Balanced MenusScenarioThe following is a lunch menu from a sea.docx

Balanced Menus
Scenario
The following is a lunch menu from a seafood restaurant. Use
the menu to answer the question related to creating a balanced
menu.
Lunch Menu*
Appetizers
Fresh Onion Soup with Brioche Crouton & Aged Swiss
Crab Bisque & Crab Fritter
Hearty Minestrone Soup
Chef’s Selection of East & West Coast Oysters
Colossal Shrimp Cocktail
Crispy Fried Crab Cake, Yellow Corn Cream
Aged Wisconsin Cheddar Fondue
Alaskan Red King Crab Legs, served with horseradish cocktail
sauce
Salads
Crisp Wedge of Iceberg, Red Onion, Smoked Bacon, Grape
Tomatoes, Bleu Cheese,
Cabernet Buttermilk Dressing
Hearts of Romaine, Parmesan Garlic Dressing & Sourdough
Crostini
House Salad: Romaine, Spinach, Granny Smith Apples, Goat
Cheese, Walnuts,
Sherry Mustard Vinaigrette
Chop Chop Salad, Hard Cooked Egg, Salami, Fresh Mozzarella,
Smoked Bacon, Club
Dressing
Beefsteak Tomato Salad, Arugula, Shaved Onion, Crumbed Bleu
Cheese Buttermilk
Dressing
Sandwiches and Entrée Salads

All sandwiches served with choice of fries, soup, or house
salad.
Chicken Club with Toasted Brioche, Swiss Cheese, Smoked
Bacon
Steak Burger with Maytag Blue Cheese, Caramelized Bacon
Maryland Crab Melt with Tillamook Cheddar Cheese, Jalapeno
Corn Relish
Soy Glazed Tuna Sandwich with Pickled Cucumber, Wasabi
Mayonnaise
Chicken Chopped Salad with Roasted Chicken, Asparagus, Goat
Cheese, Dates, Corn,
Sherry Vinaigrette
Blackened Salmon Salad with Strawberry, Cantaloupe, Walnuts,
Poppy Seed Dressing
Black & Bleu Caesar with Flat Iron Steak, Bleu Cheese
Dressing
Prime Entrées
Pecan Crusted Mountain Trout with Skillet Beans, Potato Puree,
Brown Butter
Shrimp Sautee with Angel Hair Pasta, Tabasco Cream Sauce
Ginger Salmon with Snap Peas, Sticky Rice, Soy Butter Sauce
Glazed Chilean Sea Bass with Baby Carrots, Mashed Potato
Roasted Chicken with Asparagus, Truffle Macaroni & Cheese,
Lemon Pan Jus
Flat Iron Steak & Fries with Forest Mushroom Bordelaise
New York Strip with Asparagus, Twice Baked Potatoes,
Cabernet Jus
Desserts
Blueberry Lemon Cheesecake with Graham Cracker Crust and
Blueberry Syrup
Ten Layer Carrot Cake
Chocolate Peanut Butter Pie
Sorbet with Almond Cookie
Baked Alaska – Pound Cake with Ice Cream, Toasted Meringue
and Fresh Raspberries

*Adapted from Ocean Prime. Courtesy: Cameron Mitchell
Restaurants
Questions:
1) Which items are balanced so you could leave them on the
menu as is? List at least one item in each category.
2) Which menu items could you modify to get a balanced item?
List at least two items in each category.
3) Suggest a new balanced menu item (with ingredients) for any
menu category which needs more balance.
Directions:
• Type your name, course name, case study # in the upper
right corner of the first page.
• Each case study analysis write up should be 1-2 pages,
typewritten, double-spaced-ONLY ANSWERS.
• 12 font : Times New Roman, 1 “ margin for all four side.
• No cover page is required
2
Substance Use & Misuse, 46:808–818, 2011
Copyright C© 2011 Informa Healthcare USA, Inc.
ISSN: 1082-6084 print / 1532-2491 online
DOI: 10.3109/10826084.2010.538460
ORIGINAL ARTICLE
Gender Differences in Substance Use, Consequences,
Motivation to
Change, and Treatment Seeking in People With Serious Mental
Illness

Amy Drapalski1, Melanie Bennett2 and Alan Bellack1,2
1VISN 5 Mental Illness Research, Education, and Clinical
Center, Veterans Affairs Maryland Health Care System,
Baltimore, Maryland, USA; 2Department of Psychiatry,
University of Maryland School of Medicine, Baltimore,
Maryland, USA
Gender differences in patterns and consequences of
substance use, treatment-seeking, and motivation to
change were examined in two samples of people with
serious mental illness (SMI) and comorbid substance
use disorders (SUDs): a community sample not cur-
rently seeking substance abuse treatment (N = 175)
and a treatment-seeking sample (N = 137). In both
groups, women and men demonstrated more similar-
ities in the pattern and severity of their substance use
than differences. However, treatment-seeking women
showed greater readiness to change their substance
use. Mental health problems and traumatic experi-
ences may prompt people with SMI and SUD to enter
substance abuse treatment, regardless of gender.
Keywords dual diagnosis, serious mental illness, gender
differences, motivation to change, treatment-seeking
INTRODUCTION
Substance use disorders (SUDs) among people with se-
rious mental illness (SMI) are widespread and harmful.
Depending on the psychiatric diagnosis, rates of lifetime
drug and alcohol use disorders in people with SMI gen-
erally top between 30% and 45% (Reiger et al., 1990;
Winoker et al., 1998). Despite the high prevalence, rela-
tively little is known about differences in substance use

and its consequences among subgroups of people with
SMI, or whether subgroup differences are clinically im-
portant. One important subgroup is women with SMI
and comorbid SUDs. Women with SMI have been found
to have different patterns of illness onset and course
(Angermeyer, Kuhn, & Goldstein, 1990; Childers &
1The journal’s style utilizes the category substance abuse as a
diagnostic category. Substances are used or misused; living
organisms are and can be
abused. Editor’s note.
This research was supported by grants RO1 DA 012265 (Dr.
Bellack) and R01 DA11753 (Dr. Bellack) from the National
Institute on Drug Abuse
and the VISN 5 Mental Illness Research, Education, and
Clinical Center.
Address correspondence to Dr. Amy Drapalski, VISN 5 Mental
Illness Research Education and Clinical Center, Veterans
Affairs Maryland Health
Care System, 10 North, Greene Street, Baltimore, MD 21201; E-
mail: [email protected]
Harding, 1990; Kawa et al., 2005; Kennedy et al., 2005;
Kessing, 2004; McGlashan & Bardenstein, 1990), better
social functioning (Mueser, Bellack, Morrison, & Wade,
1990), and more positive outcomes than men (Childers
& Harding, 1990; McGlashan & Bardenstein, 1990; Test,
Burke, & Wallisch, 1990). Research with primary sub-
stance users without co-occurring mental illness has
also found gender differences in substance use patterns
(Greenfield et al., 2007; Pelissier & Jones, 2005), con-
sequences (Greenfield et al., 2007; Zilberman, Taveres,
Blume, & el Guedbaly, 2003), and treatment utilization
(Greenfield et al., 2007; Weisner & Schmidt, 1992). The
high rate of substance use among individuals with SMI
and the apparent gender differences in illness course and
patterns of substance use in other groups of substance

abusers suggest the need to look at the ways in which
women with SMI and SUDs may differ from men, as well
as whether and how these differences need to be addressed
in treatment.
Few studies have examined gender differences in peo-
ple with dual SMI and SUDs. Those that have fo-
cused on gender differences have looked at how women
differ from men in terms of the nature of their sub-
stance use. For example, several studies have exam-
ined whether women with SMI and SUDs differ from
men in terms of patterns and severity of substance
use and types of substances abused.1 Overall, men and
women with SMI have been found to show similar pat-
terns and severity of substance use (Brunette & Drake,
1997; Gearon, Nidecker, Bellack, & Bennett, 2003). Dif-
ferences in drug of choice have been reported, with
women reporting higher rates of heroin and cocaine
dependence (Gearon, Nidecker, et al., 2003) and men
GENDER DIFFERENCES IN SUD AND SMI 809
higher rates of cannabis dependence (Brunette & Drake,
1997; Gearon, Nidecker, et al., 2003; Test et al., 1990)
and alcohol abuse (Frye et al., 2003). One fairly consis-
tent gender difference is in consequences of substance
use. Several studies have found higher rates of physical
and sexual victimization, greater physical health prob-
lems, and fewer legal problems in women with SMI and
SUDs than in men (Brunette and Drake, 1997; Test et al.,
1990), and dually diagnosed women report higher rates
of posttraumatic stress disorder than men (Grella, 2003).
Other research has found that women with SMI are un-
derrepresented in substance abuse treatment (Alexander,

1996; Bellack & Gearon, 1998; Comtois & Ries, 1995;
Gearon & Bellack, 1999), with women seeking treatment
only when negative consequences become severe (Rach-
Beisel, Scott, & Dixon, 1999; Weisner & Schmidt, 1992).
These findings of differences in substances of abuse,
consequences, and representation in treatment would sug-
gest that women with dual SUD and SMI have unique rea-
sons for seeking treatment or issues surrounding access to
care. However, research has not fully addressed whether
this is the case. Watkins, Shaner, and Sullivan (1999)
interviewed 21 men and women outpatients with SMI
about their treatment needs and their reasons for and bar-
riers to seeking substance abuse treatment. Few gender
differences were identified. The most frequent treatment
needs for both men and women were assistance with
housing and finances. Reasons for engagement in treat-
ment centered on staying out of legal trouble, although
men more often reported family pressure to attend treat-
ment. Both men and women reported concerns about le-
gal consequences of admitting to use, fear, and paranoia
as barriers to care. The authors speculate that such fac-
tors may disproportionately influence women to stay away
from treatment because of their high rates of victimization
(Watkins et al., 1999). Grella (2003) examined gender dif-
ferences in readiness for treatment, treatment needs, and
barriers to care among 400 individuals with dual disor-
ders recruited from several residential drug user treatment
programs. Participants were asked to rate the importance
of 25 different service needs (e.g., treatment/recovery,
health, family, basic needs, medication, trauma/domestic
violence) and whether they had experienced 10 different
barriers to receiving mental health or substance user treat-
ment (e.g., lack of money for treatment, lack of transporta-
tion to treatment, fear of negative consequences related to
treatment). Results showed no differences by gender in

readiness for treatment (as measured by a 3-point readi-
ness for treatment scale) or barriers to obtaining treatment.
Females reported a great number of service needs overall,
as well as more needs for treatment related to family and
trauma issues.
Overall, this literature suggests that there are some
ways that males and females with SMI and SUD appear
similar (patterns of substance use, self-reported barriers to
care) and some ways in which they are different (drug of
choice, consequences of use). However, several questions
related to gender differences in individuals with SMI and
SUDs remain. First, the literature on gender and substance
use in SMI is relatively small. Further comparisons of pat-
terns and severity of substance use in SMI can help to
establish whether similarities found in previous research
are consistent across samples. Second, gender differences
in variables such as motivation to change and reasons for
seeking treatment, which might impact treatment engage-
ment and outcome, have not been explored in dually di-
agnosed individuals in a comprehensive way. Third, it is
unclear whether gender differences are more or less pro-
nounced in individuals seeking substance use treatment
versus those in the community who are not seeking help
for substance abuse. The studies of gender differences
reviewed above have been conducted with samples of
individuals in treatment. Whether gender differences ex-
ist in community samples that are not seeking treatment
is not known. Dually diagnosed men and women in the
community may show differences in substance use and
severity; these differences may attenuate, as individuals
of both genders move into severer use and acknowledge
that they need to seek treatment. That is, by the time treat-
ment is initiated men and women may appear similar, but
in the community prior to seeking treatment, they may

have been quite different. Such questions are important
as we think about whether women have unique treatment
needs and whether and how to structure treatment to meet
them.
The present study sought to address each of these is-
sues. First, we explored gender differences in patterns
and consequences of SUDs, in order to determine if pre-
vious findings in non-SMI samples are relevant to indi-
viduals with SMI. Second, we examined potential gender
differences in two previously underexplored but clinically
important areas: reasons for seeking treatment and moti-
vation to change. Exploration of these domains will al-
low for a first descriptive look at how women with dual
SMI and SUDs come to treatment and what they hope to
get from it-—both important issues that need to be exam-
ined in order to better attract this group of substance users
into services. Third, we examined gender differences in
two different samples: a community sample that was not
seeking substance use treatment and a treatment-seeking
sample of clients at community mental health center that
agreed to participate in a study of an intervention for sub-
stance use designed for people with SMI. While these
samples are not balanced and so findings cannot be com-
pared across them, their use here provides the opportunity
to look descriptively at the ways in which gender differ-
ences may be manifested in different cohorts of individ-
uals with dual disorders and to identify any differences
in non-treatment-seeking and treatment-seeking samples
that may inform service use and development. Specifi-
cally, we examined gender differences in (1) psychiatric
diagnosis and symptoms, (2) patterns and severity of sub-
stance use, (3) consequences of substance use, (4) moti-
vation to change, and (5) reasons for seeking treatment.
METHOD

Participants
We used data from two studies of SMI and SUDs (see
Nidecker, DiClemente, Bennett, & Bellack, 2008) for
810 A. DRAPALSKI ET AL.
a description of the community sample and Bellack,
Bennett, Gearon, Brown, & Yang (2006) for a descrip-
tion of the treatment-seeking sample). Briefly, Study 1 in-
volved a survey of substance use and motivation to change
in nontreatment-seeking individuals with SMI and either
current cocaine dependence or cocaine dependence in re-
mission recruited from a Veterans Affairs (VA) medical
center and two community clinics in Baltimore, Mary-
land and assessed five times over 12 months (The Pro-
cess of Change in Drug Abuse by Schizophrenics, funded
by NIDA, A. Bellack, PI, n = 240 subjects, “community”
sample). The present study included data from partici-
pants with current cocaine dependence (n = 137) because
of our interest in describing gender differences among in-
dividuals with current SUDs. This sample of participants
with current cocaine dependence was 59.9% male, 77.4%
African American, 19% White, and 3.6% other, had a
mean age of 42.4 (SD = 7.6; range 22–64) and a mean
of 11.9 years of education (SD = 2.1; range = 5–18).
In terms of diagnosis, 55% of participants in the com-
munity sample had a primary diagnosis of schizophre-
nia or schizoaffective disorder, 45% mood or affective
disorder, and 2% other diagnoses. Participants reported a
mean (SD) of 6.3 (9.64) years of heroin use, 13.1 (8.05)
years of cocaine use, 11.31 (11.62) years of cannabis
use, and 12.27 (10.22) years of polydrug use. Study 2
was a randomized trial of a behavioral intervention for

substance abuse in a treatment-seeking sample of peo-
ple with SMI (Behavioral Treatment & Substance Abuse
in Schizophrenia, funded by NIDA, A. Bellack, PI, n =
175, “treatment-seeking” sample). Participants with cur-
rent cocaine, heroin, and/or marijuana dependence were
recruited from outpatient community clinics and a VA
medical center in Maryland. This sample was 63.4% male,
75.4% African American, and 22.3% White and had a
mean age of 42.7 (SD = 7.10; range 21–57) and a mean
of 11.2 years of education (SD = 2.28; range 3–18). In
terms of diagnosis, 55% of participants in the treatment-
seeking sample had a primary diagnosis of mood or affec-
tive disorder, 38% schizophrenia or schizoaffective dis-
order, and 7% other diagnoses. Cocaine was the most
frequently abused drug (69%), followed by opiates (25%)
and cannabis (7%). Participants reported a mean (SD) of
5.73 (8.76) years of heroin use, 10.2 (8.21) years of co-
caine use, 10.2 (10.4) years of cannabis use, and 12.1
(10.7) years of polydrug use.
Measures
Diagnostic and Symptom Assessments
The Structured Clinical Interview for DSM-IV (SCID–I;
First, Spitzer, Gibbon, & William, 1994) was used to es-
tablish diagnosis. Interviews were completed by doctoral-
or masters-level psychologists. Diagnoses were achieved
utilizing all available information (patient report, med-
ical records, treatment providers). Interrater reliability
(kappa) for the SCID diagnoses (psychiatric and sub-
stance abuse/dependence) was greater than 0.80. The
Positive and Negative Syndrome Scale (PANSS; Opler,
Kay, Lindenmayer, & Fiszbein, 1992) was used to as-
sess symptoms of psychiatric illness, with separate ratings
for positive symptoms, negative symptoms, and general
psychopathology. The PANSS has good reliability and va-

lidity (Kay, Fiszbein, & Opler, 1987).
Substance Use and Treatment Utilization
The Addiction Severity Index (ASI; McLellan et al., 1992)
was used at baseline to assess drug use frequency and
severity. We administered the drug, alcohol, family/social,
and legal sections of the ASI, as they are the most reli-
able sections for this population (Carey, Coco, & Correia,
1997). The Substance Use Event Survey for Severe Men-
tal Illness (SUESS; Bennett, Bellack, and Gearon, 2006)
is a relatively brief (20–30 minutes) measure that assesses
clinical issues and service utilization in individuals with
SMI and SUDs. The SUESS contains two types of items:
(1) items related to service use and (2) items to gather
descriptive information that may relate to service use in
clients with SMI. The SUESS also gathers information
about reasons for starting substance use treatment. Psy-
chometric properties and validity of the SUESS are good
(Bennett et al., 2006).
Motivation to Change
Stage of change was assessed with the University of
Rhode Island Change Assessment—Maryland (URICA-
M; Nidecker, DiClemente, Bennett, & Bellack, 2008).
The original URICA is a 32-item self-report question-
naire, which employs a 5-point Likert scale asking
respondents to rate their degree of agreement (or disagree-
ment) with each item (DiClemente & Hughes, 1990).
Each item refers to a “problem” that the patient identi-
fies. The URICA-M is a modified version designed to
suit the needs of people with SMI. A single readiness
to change score is calculated by subtracting the precon-
templation score from the sum of the contemplation, ac-
tion, and maintenance scores (Carbonari, DiClemente, &
Zweben, 1994). The possible range of the readiness score
is −2.00–14.00 with higher scores representing greater

motivation to change. Participants also completed the
Temptation to Use Drugs Scale and the Abstinence Self-
Efficacy Scale (DiClemente, Carbonari, Montgomery, &
Hughes, 1994), 20-item scales that assess the degree to
which subjects feel “tempted” to use drugs in different
situations and the degree to which they feel confident in
their ability to abstain from drug use in those situations.
Respondents made ratings using 5-point Likert scales,
and a total score was calculated. The Process of Change
Questionnaire (POC; Prochaska, Velicer, DiClemente, &
Fava, 1988) was used to assess the frequency of occur-
rence of 10 core processes used to attain the desired be-
havioral change on a 5-point Likert scale (1 = never to
5 = repeatedly). From this, we calculated a total process
score (using all 20 items), an experiential process subscore
(10 items), and a behavioral process subscore (10
items). Experiential processes involve more covert cog-
nitive and behavioral processes such as consciousness
raising (greater awareness of the problem behavior) and
dramatic relief (emotions associated with the problem be-
havior or solution to the problem are aroused). Behavioral
processes involve more overt, observable processes such
TABLE 1. Diagnostic and symptom features of a treatment-
seeking and nontreatment-seeking sample of people with SMI
and
SUD by gender
Treatment-seeking Community
Male (n = 111) Female (n = 64) Male (n = 82) Female (n = 55)
Overall MANOVA F (4, 158) = 0.18, p = .95 F (4, 132) = 1.21,

p = .31
Mean positive symptoms (SD) 1.8 (0.7) 1.9 (0.6) 2.0 (0.7) 2.1
(0.9)
Mean negative symptoms (SD) 1.8 (0.6) 1.8 (0.6) 2.0 (0.7) 2.1
(0.8)
Mean general symptoms (SD) 1.9 (0.4) 1.8 (0.4) 1.9 (0.4) 2.0
(0.6)
Percent affective diagnosis (n) 53% (59) 58% (37) 42% (34)
51% (28)
Percent schizophrenia spectrum
diagnosis (n)
40% (44) 36% (23) 56% (46) 47% (26)
as contingency management (positive behavioral changes
are rewarded) and stimulus control (planned strategies
for coping with or avoiding triggers). Psychometric prop-
erties of these scales are strong across addictive behav-
iors (DiClemente et al., 1994; Hiller, Broome, Knight, &
Simpson, 2000).
Procedures
For both studies, all procedures were approved by the Uni-
versity of Maryland Institutional Review Board. Medical
records of all new intakes at several recruitment sites (a
VA medical center and two community clinics in Mary-
land) were reviewed once per week to determine pre-
liminary eligibility, including diagnosis of SMI. All po-
tential subjects participated in a standardized informed
consent process with trained recruiters and were advised
at the time that a Federal Certificate of Confidentiality
would protect the information they provided. For both
studies, participants completed the diagnostic interview
and symptom assessment first and generally completed
the remaining baseline assessments within a week. Also
in both studies, participants subsequently completed self-

report interviews regarding their substance use and pro-
vided urine samples for drug screens at follow-up time
points.
Data Analysis
Separate multivariate analyses of variance (MANOVAs)
were used to examine gender differences in symptoms
and diagnosis, frequency and severity of substance use,
and motivation to change for each sample (treatment-
seeking and community). Chi-square tests were used to
determine differences in history of trauma/victimization,
medical problems, and probation/parole status between
men and women in each sample and reasons for seek-
ing substance use treatment in the treatment-seeking sam-
ple. T-tests were used to examine gender differences in
lifetime arrests, lifetime charges, and days incarcerated in
the past month. Owing to differences in inclusion crite-
ria between the community and treatment-seeking sam-
ples, direct comparisons of the two samples were not
done.
RESULTS
Differences in Psychiatric Diagnosis and Symptoms
by Gender
Table 1 lists diagnostic breakdown and PANSS scores
by gender for both samples. MANOVA was used to as-
sess gender differences in symptoms and diagnosis. The
MANOVA was not significant for either sample. Psychi-
atric symptoms fell within the mild to moderate range for
both samples.
Frequency and Severity of Substance Use by Gender
Separate MANOVAs were conducted to examine gender
differences in the frequency and severity of substance
use. Frequency was measured by four ASI items tap-

ping drug and alcohol use in the last 30 days (number
of days of cocaine use, heroin use, marijuana use, and
alcohol use). Severity was assessed with six additional
variables by using ASI items: number of days of drink-
ing in the past month, number of days of drinking-related
problems in the last month, number of days that more
than one substance was used in the last month, number
of days of drug-related problems in the last month, the
degree of self-reported distress from drug-related prob-
lems in the last month, and the degree of self-reported dis-
tress from alcohol-related problems in the last month. A
seventh variable was constructed that assessed the num-
ber of different substances the participant had used in
the past month. Results for both samples are presented
in Table 2. Overall, there were no differences in last-
month frequency or severity of substance use in either
sample.
Gender Differences in Victimization, Medical
Problems, and Legal Problems
Next, we examined gender differences in victimization,
medical problems, and legal problems (Table 3). First,
victimization was examined using three items from the
ASI that assess lifetime incidence of emotional, physi-
cal, and sexual abuse. Rates of victimization were high,
with over 70% of participants in both samples report-
ing emotional abuse, between 48% and 50% of the sam-
ples reporting physical abuse, and from one-quarter (com-
munity) to one-third (treatment-seeking) of participants
reporting a history of sexual abuse. Women in both
TABLE 2. Patterns and severity of substance use of a treatment-

seeking and nontreatment-seeking sample of people with SMI
and SUD by
gender
Male (n = 111) Female (n = 64) Male (n = 82) Female (n = 55)
Pattern of substance use (past month) [mean (SD)]
Overall MANOVA F (4, 170) = 1.37, p = ns F (4, 129) = 1.85, p
= ns
Days cocaine use 3.3 (5.6) 4.8 (7.1) 5.7 (6.4) 6.5 (9.2)
Days heroin use 1.2 (4.2) 2.8 (7.6) 1.7 (4.6) 1.7 (6.0)
Days marijuana use 1.2 (4.8) 2.0 (6.3) 0.7 (1.9) 1.4 (4.8)
Days alcohol use 3.2 (6.6) 3.3 (6.1) 6.4 (9.1) 3.8 (6.8)
Severity of substance use (past month) [mean (SD)]
p = .087
Days drug use 2.1 (4.7) 3.0 (5.2) 3.7 (6.0) 4.0 (7.3)
Number of substances used 1.3 (1.3) 1.5 (1.3) 2.2 (1.2) 1.9 (1.3)
Days drug problems 7.4 (10.3) 12.6 (12.3) 8.8 (11.4) 10.2 (12.5)
Distress from drug problems 1.9 (1.5) 2.4 (1.5) 2.2 (1.6) 2.1
(1.6)
Days alcohol use 3.2 (6.6) 3.3 (6.1) 6.4 (9.1) 3.8 (6.8)
Days alcohol problems 2.8 (6.6) 3.1 (7.7) 4.1 (9.0) 1.6 (4.7)
Distress from alcohol problems 0.9 (1.4) 0.7 (1.2) 1.1 (1.5) 0.5
(1.0)
samples were more likely than men to report a history of
sexual abuse (community: χ 2 = 3.88, p = .049; treatment-
seeking: χ 2 = 13.4, p < .001). There were no gender
differences in physical or emotional abuse. Violent
victimization was assessed with a separate variable
constructed using five items from the SUESS reflecting
whether or not the respondent had been a victim of a vi-

olent crime (i.e., robbed or mugged, beaten up or physi-
cally injured, raped or sexually assaulted, life-threatening
assault, any other life-threatening events, or serious in-
jury) in the 90 days prior to the assessment. Men and
women did not differ on this variable (community: χ 2
= .04, p = ns; treatment-seeking: χ 2 = 1.81, p = ns).
Second, medical problems were assessed with two items
from the SUESS: self-report of a physical/medical prob-
lem in the last 90 days and met with a doctor or nurse
about a medical problem in the last 90 days. There
were no gender differences on these variables in ei-
ther sample. Third, four legal variables from the ASI
were compared: current probation/parole, number of
lifetime arrests, number of lifetime incarcerations, and
number of days incarcerated in the last month. In the
treatment-seeking sample, men reported more crimi-
nal charges [Z (136) = −2.00, p = .045] and con-
victions [Z (136) = −2.11, p = .035] than women.
There were no gender differences in criminal charges
or convictions in the community sample [t (174) =
1.76, p = ns]. There were no gender differences in pro-
bation/parole status or number of days in the jail/prison in
either sample.
TABLE 3. Gender differences in victimization, medical
problems, and legal problems in a treatment-seeking and
nontreatment-seeking
sample of people with SMI and SUD
Variable Male (n = 111) Female (n = 64) Male (n = 82) Female
(n = 55)
History of trauma/victimization

Percent emotional abuse, lifetime (n) 73% (81) 83% (53) 74%
(60) 72% (39)
Percent physical abuse, lifetime (n) 45% (50) 59% (38) 42%
(34) 57% (30)
Percent sexual abuse, lifetime (n) 24% (27) 52% (33)∗ ∗ 21%
(17) 37%(19)∗
Percent violent victimization, past 90 days (n) 24% (27) 34%
(21) 33% (27) 35% (19)
Medical problems (past 90 days)
Percent reported physical/medical problems (n) 67% (74) 63%
(39) 56% (46) 46% (25)
Percent met with doctor/nurse (n) 77% (57) 90% (35) 76% (35)
84% (21)
Legal problems
Number on probation/parole (%) 24 (27%) 19 (12%) 24 (19%)
11 (6%)
Mean number lifetime arrests/charges (SD) 6.3 (9.4) 3.7 (5.0)∗
5.1 (7.5) 3.4 (3.8)
Mean number lifetime convictions (SD) 3.9 (7.4) 2.1 (4.0)∗ 2.6
(4.9) 1.5 (2.3)
Mean days incarcerated past month (SD) 8.1 (16.1) 4.9 (11.0)
2.0 (8.9) 0.6 (4.1)
∗ Females and males differ, p < .05.
∗ ∗ Females and males differ, p = .001.
TABLE 4. Gender comparisons in motivation to change in a
treatment-seeking and nontreatment-seeking sample of people

with SMI and
SUD
Male
(n = 111)
Female
(n = 64) F p
Male
(n = 82)
Female
(n = 55) F p
p = .81
Mean temptation to use drugs (SD) 2.7(0.9) 3.1(1.0) 7.49 .007
3.1(0.9) 3.0(1.0) 0.35 .557
Mean experiential process (SD) 3.3(0.7) 3.6(0.7) 4.26 .040
3.2(0.7) 3.1(0.8) 0.17 .678
Mean behavioral process (SD) 3.4(0.8) 3.5(0.9) 0.70 .403
3.3(0.7) 3.3(0.9) 0.07 .793
Mean readiness to change (SD) 10.2(1.6) 10.9(1.7) 7.95 .005
10.0(1.9) 9.9(2.0) 0.04 .835
Mean drug self-efficacy (SD) 3.2(0.9) 2.9(1.1) 2.93 .089
3.0(0.9) 2.8(1.0) 0.71 .401
Motivation to Change
A one-way MANOVA was used to assess gender differ-
ences in variables tapping motivation to change (temp-
tation to use drugs, experiential process of change,
behavioral processes of change, readiness to change, and
drug self-efficacy). The overall MANOVA was significant

[F (5, 165) = 3.07, p = .01]. Separate one-way analy-
ses of variance (ANOVAs; Table 4) showed that, in the
treatment-seeking sample, women reported greater temp-
tation to use drugs, greater use of experiential processes of
change, and greater overall readiness to change than men.
There were no gender differences in motivation to change
in the community sample.
Reasons for Seeking Treatment
We then explored gender differences in reasons for seek-
ing treatment in the treatment-seeking sample (Table 5).
Participants reported a number of reasons for seeking
treatment. Thinking seriously about the pros and cons
of using drugs was the most frequently cited reason
for seeking treatment (83%), followed by worsening of
psychological or emotional problems (79%), experienc-
ing a major change in lifestyle (72%), experiencing a
recent traumatic event (61%), and hitting rock bottom
(60%). Gender differences in responses were explored
via chi-square analyses. There were no significant gender
differences.
DISCUSSION
This study sought to describe the ways in which sub-
stance use and severity, motivation to change, and rea-
sons for seeking treatment differed between women and
men with SMI and SUDs. Data were collected from two
samples of participants with SMI and SUDs: a com-
munity sample and a sample seeking treatment for sub-
stance abuse. In line with previous research on gen-
der differences in dually diagnosed individuals, women
and men in both samples showed more similarities than
differences in terms of their patterns and severity of
substance use. Alcohol and cocaine were the most fre-
quently used substances for both men and women, and

there were no gender differences in severity of substance
use. Because all participants in the community sample
met criteria for current cocaine dependence, it is not
surprising that there were no gender differences in co-
caine use or problems from cocaine use. However, no
such restriction was in place for the treatment-seeking
sample. The fact that women showed similar substance
use and severity to men contrasts with findings in pri-
mary substance users. Men with primary SUDs typi-
cally evidence more problems with alcohol and mar-
ijuana use and women more problems with cocaine
use (Pelissier & Jones, 2005). The similarity of women
and men with SMI and SUDs in the treatment-seeking
TABLE 5. Reasons for seeking treatment by gender in a
treatment-seeking samplea (in %)
Variable Total (n = 77) Male (n = 47) Female (n = 30)
Thought seriously about pros and cons of use 83.3 82.2 85.2
Psychological or emotional problems worsened 78.7 75.7 83.3
Major change in lifestyle 72.2 71.1 74.1
Experienced a traumatic or very disturbing event 61.1 62.2 59.3
Hit “rock bottom” 59.7 64.4 51.9
Referred by case manager or therapist 47.3 46.7 48.1
Warned about use by family or close other 41.7 40.0 44.4
Doctor warned you about use 41.7 42.2 40.7
Physical health problems 40.3 42.2 37.0
Someone else quit using or cut down 29.2 28.9 29.6
Religious experience 27.8 28.9 25.9
Saw someone else high 20.8 17.8 25.9
Referred by court/probation/parole officer 15.3 15.6 14.8
aAll chi-square analyses were not significant.

sample in terms of frequency and severity of substance
use may be related in part to symptoms of SMI, which
may render both men and women equally vulnerable to
using substances and the negative impact of substance use
on functioning.
Gender differences were found in rates of some
substance-related negative consequences. First, women in
both the treatment-seeking and the community samples
were more likely to report sexual abuse than men, a find-
ing that is in line with other studies (Alexander, 1996;
Brunette & Drake, 1998; Gearon, Kaltman, Brown, &
Bellack, 2003; Gearon, Nidecker, et al., 2003). The fact
that this gender difference was found in both samples il-
lustrates the pervasiveness of sexual abuse among women
with SMI and SUDs and highlights trauma as an issue
that impacts women regardless of their substance abuse
treatment status. Higher rates of sexual abuse were found
among treatment-seeking women compared with women
in the community, suggesting that abuse or trauma may
play a role in the initiation of treatment. Interestingly, al-
most a quarter of men reported prior sexual abuse. There
were high rates of physical abuse, emotional abuse, or
violent victimization overall and no gender differences
in these domains in either sample, suggesting a unique
risk for women in terms of sexual abuse. Second, men
in the treatment-seeking sample were more likely to have
legal problems than women, including criminal charges
and convictions. This may reflect gender differences in
how drugs are accessed and the settings in which drugs
are used by people with SMI or the nature of the crimes
committed and/or likelihood of being prosecuted for those
crimes. Gearon, Nidecker and colleagues (2003) found
that women with SMI were more likely to purchase drugs

from, use drugs with, and get money for drugs from
friends and significant others. This close association be-
tween drug use and family may result in women with SMI
being less likely to use substances in situations that may
place them at risk for legal difficulties (i.e., using in pub-
lic places, attempting to purchase drugs from drug dealers,
using drugs with strangers). The fact that this difference
was found only in the treatment-seeking sample suggests
that increasing legal problems may be a factor that propels
men with SMI and SUDs into treatment. Third, medical
problems were equally prevalent among men and women
in both samples. A lack of gender differences in medical
problems could reflect the high rate of medical problems
among people with SMI in general and particularly among
those with comorbid SUDs.
Readiness to change variables and reasons for seeking
treatment were of particular interest in this study. Women
in the treatment-seeking sample reported greater temp-
tation to use drugs, greater use of experiential processes
of change, and greater overall readiness to change than
men. This pattern suggests that women come to treatment
with greater readiness to attempt change than men and
may have already made some change efforts. This find-
ing is in line with others that have found that women with
dual diagnoses use more experiential processes as part of
their change efforts than men (O’Conner, Carbonari, &
DiClemente, 1996). Use of experiential processes is as-
sociated with preparing for change (DiClemente et al.,
1991). The combination of higher experiential process and
readiness to change scores among women could mean that
women are more likely to seek treatment once they have
committed to change. In contrast, men may begin treat-
ment less convinced of the benefits of change and less
likely to have attempted change on their own (Watkins

et al., 1999). Interestingly, despite the fact that all partic-
ipants in the community sample met criteria for current
cocaine dependence, no gender differences were found in
motivation to change. This suggests that there is likely
some factor other than simply drug use severity that im-
pacts women’s motivation and treatment seeking. As we
have speculated, it is possible that trauma may play a role
here, as rates of trauma were higher among women in the
treatment-seeking sample.
Men and women reported similar reasons for seeking
substance abuse treatment. Engaging in an evaluation of
the advantages and disadvantages of substance use was
the most frequently endorsed reason for seeking treat-
ment. An increase in psychological health problems, hav-
ing a major change in lifestyle, and recently experiencing
a traumatic event were also frequently identified as rea-
sons for seeking treatment. The lack of gender differences
here suggests that there may be a core set of reasons for
seeking treatment in people with SMI and SUD. Factors
such as mental health problems and trauma may be among
the most important experiences that convince people with
SMI and SUDs to enter substance abuse treatment, regard-
less of gender.
These findings have implications for the identification
and clinical care of men and women with SMI and SUDs.
The high rates of abuse found here and in other stud-
ies suggest that trauma and its relationship to substance
use should to be assessed as a routine part of substance
abuse treatment for both men and women. Given that sex-
ual trauma is particularly prevalent among women with
SMI and SUDs, substance abuse treatment for women
with dual disorders may need to include strategies specif-
ically focused on reducing risk of abuse and coping
with trauma. Inclusion of these strategies could serve to

reduce incidence of trauma, minimize the impact of
trauma, and improve treatment engagement, retention,
and outcome (Bellack & Gearon, 1998). Moreover, as-
sessment of trauma by health care professionals in the
community, such as workers in primary care, outpatient
mental health, or emergency rooms, might be an impor-
tant step in getting women with SMI and SUD in the com-
munity to think about the harm caused by their substance
use and perhaps consider treatment for it. In addition, our
findings suggest that when women with SMI and SUDs do
come to treatment, they are more highly motivated than
men to make a change and may already be engaging in
change efforts. This suggests that the initial activities as-
sociated with treatment should involve assessment of mo-
tivation and tailoring, depending on a woman’s level of
readiness to change. Women who are already involved in
change may want different sorts of advice, assistance, or
support from a clinician than others who are less ready
or who may be still deciding whether and how to make a
change.
Study’s Limitations
Several limitations of this study should be noted. Individu-
als in the treatment-seeking sample were selected because
they reported current dependence on at least one of sev-
eral drugs (cocaine, heroin, or marijuana), while those in
the community sample were selected for current depen-
dence on cocaine only (although they could meet criteria
for dependence on other drugs in addition to cocaine). Be-
cause of these differences, we were unable to directly ex-
amine gender differences across samples. A direct com-

parison of gender differences in treatment-seeking and
nontreatment-seeking people with SMI and SUD could
provide important information concerning factors that fa-
cilitate or impede treatment seeking in this group. In ad-
dition, the studies from which these data were taken were
not designed to assess gender differences and so may
not have captured relevant variables. For example, the
list of reasons for seeking treatment used here was de-
signed for substance users regardless of gender and so
did not include reasons that may be especially relevant for
women such as reasons related to child custody, interac-
tions with child protective services, and housing issues.
Future research should include these sorts of reasons for
seeking treatment that might be especially important to
women.
While these findings provide a useful first step, more
remains to be examined and understood about women
with SMI and SUDs. Future studies should move be-
yond patterns and consequences of use and directly assess
whether women with SMI and SUDs experience unique
barriers to treatment. People with SMI and SUDs have
reported numerous barriers to treatment, including cost
of treatment, fears about what happens in treatment, and
about being hospitalized (Nidecker, Bennett, Gjonbalaj-
Marovic, RachBeisel, & Bellack, 2009). It remains un-
clear if women experience additional barriers such as
family-related responsibilities including care of children
or other family members and fear about how treatment
may impact important social and family relationships. In
addition, our finding that women may be more ready to
change and may have attempted some behavior change
prior to seeking treatment needs to be understood in light
of other findings that women with SMI and SUDs are
less likely to seek formal treatment than men (Alexander,
1996; Bellack & Gearon, 1998; Comtois & Ries, 1995).

Women may attempt more change on their own, seeking
out professional assistance only when their change efforts
have failed. Thus, motivation and change efforts may ac-
tually serve as a barrier to formal care for some women
who believe they can change on their own. A better un-
derstanding of the factors that keep women away from
treatment could lead to the development of relevant and
effective outreach and treatment approaches that address
or overcome barriers to care for women with SMI and
SUDs.
Declaration of Interest
The authors report no conflicts of interest. The authors
alone are responsible for the writing and content of the
article.
RÉSUMÉ
Différences dans les modèles et conséquences de la
dépendance aux substances, dans la recherche de
traitements et dans la motivation de vouloir changer
selon les sexes
Différences dans les modèles et conséquences de la
dépendance aux substances, dans la recherche de traite-
ments et dans la motivation de vouloir changer selon les
sexes. Deux échantillons ont été examines: un échantillon
comprenait des gens avec des maladies mentales graves et
l’autre échantillon comprenait des gens avec des troubles
liés à l’usage de substances. Un des échantillons compre-
nait des gens dans la communauté qui ne recherchaient pas
présentement de traitements pour la dépendance aux sub-
stances (N = 175) et l’autre échantillon comprenait des
gens qui recherchaient un traitement (N = 137). Dans les

deux groupes, les femmes et les hommes ont démontrées
plus de similarités que de différences dans les modèles et
sévérité d’utilisation de leurs substances. Par contre, les
femmes qui recherchaient un traitement ont démontrées
une facilité plus importante à changer leurs dépendances
aux substances. Les problèmes de maladies mentales et les
expériences traumatiques pourraient faire en sorte que les
gens faisant partie de ses deux groupes sont incites à entrer
dans un traitement d’abus de substances indépendamment
de leur sexe.
RESUMEN
Diferencias de género de su uso de sustancia, con-
secuencias, el motivo para cambiar, y buscando-
tratamiento en personas con trastornos mentales
crónico/grave
Diferencias de género en pautas y consecuencias del uso
de sustancia, buscando-tratamiento, y el motivo para cam-
biar fueron examinado en dos muestras de personas con
trastornos mentales crónico/grave (TMC) y comorbilidad
de trastornos por uso de sustancias (TUS): una muestra
de la comunidad cual presentemente no está buscando
tratamiento de abuso de sustancia (N = 175) y una mues-
tra buscando tratamiento (N = 137). En ambos grupos, las
mujeres y los hombres demostraron más similitudes que
diferencias en la pauta y la severidad de su uso de sus-
tancia. Sin embargo, mujeres cual buscaron-tratamiento
mostraron la prontitud más grande para cambiar su uso
de sustancia. Los problemas de la salud mental y experi-
encias traumáticas pueden incitar a personas con TMC y

TUS a entrar tratamiento de abuso de sustancia, a pesar de
género.
THE AUTHORS
Amy Drapalski, Ph.D., is
Administrative Core Manager
at the Veterans Affairs (VA)
Capital Health Care Network
Mental Illness, Research,
Education and Clinical Center
(MIRECC). Her research has
primarily focused on identifying
barriers and facilitators of
recovery and developing,
evaluating, and implementing
psychosocial treatments
for individuals with serious
mental illness and their families. Her current research is aimed
at
understanding the impact of self-stigma and other related
factors
on recovery and developing interventions aimed at reducing
internalized stigma and its effects in people with serious mental
illness.
Melanie Bennett, Ph.D., is a
Clinical Associate Professor in
the Department of Psychiatry
at the University of Maryland,
School of Medicine. Her
primary research focus has
been on the assessment and
treatment of substance use
disorders in people with serious

mental illness. Her current
research focuses on developing
behavioral treatment programs
to alcohol, drug, and nicotine
dependence in people with schizophrenia and other forms of
serious mental illness. She is also interested in ways to improve
treatment engagement and outcome via motivational
enhancement
strategies that are adapted for individuals with serious mental
illness.
Alan S. Bellack, Ph.D., A.B.P.P., received his Ph.D. from the
Pennsylvania State University in 1970. He currently is Professor
of Psychiatry and Director of the Division of Psychology at the
University of Maryland School of Medicine and Director of the
VA Capital Health Care Network Mental Illness Research,
Education, and Clinical Center (MIRECC). He was formerly
Professor of Psychiatry and Director of Psychology at the
Medical
College of Pennsylvania and Professor of Psychology and
Director
of Clinical Training at the University of Pittsburgh. He is a Past
President of the Association for Advancement of Behavior
Therapy and of the Society for a Science of Clinical
Psychology.
He is a Diplomate of the American Board of Behavior Therapy
and the American Board of Professional Psychology and a
fellow
of the American Psychological Association, the American
Psychological Society, the Association for Clinical
Psychosocial
Research, and the American Psychopathological Association. He
was the first recipient of the American Psychological
Foundation
Gralnick Foundation Award for his lifetime research on

psychosocial aspects of schizophrenia and was the first
recipient of
the Ireland Investigator Award from NARSAD. He received an
National Institute of Mental Health (NIMH) MERIT award and
has had continuous funding from NIH since 1974 for his work
on
schizophrenia, depression, social skills training, and substance
abuse. He chaired the VA Recovery Transformation Workgroup
and is Chair of the VA National Recovery Advisory Committee.
He is founding Coeditor of the journals Clinical Psychology
Review and Behavior Modification and serves on a number of
other editorial boards and a VA Merit review study
section.
Dr. Bellack has published 175
journal articles and 52 book
chapters. He is Coauthor or
Coeditor of 31 books, including
Bellack, A. S., Mueser, K. T.,
Gingerich, S., & Agresta, J.
(2004). Social Skills Training
for Schizophrenia: A Step-by-
Step Guide (Second Edition).
New York: Guilford Press, and
Bellack, A. S., Bennett, M. E., &
Gearon, J. S. (2007). Behavioral
Treatment for Substance Abuse
in People With Serious and Persistent Mental Illness. New
York:
Taylor and Francis.
GLOSSARY
Behavioral Processes of Change: More overt, observable

processes (i.e., reinforcement management, helping re-
lationships, stimulus control, etc.) by which behavior
change may occur.
Dual Diagnosis or Co-Occurring Substance Disorder:
having both a psychiatric condition or illness and a sub-
stance use disorder.
Experiential Processes of Change: More covert cognitive,
and behavioral processes (i.e., increasing awareness of
a problem behavior, assessing the impact of behav-
ior on the surrounding environment, self-reevaluation,
etc.) by which behavior change may occur.
Stages of Change: A key construct in the Transtheoretical
Model of Change (Prochaska & DiClemente, 1983) re-
ferring to the stages through which an individual pro-
gresses when making a behavior change; stages in-
clude precontemplation (not thinking about/planning
to change in the near future), contemplation (aware-
ness of a desire to change in the near future), prepa-
ration (plans made to change in the near future), action
(changes in behavior occur), and maintenance (behav-
ior change has occurred and has been maintained for a
least 6 months).
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Theories of Motivation in Addiction Treatment:
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Model of Change and Self-Determination Theory
Kerry Kennedy PhD & Thomas K. Gregoire PhD
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Copyright © Taylor & Francis Group, LLC
ISSN: 1533-256X print/1533-2578 online
DOI: 10.1080/15332560902852052
WSWP1533-256X1533-2578Journal of Social Work Practice in
the Addictions, Vol. 9, No. 2, March 2009: pp. 1–38Journal of
Social Work Practice in the Addictions
Theories of Motivation in Addiction
Treatment: Testing the Relationship
of the Transtheoretical Model of Change
and Self-Determination Theory
Theories of Motivation in Addiction TreatmentK. Kennedy and
T. K. Gregoire
KERRY KENNEDY, PHD
Assistant Professor, Department of Social Work and
Gerontology, Weber State University,
Ogden, Utah, USA

THOMAS K. GREGOIRE, PHD
Associate Professor, College of Social Work, Ohio State
University, Columbus, Ohio, USA
This study explored the relationship between 2 theories of
motivation:
self-determination theory (SDT) and the transtheoretical model
of
change (TTM), and sought to determine whether the source of
moti-
vation described by SDT would predict TTM’s stage of change.
SDT
was operationalized as the level of internal or external
motivation
for treatment, and TTM was operationalized as 3 stages of
change:
precontemplation, contemplation, and action. Our data came
from
the Drug Abuse Treatment Outcome Study published in 2004. A
multinomial logistic regression analysis indicated that there was
a
significant relationship between source of motivation and stage
of
change at intake. Controlling for severity, treatment history,
legal
status, and primary substance use, persons entering treatment
with higher levels of internal motivation were more likely to be
in
the action stage than the precontemplation stage. Higher levels
of
internal motivation also predicted a greater likelihood of being
in
the contemplation rather than the precontemplation stage.
KEYWORDS addiction, motivation, stages of change

Received July 18, 2006; accepted February 22, 2007.
Address correspondence to Kerry Kennedy, Department of
Social Work and Gerontology,
Weber State University, 1211 University Circle #148, Ogden,
UT 84041, USA. E-mail: [email protected]
weber.edu
164 K. Kennedy and T. K. Gregoire
Recent years have seen an evolution in thought about the role of
motivation
in substance abuse treatment. Motivation was once viewed as a
function of
individual differences and largely related to personality traits.
Individuals
who did not comply with treatment were considered to be
unmotivated
(Clancy, 1961). Motivation is now considered more a function
of an interac-
tion of individual and environmental factors (Miller & Rollnick,
2002). Current
views of motivation in treatment place the onus for client
motivation on the
clinician, recognizing that the interaction of the clinician and
client “has a
crucial impact on how they respond and whether treatment is
successful”
(Center for Substance Abuse Treatment, 1999, p. 3). Clients
once viewed as
not ready to change their behavior are now more likely seen as
in need of
different interventions than those perceived as more motivated

(Prochaska,
DiClemente, & Norcross, 1992). Interventions such as
motivational inter-
viewing are based on the demonstrated assumption that the
clinician need
not wait for a client’s readiness but instead can impact a
client’s level of
motivation and guide them toward prosocial behavior (Miller &
Rollnick,
2002).
The change in the perception of the role of motivation has been
heavily influenced by the emergence of the transtheoretical
model (TTM;
DiClemente, 2003; Prochaska, 1979; Prochaska & DiClemente,
1983; Prochaska
et al., 1992). As described later, this theoretical perspective on
a client’s
readiness to change has led practitioners to recognize that
motivation to
change might be a malleable, dynamic, and nonlinear process.
Although less well known than TTM, self-determination theory
(SDT;
Deci & Ryan, 1985) describes the role of internal and external
factors in
understanding motivation. The two theories provide distinct
approaches to
understanding the role of motivation in affecting change among
persons
who misuse substances. This article explores the relationship of
these two
theories and considers if the two approaches in tandem provide
a greater
understanding of motivation among persons with substance use
disorders.

THE TRANSTHEORETICAL MODEL
The TTM was derived from a compilation of 18 different
psychological and
behavioral theories and provides a temporal framework for
describing
intentional behavior change (Prochaska, 1979). Developed
originally as a
model for understanding client-initiated attempts to modify
their nicotine
addiction (Prochaska & DiClemente, 1983), TTM has been
adapted to a
wide range of behaviors including substance use (DiClemente &
Hughes,
1990).
DiClemente (2003) described TTM as consisting of four
interrelated
dimensions of change that include stages, processes, markers,
and a context
of change. The stages of change are delineated with a time
frame and tasks
Theories of Motivation in Addiction Treatment 165
associated with movement through that stage. There are five
stages of change
(Prochaska et al., 1992). Persons identified in the
precontemplation stage
have no current intention to modify their behavior, and might
not acknowl-
edge that they have a problem. Persons characterized as
contemplators are

thinking about addressing their problem, but have yet to take
action. The
preparation stage reflects a client’s intention to make some
change attempt
during the next month. Persons who have made an unsuccessful
change
effort in the preceding year are also in this stage of change. The
action stage
is associated with the initiation of modified substance use
behavior. Typically,
the action stage extends from 3 to 6 months (DiClemente,
2003). During the
final maintenance stage, beginning at 6 months after behavior
change, persons
take steps to avoid relapse and consolidate their new lifestyle.
These stages are accompanied by processes of change. Each
process
consists of interventions appropriate to assist a person in
moving to the next
stage of change (Prochaska et al., 1992). The context of change
attempts to
address other areas that might contribute to maintenance of the
problem
including interpersonal, social, and environmental dimensions
(DiClemente,
2003).
TTM emphasizes the importance of attempting and then
maintaining
new behavior in understanding motivation for change. The
concept of deci-
sional balance refers to the weight of the evidence for and
against a certain
behavior. In TTM, persons initiate change as the balance tips
against the

benefits of the addictive behavior. TTM does not appear to
distinguish
between the importance of internal and external sources of
motivation with
respect to decisional balance, although early interventions do
tend to empha-
size external pressure.
The literature on the influence of motivation type on stage of
change
is limited and divided. O’Hare (1996) found that people referred
through
the court were more likely to be in the precontemplation stage
than peo-
ple who were not court referred. However, Gregoire and Burke
(2004)
reported that persons who were legally coerced to outpatient
substance
abuse treatment were more likely to be in the action stage of
change than
precontemplation.
A considerable literature has emerged on TTM, with mixed
findings on
its efficacy as a tool for understanding addiction and recovery.
Knowledge
of a client’s stage of change has been demonstrated in some
studies as an
important tool for predicting treatment outcomes. In particular,
studies have
associated a client’s stage of change with subsequent alcohol
use (Heather,
Rollnick, & Bell, 1993), heroin or cocaine use (Henderson,
Galen, & Saules,
2004; Prochaska et al., 1994), and treatment dropout (Callaghan
et al., 2005;

Edens & Willoughby, 2000; Haller, Miles, & Cropsey, 2004;
Simpson & Joe,
1993). In general, clients identified as precontemplators fared
worse on sub-
sequent outcomes than those who presented for treatment in
more advanced
stages of change.
However, other authors reported finding no relationship
between stage
of change and either posttreatment substance use (Belding,
Iguchi, & Lamb,
1997; Burke & Gregoire, 2007) or Addiction Severity Index
composite scores
(Burke & Gregoire). Another study found the stages of change
model
unable to predict either treatment attendance or the percentage
of days
abstinent (Blanchard, Morgenstern, Morgan, Labouvie, & Bux,
2003).
Critics of the TTM argue that this model is arbitrary in its
division of the
stages of change and the time frame of each stage (Sutton,
2001). Bandura
(1997) described the TTM as “arbitrary pseudo-stages” (p. 8)
and not a true
stage theory. Davidson (1998) and Bandura (1997) suggested
that the TTM
does not include stages that are distinctly, qualitatively
different from each
other, noting that one stage can be considered an extension of

the previous
stage. Weinstein, Rothman, and Sutton (1998) also described
the specific
time points of the stages as arbitrary.
DiClemente (2003) acknowledged that the TTM stage model
does not
produce fixed stages with either a determinant order or a single
linear path-
way. Instead the stages are best understood as a developmental
model of
recovery in which the resolution of earlier tasks impacts the
later stages.
SELF-DETERMINATION THEORY
SDT (Deci & Ryan, 1985) has been applied to many areas, such
as medica-
tion adherence, weight loss, and test-taking behavior in school-
aged children.
However, the application of this theory specifically to substance
abuse has
been limited (Ryan, Plant, & O’Malley, 1995; Zeldman, Ryan,
& Fiscella,
2004). Nevertheless, the application of SDT might complement
TTM as a
framework for understanding motivation in substance misuse
treatment.
Whereas TTM does not place a major emphasis on the internal
or external
aspects of a client’s motivation to change, SDT addresses the
source of
motivation specifically by outlining a framework for
understanding internal
and external sources of motivation and the impact of each on
treatment out-

comes. SDT defines motivation as consisting of six categories
that describe a
continuum of external to internal motivation.
SDT postulates that persons with higher internal motivation
should
have better treatment outcomes, and that high external
motivation in the
absence of internal motivation is associated with less positive
outcomes.
Zeldman et al. (2004) found that clients in a methadone
maintenance pro-
gram with higher levels of internal motivation had lower relapse
rates and
higher program participation than externally motivated persons.
Ryan et al. (1995) also found that higher internalized
motivation was
negatively correlated with dropping out of treatment. Their
study also
observed an interaction between internal and external
motivation, as per-
sons with both high internal and high external motivation were
most likely
to persist in treatment. External motivation was positively
related to out-
comes only when internal motivation was present.
RELATIONSHIP BETWEEN SDT AND TTM
Both theories make important contributions to understanding

motivation
among persons with substance use disorders. SDT identifies the
forces that
might influence an individual to initiate behavior change and
the psycho-
logical mechanisms that drive an individual to accomplish
change. TTM
identifies the change processes in individuals within a temporal
dimension
of motivation and provides a framework for understanding
movement toward
behavior change.
Together, SDT and the TTM offer a more comprehensive view
of moti-
vation than either do independently. Whereas SDT emphasizes
the influ-
ence of perceived antecedents of behavior change on the
individual, TTM
provides structure to understand how clients move through a
progression of
behavior change. Although both theories incorporate
information about the
source of motivation, SDT more explicitly distinguishes the
influence of
internal and external sources of motivation. TTM refers to two
markers of
change—decisional balance and self-efficacy—and suggests that
early influ-
ences to change tend to be external as the decisional balance
scale is tipped.
TTM suggests that internal forces are at work as self-efficacy is
acquired and
change is maintained. However, efficacy refers less explicitly to
motivation
and more to the development of a confidence that one might

succeed in a
change effort.
Despite the utility of each in informing a greater understanding
of
motivation, a specific relationship between these two theories
has yet to
be determined within a population of persons who misuse
substances.
The purpose of this research is to explore the relationship of
TTM and
SDT among a sample of substance use treatment participants. In
particu-
lar, we seek to model the relationship between source of
motivation as
described by SDT and TTM’s stage of change to test the
following
hypotheses: (a) Higher levels of external motivation will predict
mem-
bership in the precontemplation stage of change; and (b) greater
internal
motivation will predict later stages of change, specifically
contemplation
and action.
METHOD
Data for this study were obtained from the Drug Abuse
Treatment Outcome
Study–Adult (DATOS) published in 2004 (U.S. Department of
Health and
Human Services, National Institute on Drug Abuse, 2004). The
DATOS study

was conducted by Research Triangle Institute and funded by the
National
Institute on Drug Abuse. DATOS data were derived from a
longitudinal pro-
spective cohort design of 10,010 persons aged 18 or older.
Clients from
96 programs in 11 cities were purposively chosen and
interviewed at intake
and again at several points during treatment. The selection of
communities
and programs reflected typical drug treatment programs in
medium and
large-sized U.S. cities and consisted of both publicly and
privately funded
entities.
Data were collected from 1991 to 1993 via face-to-face
interviews con-
ducted with participants in four different treatment modalities:
outpatient
methadone maintenance, long-term residential, outpatient drug
free, and
short-term inpatient. Data for our study consisted of two waves
of interviews.
The first wave was from an initial interview conducted as soon
as possible
after admission to treatment and the second wave came from an
interview
that was conducted about 1 week later.
Participants
Of the 10,010 participants in the total sample, 8,725 had a
completed first

and second intake interview on record. Although the initial two
waves of
DATOS data consisted of a purposive sample of treatment
admissions, data
for the DATOS follow-up interviews were derived from a
random sample.
The sample employed for our analysis consisted of a random
sample drawn
from those individuals with completed Wave 1 and 2 data. Table
1 describes
the characteristics of the sample in greater detail.
The majority of the 4,347 participants were male (67.1%), and
most
were over the age of 30 (58.2 %), with a mean age of 32.5 (SD
= 7.4). African
Americans made up 46.7% of our sample, 38.7% were White,
and 11.9% were
Hispanic. Most respondents (55.0%), had no current legal
involvement,
although 31.9% reported being on probation or parole.
Most of the respondents reported being unmarried at the time of
their
first interview; 45.4% were never married, 19.5% were
currently married,
12.1% were living as married, and 13.5% reported being
divorced. In terms
of education, about one third (38.4%) of the respondents had a
high school
degree, and another third (35.8%) had less than a high school
degree. Most
respondents were not working at the time of their initial
interview. Slightly
less than half of the respondents (42.4%) identified the major
source of

income as legal work, and about one fifth (20.4%) stated that
public assis-
tance was their major source of income.
Table 2 describes participant substance use. The majority
(51.1%) iden-
tified crack or cocaine as their primary drug problem, followed
by heroin
and other opiates (20.6%), alcohol (12.2%), marijuana (3.0%),
and amphet-
amines (2.4%).
The average number of prior treatments for substance use
disorders
was 1.95 (SD = 4.4). Slightly less than half of the participants
(44.7%) indi-
cated that this was their first treatment experience for substance
misuse. About
one in five persons indicated that this was their second
treatment experi-
ence (20.5%) with the remaining participants reporting between
three and
six prior treatment episodes. About one third (34.7%) of the
participants
identified their primary referral source as self, followed by
family or friends
(30.9%), and the legal system (21.9%).
TABLE 1 Sample Characteristics
Variable N %

Gender
Male 2,915 67.1
Female 1,432 32.9
Ethnicity
African American 2,029 46.7
White 1,684 38.7
Hispanic 517 11.9
Other 117 2.7
Marital status at intake
Never married 1,974 45.4
Married or living as married 1,374 31.6
Previously married 992 22.8
Missing 7 0.2
Age at admission
18–20 124 2.9
21–25 656 15.1
26–30 1,033 23.8
31–35 1,147 26.4
36–44 1,117 25.7
44 + 270 6.2
Educational level
No high school degree 1,555 35.8
High school degree 1,670 38.4
Some college 741 17.0
College degree 377 8.7
Missing 4 0.1
Major source of income
Legal work 1,843 42.4
Public assistance 887 20.4
Illegal sources 642 14.8
Family or friends 291 6.7

Social Security 198 4.6
No income 198 4.6
Other 61 1.4
Missing 227 5.2
Criminal justice status
No current legal status 2,389 55.0
Current legal status 1948 44.8
Missing 10 0.2
We assessed problem severity by considering frequency of
substance
use and reports of regular use of more than one substance.
Almost half of
the respondents (48.4%) indicated that they used their primary
drug daily,
almost every day, or on multiple occasions in a single day. An
additional
35.5% reported using one to six times per week, 3.3% reported
using less
than once a week, and 4.6% reported no use at the time of their
initial inter-
view. The number of different drugs used weekly ranged from
zero to eight.
Measures and Procedure
To control for the role of problem severity we created a severity
measure
and categorized respondents into one of three severity groups.
Participants
were assigned to a low-severity group when the frequency of

recent sub-
stance use was reported as none or less than one time per week
and the
number of lifetime substances used was zero or one. Moderate-
severity
TABLE 2 Substance Use and Treatment History
Primary drug problem N %
Cocaine/crack 2,221 51.1
Opiates 894 20.6
Alcohol 530 12.2
Other/not specified 374 8.6
Marijuana 129 3.0
Amphetamines 105 2.4
Missing 94 2.2
Treatment modality
Short-term inpatient 1,379 31.7
Residential 1,193 27.4
Outpatient drug free 1,098 25.3
Methadone maintenance 677 15.6
Previous treatment
No prior treatment 1,943 44.7
One prior treatment 893 20.5
Two or more prior treatments 1,499 34.5
Missing 12 0.3
Referral source
Self 1,509 34.7
Family or friends 1,344 30.9
Legal 950 21.9
Other professional 535 12.3
Missing 9 0.2

Frequency of primary drug use
1–6 times per week 1,543 35.5
2 + times per day 1,118 25.7
Daily or almost every day 987 22.7
Less than once per week 145 3.3
None 202 4.6
Missing 352 8.1
participants reported frequency of use of less than one time per
week and
the use of three or more substances, or for those who reported
daily use, of
one or more substances. Persons who reported daily or more
than daily use
of one or two substances were also assigned to moderate
severity. The
high-severity groups included those reporting daily or more
than daily use
who also reported using three or more substances. This group
also included
people who reported using one to five times per week who were
also using
four or more substances. Determining severity in this manner
resulted in
13.5% in the low-severity group, 57.3% in the moderate group,
and the
remaining 29.2% in the high-severity group.
To measure the independent variable of type of motivation
(internal or
external), we recoded responses to the question “What is the

most impor-
tant reason you are in treatment?” into three categories:
internal, external, or
unidentified. There were 57 possible responses for each of the
questions:
Responses were recorded as open-ended questions and then
coded into a
fixed category. Examples of external motivation include drug
availability,
custody issues with children, and court. Examples of internal
motivation
include disgusted with lifestyle, fear, and wanting to get off
drugs. Unidentified
responses were those that could have fallen into either internal
or external
motivation, such as religious reasons. A total of 47 of the
possible responses
were coded as external, 7 of the possible responses were coded
as internal,
and 3 of the possible responses were coded as undecided.
The survey repeated the important reason question three times
to identify
the primary, secondary, and tertiary reason for the client’s
referral. Based on
the responses to the three questions, a respondent could have
endorsed
three external responses, three internal responses, or a
combination of the
two types. Responses that were considered external were
recoded as a neg-
ative number. Responses were summed from the recoded
responses for the
three questions to create a motivation continuum, a scale
ranging from –3
(totally external) to +3 (totally internal).

Respondents were initially categorized into six categories based
on the
source of motivation, ranging from –3 (totally extrinsic) to +3
(totally intrinsic).
Scores of –2 and +2 were associated with somewhat external
and somewhat
internal motivation, respectively. Because of the low number of
respondents in
the somewhat external (n = 2) and somewhat internal categories
(n = 4),
these categories were collapsed under the lower heading.
Somewhat inter-
nal respondents were included with the slightly internal
respondents, and
somewhat external respondents were included with the slightly
external
respondents. The final source of motivation variable contained
four levels:
totally external, slightly external, totally internal, and slightly
internal.
Approximately one third of the respondents had a motivation
score of
+1, indicating slight internal motivation (34.9%) and a similar
percentage,
with a motivation score of –1, were coded as slightly externally
motivated
(37.3%). Only 5.8% of the respondents had a score of –3,
indicating totally
external motivation, and 21.4% scored +3 on the measure,

indicating a
totally internal source of motivation.
The DATOS study did not assess stage of change until the 3-
month
interview. At this point, the sample size was dramatically
reduced (n = 3,180)
both due to sample selection and attrition. Having participated
in treatment
for 3 months, the vast majority of the respondents were
observed to be in
the action stage. Given our goal of exploring the relationship
between moti-
vation type and stage of change at treatment inception, it was
not a viable
option to employ a sample based on 3 months of treatment
participation
and 30% attrition. Further, in light of the strong literature
support that this
attrition might be biased against earlier stages of change, we
saw the need
to create a measure of change at the outset of treatment
(Callaghan et al.,
2005; Edens & Willoughby, 2000; Haller et al., 2004; Simpson
& Joe, 1993).
We created a proxy for stage of change from data available in
the two
interviews conducted within 1 week of treatment admission. The
second of
the two initial interviews included six questions from the
Circumstances,
Motivation, Readiness, and Suitability Scale (CMRS). The
CMRS has been
shown to have high internal consistency reliability and, as a
measure of

validity, is effective in predicting treatment retention (DeLeon,
Melnick,
Kressel, & Jainchill, 1994).
Our proxy measures employed responses to the six CMRS
questions to
assign clients to one of three mutually exclusive stages:
precontemplation,
contemplation, or action stage. The basis for assigning a stage
was determined
by whether a participant recognized a substance use problem,
and whether
they had taken any action in the past month to address that
problem.
If a participant responded not at all to the statement “My drug
use is a
very serious problem in my life,” and responded very much
agree to the
statement “I don’t really need treatment, I’m here because of
pressure on
me,” we categorized them into the precontemplation stage. If a
participant
responded agree somewhat or very much agree to the four
questions, “I feel
that my drug use and the way I’ve been living have hurt a lot of
people,” “I
am really tired of using drugs and want to change,” “I really do
need to be
completely drug free in order to live the way I want to,” and
“My drug use
is a very serious problem in my life,” they were categorized into
the con-
templation stage.
We classified persons into the action stage based on their

problem rec-
ognition and whether they had engaged in change-oriented
behavior in the
30 days prior to the interview. We employed each participant’s
response to
an inquiry about their attendance at self-help group meetings in
the past
30 days or whether they had participated in any other treatment
in the past
30 days. Affirmative responses to this question resulted in the
participant
being categorized into the action stage.
Slightly less than two thirds of the respondents (65.6%) were in
the
contemplation stage, one third were in the action stage (32.9%)
and the
remaining 1.5% were in the precontemplation stage. Our
approach to classi-
fication represents what Migneault, Adams, and Read (2005)
referred to as
an algorithmic approach, in which individuals are classified into
mutually
exclusive stages of change based on specific responses to their
current sub-
stance use history. This approach has been used successfully in
prior studies to
classify individuals with substance use problems (Belding,
Iguchi, Lamb, &
Lakin, 1995; Migneault, Pallonen, & Velicer, 1997).

RESULTS
We began the process of model building by analyzing a series of
bivariate
relationships between the dependent variable stage of change
and a num-
ber of demographic and substance use variables that might be
related to it.
The purpose of the procedure was to develop a model that
controlled for
alternative explanations of variability in the dependent variable.
Table 3 describes the bivariate relationship of motivation source
and
the stage of change. Persons with an external source of
motivation were
more likely to be in the precontemplation stage. High internal
source of
motivation was associated with membership in the action stage.
This rela-
tionship was significant, c2(6, N = 4,347) = 54.385, p < 001, v
= .079.
Our next analysis consisted of a series of cross-tabulations
between
stage of change and demographic variables described in Table 4.
Because
the chi-square statistic is dependent on sample size, our large
sample size
was likely to result in significant chi-square statistics despite
trivial differ-
ences between the stage of change groups. To address this
concern we
added a measure of effect size as an additional criterion for
inclusion into
the model. For these cross-tab analyses, we employed the

Cramer’s v statistic
using Cohen’s (1988) criteria of .10 for a small effect size.
Table 4 describes these bivariate relationships. Using the
criteria of sta-
tistical significance and a small effect size, only legal
involvement (p < .001,
v = .102) was significantly related to stage of change. No other
demographic
variable met the criteria for significance.
Our model-building procedure also considered a number of
treatment
and substance use variables that might be expected to impact
stage of change.
TABLE 3 Percent Distribution of Motivation Source by Stage of
Change
Precontemplationa Contemplationb Actionc
Total externald 15.9 5.4 6.1
Slight externale 68.1 36.0 36.8
Slight internalf 13.0 37.0 35.3
Total internalg 2.9 21.6 21.8
an = 69; bn = 2,852; cn = 1,426; dn = 252; en = 1,599; fn =
1,566; gn = 930.
There were five drug and alcohol use indicators that met
inclusion criteria:
referral source (p < .001, v = .137), drug of choice (p < .001, v

= .222), and the
number of prior treatments (p < .001, v = .120). Although
severity did not
meet the inclusion criteria of v > .1, we included it in the model
due to the
conceptual importance of the indicator (p < .001, v = .073).
Because of the
extensive literature on gender differences in treatment, we
initially included
this variable despite its lack of significance in the bivariate
model (p = .071,
v = .035). Gender remained nonsignificant in the multivariable
model and
was excluded from further analysis.
We then conducted a multinomial logistic regression to assess
the influ-
ence of source of motivation on stage of change. Stage of
change was regressed
on the following variables: source of motivation, drug of
choice, treatment
modality, legal status, educational level, referral source, number
of prior drug
treatments, and severity.
Our initial analyses were somewhat stymied by problems of
quasi-
separation in the data. This problem is caused by zero cell
counts that occur
when the dependent variable does not vary for some values of
the categorical
independent variables (Menard, 2002). This was not an
unexpected problem
given both the number of categorical independent variables in
the initial
analysis and the small number of people in the precontemplation

group
relative to the other two stage of change groups. Menard (2002)
notes that
although this problem does not impact the overall fit of a
model, it does
result in higher standard errors that will influence the individual
regression
coefficients. We took a twofold approach to addressing this
problem: drop-
ping the variables of educational status, referral source, and
modality when
preliminary results suggested their bivariate relationships to the
dependent
TABLE 4 Bivariate Relationship with Stage of Change
Variable c2 df V
Demographic information
Gender 5.29 2 .035
Age group 27.24* 10 .056
Ethnicity 28.45* 6 .057
Marital status 24.77* 4 .053
Educational level 8.77 6 .032
Major source of income 8.41 12 .025
Criminal justice status 45.10* 2 .102
Alcohol and drug indicators
Primary drug 210.30* 10 .222
Treatment modality 24.77* 4 .053
Referral source 162.19* 8 .137
Previous treatment 124.21* 4 .120
Severity indicators
Severity 42.70* 4 .073

*p < . 05.
variable were eliminated in this multivariable analysis; and
collapsing the
two categorical variables of prior treatment and drug of choice.
The number
of persons with three or more prior treatments was merged with
those with
two prior treatments, and persons reporting sedative (0.8% of
sample),
hallucinogenic (1.5%), or inhalant (0.1%) drugs of choice were
merged into
the other drug use category.
With the exclusion of cases with missing values, a total of 4,347
cases
were included in this analysis. Multinomial logistic regression
uses the chi-
square as a test of the model’s significance. The full model –2
log likelihood test
for source of motivation was significant, c2(26, N = 4,357) =
320.85, p < .001,
indicating that the specified model was a significant
improvement over a
constant only, or null model. Taken as a measure of effect size,
the Nagelkerke
pseudo-R2 value of .104 represented a relatively small effect
for the full
model. The –2 log likelihood ratio test for source of motivation
was signifi-
cant, c2(6, N = 4,357) = 20.57, p = .002, indicating that a

client’s source of
motivation was significantly related to his or her stage of
change.
Parameter estimates are created in this approach by making
pairwise
comparisons of each categorical outcome to a reference group.
We employed
the action stage of change as the reference category and
compared those in
the action stage with persons in the precontemplation or
contemplation
stages. By repeating the analysis with the precontemplation as
the reference
group we were also able to conduct a comparison of
precontemplation to
contemplation. Table 5 provides the results of these two
analyses.
In addition to designating a reference group for the dependent
variable, it
is also necessary to designate reference groups for categorical
independent
variables. The totally internal source of motivation is the
reference group in
this analysis. The analysis makes comparisons of totally
internally motivated
persons to somewhat internally motivated, and to both the
externally moti-
vated categories. Reference groups for other variables are
identifiable in
Table 5 by their lack of parameter estimates.
Precontemplation Versus Action
External source of motivation was associated with an increased

likelihood of
being in the precontemplation rather than the action group.
Persons who scored
in the totally external source of motivation group were 15.7
times more likely
than totally internal persons to be in the precontemplation rather
than the action
stage. Those scoring in the slightly external range were 10.1
times more likely to
be in the precontemplation group than the action group. Being
slightly internally
motivated did not predict precontemplation versus action group
membership.
The probability of being in the precontemplation rather than
action
group appeared to be greater with a decline in substance use
severity, as
persons in the lower severity category were four times more
likely to be
TABLE 5 Parameter Estimates of Multinomial Regression
Predicting Stage of
Change from Motivation Source
B SE B df Odds ratio
Precontemplation vs. action
Motivation source
Total external 2.754 1.106 1 15.698*
Slight external 2.311 1.029 1 10.081*

Slight internal 0.957 1.103 1 2.603
Total internal
Substance use severity
Low severity 1.390 0.591 1 4.017*
Moderate severity 0.460 0.572 1 1.584
High severity
Legal status
No legal status –0.511 0.365 1 0.600
Current legal status
Number of prior treatments
No prior treatment 2.968 1.035 1 19.448*
One prior treatment 2.974 1.055 1 19.562*
Two or more treatments
Drug of choice
Alcohol 1.326 1.087 1 3.766
Marijuana 3.143 1.104 1 23.177*
Cocaine 0.356 1.076 1 1.428
Opiate 0.698 1.267 1 2.011
Amphetamine 1.508 1.200 1 4.517
Other
Contemplation vs. action
Motivation source
Total external 0.017 0.168 1 1.017
Slight external 0.037 0.094 1 1.038
Total internal
Low severity –0.272 0.116 1 0.762*
Moderate severity –0.035 0.082 1 0.966

High severity
Legal status
No legal status 0.290 0.071 1 1.336*
No prior treatment 0.890 0.083 1 2.436
One prior treatment 0.482 0.096 1 1.619
Drug of choice
Alcohol 0.004 0.218 1 1.004
Marijuana 0.700 0.291 1 2.014
Cocaine 0.346 0.201 1 1.414
Opiate 1.060 0.214 1 2.886*
Amphetamine 0.145 0.288 1 1.155
Other
(Continued )
found in the precontemplation group as compared to those in the
high-
severity group. Persons in the no prior and one prior treatment
group were
more likely than those in the multiple prior treatment group to
be identified
as in precontemplation. Marijuana users were also more likely
to be in the
precontemplation stage of change.
Contemplation Versus Action

The source of motivation did not distinguish persons in the
contemplation
stage from those in the action stage of change. Low problem
severity was
associated with a reduced likelihood of being in the
contemplation group
(OR = .762). Persons with no current legal involvement were
more likely to
be in the contemplation group than the action group. As with
the prior
comparison, the odds of being in the contemplation group
declined as the
number of treatments increased. Those with no treatment history
were 2.4
times more likely to be in the contemplation group and those
with a single
treatment episode were 1.6 times more likely to be in this group
than in the
action group. Opiate and marijuana users were also more likely
to be in the
contemplation rather than the action group.
TABLE 5 (Continued)
B SE B df Odds ratio
Precontemplation vs.contemplation
Motivation source
Total external 2.737 1.102 1 15.439*
Slight external 2.274 1.027 1 9.714*
Total internal

Low severity 1.662 0.587 1 5.271*
Moderate severity 0.495 0.570 1 1.640
High severity
Legal status
No legal status –0.801 0.362 1 .449*
No prior treatment 2.077 1.034 1 7.983*
One prior treatment 2.492 1.054 12.079*
Drug of choice
Alcohol 1.322 1.085 1 3.751
Marijuana 2.443 1.092 11.507*
Cocaine 0.010 1.073 1 1.010
Opiate –0.362 1.263 1 .697
Amphetamine 1.363 1.194 1 3.910
Other
Note. N = 4,347.
*p < .05.
Precontemplation Versus Contemplation
External motivation was associated with an increased likelihood
of being in
the precontemplation rather than the contemplation group.
Totally exter-
nally motivated individuals were 15.4 times more likely to be in
the precon-

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