahy, J., Mundt, M., Parthasarathy, S., Polsky, D.,
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121116
Ray, G. T., Sterling, S., & Weisner, C. (2003). Services integration
and cost-effectiveness. Alcoholism: Clinical and Experimental
Research,27, 271 – 280.
Hoffman, J. A., Caudill, B. D., Koman, J. J., Luckey, J. W., Flynn, P. M., &
Hubbard, R. L. (1994). Comparative cocaine abuse treatment strategies:
Enhancing client retention and treatment exposure. Journal of Addictive
Diseases,13, 115 – 128.
Holmes, S. E., & Kivlighan Jr., D. M. (2000). Comparison of therapeutic
factors in group and individual treatment processes. Journal of
Counseling Psychology,47, 478 – 484.
Horvath, A. O., & Greenberg, L. S. (1989). Development and validation of
the Working Alliance Inventory. Journal of Counseling Psychology,36,
223 – 233.
Horvath, A. O., & Symonds, B. D. (1991). Relation between working
alliance and outcome in psychotherapy: A meta-analysis. Journal of
Counseling Psychology,38, 139 – 149.
Howard, K. I., Moras, K., Brill, P. B., Martinovich, Z., & Lutz, W. (1996).
Evaluation of psychotherapy: Efficacy, effectiveness, and patient prog-
ress. American Psychologist,51, 1059 – 1064.
Hser, Y., Polinsky, M. L., Maglione, M., & Anglin, M. D. (1999).
Matching clients’ need with drug treatment services. Journal of
Substance Abuse Treatment,16, 299 – 305.
Hubbard, R. L., Craddock, S. G., & Anderson, J. (2003). Overview of 5-year
followup outcomes in the Drug Abuse Treatment Outcome Studies
(DATOS). Journal of Substance Abuse Treatment,25, 125 – 134.
Hubbard, R. L., Marsden, M. E., Rachal, J. V., Harwood, H. J., Cavanaugh,
E. R., & Ginzburg, H. M. (1989). Drug abuse treatment: A
natural study of effectiveness. Chapel Hill7University of North
Carolina Press.
Humphreys, K., Noke, J. M., & Moos, R. H. (1996). Recovering substance
abuse staff members’ beliefs about addiction. Journal of Substance
Abuse Treatment,13, 75– 78.
Institute of Medicine. (1996). Pathways of addiction: Opportunities in
drug abuse research. Washington, DC7National Academy Press.
Jaffe, J. H. (1979). The swinging pendulum: The treatment of drug users
in America. In R. L. DuPont, A. Goldstein, & J. O’Donnell (Eds.),
Handbook on drug abuse (pp. 3 – 16). Washington, DC7U.S. Govern-
ment Printing Office.
Joe, G. W., Broome, K. M., Rowan-Szal, G. A., & Simpson, D. D. (2002).
Measuring patient attributes and engagement in treatment. Journal
of Substance Abuse Treatment,22, 183 – 196.
Joe, G. W., Dansereau, D. F., Pitre, U., & Simpson, D. D. (1997).
Effectiveness of node-link mapping enhanced counseling for opiate
addicts: A 12-month posttreatment follow-up. Journal of Nervous
and Mental Disease,185, 306 – 313.
Joe, G. W., Simpson, D. D., & Broome, K. M. (1998). Effects of readiness
for drug abuse treatment on client retention and assessment of process.
Addiction,93, 1177 – 1190.
Joe, G. W., Simpson, D. D., & Broome, K. M. (1999). Retention and patient
engagement models for different treatment modalities in DATOS. Drug
and Alcohol Dependence,57, 113 – 125.
Joe, G. W., Simpson, D. D., Dansereau, D. F., & Rowan-Szal, G. A.
(2001). Relationships between counseling rapport and drug abuse
treatment outcomes. Psychiatric Services,52, 1223 – 1229.
Joe, G. W., Simpson, D. D., Greener, J. M., & Rowan-Szal, G. A.
(1999). Integrative modeling of client engagement and outcomes
during the first 6 months of methadone treatment. Addictive
Behaviors,24, 649 – 659.
Joe, G. W., Simpson, D. D., Greener, J. M., & Rowan-Szal, G. A. (in press).
Development and validation of a client problem profile and index for
drug treatment. Psychological Reports.
Joe, G. W., Simpson, D. D., & Hubbard, R. L. (1991). Treatment predictors
of tenure in methadone maintenance. Journal of Substance Abuse,3,
73 – 84.
Joe, G. W., Simpson, D. D., & Rowan-Szal, G. A. (in press). Interaction of
counseling rapport and topics discussed in sessions with methadone
clients. Substance Use & Misuse.
Joe, G. W., Simpson, D. D., & Sells, S. B. (1994). Treatment process and
relapse to opioid use during methadone maintenance. American Journal
of Drug and Alcohol Abuse,20, 173– 197.
Kasarabada, N. D., Hser, Y., Boles, S. M., & Huang, Y. (2002). Do patients’
perceptions of their counselors influence outcomes of drug treatment?
Journal of Substance Abuse Treatment,23, 327 – 334.
Kasarabada, N. D., Hser, Y., Parker, L., Hall, E., Anglin, M. D., & Chang,
E. (2001). A self-administered instrument for assessing therapeutic
approaches of drug-user treatment counselors. Substance Use & Misuse,
36, 273 – 299.
Katz, E. C., Brown, B. S., Schwartz, R. P., Weintraub, E., Barksdale, W., &
Robinson, R. (2004). Role induction: A method for enhancing early
retention in outpatient drug-free treatment. Journal of Consulting and
Clinical Psychology,72, 227–234.
Kivlighan Jr., D. M., & Schmitz, P. J. (1992). Counselor technical activity
in cases with improving working alliances and continuing-poor working
alliances. Journal of Counseling Psychology,39, 32 – 38.
Kleber, H. D., & Slobetz, F. (1979). Outpatient drug-free treatment. In R. L.
Dupont, A. Goldstein, & J. O’Donnell (Eds.), Handbook on drug abuse
(pp. 31 38). Washington, DC7U.S. Government Printing Office.
Knight, D. K., Cross, D. R., Giles-Sims, J., & Simpson, D. D. (1995).
Psychosocial functioning among adult drug users: The role of
parental absence, support, and conflict. International Journal of the
Addictions,30, 1271 – 1288.
Knight, D. K., Joe, G. W., & Simpson, D. D. (2003). Social relationships
and treatment process for women in residential substance abuse
treatment. Manuscript submitted for publication.
Knight, D. K., & Simpson, D. D. (1996). Influences of family and friends
on client progress during drug abuse treatment. Journal of Substance
Abuse,8, 417 – 429.
Knight, K., Hiller, M. L., Broome, K. M., & Simpson, D. D. (2000). Legal
pressure, treatment readiness, and engagement in long-term residential
programs. Journal of Offender Rehabilitation,31, 101 – 115.
Knight, K., Simpson, D. D., Chatham, L. R., & Camacho, L. M. (1997). An
assessment of prison-based drug treatment: Texas’ in-prison therapeutic
community program. Journal of Offender Rehabilitation,24, 75 – 100.
Knight, K., Simpson, D. D., & Dansereau, D. F. (1994). Knowledge
mapping: A psychoeducational tool in drug abuse relapse prevention
training. Journal of Offender Rehabilitation,20, 187– 205.
Knight, K., Simpson, D. D., & Hiller, M. L. (1999). Three-year re-
incarceration outcomes for in-prison therapeutic community treatment
in Texas. The Prison Journal,79, 337 – 351.
Kolden, G. G. (1996). Change in early sessions of dynamic therapy:
Universal processes and the generic model of psychotherapy. Journal
of Consulting and Clinical Psychology,64, 489 – 496.
Kordy, H., Hannfver, W., & Richard, M. (2001). Computer-assisted
feedback-driven quality management for psychotherapy: The Stuttgart-
Heidelberg Model. Journal of Consulting and Clinical Psychology,69,
173 – 183.
Krause, M. S., & Howard, K. I. (2003). What random assignment does and
does not do. Journal of Clinical Psychology,59, 751 – 766.
Kressel, D., De Leon, G., Palij, M., & Rubin, G. (2000). Measuring client
clinical progress in therapeutic community treatment: The therapeutic
community Client Assessment Inventory, Client Assessment Summary,
and Staff Assessment Summary. Journal of Substance Abuse Treatment,
19, 267 – 272.
Lamb, S., Greenlick, M. R., & McCarty, D. (1998). Bridging the gap
between practice and research: Forging partnerships with community-
based drug and alcohol treatment. Washington, DC7National Academy
Press.
Lambert, M. J., Hansen, N. B., & Finch, A. E. (2001). Patient-focused
research: Using patient outcome data to enhance treatment effects.
Journal of Consulting and Clinical Psychology,69, 159 – 172.
Landau, J., Garrett, J., Shea, R. R., Stanton, M. D., Brinkman-Sull, D., &
Baciewicz, G. (2000). Strength in numbers: The ARISE method for
mobilizing family and network to engage substance abusers in treatment.
American Journal of Drug and Alcohol Abuse,26, 379 – 398.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 117
Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth
ten thousand words. Cognitive Science,11, 65 – 99.
Lehman, W. E. K., Greener, J. M., & Simpson, D. D. (2002). Assessing
organizational readiness for change. Journal of Substance Abuse
Treatment,22, 197– 209.
Lemak, C. H., Alexander, J. A., & Campbell, C. (2003). Administrative
burden and its implications for outpatient substance abuse treatment
organizations. Psychiatric Services,54, 705 – 711.
Lemke, S., & Moos, R. H. (2002). Prognosis of older patients in mixed-age
alcoholism treatment programs. Journal of Substance Abuse Treatment,
22, 33 – 43.
Leon, S. C., Kopta, S. M., Howard, K. I., & Lutz, W. (1999). Predicting
patients’ responses to psychotherapy: Are some more predictable than
others? Journal of Consulting and Clinical Psychology,67, 698 – 704.
Leonhard, C., Mulvey, K., Gastfriend, D. R., & Shwartz, M. (2000). The
Addiction Severity Index: A field study of internal consistency and
validity. Journal of Substance Abuse Treatment,18, 129 – 135.
Liddle, H. A., Rowe, C. L., Diamond, G. M., Sessa, F., Schmidt, S., &
Ettinger, D. (2000). Towards a developmental family therapy: The
clinical utility of adolescent development research. Journal of Marital
and Family Therapy,26, 491 – 505.
Liddle, H. A., Rowe, C. L., Quille, T. J., Dakof, G. A., Mills, D. S., Sakran,
E., & Biaggi, H. (2002). Transporting a research-based adolescent drug
treatment into practice. Journal of Substance Abuse Treatment,22,
231 – 243.
Longshore, D., Grills, C., Anglin, M. D., & Annon, K. (1997). Desire for
help among African-American drug users. Journal of Drug Issues,27,
755 – 770.
Luborsky, L., McLellan, A. T., Diguer, L., Woody, G., & Seligman, D. A.
(1997). The psychotherapist matters: Comparison of outcomes across
twenty-two therapists and seven patient samples. Clinical Psychology:
Science and Practice,4, 53 – 65.
Luborsky, L., McLellan, A. T., Woody, G. E., O’Brien, C. P., & Auerbach,
A. (1985). Therapist success and its determinants. Archives of General
Psychiatry,42, 602 – 611.
Magura, S., Rosenblum, A., Fong, C., Villano, C., & Richman, B. (2002).
Treating cocaine-using methadone patients: Predictors of outcomes in a
psychosocial clinical trial. Substance Use & Misuse,37, 1927– 1955.
Mallinckrodt, B. (1991). Clients’ representations of childhood emotional
bonds with parents, social support, and formation of the working
alliance. Journal of Counseling Psychology,38, 401 – 409.
Mallinckrodt, B. (1993). Session impact, working alliance, and treatment
outcome in brief counseling. Journal of Counseling Psychology,40,
25 – 32.
Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention. New York7
Guilford.
Marshall, M. N., Shekelle, P. G., Davies, H. T. O., & Smith, P. C. (2003).
Public reporting on quality in the United States and the United
Kingdom. Health Affairs,22, 134 – 148.
Martin, D. J., Garske, J. P., & Davis, M. K. (2000). Relation of the
therapeutic alliance with outcome and other variables: A meta-
analytic review. Journal of Consulting and Clinical Psychology,68,
438 – 450.
Martin, S. S., Butzin, C. A., Saum, C. A., & Inciardi, J. A. (1999). Three-
year outcomes of therapeutic community treatment for drug-involved
offenders in Delaware: From prison to work release to aftercare. The
Prison Journal,79, 294 – 320.
Martin, S. S., O’Connell, D. J., Inciardi, J. A., Surratt, H. L., & Beard, R. A.
(2003). HIV/AIDS among probationers: An assessment of risk and
results from a brief intervention. Journal of Psychoactive Drugs,35,
435 – 443.
McCaughrin, W. C., & Howard, D. L. (1996). Variation in access to
outpatient substance abuse treatment: Organizational factors and
conceptual issues. Journal of Substance Abuse,8, 403 – 415.
McGlothlin, W. H. (1979). Criminal justice clients. In R. L. Du-
Pont, A. Goldstein, & J. O’Donnell (Eds.), Handbook on drug abuse
(pp. 203 – 209). Washington, DC7U.S. Government Printing Office.
McLellan, A. T. (2002). Have we evaluated addiction treatment
correctly? Implications from a chronic care perspective. Addiction,
97, 249 – 252.
McLellan, A. T., Alterman, A. I., Metzger, D. S., Grissom, G. R., Woody,
G. E., Luborsky, L., & O’Brien, C. P. (1994). Similarity of outcome
predictors across opiate, cocaine, and alcohol treatments: Role of
treatment services. Journal of Consulting and Clinical Psychology,62,
1141– 1158.
McLellan, A. T., Arndt, I. O., Metzger, D. S., Woody, G. E., & O’Brien,
C. P. (1993). The effects of psychosocial services in substance
abuse treatment. Journal of the American Medical Association,269,
1953 – 1959.
McLellan, A. T., Grissom, G. R., Brill, P., Durell, J., Metzger, D. S., &
O’Brien, C. P. (1993). Private substance abuse treatments: Are some
programs more effective than others? Journal of Substance Abuse
Treatment,10, 243– 254.
McLellan, A. T., Grissom, G. R., Zanis, D. A., Randall, M., Brill, P., &
O’Brien, C. P. (1997). Problem-service dmatchingTin addiction treat-
ment: A prospective study in 4 programs. Archives of General
Psychiatry,54, 730 – 735.
McLellan, A. T., Hagan, T. A., Levine, M., Gould, F., Meyers, K.,
Bencivengo, M., & Durell, J. (1998). Supplemental social services
improve outcomes in public addiction treatment. Addiction,93,
1489 – 1499.
McLellan, A. T., Hagan, T. A., Levine, M., Meyers, K., Gould, F.,
Bencivengo, M., Durell, J., & Jaffe, J. H. (1999). Does clinical case
management improve outpatient addiction treatment. Drug and Alcohol
Dependence,55, 91 – 103.
McLellan, A. T., Kushner, H., Metzger, D., Peters, R., Smith, I., Grisson, G.,
Pettinati, H., & Argeriou, M. (1992). The fifth edition of the Addiction
Severity Index. Journal of Substance Abuse Treatment,9, 199 – 213.
McLellan, A. T., Lewis, D. C., O’Brien, C. P., & Kleber, H. D. (2000). Drug
dependence, a chronic medical illness: Implications for treatment,
insurance, and outcomes evaluation. Journal of the American Medical
Association,284, 1689 – 1695.
McLellan, A. T., Woody, G. E., Luborsky, L., & Goehl, L. (1988). Is the
counselor an bactive ingredientQin substance abuse rehabilitation? An
examination of treatment success among four counselors. Journal of
Nervous and Mental Disease,176, 423– 430.
McLellan, A. T., Woody, G. E., Metzger, D., McKay, J., Durell, J.,
Alterman, A. I., & O’Brien, C. P. (1997). Evaluating the effective-
ness of addiction treatments: Reasonable expectations, appropriate
comparisons. In J. A. Egertson, D. M. Fox, & A. I. Leshner (Eds.),
Treating drug abusers effectively (pp. 7 – 40). Cambridge, MA7Black-
well Publishers of North America.
Mee-Lee, D. (Ed.) (2001). ASAM Patient placement criteria for the
treatment of substance-related disorders (2nd rev. ed.). Chevy Chase,
MD7American Society of Addiction Medicine.
Melnick, G., De Leon, G., Thomas, G., Kressel, D., & Wexler, H. K.
(2001). Treatment process in prison therapeutic communities: Motiva-
tion, participation, and outcome. American Journal of Drug and
Alcohol Abuse,27, 633 – 650.
Melville, C. L., Davis, C. S., Matzenbacher, D. L., & Clayborne, J. (2004).
Node-link mapping-enhanced group treatment for pathological gam-
bling. Addictive Behaviors,29, 73 – 87.
Messer, S. B. (2002). Empirically supported treatments: Cautionary notes.
[Retrieved March 10, 2003] Available: http://www.medscape.com/
viewarticle/445082_print.
Meyers, R. J., Miller, W. R., Smith, J. E., & Tonigan, J. S. (2002). A
randomized trial of two methods for engaging treatment-refusing drug
users through concerned significant others. Journal of Consulting and
Clinical Psychology,70, 1182 – 1185.
Miller, W. R. (1985). Motivation for treatment: A review with special
emphasis on alcoholism. Psychological Bulletin,98, 84 – 107.
Miller, W. R. (1989). Increasing motivation for change. In R. K. Hester &
W. R. Miller (Eds.), Handbook of alcoholism treatment approaches:
Effective alternatives (pp. 67 80). New York7Pergamon Press.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121118
Miller, W. R. (1996). Motivational interviewing: Research, practice, and
puzzles. Addictive Behaviors,21, 835 – 842.
Miller, W. R. (2000). Rediscovering fire: Small interventions, large effects.
Psychology of Addictive Behaviors,14, 6– 18.
Miller, W. R. (2003). A collaborative approach to working with families.
Addiction,98,56.
Miller, W. R., Meyers, R. J., & Tonigan, J. S. (1999). Engaging the
unmotivated in treatment for alcohol problems: A comparison of three
strategies for intervention through family members. Journal of
Consulting and Clinical Psychology,67, 688 – 697.
Miller, W. R., & Rollnick, S. (1991). Motivational interviewing: Preparing
people to change addictive behavior. New York7Guilford Press.
Miller, W. R., & Rollnick, S. (2002). Motivational interviewing: Preparing
people for change (2nd ed.). New York7Guilford Press.
Moos, R. H. (2003). Addictive disorders in context: Principles and puzzles
of effective treatment and recovery. Psychology of Addictive Behaviors,
17, 3 – 12.
Moos, R. H., Finney, J. W., & Moos, B. S. (2000). Inpatient substance
abuse care and the outcome of subsequent community residential and
outpatient care. Addiction,95, 833 – 846.
Moos, R. H., King, M. J., Burnett, E. B., & Andrassy, J. M. (1997).
Community residential program policies, services, and treatment
orientations influence patients’ participation in treatment. Journal of
Substance Abuse,9, 171 – 187.
Moos, R. H., Pettit, B., & Gruber, V. (1995). Longer episodes of
community residential care reduce substance abuse patients’ readmis-
sion rates. Journal of Studies on Alcohol,56, 433 – 443.
Morral, A. R., Belding, M. A., & Iguchi, M. Y. (1999). Identifying
methadone maintenance clients at risk for poor treatment response:
Pretreatment and early progress indicators. Drug and Alcohol Depend-
ence,55, 25 – 33.
Moyer, A., & Finney, J. W. (2002). Randomized versus nonrandomized
studies of alcohol treatment: Participants, methodological features and
posttreatment functioning. Journal of Studies on Alcohol,63, 542 – 550.
National Institute on Drug Abuse. (1999). Principles of drug addiction
treatment: A research-based guide (NIH Publication No. 99–4180).
Bethesda, MD7National Institutes of Health.
Newbern, D., Dansereau, D. F., & Dees, S. M. (1997). Node-link mapping
in substance abuse: Probationers’ ratings of group counseling. Journal
of Offender Rehabilitation,25, 83– 95.
Norcross, J. C., & Goldfried, M. R. (1992). Handbook of psychotherapy
integration. New York7Basic Books.
Nwakeze, P. C., Magura, S., & Rosenblum, A. (2002). Drug problem
recognition, desire for help, and treatment readiness in a soup kitchen
population. Substance Use & Misuse,37, 291– 312.
Obert, J. L., London, E. D., & Rawson, R. A. (2002). Incorporating brain
research findings into standard treatment: An example using the Matrix
Model. Journal of Substance Abuse Treatment,23, 107 – 113.
Orlinsky, D. E., & Howard, K. I. (1978). The relation of process to
outcome in psychotherapy. In S. L. Garfield & A. E. Bergin (Eds.),
Handbook of psychotherapy and behavior change: An empirical
analysis (pp. 283– 329). New York7John Wiley & Sons.
Orlinsky, D. E., & Howard, K. I. (1986). Process and outcome in
psychotherapy. In S. L. Garfield, & A. E. Bergin (Eds.), Handbook
of psychotherapy and behavior change, (3rd ed., pp. 311–381). New
York7John Wiley & Sons.
Orlinsky, D. E., Rbnnestad, M. H., & Willutzki, U. (2004). Fifty years of
psychotherapy process-outcome research: Continuity and change. In
M. J. Lambert (Ed.), Bergin and Garfield’s Handbook of psychotherapy
and behavior change (5th ed., pp. 307–389). New York7John Wiley &
Sons.
Pantalon, M. V., Nich, C., Frankforter, T., & Carroll, K. M. (2002). The
URICA as a measure of motivation to change among treatment-seeking
individuals with concurrent alcohol and cocaine problems. Psychology
of Addictive Behaviors,16, 299– 307.
Patkar, A. A., Thornton, C. C., Berrettini, W. H., Gottheil, E., Weinstein,
S. P., & Hill, K. P. (2002). Predicting treatment-outcome in cocaine
dependence from admission urine drug screen and peripheral
serotonergic measures. Journal of Substance Abuse Treatment,23,
33 – 40.
Pearson, F. S., Lipton, D. S., Cleland, C. M., & Yee, D. S. (2002). The
effects of behavioral/cognitive-behavioral programs on recidivism.
Crime & Delinquency,48, 476 – 496.
Petry, N. M., & Bickel, W. K. (1999). Therapeutic alliance and psychiatric
severity as predictors of completion of treatment for opioid dependence.
Psychiatric Services,50, 219 – 227.
Petry, N. M., & Martin, B. (2002). Low-cost contingency management for
treating cocaine- and opioid-abusing methadone patients. Journal of
Consulting and Clinical Psychology,70, 398 – 405.
Petry, N. M., Martin, B., Cooney, J. L., & Kranzler, H. R. (2000). Give
them prizes, and they will come: Contingency management for
treatment of alcohol dependence. Journal of Consulting and Clinical
Psychology,68, 250 – 257.
Petry, N. M., Petrakis, I., Trevisan,L., Wiredu, G., Boutros, N. N., Martin, B.,
& Kosten, T. R. (2001). Contingency management interventions: From
research to practice. American Journal of Psychiatry,158, 694– 702.
Petry, N. M., & Simcic Jr., F. (2002). Recent advances in the dissemination
of contingency management techniques: Clinical and research perspec-
tives. Journal of Substance Abuse Treatment,23, 81 – 86.
Pitre, U., Dansereau, D. F., & Joe, G. W. (1996). Client education levels
and the effectiveness of node-link maps. Journal of Addictive Diseases,
15, 27 – 44.
Pitre, U., Dansereau, D. F., Newbern, D., & Simpson, D. D. (1998).
Residential drug abuse treatment for probationers: Use of node-link
mapping to enhance participation and progress. Journal of Substance
Abuse Treatment,15, 535 – 543.
Prendergast, M. L., Podus, D., Chang, E., & Urada, D. (2002). The
effectiveness of drug abuse treatment: A meta-analysis of comparison
group studies. Drug and Alcohol Dependence,67, 53– 72.
Prochaska, J. O., & DiClemente, C. C. (1986). Toward a comprehensive
model of change. In W. R. Miller, & N. Heather (Eds.), Treating
addictivebehaviors:Processes ofchange (pp. 3 – 27). New York7Plenum
Press.
Rawson, R. A., Huber, A., McCann, M., Shoptaw, S., Farabee, D., Reiber,
C., & Ling, W. (2002). A comparison of contingency management
and cognitive-behavioral approaches during methadone maintenance
treatment for cocaine dependence. Archives of General Psychiatry,59,
817 – 824.
Rawson, R. A., Marinelli-Casey, P., & Ling, W. (2002). Dancing with
strangers: Will U.S. substance abuse practice and research organiza-
tions build mutually productive relationships? Addictive Behaviors,27,
941 – 949.
Rawson, R. A., Shoptaw, S. J., Obert, J. L., McCann, M. J., Hasson, A. L.,
Marinelli-Casey, P. J., Brethen, P. R., & Ling, W. (1995). An intensive
outpatient approach for cocaine abuse treatment: The matrix model.
Journal of Substance Abuse Treatment,12, 117– 127.
Robbins, M. S., Bachrach, K., & Szapocznik, J. (2002). Bridging the
research-practice gap in adolescent substance abuse treatment: The case
of brief strategic family therapy. Journal of Substance Abuse Treatment,
23, 123 – 132.
Robles, E., Stitzer, M. L., Strain, E. C., Bigelow, G. E., & Silverman,
K. (2002). Voucher-based reinforcement of opiate abstinence
during methadone detoxification. Drug and Alcohol Dependence,65,
179 – 189.
Rogers, C. R. (1959). A theory of therapy, personality, and interpersonal
relationships as developed in the client-centered framework. In S.
Koch (Ed.), Psychology: A study of a science. Vol. 3. Formulations of
the person and the social context (pp. 184 – 256). New York7McGraw
Hill.
Roman, P. M., & Johnson, J. A. (2002). Adoption and implementation of
new technologies in substance abuse treatment. Journal of Substance
Abuse Treatment,22, 211– 218.
Rosenblum, A., Magura, S., Foote, J., Palij, M., Handelsman, L., Lovejoy,
M., & Stimmel, B. (1995). Treatment intensity and reduction in drug
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 119
use for cocaine-dependent methadone patients: A dose-response
relationship. Journal of Psychoactive Drugs,27, 151 – 159.
Rowan-Szal, G. A., Bartholomew, N. G., Chatham, L. R., & Simpson, D.
D. (2002). A combined cognitive and behavioral intervention for
cocaine-using methadone clients. Manuscript submitted for publication.
Rowan-Szal, G. A., Chatham, L. R., Greener, J. M., Joe, G. W., Payte, J. T.,
& Simpson, D. D. (2002). Structure as a determinant of treatment dose.
Manuscript submitted for publication.
Rowan-Szal, G. A., Joe, G. W., Chatham, L. R., & Simpson, D. D. (1994).
A simple reinforcement system for methadone clients in a community-
based treatment program. Journal of Substance Abuse Treatment,11,
217 – 223.
Rowan-Szal, G. A., Joe, G. W., Hiller, M. L., & Simpson, D. D. (1997).
Increasing early engagement in methadone treatment. Journal of
Maintenance in the Addictions,1, 49 – 61.
Rowan-Szal, G. A., Joe, G. W., & Simpson, D. D. (2000). Treatment
retention of crack and cocaine users in a national sample of long term
residential clients. Addiction Research,8, 51 – 64.
Ryan, R. M., Plant, R. W., & O’Malley, S. (1995). Initial motivations for
alcohol treatment: Relations with patient characteristics, treatment
involvement, and dropout. Addictive Behaviors,20, 279 – 297.
Schippers, G. M., Schramade, M., & Walburg, J. A. (2002). Reforming
Dutch substance abuse treatment services. Addictive Behaviors,27,
995 – 1007.
Schneider, B., Salvaggio, A. N., & Subirats, M. (2002). Climate strength: A
new direction for climate research. Journal of Applied Psychology,87,
220 – 239.
Sells, S. B. (1979). Treatment effectiveness. In R. L. DuPont, A. Goldstein,
& J. O’Donnell (Eds.), Handbook on drug abuse (pp. 105 – 118).
Washington, DC7U.S. Government Printing Office.
Sells, S. B. (Ed.) (1974). The effectiveness of drug abuse treatment,vols. 1–2.
Cambridge, MA7Ballinger.
Sells, S. B., Demaree, R. G., Simpson, D. D., Joe, G. W., & Gorsuch, R. L.
(1977). Issues in the evaluation of drug abuse treatment. Professional
Psychology,8, 609 – 640.
Sells, S. B., & Simpson, D. D. (1979). Implications for evaluation of drug
dependency treatment based on data provided by a management
information system. Addictive Diseases,3, 533 – 554.
Sells, S. B., & Simpson, D. D. (Eds.) (1976). The effectiveness of drug
abuse treatment,vols. 3–5. Cambridge, MA7Ballinger.
Sia, T. L., Czuchry, M., & Dansereau, D. F. (1999). Considering personal
strengths: The effect of three different methods on mood, arousal, and
self-esteem. Journal of Applied Social Psychology,29, 1151 – 1171.
Sia, T. L., Dansereau, D. F., & Czuchry, M. (2000). Treatment readiness
training and probationers’ evaluation of substance abuse treatment in a
criminal justice setting. Journal of Substance Abuse Treatment,19,
459 – 467.
Siegal, H. A., Rapp, R. C., Li, L., Saha, P., & Kirk, K. D. (1997). The role
of case management in retaining clients in substance abuse treatment:
An exploratory analysis. Journal of Drug Issues,27, 821– 831.
Silverman, K., Higgins, S. T., Brooner, R. K., Montoya, I. D., Cone, E. J.,
Schuster, C. R., & Preston, K. L. (1996). Sustained cocaine abstinence
in methadone maintenance patients through voucher-based reinforce-
ment therapy. Archives of General Psychiatry,53, 409 – 415.
Silverman, K., Wong, C., Higgins, S., Brooner, R., Montoya, I.,
Contoreggi, C., Umbricht-Schneiter, A., Schuster, C., & Preston, K.
(1996). Increasing opiate abstinence through voucher-based reinforce-
ment therapy. Drug and Alcohol Dependence,41, 157 – 165.
Simpson, D. D. (1979). The relation of time spent in drug abuse treatment
to posttreatment outcomes. American Journal of Psychiatry,136,
1449 – 1453.
Simpson, D. D. (1981). Treatment for drug abuse: Follow-up outcomes and
length of time spent. Archives of General Psychiatry,38, 875 – 880.
Simpson, D. D. (2001). Modeling treatment process and outcomes.
Addiction,96, 207 – 211.
Simpson, D. D. (2002). A conceptual framework for transferring research to
practice. Journal of Substance Abuse Treatment,22, 171 – 182.
Simpson, D. D., & Brown, B. S. (Eds.) (1999). Special issue: Treatment
process and outcome studies from DATOS. Drug and Alcohol
Dependence,57.
Simpson, D. D., Chatham, L. R., & Joe, G. W. (1993). Cognitive
enhancements to treatment in DATAR: Drug abuse treatment for AIDS
risks reduction. In J. A. Inciardi, F. M. Tims, & B. W. Fletcher (Eds.),
Innovative approaches to the treatment of drug abuse: Volume 1:
Program models and strategies (pp. 161 – 177). Westport, CT7Green-
wood Press.
Simpson, D. D., & Curry, S. J. (Eds.) (1997). Special issue: Drug Abuse
Treatment Outcome Study (DATOS). Psychology of Addictive Behav-
iors,11.
Simpson, D. D., Dansereau, D. F., & Joe, G. W. (1997). The DATAR
project: Cognitive and behavioral enhancements to community-based
treatments. In F. M. Tims, J. A. Inciardi, B. W. Fletcher, & A. M.
Horton, Jr. (Eds.), The effectiveness of innovative approaches in the
treatment of drug abuse (pp. 182– 203). Westport, CT7Greenwood
Press.
Simpson, D. D., & Joe, G. W. (1993). Motivation as a predictor of early
dropout from drug abuse treatment. Psychotherapy,30, 357 – 368.
Simpson, D. D., & Joe, G. W. (2004). A longitudinal evaluation of treat-
ment engagement and recovery stages. Journal of Substance Abuse
Treatment,27, 89–97.
Simpson, D. D., Joe, G. W., & Bracy, S. A. (1982). Six-year follow-up of
opioid addicts after admission to treatment. Archives of General
Psychiatry,39, 1318 – 1323.
Simpson, D. D., Joe, G. W., & Broome, K. M. (2002). A national 5-year
follow-up of treatment outcomes for cocaine dependence. Archives of
General Psychiatry,59, 538 – 544.
Simpson, D. D., Joe, G. W., Broome, K. M., Hiller, M. L., Knight, K., &
Rowan-Szal, G. A. (1997). Program diversity and treatment retention
rates in the Drug Abuse Treatment Outcome Study (DATOS).
Psychology of Addictive Behaviors,11, 279 – 293.
Simpson, D. D., Joe, G. W., & Brown, B. S. (1997). Treatment retention
and follow-up outcomes in the Drug Abuse Treatment Outcome Study
(DATOS). Psychology of Addictive Behaviors,11, 294 – 307.
Simpson, D. D., Joe, G. W., Dansereau, D. F., & Chatham, L. R. (1997).
Strategies for improving methadone treatment process and outcomes.
Journal of Drug Issues,27, 239– 260.
Simpson, D. D., Joe, G. W., Fletcher, B. W., Hubbard, R. L., & Anglin, M.
D. (1999). A national evaluation of treatment outcomes for cocaine
dependence. Archives of General Psychiatry,56, 507 – 514.
Simpson, D. D., Joe, G. W., Greener, J. M., & Rowan-Szal, G. A. (2000).
Modeling year 1 outcomes with treatment process and post-treatment
social influences. Substance Use & Misuse,35, 1911 – 1930.
Simpson, D. D., Joe, G. W., & Rowan-Szal, G. A. (1997). Drug abuse
treatment retention and process effects on follow-up outcomes. Drug
and Alcohol Dependence,47, 227 – 235.
Simpson, D. D., Joe, G. W., Rowan-Szal, G. A., & Greener, J. M. (1997).
Drug abuse treatment process components that improve retention.
Journal of Substance Abuse Treatment,14, 565 – 572.
Simpson, D. D., & Sells, S. B. (1982). Effectiveness of treatment for drug
abuse: An overview of the DARP research program. Advances in
Alcohol and Substance Abuse,2, 7– 29.
Simpson, D. D., & Sells, S. B. (Eds.) (1990). Opioid addiction and
treatment: A 12-year follow-up. Malabar, FL7Krieger.
Snell, M. N., Mallinckrodt, B., Hill, R. D., & Lambert, M. J. (2001).
Predicting counseling center clients’ response to counseling: A 1-year
follow-up. Journal of Counseling Psychology,48, 463 – 473.
Sobell, M. B., & Sobell, L. C. (2000). Stepped care as a heuristic approach
to the treatment of alcohol problems. Journal of Consulting and
Clinical Psychology,68, 573 – 579.
Soldz, S., Panas, L., & Rodriguez-Howard, M. (2002). The reliability of the
Massachusetts Substance Abuse Management Information System.
Journal of Clinical Psychology,58, 1057 – 1069.
Stark, M. J. (1992). Dropping out of substance abuse treatment: A clinically
oriented review. Clinical Psychology Review,12, 93 – 116.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121120
Stiles, W. B. (1980). Measurement of the impact of psychotherapy sessions.
Journal of Consulting and Clinical Psychology,48, 176 – 185.
Stiles, W. B., Reynolds, S., Hardy, G. E., Rees, A., Barkham, M., & Shapiro,
D. A. (1994). Evaluation and description of psychotherapy sessions by
clients using the Session Evaluation Questionnaire and the Session
Impacts Scale. Journal of Counseling Psychology,41, 175 – 185.
Stiles, W. B., & Snow, J. S. (1984). Dimensions of psychotherapy session
impact across sessions and across clients. British Journal of Clinical
Psychology,23, 59 – 63.
Sturm, R. (2002). Evidence-based health policy versus evidence-based
medicine. Psychiatric Services,53, 1499.
Substance Abuse and Mental Health Services Administration. (2003).
National Survey of Substance Abuse Treatment Services (N-SSATS).
Drug & Alcohol Services Information System. [Retrieved June 11,
2003] Available: http://wwwdasis.samhsa.gov/dasis2/nssats.htm.
Szapocznik, J., & Kurtines, W. M. (1993). Family psychology and cultural
diversity: Opportunities for theory, research, and application. American
Psychologist,48, 400 – 407.
Thomas, C. P., Wallack, S. S., Lee, S., McCarty, D., & Swift, R. (2003).
Research to practice: Adoption of naltrexone in alcoholism treatment.
Journal of Substance Abuse Treatment,24,111.
Thornton, C. C., Gottheil, E., Weinstein, S. P., & Kerachsky, R. S. (1998).
Patient-treatment matching in substance abuse: Drug addiction severity.
Journal of Substance Abuse Treatment,15, 505 – 511.
Timko, C., Lesar, M., Calvi, N. J., & Moos, R. H. (2003). Trends in acute
mental health care: Comparing psychiatric and substance abuse treat-
ment programs. Journal of Behavioral Health Services & Research,30,
145 – 160.
Toumbourou, J. W., Hamilton, M., & Fallon, B. (1998). Treatment level
progress and time spent in treatment in the prediction of outcomes
following drug-free therapeutic community treatment. Addiction,93,
1051 – 1064.
Toumbourou, J. W., Hamilton, M., U’Ren, A., Stevens-Jones, P., & Storey,
G. (2002). Narcotics Anonymous participation and changes in
substance use and social support. Journal of Substance Abuse Treat-
ment,23, 61 – 66.
Tracey, T. J., & Kokotovic, A. M. (1989). Factor structure of the Working
Alliance Inventory. Journal of Consulting and Clinical Psychology,1,
207 – 210.
Unqtzer, J., Choi, Y., Cook, I. A., & Oishi, S. (2002). A web-based data
management system to improve care for depression in a multicenter
clinical trial. Psychiatric Services,53, 671 – 673.
Valle, S. K. (1981). Interpersonal functioning of alcoholism counselors and
treatment outcome. Journal of Studies on Alcohol,42, 783– 790.
Weisner, C., McLellan, A. T., & Hunkeler, E. M. (2000). Addiction
Severity Index data from general membership and treatment samples of
HMO members: One case of norming the ASI. Journal of Substance
Abuse Treatment,19, 103 – 109.
Weiss, R. D., Griffin, M. L., Gallop, R., Onken, L. S., Gastfriend, D. R.,
Daley, D., Crits-Cristoph, P., Bishop, S., & Barber, J. P. (2000). Self-
help group attendance and participation among cocaine dependent
patients. Drug and Alcohol Dependence,60, 169– 177.
Weissberg, R. P., & Greenberg, M. T. (1998). Prevention science and
collaborative community action research: Combining the best from both
perspectives. Journal of Mental Health,7, 479 – 492.
Westermeyer, J. (1989). Monitoring recovery from substance abuse:
Rationales, methods and challenges. Advances in Alcohol & Substance
Abuse,8, 93 – 106.
Wexler, H. K. (2003). The promise of prison-based treatment for
dually diagnosed inmates. Journal of Substance Abuse Treatment,25,
223 – 231.
Wexler, H. K., Melnick, G., Lowe, L., & Peters, J. (1999). Three-year
reincarceration outcomes for Amity in-prison therapeutic community
and aftercare in California. The Prison Journal,79, 321 – 336.
Whiston, S. C., & Sexton, T. L. (1993). An overview of psychotherapy
outcome research: Implications for practice. Professional Psychology:
Research and Practice,24, 43 – 51.
Wolff, N., Plemmons, D., Veysey, B., & Brandli, A. (2002). Release
planning for inmates with mental illness compared with those who have
other chronic illnesses. Psychiatric Services,53, 1469 – 1471.
Woody, G. E., McLellan, A. T., Luborsky, L., O’Brien, C. P., Blaine, J., Fox,
S., Herman, I., & Beck, A. T. (1984). Severity of psychiatricsymptoms as
a predictor of benefits from psychotherapy: The VeteransAdministration-
Penn Study. American Journal of Psychiatry,141, 1172 – 1177.
Zanis, D. A., McLellan, A. T., Belding, M. A., & Moyer, G. (1997). A
comparison of three methods of measuring the type and quantity of
services provided during substance abuse treatment. Drug and Alcohol
Dependence,49, 25 – 32.
D.D. Simpson / Journal of Substance Abuse Treatment 27 (2004) 99–121 121
... Outcomes of studies investigating the relationship between drug and alcohol clinician knowledge and attitudes and implementation fidelity have been mixed. For instance, some studies have demonstrated that clinicians who are prepared for change and who have positive attitudes to EBP are more likely to implement such practices [18,[35][36][37], while others have not found a strong relationship between treatment fidelity and attitudes such as interest, confidence and commitment to EBPs [21,29,38]. Clinician perceptions of the organisational climate comprise a distinct set of beliefs found to influence implementation outcomes in drug and alcohol settings [29,39]. ...
... Although the relationship between self-efficacy and comorbidity practice was not significant, there was a trend toward significance observed in this small population which would warrant further investigation in a larger sample. In terms of clinican attitudes toward EBP, our findings did not confirm those of previous studies, which have shown that clinicians who are prepared for change and who have positive attitudes to EBP [18,[35][36][37], and treatment manuals [51] are more likely to implement such practices. On the contrary, attitudes of clinicians in this study are more consistent with alternate evidence suggesting that attitudes such as interest, confidence, and commitment to EBPs do not have a strong relationship with treatment fidelity [21,29,38]. ...
Article
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Background The process of determining the best strategy for increasing the uptake of evidence-based practice might be improved through an understanding of relevant clinician-level factors. The Pathways to Comorbidity Care (PCC) training program (Louie E, et al., J Dual Diagnosis 17:304–12, 2021) aimed to facilitate integrated management of comorbid drug and alcohol and mental disorders amongst drug and alcohol clinicians. We hypothesised that uptake of integrated management of comorbidity following the implementation of the PCC program would be associated with clinician-level: (i) demographics (gender, education, experience), (ii) attitudes (evidence-based practice, therapist manuals, counselling self-efficacy), and (iii) organisational readiness to change. Methods Twenty clinicians participated in the 9-month PCC training program. Attitudes towards evidence-based practices and psychotherapist manuals, self-efficacy, and organisational readiness to change, along with demographics, were measured at baseline. At follow-up, change in Comorbidity Practice (CoP) scores related to integrated comorbidity management were obtained using a file audit checklist and categorised into high (at least 60% increase in CoP), medium or low (a decrease of − 20% or less in CoP). Clinician-level characteristics were examined across the implementation categories. Results There were no significant differences found between implementation groups on sociodemographic variables (p’s > 0.30), attitudes to evidence-based practices, attitudes to therapist manuals, and self-efficacy (p’s > 0.52). The high implementation group demonstrated significantly higher scores on leadership practices aspect of organisational readiness to change relative to the low and medium implementation group ((F(2, 16) = 3.63, p = 0.05; Cohen’s d = .31) but not on the other subscales (p’s > 0.07). Conclusions Confidence that leadership will play a positive role in the implementation process may improve effectiveness of comorbidity training programs for drug and alcohol clinicians. On the other hand, contrary to our hypothesis, counselling self-efficacy, evidence-based practice attitudes, attitudes towards therapist manuals, gender, education and experience were not distinguishing factors.
... The results of this study can potentially guide NPO leadership to evaluate their rationale for engaging in a collaboration that is mainly guided by a mechanism to secure rewards (funds) and avoid punishment (lack of funds). Information sharing behavior in collaboration leads to the access of new information that has implications for the transfer and application of innovative practices into everyday community settings (Schoenwald & Hoagwood, 2001;Simpson, 2002). However, if organizations do not fully collaborate and are not exchanging new or innovative knowledge through information exchange behaviors, the members of their communities ultimately suffer the consequences. ...
Thesis
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Federal funding agencies that administer financial support in the form of program grants to non-profit organizations (NPOs) that provide child and family services increasingly require NPOs to formalize inter-organizational partnerships in order to receive this vital source of funding. That is, by mandate NPOs must participate in inter-organizational collaboration networks to receive these essential federal funds. Therefore, there is a need to understand the collaboration behavior of NPOs in a policy-mandated environment. This study considers collaboration behavior as information sharing and advice-seeking between the organizations who are part of a collaboration network as a result of a policy mandate. Drawing on collaboration theory, social capital theory, and social network theory, this study examines the evolution of a collaboration network by assessing how NPOs in a policy-mandated context chose to engage in information-sharing behaviors and how these behaviors changed over time as NPOs developed a working history together. This research examined the production and distribution of social capital as the primary mechanism for motivating collaboration (i.e., information exchange) as the network evolved. Using Louisiana’s Project Linking Actions for Unmet Needs in Children’s Health (LAUNCH) as a case study, this study analyzed five years of self-reported organization-level data on collaboration behaviors and information exchanged among NPOs within the LAUNCH network. A social network approach was used to analyze the evolution of collaboration practices and found that existing ties play a pivotal role in facilitating information exchange behaviors among the NPOs in the study. That is, organizations are more likely to create information-sharing partnerships with other organizations that have been endorsed and vouched for by an existing partner, or they share information with organizations that have already shared information with them in the past. This showed a tendency towards bonding social capital wherein organizations are provided security against the high levels of risk within a policy-mandated collaboration by the convenience and accessibility offered by maintaining existing relationships. Results of this study were consistent throughout the different model specifications employed in the analysis, and reveal key implications for organizations engaged in policy-mandated partnerships, as well as for funders who require collaboration.
... Unfortunately, there are relatively few complementary measures that assess for training implementation outcomes (e.g., training acceptability, posttraining attitudes, knowledge, and adoption), and even fewer have demonstrated reliability or validity Frank et al., 2020;Herschell et al., 2010;Lewis et al., 2015). While there are several notable exceptions, including measures of provider attitudes toward EBPs (e.g., EBP Attitudes Scale [Aarons, 2004]; Modified Practice Attitudes Scale [Borntrager et al., 2009]) and knowledge of EBPs (e.g., Knowledge of EBP Strategies Questionnaire; Stumpf et al., 2009), a recent review of implementation outcome measures found that many training implementation outcome measures (e.g., Training Acceptability Rating Scale [Milne, 2010]; Training/Practice acceptability, feasibility, appropriateness scale [AFAS; Lyon, 2011]; Workshop Evaluation Form [Simpson, 2002]) have limited or unknown psychometric properties (Mettert et al., 2020). Thus, psychometric evaluation of existing training implementation outcome measures is needed to guide ongoing measure development (Mettert et al., 2020). ...
Article
There is a well-documented gap between research and practice in the treatment of mental health problems. One promising approach to bridging this gap is training community-based providers in evidence-based practices (EBPs). However, a paucity of valid, reliable measures to assess a range of outcomes of such trainings impedes our ability to evaluate and improve training toward this end. The current study examined the factor structure of the Acceptability, Feasibility, Appropriateness Scale (AFAS), a provider-report measure that assesses three perceptual implementation outcomes of trainings that may be leading indicators of training success (i.e., acceptability, feasibility, and appropriateness). Providers who attended half-day EBP trainings for common mental health problems reported on the acceptability, feasibility, and appropriateness of these trainings using the AFAS ( N = 298). Confirmatory factor analysis indicates good fit to the hypothesized three-factor structure (RMSEA = .058, CFI = .990, TLI = .987). Acceptability, feasibility, and appropriateness were three distinct but related constructs. Cronbach's alpha ranged from .86 to .91, indicating acceptable internal consistency for the three subscales. Acceptability and feasibility, but not appropriateness, scores varied between workshops, though variability across workshops was generally limited. This initial evaluation of the AFAS is in line with recent efforts to enhance psychometric reporting practices for implementation outcome measures and provides future directions for further development and refinement of the AFAS. Plain Language Summary Clinician training in evidence-based practices is often used to increase implementation of evidence-based practices in mental health service settings. However, one barrier to evaluating the success of clinician trainings is the lack of measures that reliably and accurately assess clinician training outcomes. This study was the initial evaluation of the Acceptability, Feasibility, Appropriateness Scale (AFAS), a measure that assesses the immediate outcomes of clinician trainings. This study found some evidence supporting the AFAS reliability and its three subscales. With additional item refinement and psychometric testing, the AFAS could become a useful measure of a training's immediate impact on providers.
... Inquiry group processes may also suffer from a lack of time and resource allocation, as well as attention to key relationships (Patterson & Goulter, 2015) which are crucial in groupwork processes, generally. Organisational buy-in makes a difference (Fixsen et al, 2005;Greenhalgh et al, 2004;Schoenwald et al, 2008;Simpson, 2002) and inter-organisational collaboration requires additional communications and information-sharing channels as well as adequate resources in order to succeed (Palinkas et al, 2012). ...
Article
The long-term impacts of Adverse Childhood Experiences (ACEs) are of increasing interest to researchers and practitioners. While attention is being paid to the effectiveness of screening for ACEs to improve health and social outcomes, how to implement such practices has become key. The Irish study upon which this article is based used an action research approach to implement ACEs routine enquiry within a domestic violence service, while also utilising co-operative inquiry groups for practitioners within the organisation (n=10) and those working in associated fields of infant mental health, child protection and welfare and community support (n=7). This article documents the process of enacting the co-operative inquiry groups and outlines learning points, challenges and lessons. Drawing on Finlay’s (2002a, 2002b) theories on the variants of reflexivity in research processes, six themes emerged on the research process: insider and outsider status; the structure and approach to the groupwork process; participation in the group process; addressing fears; practice concerns; and motivations to act. We conclude with suggestions for effective co-operative inquiry processes and highlight the factors related to groupwork that may contribute to improvements in practice, organisational and community change.
... Community readiness (CR) is defined as the extent to which a community can be prepared to take action against or make changes on an issue (9). Readiness assessment informs planners about the feasibility of conducting a prevention program and helps to explore tailored capacity-building strategies according to the readiness stage of a community (10,11). ...
Article
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Background: Community Readiness Improvement for Tackling Childhood Obesity (CRITCO) study has been developed based on the community readiness model (CRM) to improve the readiness of targeted local communities from two diverse socioeconomic districts of Tehran for tackling childhood obesity (CO) of late primary school children (10 - 12 years of age). Objectives: This study aimed to describe the rationale and process of developing the fourth phase of CRITCO study by developing an intervention package. Methods: The readiness level data was used to analyze strengths, weaknesses, opportunities, and threats (SWOT) of four intervention sites to guide proper strategies in partnership with key community members. Then, the action plan was developed as a living document to guide a 6-month quasi-experimental community-based intervention around the six dimensions of CRM. Finally, the community engagement process, outcome, and evaluation process were explained. Results: The current report described the action plan and strategy development of the CRITCO study. Expectedly, this study can provide valuable information to guide the public health policymakers in planning and executing relevant interventions.
Article
The necessity to implement evidence-based programs to support the healthy development of youth and families is becoming part of national policy. Organizations that are not “ready” to do so will likely lose resources, disallowing them to serve as they have set out to do. Consequently, the current survey study draws from a national sample of Cooperative Extension personnel in 2009 to examine characteristics within their organizational context that facilitate successful change related to youth/family programming. Data were collected from 946 4-H/youth development or Family and Consumer Sciences employees at all levels. Self-reported indices of each state organization’s openness to change, leadership, morale, communication, and resources were constructed to assess the organizational context. Dependent variables included indicators of readiness to implement prevention and evidence-based programming. Results suggest that the organizational context was strongly associated with indicators of readiness for evidence-based prevention programming, and specifically, the clarity of communication was most important.
Article
Short, valid, and reliable tools that measure organizational readiness are needed in practice. This study adapted and tested a previously developed instrument for measuring organizational readiness in a Medicaid Home and Community-Based Services (HCBS) program. The Texas Christian University Organizational Readiness for Change (TCU-ORC) scale was adapted and tested for validity and reliability in a sample of 522 registered nurses and social workers employed at 18 program sites. Structural validity was established using the exploratory factor analysis. Convergent validity was evaluated via correlations with the Implementation Leadership Scale (ILS) score. The adapted ORC scale consisted of 23 items. Cronbach's alphas for 5 subscales, Climate, Culture, Training, Motivation, and Pressure to Change exceeded .70. Convergent validity was supported by significant moderate correlations with the ILS. The adapted 23-item TCU-ORC scale is a valid and reliable instrument for measuring the organizational readiness for change in the Medicaid Home and Community-Based Services programs.
Article
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Background The last decade has seen growing interest in scaling up of innovations to strengthen healthcare systems. However, the lack of appropriate methods for determining their potential for scale-up is an unfortunate global handicap. Thus, we aimed to review tools proposed for assessing the scalability of innovations in health. Methods We conducted a systematic review following the COSMIN methodology. We included any empirical research which aimed to investigate the creation, validation or interpretability of a scalability assessment tool in health. We searched Embase, MEDLINE, CINAHL, Web of Science, PsycINFO, Cochrane Library and ERIC from their inception to 20 March 2019. We also searched relevant websites, screened the reference lists of relevant reports and consulted experts in the field. Two reviewers independently selected and extracted eligible reports and assessed the methodological quality of tools. We summarized data using a narrative approach involving thematic syntheses and descriptive statistics. Results We identified 31 reports describing 21 tools. Types of tools included criteria (47.6%), scales (33.3%) and checklists (19.0%). Most tools were published from 2010 onwards (90.5%), in open-access sources (85.7%) and funded by governmental or nongovernmental organizations (76.2%). All tools were in English; four were translated into French or Spanish (19.0%). Tool creation involved single (23.8%) or multiple (19.0%) types of stakeholders, or stakeholder involvement was not reported (57.1%). No studies reported involving patients or the public, or reported the sex of tool creators. Tools were created for use in high-income countries (28.6%), low- or middle-income countries (19.0%), or both (9.5%), or for transferring innovations from low- or middle-income countries to high-income countries (4.8%). Healthcare levels included public or population health (47.6%), primary healthcare (33.3%) and home care (4.8%). Most tools provided limited information on content validity (85.7%), and none reported on other measurement properties. The methodological quality of tools was deemed inadequate (61.9%) or doubtful (38.1%). Conclusions We inventoried tools for assessing the scalability of innovations in health. Existing tools are as yet of limited utility for assessing scalability in health. More work needs to be done to establish key psychometric properties of these tools. Trial registration We registered this review with PROSPERO (identifier: CRD42019107095)
Article
Background: Many primary care providers (PCPs) in the Veterans Health Administration need updated clinical training in women's health. The objective was to design, implement, and evaluate a training program to increase participants' comfort with and provision of care to women Veterans, and foster practice changes in women's health care at their local institutions. Methods: The Women's Health Mini-Residency was developed as a multi-day training program, based on principles of adult learning, wherein knowledge gleaned through didactic presentations was solidified during small-group case study discussions and further enhanced by hands-on training and creation of a facility-specific action plan to improve women Veterans' care. Pre, post, and 6-month surveys assessed attendees' comfort with and provision of care to women. The 6-month survey also queried changes in practice, promulgation of program content, and action plan progress. Results: From 2008 to 2019, 2912 PCPs attended 26 programs. A total of 2423 (83.2%) completed pretraining and 2324 (79.3%) completed post-training surveys. The 6-month survey was sent to the 645 attendees from the first 14 programs; 297 (46.1%) responded. Comparison of pre-post responses indicated significant gains in comfort managing all 19 content areas. Six-month data showed some degradation, but comfort remained significantly improved from baseline. At 6 months, participants also reported increases in providing care to women, including performing more breast and pelvic examinations, dissemination of program content to colleagues, and progress on action plans. Conclusions: This interactive program appears to have been successful in improving PCPs' comfort in providing care for women Veterans and empowering them to implement institutional change.
Preprint
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Background A more nuanced understanding of the interrelationships between clinician-level factors and implementation outcomes may enhance the utility of evidence-based practice implementation strategies. The Pathways to Comorbidity Care (PCC) training program [1] aimed to facilitate integrated management of comorbid drug and alcohol and mental disorders amongst drug and alcohol clinicians. We hypothesised that implementation outcomes would be associated with clinician-level: (i) demographics (gender, education, experience), (ii) attitudes (evidence-based practice, therapist manuals, counselling self-efficacy), and (iii) organisational readiness to change. Methods Twenty clinicians participated in the 9-month PCC training program. Attitudes towards evidence-based practices and psychotherapist manuals, self-efficacy, and organisational readiness to change, along with demographics, were measured at baseline. At follow-up, change in Comorbidity Practice (CoP) scores related to integrated comorbidity management were obtained using a file audit checklist and categorised into high (at least 60% increase in CoP), medium or low (a decrease of -20% or less in CoP). Clinician-level characteristics were examined across the implementation categories. Results There were no significant differences found between implementation groups on sociodemographic variables (p’s > 0.30), attitudes to evidence-based practices, attitudes to therapist manuals, and self-efficacy (p’s > 0.52). The high implementation group demonstrated significantly higher scores on leadership practices aspect of organisational readiness to change relative to the low and medium implementation group ((F(2, 16) = 3.63, p = 0.05; Cohen’s d = .31) but not on the other subscales (p’s > 0.07). Conclusions Confidence that leadership will play a positive role in the implementation process may improve effectiveness of comorbidity training programs for drug and alcohol clinicians. On the other hand, contrary to our hypothesis, counselling self-efficacy, evidence-based practice attitudes, attitudes towards therapist manuals, gender, education and experience were not distinguishing factors.
Book
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Hailed by one reviewer as "the bible of the integration movement," the inaugural edition of Handbook of Psychotherapy Integration was the first compilation of the early integrative approaches to therapy. Since its publication psychotherapy integration has grown into a mature, empirically supported, and international movement, and the current edition provides a comprehensive review of what has been done. Reflecting the considerable advances in the field since the previous edition's release in 2005, this third edition of Handbook of Psychotherapy Integration continues to be the state-of-the-art description of psychotherapy integration and its clinical practices by some of its most distinguished proponents. Six chapters new to this edition describe growing areas of psychotherapy research and practice: common factors therapy, principle-based integration, integrative psychotherapy with children, mixing psychotherapy and self-help, integrating research and practice, and international themes. The latter two of these constitute contemporary thrusts in the integration movement: blending research and practice, and recognizing its international nature. Also closely examined are the concepts, history, training, research, global themes, and future of psychotherapy integration. Each chapter includes a new section on cultural considerations, and an emphasis is placed throughout the volume on outcome research. Charting the remarkable evolution of psychotherapy integration itself, the third edition of this Handbook will continue to prove invaluable to practitioners, researchers, and students alike.