Assignment: Research Design and Sampling – SOCW 6301 Week 7

See the attachment for ALL of the requirements and the week 5 source document  

Using the empirical research article that your instructor approved in the Week 5 assignment, ask yourself: “Is this a quantitative research article or a qualitative research article?” Remember, in quantitative research, the emphasis is on measuring social phenomenon because it is assumed that everything can be observed, measured, and quantified. On the other hand, in qualitative research, it is assumed that social phenomenon cannot be easily reduced and broken down into concepts that can be measured and quantified. Instead, there may be different meanings to phenomenon and experiences. Often in qualitative research, researchers use interviews, focus groups and observations to gather data and then report their findings using words and quotations.

Consider how these different methods affect the sampling design and recruitment strategy, and ask yourself how the recruitment of research participants will affect the findings.

For this Assignment, submit a 3-4 page paper. 

Complete the following:

· Read your selected empirical research article, and identify whether the study is a quantitative or qualitative study. Justify the reasons why you believe it is a quantitative or qualitative study. (Your instructor will indicate to you if you are correct in identifying the research design. This will point you to whether you will use the “Quantitative Article and Review Critique” or the “Qualitative Article and Review Critique” guidelines for the final assignment in week 10.)

· Using the empirical research article, focus on the sampling method in the study and begin to evaluate the sampling method by answering the following:

o Describe the sampling methods in your own words (paraphrase, do not quote from the article).

o Describe the generalizability or the transferability of the research finding based on the sampling method.

o Discuss the limitations the article identified with the sample and how those limitations affect the reliability or credibility.

o Explain one recommendation you would make to improve the sampling plan of the study that would address these limitations in future research.

SOCW 6301 Week 6

Assignment: Research Design and Sampling

Using the empirical research article that your instructor approved in the Week 5 assignment, ask yourself: “Is this a quantitative research article or a qualitative research article?” Remember, in quantitative research, the emphasis is on measuring social phenomenon because it is assumed that everything can be observed, measured, and quantified. On the other hand, in qualitative research, it is assumed that social phenomenon cannot be easily reduced and broken down into concepts that can be measured and quantified. Instead, there may be different meanings to phenomenon and experiences. Often in qualitative research, researchers use interviews, focus groups and observations to gather data and then report their findings using words and quotations.

Consider how these different methods affect the sampling design and recruitment strategy, and ask yourself how the recruitment of research participants will affect the findings.

For this Assignment, submit a 3-4 page paper.

Complete the following:

· Read your selected empirical research article, and identify whether the study is a quantitative or qualitative study. Justify the reasons why you believe it is a quantitative or qualitative study. (Your instructor will indicate to you if you are correct in identifying the research design. This will point you to whether you will use the “Quantitative Article and Review Critique” or the “Qualitative Article and Review Critique” guidelines for the final assignment in week 10.)

· Using the empirical research article, focus on the sampling method in the study and begin to evaluate the sampling method by answering the following:

· Describe the sampling methods in your own words (paraphrase, do not quote from the article).

· Describe the generalizability or the transferability of the research finding based on the sampling method.

· Discuss the limitations the article identified with the sample and how those limitations affect the reliability or credibility.

· Explain one recommendation you would make to improve the sampling plan of the study that would address these limitations in future research.

· Uses a reference and citation from Yegidis, Weinbach, and Myers (2018)

Proper English with no run-on sentences is an absolute requirement!

The paper must contain a minimum of 5 references and citations.

Yegidis, B. L., Weinbach, R. W., & Myers, L. L. (2018). Research methods for social workers (8th ed.). New York, NY: Pearson.

· Chapter 9, “Sampling Issues and Options” (pp. 202-222)

Substance Use & Misuse, 50:

653

–663, 2015
Copyright C© 2015 Foundations Recovery Network
ISSN: 1082-6084 print / 1532-2491 online
DOI: 10.3109/10826084.2014.997828

ORIGINAL ARTICLE

Gender Differences in Treatment Retention Among Individuals with
Co-Occurring Substance Abuse and Mental Health Disorders

Sam Choi1, Susie M. Adams2, Siobhan A. Morse3 and Sam MacMaster1

1School of Social Work, University of Tennessee—Knoxville, Nashville, Tennessee, USA; 2Vanderbilt University,
Nashville, Tennessee, USA; 3Research and Fidelity, Foundations Recovery Network, Brentwood, California, USA

Background: A significant number of individuals with
co-occurring substance abuse and mental health dis-
orders do not engage, stay, and/or complete residen-
tial treatment. Although prior research indicates that
women and men differ in their substance abuse treat-
ment experiences, our knowledge of individuals with
co-occurring substance abuse and mental health disor-
ders as well as those attending private residential treat-
ment is limited. Objectives: The purpose of this study
is to examine gender differences on treatment reten-
tion for individuals with co-occurring substance abuse
and mental health disorders who participate in private
residential treatment. Methods: The participants were
1,317 individuals (539 women and 778 men) with co-
occurring substance abuse and mental health disorders
receiving treatment at three private residential treat-
ment centers. Bivariate analyses, life tables, and Cox
regression (survival analyses) were utilized to exam-
ine gender effects on treatment retention, and iden-
tify factors that predict treatment retention for men
and women. Results: This study found that women
with co-occurring disorders were more likely to stay
longer in treatment when compared to men. The find-
ings indicate the factors influencing length of stay dif-
fer for each gender, and include: type of substance used
prior to admission; Addiction Severity Index Compos-
ite scores; and Readiness to Change/URICA scores.
Age at admission was a factor for men only. Conclu-
sions/Importance: These findings can be incorporated
to develop and initiate program interventions to min-
imize early attrition and increase overall retention in
private residential treatment for individuals with co-
occurring substance use and mental health disorders.

Keywords gender differences, co-occurring disorders, dual
diagnosis, substance abuse and mental health disorder,
retention, predictors, residential treatment

Address correspondence to Siobhan A Morse, Foundations Recovery Network, Research and Fidelity, 5409 Maryland Way, Brentwood, CA 37027
USA; E-mail: siobhan.morse@frnmail.com

Length of stay (LOS) in substance abuse treatment is
a strong predictor of treatment outcomes with longer
lengths of stay in treatment associated with lower post-
treatment substance use rates (DeLeon & Schwartz, 1984;
Greenfield et al. 2003; Simpson, Joe, & Rowan-Szal,
1997). Longer periods of treatment engagement are also
associated with lower readmission rates (Moos & Moos,
1995). Although the significance of remaining in treat-
ment is well established, leaving treatment prior to com-
pletion or against clinical advice remains a treatment con-
cern (Ball, Carroll, Canning-Ball, & Rounsaville, 2006)
and is associated with poor treatment outcomes (Deane,
Wootton, Hsu, & Kelly, 2012).

Treatment retention is a widely used proxy for treat-
ment outcomes such as substance use relapse, recidivism
to crime, and sustained recovery. Factors related to both
treatment program characteristics and individual patient
characteristics have been investigated for their impact on
retention in treatment. Program factors such as therapeu-
tic alliance, the use of motivational interviewing, gender-
specific programming, gender-specific interventions, pay-
ment source, and inclusion of treatment for co-morbid
mental health issues have all been studied for their impact
on retention.

Therapeutic alliance is strongly associated with re-
maining in treatment (Marsh, Angell, Andrews, & Curry,
2012; Simpson, Joe, Rowan-Szal, & Greener, 1997) and
treatment attendance (Mullins, Suarez, Ondersma, &
Page, 2004) but appears to impact outcomes somewhat
less significantly than retention or attendance (Simpson,
Joe, & Rowan-Szal, 1997). Motivation for treatment is
considered a robust predictor of retention (Adamson,
Sellman, & Frampton, 2009). The impact of using mo-
tivational interviewing or enhancement on retention has
mixed findings with positive results for the early stages
of treatment retention (Carroll et al., 2006). However,
Mullins and colleagues (2004) found that the use of

653

654 S. CHOI ET AL.

motivational interviewing did not demonstrate significant
improvement over education counseling. Gender-specific
programming has been found to be a significant factor
in both treatment retention and outcomes for women
(Ashley, Marsden, & Brady, 2003; Greenfield et al.,
2008; Greenfield, Cummings, Kuper, Wigderson, &
Koro-Ljungberg, 2013). Further studies have found that
attention to issues, such as employment, trauma, and
mental health in a gender-specific manner resulted in
positive retention and treatment outcomes (Adams et al.,
2011; Brady & Ashley, 2005; Grella, 2008; Green, Polen,
Dickinson, Lynch, & Bennett, 2002; Greenfield, Back,
Lawson, & Brady, 2010). Payment source has also been
associated with LOS in treatment with private insurance
patients having shorter lengths of stay (Brady & Ashley,
2005). The recognition of the co-occurring nature of
psychiatric disorders and substance use disorder and
the need to simultaneously treat both has resulted in the
development of specialty treatment programs based on
an integrated model (SAMHSA, DASIS Report, 2003),
positively impacting both outcomes and retention.

Personal and substance use characteristics have been
found to be inconsistent predictors of treatment reten-
tion and outcomes (Hiller, Knight, Leukefeld, & Simpson,
2002; Hser, Joshi, Maglione, Chou, & Anglin, 2001; Joe,
Simpson, & Broome, 1998, 1999). Age is the only socio-
demographic characteristic that consistently predicts re-
tention in substance abuse treatment regardless of gen-
der, with older age associated with longer lengths of stay
(Adams et al., 2011; Hall, Prendergast, Wellisch, Pat-
ten, & Cao, 2004: Pelissier, Motivans, & Rounds-Bryant,
2005). Primary drug of choice has been investigated as
a predictor of retention in 90-day residential treatment
with alcohol use associated with longer retention when
compared to other substance use (Deane, et al., 2012).
Drug Abuse Treatment Outcomes Survey data analysis re-
vealed that programs serving patients with greater psycho-
logical severity and higher cocaine and alcohol use had
shorter lengths of stay (Simpson, Joe, & Brown, 1997).
Patients with a higher motivation at intake are also more
likely to remain in treatment than those with lower moti-
vation at intake (Simpson et al., 1997). A history of trauma
(Tull, Gratz, Coffey, Weiss, & McDermott, 2013; Logan,
Walker, Jordan, & Leukefeld, 2006), stress response (Tull
et al., 2013), multiple life stressors (Kelly, Blacksin, &
Mason, 2001; Comfort, Sockloff, Loverro, & Kaltenbach,
2003), and self-efficacy (Cummings, Gallop, & Green-
field, 2010) have also been found to impact retention.

A study of insured outpatient treatment attendees re-
vealed that having fewer and less severe drug problems
improved retention and that there were gender differences
in the factors that impacted retention (Mertens & Weis-
ner, 2000). In women, higher retention was associated
with being married, having a higher income, not being
African American, having lower psychiatric severity, and
being unemployed; and in men, age, employer involve-
ment, and abstinence goals were predictors of higher re-
tention (Mertens & Weisner, 2000). By contrast, a study of
268 patients at a publicly funded outpatient center, patient

substance use did not predict retention; however, being
male, Caucasian and having higher severity in employ-
ment composite score as measured by the ASI were as-
sociated with longer retention and greater attendance in
outpatient treatment (McCaul, Svikis, & Moore, 2001).

Overall, women appear to be less likely to use sub-
stance use treatment services (Wu, Ringwalt, & William,
2003; Kim et al. 2011), and are among those at most risk
for not accessing mental health treatment when needed
(Roll, Kennedy, Tran, & Howell, 2013). Although, in gen-
eral, women use substances for shorter periods of time
prior to entering treatment than men, they appear to enter
treatment with greater severity of issues (Greenfield et al.,
2010; Piazza, Vrbka, & Yeager, 1989; Hernandez-Avila,
Rounsaville, & Kranzler, 2004; Arfken, Klein, di Menza,
& Schuster, 2001). Some research has shown significant
differences in retention by gender (Arfkin et al., 2001;
Kim et al., 2011; Choi, Adams, MacMaster, & Seiters,
2013) and others have stipulated that while gender itself
has not been predictive of retention, issues traditionally
associated with gender, such as child-care, employment
and trauma, are related to variations found in retention by
gender (Mertens & Weisner, 2000; Green et al., 2002; Tull
et al., 2013; Greenfield et al., 2007). Despite having more
severe and complex problems, studies found that incar-
cerated women with substance abuse histories were less
likely to relapse than their male counterparts (Fiorentine,
Anglin, Gil-Rivas, & Taylor, 1997; Gil-Rivas, Fiorentine,
& Anglin, 1996; Grella, Stein, & Greenwell, 2005). This
“gender paradox” in has been explained by the hypothesis
that women engage more readily and actively in counsel-
ing and other treatment services (Fiorentine et al., 1997;
Kim et al., 2011).

In studies focusing on factors impacting the retention
of women in treatment, higher income and education and
lower psychiatric severity are often predictors of higher
retention and completion rates (Mertens & Weisner, 2000;
Kelly et al., 2001). The impact of psychiatric severity
on employability and thus the level of distress and anx-
iety on substance abusing women has been suggested
(Hernandez-Avila et al., 2004) which could lead to shorter
stays in treatment for women. Women also appear to ben-
efit most from single-gendered treatment groups (Cum-
mings et al., 2010; Greenfield et al., 2013; Greenfield
et al., 2008; Green et al., 2002).

In summary, research indicates that there are signif-
icant differences by gender in the factors that influence
treatment retention. Although women have less access to
services overall, once they enter treatment; they do so
with more serious substance dependencies, and with more
health and social problems than do men (Kim et al., 2011;
Wu et al., 2003). Women are also more likely than men
to use, and benefit from, services available in comprehen-
sive programs (Greenfield et al., 2013; Green et al., 2002).
Through prior investigation of this dataset, Choi and col-
leagues (2013) found that in private residential treatment,
age, gender, types of drug used, ASI medical and psy-
chiatric severity scores and URICA readiness to change
scores predicted treatment retention at 30 days of their

GENDER EFFECTS ON TREATMENT RETENTION 655

initial treatment. The prior study used bivariate analysis
to identify independent variables significantly correlated
with 30-day treatment retention, then used logistic regres-
sion to determine predictors of treatment outcomes (Choi
et al., 2013). Based on the prior study (Choi et al., 2013)
and what we know from the available literature on the im-
pact of gender on treatment retention, specifically Merten
and Weisner (2000), this study was designed to further
examine the effect of gender on treatment retention us-
ing survival analyses, and to identify factors that predict
treatment retention in each gender while enrolled specifi-
cally in private, residential treatment for individuals with
co-occurring mental health and substance use disorders.

METHODS

Setting
Data were collected at three private residential facilities
that provide integrated substance abuse and mental health
treatment services in Memphis, Tennessee, Malibu, Cali-
fornia, and Palm Springs, California. Foundations Recov-
ery Network (FRN), a private for-profit substance abuse
treatment provider offering residential and outpatient sub-
stance abuse treatment services, operates all three pro-
grams. Service recipients’ at all three facilities are drawn
from across the United States and Canada. Treatment ser-
vices are based on an integrated model of mental health
and substance abuse services consisting of both individual
and group evidence-based interventions (Foundations Re-
covery Network, 2010). In most cases, the expected LOS
is between 28 and 40 days. Recommended length of time
in treatment is individualized on the basis of clinical as-
sessment and medical necessity; however, other factors
may contribute to the actual LOS.

Participants
All program participants who enter residential services are
offered an opportunity to participate in an ongoing evalua-
tion during the initial phase of treatment. A trained intake
person located at each facility describes the evaluation, re-
views and obtains informed consent, and collects the loca-
tor information for post discharge interviews. The Addic-
tion Severity Index (ASI) (McClellan, 1983; McClellan
et al., 1992) from the initial clinical assessment is used
as the baseline ASI assessment if informed consent is ob-
tained. Masters’ level clinicians complete the initial clini-
cal assessment within the first 4 days following admission
to FRN’s residential programs. Data are collected at three
additional time points: 1, 6, and 12 months post discharge.
A community-based Institutional Review Board reviewed
study protocols to assure the protection of Human
Subjects.

Data for this study are drawn only from the baseline in-
terview and LOS in treatment days. The participants were
1,317 individuals who voluntarily sought residential treat-
ment at one of the three treatment centers. All participants
received an intake assessment by a multidisciplinary team
which provides the basis for an individual treatment plan
to address substance use, psychiatric disorder, and medi-

cal and social service needs. At this time, the evaluation
data was collected for those individuals who were will-
ing to consent to participating in the evaluation process.
Compared to all patients who attended treatment during
the same time period, the population who agreed to par-
ticipate in the research project was not appreciably differ-
ent in terms of demographic characteristics. The average
age for the overall population was 36.6 versus 36.0 for
the study population. The overall population was 60.8%
male and the study population was 59.1%. Caucasians
represented the largest percent by far in both the general
population and the study group. The average LOS was
29.90 for the overall population compared to 32.08 for our
sample. Co-occurring disorders were assessed over the
course of treatment starting with initial screening, assess-
ment, and psychiatric evaluation. A master’s level clini-
cian conducted a complete psychiatric evaluation with one
of the programs psychiatrists within 72 hours after arriv-
ing to the residential treatment facility. Each patient is as-
signed to one of the program’s licensed clinicians who
utilize the information gathered through initial screen-
ing and assessment to develop the initial treatment plan
with the patient during an initial individual session with
the first week of treatment. Ongoing psychiatric and in-
dividual therapy sessions are utilized along with weekly
treatment team meetings to update each patient’s treat-
ment plan, including updates/confirmation of specific co-
occurring substance use and psychiatric disorders. This
process provides input from a multidisciplinary team of
clinicians in order to thoroughly assess co-occurring dis-
orders throughout treatment as symptoms may change or
become clearer during the course of treatment. Retention
was measured in treatment days through a retrospective
review of discharge records, which also included the base-
line interview data.

Instruments
Addiction Severity: The scalable questions that make up
the composite scores of the Addiction Severity Index
(ASI) (McClellan et al., 1992) were utilized to measure
addiction severity. The ASI was developed to measure
problem severity in each of seven potential problem areas
that include: medical, employment, alcohol, drug, legal,
family/social, and psychiatric problems. In order to ensure
that each question within a given problem area is given the
same weight in calculation of the composite score each
item in a subscale is divided by its maximum value and by
the total number of questions in a composite. This scoring
yields a score from 0 to 1 in each composite.

Readiness for Change: The University of Rhode Island
Change Assessment (URICA) (DiClemente & Hughes,
1990) is a measure of readiness to change that has been
studied with a range of different populations. The instru-
ment consists of 32 statements that subjects endorse on
a 5-point scale from strongly agree to strongly disagree.
The URICA yields scores on each of four scales; Precon-
templation, Contemplation, Action, and Maintenance, or
each of the stages of change described by Prochaska, Di-
Clemente, & Norcross (1992). In addition, the scores from

656 S. CHOI ET AL.

these scales are used to create a Readiness to Change com-
posite score. The Readiness to Change score was derived
for this study in the same manner used in Project MATCH
Research Group (1997). The average Contemplation, Ac-
tion, and Maintenance scores were added and the Precon-
templation score was subtracted from the sum. The Readi-
ness to Change score was used as a predictor variable in
subsequent analysis.

Treatment Retention: This study focuses on treatment
retention. Retention status was observed and calculated by
days between a program start date and a discharge date.
Data were collected on all admissions between February
1, 2008 and through July 31, 2010 with final observation
of discharge data on August 31, 2010.

Data Analysis
Initial analyses consisted of basic descriptive statistics
and bivariate analyses to identify and examine any gen-
der difference on any pre-treatment demographic and use-
related factors as well as components of treatment reten-
tion. Next, a life table was developed to investigate the
trajectory of treatment retention by gender. Finally, Cox
regression was employed to investigate the impact of var-
ious predictors of treatment retention by gender. One of
the essential features of Cox regression is that the tech-
nique allows for the unbiased analysis of time to event
data controlling for covariates. The event of interest in the
current study is a discharge from treatment. Like logis-
tic regression, the exponential of the coefficients from the
Cox model gives the relative risk of the odds for the co-
variate. Cox regression also proves superior to ordinary
least squares regression (OLS), in that the Cox regression
algorithm allows for censoring of persons who discontin-
ued or did not experience the event (treatment retention
in the current study) during the study period. In this study,
Cox regression was developed in three steps. Model 1 con-
tains demographic characteristics, types of substance use
disorders, types of mental health disorders, and treatment
location. Model 2 also includes the various scalable ASI
subscale measures. Finally, in Model 3, the score of readi-
ness to change was added in the final model.

RESULTS

Sample Description
Demographic, substance use, and mental health disorder
and treatment characteristics are provided in Table 1. The
mean age in this study sample was 36 years (SD = 12.1)
with 40.9% of the sample being female. The majority of
study participants (90.2%) were Caucasian, 8.1% were
African American, and 1.7% were Latino. Nearly 56%
were employed in last 30 days. Approximately 67% had
alcohol-related disorders (alcohol abuse or dependence),
18.8% had opioid related disorders (opioids abuse or
dependence), and 18.1% had cocaine-related disorders
(cocaine abuse or dependence). In terms of identified
mental health disorders, the majority of study partici-
pants (82.9%) had a diagnosis of an anxiety disorder,
followed by major depression (74.9%), and mood dis-

order (26.6%). The majority of participants (80%) were
diagnosed with more than one mental health disorder. Of
the 1,317 study participants, 43.7% stayed in treatment
for at least 30 days. The average LOS in treatment for the
study sample was 32.08 days (SD = 19.29).

Bivariate Analysis
Bivariate analyses were conducted to determine the
relationship between various independent variables
and gender using chi-square and t tests. The results of
bivariate analyses are displayed in the second and third
column of Table 1. Statistically significant differences
were found in participants’ age, employment in last
30 days, and race/ethnicity. Men in this study were
younger than women. Men also had a higher rate of
employment in the last 30 days (60.0%) compared to
women (46.6%). In terms of race and ethnicity, women
were more predominately Caucasians (94.1%) compared
to men (87.5%). Women stayed in treatment longer than
men by an average of approximately four and a half
days. In addition, women had a higher rate of treatment
retention (47.7%) at 30 days compared to men (40.9%).

Statistically significant gender differences were found
in six of the seven domains of the ASI composite score
measurement. Women had higher mean ASI composite
scores in the areas of medical, employment/support, fam-
ily/social relationships, and psychiatric issues indicating
greater severity in these areas than men. Men had higher
mean ASI scores in areas of drug, and legal measures than
women. ASI composite score for alcohol severity were
slightly higher in men. Men entered treatment with higher
URICA scores in the precontemplative stage. Women
were less likely to be in the precontemplative stage and
therefore more likely to be in a stage reflecting greater
readiness for change; however, overall URICA and other
URICA subscale scores did not reflect significant differ-
ences between men and women. Gender differences were
also found in type of substance use and mental health dis-
order. Men had higher rates of cocaine and cannabis use
disorders as compared to women. Women had higher rates
of major depression, anxiety disorders, mood disorders,
and eating disorders compared to men.

Life Table
The life table was developed to investigate the trajectory
of treatment retention by gender. Figure 1 illustrates the
survival lines indicating time to discharge for both men
and women. Observation of Figure 1 revealed that the sur-
vival lines are similar for the first 20 days of treatment.
These lines split into different trajectories after approxi-
mately 20 days which continues to broaden as time pro-
gresses. The largest gap appears from 30 days forward.
At 30 days, approximately 30% of men remained in treat-
ment compared to 40% of women. Comparison of the sur-
vival lines was performed using the Wilcoxon (Gehan)
statistic (13.415, df = 1. p < .000). The result further high- lights the statistical significance of the differences in the trajectories of these lines.

GENDER EFFECTS ON TREATMENT RETENTION 657

TABLE 1. Sample description

Total Sample N = 1,317 Men N = 778 (59.1%) Women N = 539 (40.9%)
Mean (SD) Mean (SD) Mean (SD)

Age∗∗ 36 (12.11) 36.08 (11.94) 38.26 (12.26)
ASI: Medical∗∗∗ .26 (.36) .21 (.33) .34 (.38)
ASI: Employment/Support∗∗ .40 (.27) .38 (.28) .43 (.26)
ASI: Alcohol .38 (.34) .40 (.33) .38 (.35)
ASI: Drug∗ .17 (.16) .18 (.16) .16 (.16)
ASI: Legal∗ .11 (.21) .12 (.21) .10 (.20)
ASI: Family/Social

Relationships∗∗∗
.30 (.26) .28 (.25) .34 (.26)

ASI: Psychiatric∗∗∗ .49 (.20) .45 (.21) .55 (.18)
Readiness for Change 10.77 (1.56) 10.66 (1.61) 10.94 (1.47)
Precontemplation∗∗∗ 1.67 (.52) 1.73 (.54) 1.57 (.46)
Contemplation 4.41 (.44) 4.38 (.46) 4.45 (.41)
Action 4.27 (.47) 4.25 (.45) 4.30 (.49)
Maintenance 3.75 (.64) 3.75 (.63) 3.76 (.64)
Days in Treatment∗∗∗ 32.08 (19.29) 30.17 (16.4) 34.85 (22.5)

N (%) N (%) N (%)

Treatment Retention at 30 days∗∗ 575 (43.7) 318 (40.9) 257 (47.7)
Race/Ethnicity∗∗∗

African American 107 (8.1) 83 (10.7) 24 (4.5)
Caucasian 1,188 (90.2) 681 (87.5) 507 (94.1)
Latino 22 (1.7) 14 (1.8) 8 (1.5)
Employment in last
30 days-Yes∗∗∗

733 (55.7) 467 (60.0) 250 (46.4)

No 584 (44.3) 311 (40.0) 289 (53.6)
Type of Substance Use Disorders

Alcohol 885 (67.2) 515 (66.2) 370 (68.6)
Cocaine∗∗∗ 238 (18.1) 170 (21.9) 68 (12.6)
Cannabis∗ 88 (6.7) 60 (7.7) 28 (5.2)
Opioid 248 (18.8) 147 (18.9) 101 (18.7)
Poly Substance 145 (11.0) 87 (11.2) 58 (10.8)
Others 29 (2.2) 4 (1.3) 6 (2.3)

Type of Mental Health Disorders
Major Depression∗∗∗ 986 (74.9) 540 (69.4) 446 (82.7)
Anxiety Disorder∗∗∗ 1090 (82.8) 617 (79.3) 473 (87.8)
Mood Disorder∗∗ 350 (26.6) 186 (23.9) 164 (30.4)
Bi-Polar Disorder 16 (1.2) 11 (1.4) 5 (0.9)
Eating Disorder∗ 9 (0.7) 2 (0.3) 7 (1.3)
ADHD 10 (0.8) 5 (0.6) 5 (1.9)
Dementia 19 (1.4) 13 (1.7) 6 (1.1)
Missing∗∗∗ 89 (6.8) 70 (9.0) 17 (3.5)

Locations
A 291 (22.1) 176 (22.6) 115 (21.3)
B 64 (4.9) 29 (3.7) 35 (6.5)
C 962 (73.0) 573 (73.7) 389 (72.7)

∗p < .05. ∗∗p < .01. ∗∗∗p < .00.

Cox Regression
Table 2 provides the results of models constructed to as-
sess relative effects on the likelihood of retention for in-
dividuals with co-occurring substance abuse and mental
disorders. The results for men suggest that age, an ADHD
diagnosis, location, and ASI employment subscale com-
posite score were associated with treatment retention. In
interpreting these results, it is important to note that a
hazard ratio greater than 1 indicates a higher likelihood

of treatment retention. Age was significantly and posi-
tively related to retention. The Exp (b) of 1.009 indicates
that for each increase in age by a year, the likelihood of
treatment retention increased by 0.9%. Men diagnosed
with ADHD were 41% more likely to stay in the treat-
ment longer than men diagnosed with mood disorder. The
ASI employment score was significantly and negatively
associated with the likelihood of retention for men. Men
with higher ASI employment scores, reflecting greater

658 S. CHOI ET AL.

FIGURE 1. Life Table—Treatment Retention by Gender.

severity of employment issues, were less likely to stay in
treatment. There were significant differences in male re-
tention rates predicted by treatment location. Readiness to
change was not predictive of treatment retention for men.

The Cox regression model for women suggests that co-
caine use, depression, location, ASI alcohol subscale com-
posite score, and readiness to change were significantly
correlated with treatment retention. Women who were co-
caine dependent were approximately 41% less likely to re-
main in treatment compared to women who were alcohol
dependent. However, having a higher ASI drug subscale
composite score (indicating greater severity) was not pre-
dictive of retention; but greater severity on the ASI alco-
hol subscale composite was predictive of treatment reten-
tion for women. The likelihood of remaining in treatment
improved by 56% for every point increase on the ASI al-
cohol composite subscale. In addition, women diagnosed
with depression were 92% more likely to remain in treat-
ment longer than women diagnosed with a mood disor-
der. Location was also predictive of the decision to remain
in treatment in women. Women who were scored in the
precontemplative or contemplative stages on the URICA
readiness to change scale were significantly less likely to
remain in treatment than those who scored in the action or
maintenance stages of change.

DISCUSSION

The purpose of the current study was to (1) examine gen-
der effects on treatment retention, and (2) identify fac-
tors that predict treatment retention for men and women
in private residential treatment for individuals with co-
occurring substance abuse and mental health disorders.
This study identifies significant differences by gender
in treatment retention for individuals with co-occurring
substance abuse and mental health disorders. The find-

ings indicate that women are more likely to stay in treat-
ment compared to men. Men with co-occurring sub-
stance abuse and mental health disorders in this study
had more difficulty staying in treatment than women. For
example, men stayed an average of 30.17 days in treat-
ment, 4.5 days fewer than women (34.85 days). Similarly,
women were significantly more likely to remain in treat-
ment for 30 days. Typically, more intensive treatment has
been associated with lower retention for women. For ex-
ample, Arfken and colleagues (2001), in their study of
publicly funded residential and intensive outpatient treat-
ment in a major metropolitan area, found that women had
lower retention and completion rates than men. In con-
trast, in a study of substance abuse care provided in pri-
mary care settings women remained in treatment on av-
erage longer than men (Kim et al., 2011). However, both
studies, as well as much of the research evaluating treat-
ment retention have been conducted in publicly funded
programs. This research provides an important evidence
that funding source may be an over-riding factor in patient
decisions to remain in private, residential treatment.

The study also finds that different factors appear to con-
tribute to the likelihood of remaining in treatment for each
gender. Women appear to enter treatment with greater
severity in the areas of medical, employment/support,
family/social relationships, and psychiatric measures ev-
idenced by higher mean ASI composite scores. This re-
flects findings in studies by Greenfield and colleagues
(2010) and others noted in the literature review.

Consistent with the literature, age is associated with
treatment retention in men with older men more likely
to remain in treatment longer (Adams et al., 2011; Hall,
Prendergast, Wellisch, Patten, & Cao, 2004: Pelissier, Mo-
tivans, & Rounds-Bryant, 2005); however, this was not the
case with women. Age was not found to be predictive of
treatment retention in females.

Drug use characteristics impacted treatment retention
differently for males and females. Similar to the Drug
Abuse Treatment Outcomes Survey data analysis results,
cocaine use in women was associated with shorter stays in
treatment (Simpson, Joe, & Brown, 1997) when compared
to alcohol as the control group. In contrast to Simpson, Joe
and Brown (1997), women whose ASI alcohol subscale
composite score indicated greater severity than their coun-
terparts were more likely to stay in treatment. This was
similar to results found by Deane and colleagues (2012)
who found that women who used alcohol were likely to re-
main longer in treatment than those who used other drugs.

Women with depression were also more likely to re-
main in treatment longer than their counterparts with
mood disorders. Men diagnosed with ADHD were signif-
icantly more likely to remain in treatment for longer com-
pared to those diagnosed with a mood disorder. Mertens
& Weisner, 2000 found that lower psychiatric severity was
related to improved retention; however we did not find that
psychiatric severity measured by the ASI psychiatric sub-
scale was influential in retention for either gender.

Similar to findings in the literature (Grella, Stein, &
Greenwell, 2005) women did enter treatment with greater

GENDER EFFECTS ON TREATMENT RETENTION 659

TABLE 2. Cox regression models for treatment retention

Model for Men Model for Women

Variables B SE Exp (B) p-Value 95% CI B SE Exp (B) P-Value 95% CI

Age .009 .005 1.009 .048 (1.000–1.018) −.003 .005 .997 .575 (.986–1.008)
Caucasian1 −.271 .147 .762 .065 (.571–1.017) −.262 .275 .760 .339 (.449–1.318)
Employed in last

30days–yes
−.029 .127 .971 .819 (.756–1.247) −.220 .148 .803 .137 (.600–1.073)

Opiate
Abuse/Dependence2

.195 .142 1.216 .168 (.921–1.605) .107 .143 1.113 .452 (.842–1.472)

Cocaine
Abuse/Dependence2

−.155 .133 .857 .243 (.660–1.111) −.541 .169 .582 .001 (.418–.811)

Cannabis
Abuse/Dependence2

.043 .127 1.044 .735 (.814–1.339) .136 .163 1.146 .403 (.833–1.575)

Poly Substance
Abuse/Dependence2

.020 .148 1.020 .892 (.763–1.365) −.255 .166 .775 .123 (.560–1.072)

Depression3 .025 .144 1.025 .862 (.773–1.360) .656 .207 1.928 .002 (1.285–2.892)
Anxiety Disorder3 −.149 .167 .861 .371 (.621–1.195) −.006 .217 .994 .977 (.649–1.521)
Bipolar3 −.132 .621 .876 .829 (.264–2.907) −.594 1.206 .552 .622 (.052–5.565)
Eating Disorder3 −1.385 1.031 .250 .179 (.033–1.889) −.011 .436 .989 .980 (.421–2.324)
ADHD3 1.528 .594 4.611 .010 (1.438–14.782) .267 .527 1.306 .612 (.465–3.666)
Dimentia3 .282 .332 1.326 .396 (.691–2.544) −.121 .531 .886 .819 (.313–2.508)
Missing Mental

Disorders–Yes
.048 .246 1.049 .847 (.647–1.699) .138 .560 1.148 .805 (.383–3.443)

Location A4 −.313 286 .731 .272 (.418–1.279 −1.182 .286 .307 .000 (.175–.537)
Location B4 .320 .123 1.377 .009 (1.083–1.750) .049 .151 1.051 .744 (.781–1.413)
ASI: Medical −.085 .149 .918 .566 (.686–1.229) −.052 .157 .950 .742 (.698–1.292)
ASI: Employment −.549 .218 .577 .012 (.376–.886) −.244 .288 .784 .397 (.446–1.378)
ASI: Alcohol −.020 .172 .817 .240 (.584–1.144) .446 .191 1.562 .019 (1.075–2.269)
ASI: Drug −.077 .544 .926 .888 (.319–2.689) 1.061 .570 2.890 .062 (.946–8.830)
ASI: Legal −.188 .244 .828 .441 (.513–1.337) −.468 .287 .626 .103 (.356–1.100)
ASI: Family/Support −.011 .208 .989 .958 (.658–1.487) −.236 .233 .790 .311 (.500–1.247)
ASI: Psychiatric −.463 .351 .629 .187 (.316–1.253) −1.612 .570 2.890 .062 (.946–8.830)
Readiness to Change
Precontemplation .181 .121 1.199 .133 (.946–1.519) −.393 .181 .675 .029 (.474–.962)
Contemplation −.153 .170 .858 .366 (.615–1.196) −.549 .241 .577 .023 (.360–.926)
Action .040 .142 1.041 .778 (.788–1.374) .144 .158 1.155 .362 (.847–1.575)
Maintenance .053 .089 1.055 .549 (.886–1.255) −.195 .102 .823 .054 (.674–1.004)
-2 Log Likelihood 4868.477 3326.112
χ 2, df, p-value 58.795,27,000∗∗∗ 82.221, 27, .000∗∗∗

1African American and Latino were the reference group.
2Alcohol abuse/dependences and others were the reference group.
3Mood disorders was the reference group.
4Location C was the reference group.

severity of 4 of the 7 subscale composite scores in the ASI.
However, despite entering treatment with less severity in
ASI employment composite score than females, employ-
ment issues were a factor in predicting retention for males
but not females.

Females in the early stages of readiness were less likely
to remain in treatment; however, readiness for change as
measured by the URICA was not a factor in predicting re-
tention for men. This is in contrast to the results of Adam-
son, Sellman, & Frampton (2009) whom results report that
motivation is of the most robust predictors of retention.

This study also found differences in retention for
men by location. Further investigation is required to
determine the factors causing these differences; however,

an interesting point is raised. Although those three
different facilities have similar therapeutic philosophies
and operation systems, the findings of this study indicate
the needs of recognizing the uniqueness of each program.
According to Simpson, Joe and Brown (1997), each
treatment facility differ in staff skills, resources, service
intensity, environmental setting, and client demands
which may impact their retention and effectiveness. Ac-
cordingly, treatment evaluation studies must recognize the
multi-level factors—client level and program level—on
treatment retention (Simpson et al., 1997; Simpson, Joe,
& Brown, 1997). Organizational program planners may
want to consider a variety of factors that can influence
retention differences across sites, including state and

660 S. CHOI ET AL.

local legislative requirements, payer mix, population dif-
ferences, and other factors across both the organizations
and the patients that could prove to be significant.

These findings show patterns in treatment retention that
are different from prior studies. There may be several
reasons as to why these findings are different. Further
research is needed to investigate the impact of funding
source; e.g., private versus public treatment, on retention
patterns in both genders. The data for this study was drawn
only from private treatment which serves as both a limita-
tion to the findings and a strength of the article due to the
paucity of research drawn from this source.

These results further demonstrate what was found in
the earlier study by Choi and colleagues (2013) using
bivariate analysis and logistic regression to determine
predictors of retention. These researchers found that
gender was a significant predictor of treatment retention
at 30 days in private, residential, dual diagnosis treat-
ment. This study adds to the current body of literature
investigating factors impacting retention in treatment and
differences by gender in dual diagnosis treatment and
identifies several key predictors which may be addressed
through programming interventions. It is also important
to note that historically individuals with co-occurring
substance abuse and mental health disorders have low
retention rates. Prior research indicates that longer lengths
of stay in treatment generally predicts better treatment
outcomes at follow-up (Simpson et al., 1997). Accord-
ingly, this finding highlights the importance of early
engagement efforts for both men and women developing
gender-specific strategies to improve treatment retention
for individuals with co-occurring substance abuse and
mental health disorders.

The current study makes a unique contribution to the
literature for individuals with substance abuse and men-
tal disorders, despite some limitations. First, the results
of this study may be unique to private residential treat-
ment programs. The predictors of treatment retention in
publicly funded residential treatment programs may be
different. In addition, this study assumes that three treat-
ment programs operate identically, although there is some
variability in staff skills, resources, service intensity, envi-
ronmental setting, and client demands which may impact
their effectiveness (Simpson, Joe, & Brown, 1997). This
study was limited to client-level variables and did not ex-
amine the impact of program level variables on treatment
retention. Although the findings and implications are im-
portant, one important limitation may exist in the sample
of research participants. While individuals who partici-
pated in the research component appear to be demograph-
ically similar to all other treatment participants, there was
a slight difference in the overall LOS between individuals
who participated in the research component and those did
not.

In summary, the current study investigated gender-
specific factors that may explain treatment retention vari-
ations for individuals with co-occurring substance abuse
and mental health disorders in private residential treat-

ment settings. The findings suggest the importance of fur-
ther research investigating the factors predicting retention
in privately funded treatment.

Declaration of Interest

Siobhan A. Morse is employed by Foundations Recovery
Network, the operator of the sites supplying the data. She
received compensation in the form of salary as Director
of Research. Samuel A. MacMaster was under contract
with Foundations Recovery Network, the operator of the
sites supplying the data. He received compensation from
Foundations Recovery Network. The authors alone are re-
sponsible for the content and writing of the article.

THE AUTHORS
Siobhan A. Morse, MHSA,
CRC, CAI, MAC is the
Director of Research and
Fidelity at Foundations
Recovery Network. Her current
research interests focus on
providing high-quality care and
outcomes research in private
residential dual diagnosis
treatment.

Samuel MacMaster, Ph.D. is
an Associate Professor at the
University of Tennessee within
the College of Social Work. Dr.
MacMaster’s research interests
center on the intersection of
substance use and HIV/AIDS;
and have focused specifically on
the development of culturally
appropriate interventions to
overcome barriers to service
access for underserved and
incarcerated populations.

Sam Choi, Ph.D is a director at
Tennessee Korean American
Social Service Center and
research fellow at Children and
Family Research Center. Dr.
Choi’s research revolves around
two main areas: the relations
of service delivery to child
welfare and treatment outcomes
for parents with co-occurring
problems and the relations of
service delivery to treatment
outcomes for individuals with

co-occurring substance abuse and mental health problems.

GENDER EFFECTS ON TREATMENT RETENTION 661

Susan M. Adams, PhD, RN, is
Faculty Scholar for Community
Engaged Behavioral Health at
Vanderbilt University School
of Nursing in Nashville, TN.
Dr. Adams’ current research
concerns are efficacy of trauma-
informed interventions and
sustained recovery for women
with co-occurring substance use
and mental health disorders in
community based programs.

GLOSSARY

Co-occurring Disorders [COD] (previously called Dual
Diagnosis): refers to individuals who have one or more
disorders relating to the use of alcohol and/or other sub-
stances of abuse as well as one or more mental health
disorders. The diagnosis of co-occurring disorders is
used when at least one disorder of each type occurs in-
dependent of the other and is not a cluster of symp-
toms resulting from one disorder alone. COD replaces
the term Dual Diagnosis which can be confusing since
it has been used to identify other co-morbid disorders
such as a primary medical disorder and a mental health
disorder.

Cox regression (or proportional hazards regression): is a
method for investigating the effect of several variables
upon the time a specified event takes to happen (such as
in treatment). The method does not assume a specific
“survival model,” although it is not truly nonparametric
because it does make the assumptions that the effects
of the predictor variables on survival are constant over
time and that they are additive in one scale.

Life table: is a statistical calculation of survival analysis
that deals with “time to an event” such as death, relapse,
time in treatment, or other health events. It can answer
the question of the chance of survival after diagnosis or
entry to treatment. It can address the variable of entry
and withdrawal from treatment. The life table generates
a survival curve.

Predictors, sometimes called independent variables, are
factors or variables that can be used to “predict” or
forecast the value of another variable, called the depen-
dent or outcome variable, based on observations and
measurements. Within the addictions field, predictor
variables can include characteristics of an individual or
population (such as age, gender, education, severity of
disorder, involvement in criminal justice system, readi-
ness to change, motivation for treatment, etc.), char-
acteristics of the treatment environment, theoretical
approach to treatment, models of service delivery, char-
acteristics of the therapist/counselor and therapeutic
alliance.

Private residential treatment: is a 24 hour/7 days a week
treatment program for co-occurring substance abuse
and mental health disorders provided in a residential

setting for extended time periods (up to 6–12 months)
beyond an acute detoxification or psychiatric inpatient
hospitalization stay. Residential treatment may be pub-
licly funded (Medicaid/Medicare, state/federal block
grants, or nonprofit agencies without fees) or privately
funded (private insurance or direct out-of-pocket pay-
ment).

Treatment retention: refers to the quantity or amount of
time in treatment. Most commonly treatment reten-
tions refer to the length of stay in treatment measured
by days, months, or specific time period. Historically,
longer treatment retention is a consistent predictor of
better post-treatment outcomes.

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