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A Multisite Evaluation of Prison-Based Therapeutic Community Drug Treatment

November 2007
Criminal Justice and Behavior 34(11):1481-1498

November 2007
34(11):1481-1498

Authors:

Temple University

A quasi-experimental study examined multiple postrelease outcomes up to 2 years for inmates who participated in therapeutic community (TC) drug treatment programs (n = 217) or comparison groups (n = 491) at five state prisons. Statistical controls included level of need for treatment, current and prior criminal history, and postrelease employment. Prison TC was effective even without mandatory community aftercare, although main effects and interactions varied somewhat across different outcome measures and sites. TC significantly reduced rearrest and reincarceration rates but not drug relapse rates. Postrelease employment predicted drug relapse and reincarceration, and employment interacted with age to predict rearrest. Two sites had higher drug relapse rates than the other three. Implications for research and policy are discussed.

: Sample Characteristics

…

: Stepwise Logistic Regression of Reincarceration, Rearrest, and Relapse on Predictor and Control Variables

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Criminal Justice and Behavior

DOI: 10.1177/0093854807307036

2007; 34; 1481 Criminal Justice and Behavior

Wayne N. Welsh

A Multisite Evaluation of Prison-Based Therapeutic Community Drug Treatment

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A MULTISITE EVALUATION OF

PRISON-BASED THERAPEUTIC

COMMUNITY DRUG TREATMENT

WAYNE N. WELSH

Temple University

A quasi-experimental study examined multiple postrelease outcomes up to 2 years for inmates who participated in therapeu-

tic community (TC) drug treatment programs (n = 217) or comparison groups (n = 491) at five state prisons. Statistical con-

trols included level of need for treatment, current and prior criminal history, and postrelease employment. Prison TC was

effective even without mandatory community aftercare, although main effects and interactions varied somewhat across dif-

ferent outcome measures and sites. TC significantly reduced rearrest and reincarceration rates but not drug relapse rates. Post-

release employment predicted drug relapse and reincarceration, and employment interacted with age to predict rearrest. Two

sites had higher drug relapse rates than the other three. Implications for research and policy are discussed.

Keywords: adult offenders; corrections; drug use; drug treatment; rehabilitation

rug-involved offenders comprise a large portion of local, state, and federal correctional

populations. At midyear 2005, 2.2 million inmates were incarcerated in U.S. jails and

prisons, a rate of 738 per 100,000 adults (up from 601 per 100,000 in 1995; P. M. Harrison

& Beck, 2006). Drug offenses accounted for 21% of sentenced prisoners under state juris-

diction in 2002, and 55% of sentenced prisoners under federal jurisdiction in 2003 (P. M.

Harrison & Beck, 2005).

The existing delivery of correctional drug treatment is inadequate relative to need. In the

1997 Survey of Inmates in State and Federal Correctional Facilities, about two out of three

inmates admitted drug histories, but fewer than 15% received any professional treatment

while in prison (Mumola, 1999). Belenko and Peugh (2005) estimated that about one third

of male inmates and more than half of female inmates in this sample needed long-term res-

idential treatment. Although inmates in the most severe drug use categories were more

likely to receive treatment while incarcerated, only about one fifth received any clinical

treatment services.

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CRIMINAL JUSTICE AND BEHAVIOR, Vol. 34 No. 11, November 2007 1481-1498

DOI: 10.1177/0093854807307036

AUTHORS’ NOTE: The U.S. Department of Justice, National Institute of Justice (NIJ; Grant No 99-CE-VX-

0009) supported the research reported here, along with the Pennsylvania Commission on Crime and Delinquency

(PCCD; Subgrant No. 1999/2000-DS-011188). Opinions expressed here are those of the author and not neces-

sarily of PCCD or the U.S. Department of Justice. Any errors or omissions are the responsibility of the author

alone. The author gratefully acknowledges the valuable contributions of Gary Zajac, chief of research and eval-

uation for Pennsylvania Department of Corrections, who chaired the Steering Committee for this project and

provided access to Pennsylvania Department of Corrections data; all Department of Corrections personnel on

the Steering Committee, for input and advice throughout this project; Doug Hoffman and Deborah Almoney of

PCCD, and James Alibrio of the Pennsylvania Board of Probation and Parole, who provided important postre-

lease data for this study; and graduate research associates Patrick McGrain and Nicole Salamatin, who capa-

bly assisted with data collection and coding.

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In the 2004 Survey of Inmates in State and Federal Correctional Facilities, the Bureau

of Justice Statistics included for the first time measures of drug dependence and abuse

based on criteria specified in the Diagnostic and Statistical Manual of Mental Disorders

(DSM-IV; American Psychiatric Association, 1994; see Mumola & Karberg, 2006). Fifty-

three percent of state and 45% of federal prisoners met DSM-IV criteria for drug depen-

dence or abuse. Among drug dependent prisoners, 40% of state and 49% of federal inmates

took part in some type of drug abuse program, including self-help groups, peer counseling,

and drug education. However, the percentage who took part in treatment programs with a

trained professional (15%) remained unchanged from 1997.

Prison-based therapeutic community (TC) drug treatment has been shown to be effective in

breaking the cycle of relapse and recidivism among seriously drug-involved offenders (Gaes,

Flanagan, Motiuk, & Stewart, 1999; Mitchell, MacKenzie, & Wilson, 2006; Pearson & Lipton,

1999). However, important questions remain about the magnitude and generalizability of treat-

ment effects across different measures, sites, and contexts (Farabee et al., 1999; Fletcher &

Tims, 1992; Office of National Drug Control Policy [ONDCP], 1996, 1999; Pearson & Lipton,

1999; Welsh & Zajac, 2004a, 2004b).

IN-PRISON SUBSTANCE-ABUSE TREATMENT

An in-prison TC is a residential treatment program that provides an intensive, highly struc-

tured prosocial environment for the treatment of substance abuse and addiction. It differs from

other treatment approaches principally in its use of the community as the key agent of change,

in which treatment staff and recovering clientele interact in both structured and unstructured

ways to influence attitudes, perceptions, and behaviors associated with drug use (De Leon,

2000). The TC uses a staged, hierarchical model in which treatment stages are related to

increased levels of individual and social responsibility. Peer influence, mediated through a

variety of group processes, is used to help residents learn and assimilate social norms and

develop more effective social skills. The therapeutic approach generally focuses on changing

negative patterns of thinking and behavior through individual and group therapy, group ses-

sions with peers, and participation in a therapeutic milieu with hierarchical roles, privileges,

and responsibilities. Strict and explicit behavioral norms are emphasized and reinforced with

specific contingencies (rewards and punishments) directed toward developing self-control

and responsibility.

Conclusions about the impact of in-prison TC treatment and aftercare on postrelease

drug abuse and criminal behavior have been based largely on the intensive evaluation of

three model programs: (a) KEY/CREST in Delaware, (b) the Amity prison in California,

and (c) Kyle New Vision in Texas. Each found that graduates of prison TC had lower rates

of recidivism than comparison samples, especially when prison TC was combined with

structured aftercare following release from prison.

In Delaware, prisoners with a history of drug-related problems are referred to the 12-

month KEY Therapeutic Community program, and following prison release, these individ-

uals go to the CREST program, a 6-month TC-based work-release program for transitional

aftercare (Inciardi, Martin, Butzin, Hooper, & Harrison, 1997; Lockwood, Inciardi, &

Surratt, 1997; Nielsen, Scarpitti, & Inciardi, 1996). Finally, after release from residential

aftercare, the clients receive supervised outpatient-based aftercare. Random assignment

was used only for one cohort of inmates randomly assigned to work release (CREST) or

not. No random assignment was used to assign subjects to the experimental treatment

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(KEY) or control group. Three years following their release to the community, significantly

more of the clients who completed the in-prison program and the transitional aftercare

program remained arrest-free (55%) than those in an untreated comparison group (29%;

Martin, Butzin, Saum, & Inciardi, 1999). Those who also received outpatient aftercare fol-

lowing the transitional residential treatment had the best outcomes (69% arrest-free after 3

years). Results for relapse to drug use (as measured by both self-reports and urinalysis)

were similar, with 17% of those who completed only the in-prison TC, 27% who had the

in-prison treatment and the transitional residential treatment, and 35% who also had out-

patient aftercare remaining drug-free during the follow-up period, compared to only 5% of

the comparison group. Findings reported for 5-year outcomes were similar, with those who

went through both KEY and CREST or through CREST alone having significantly lower

recidivism rates than the comparison group (Inciardi, Martin, & Butzin, 2004). According

to Inciardi et al. (2004, p. 103), participation in prison TC treatment alone did not signifi-

cantly improve 5-year outcomes, although those analyses were not presented.

In the Amity, California prison study (Wexler, Melnick, Lowe, & Peters, 1999), researchers

randomly assigned inmates who had volunteered for treatment to either TC or a wait-listed,

“intent-to-treat” comparison group. Volunteers were deemed eligible for TC if they had a drug

problem and had at least 9 to 14 months remaining in their sentence prior to parole eligibil-

ity. Inmates remained in the TC-eligible pool until they had less than 9 months to serve; then

they were removed from the pool and designated as members of the “no-treatment” control

group. On release from prison, Amity parolees could volunteer for aftercare in a 40-bed,

community-based TC program, so not all of those who received in-prison treatment also

received aftercare on return to the community.

Three-year postparole outcome data showed that only 27% of those who received both in-

prison and aftercare treatment were reincarcerated during the follow-up interval, compared

to a 75% reincarceration rate for those in the comparison group, 79% who completed only

the in-prison treatment, and 82% for those who were in-prison treatment dropouts (Wexler

et al., 1999). However, when the entire treatment group (i.e., before removing dropouts from

TC or aftercare) was considered together (as it should be, to avoid biased outcomes in favor

of the treatment group), the reincarceration rate for the treatment group increased to 69%, a

difference that was no longer statistically significant. Interestingly, the 5-year outcome

results for this sample suggested a “rebound” effect: The in-prison TC treatment group had

a significantly lower reincarceration rate (76%) than the no-treatment control group (83%;

Prendergast, Hall, Wexler, Melnick, & Cao, 2004). There is evidence, therefore, that prison

TC reduced recidivism independently of community aftercare, although prison TC followed

by aftercare produced the strongest effects in bivariate analyses. Five-year effects of treat-

ment on postrelease employment and drug use were nonsignificant in multivariate analyses,

although unverified self-report measures were used for both outcomes.

It is difficult to interpret the stability of these findings, the authors acknowledge, because

“the unbiased assignment of randomization no longer operates, and selection bias becomes

a possible (although by no means exclusive) explanation for the findings” (Prendergast et al.,

2004, p. 53). In addition, bivariate analyses of four treatment subgroups (TC dropouts, TC

completers, aftercare dropouts, TC + aftercare completers) were potentially limited by small

sample sizes. Statistical power depends on the sample size, the size of the treatment effect,

and the criterion of significance (alpha level; Borenstein, Rothstein, & Cohen, 2001).

Studies of prison TC in Texas provide the third exemplar. The Kyle New Vision program

is a 500-bed facility that provides treatment to inmates during the final 9 months of their

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prison term (Eisenberg & Fabelo, 1996). After release, parolees were mandated to attend

3 months of residential aftercare in a transitional therapeutic community (TTC), followed by

up to another year of supervised outpatient aftercare. Authors constructed a matched com-

parison sample (n = 103) based on TC-eligible inmates who were either rejected by the

parole board or who had too little time remaining on their sentence (Knight, Simpson, &

Hiller, 1999). TC-eligible parolees were rejected because the parole board judged them

either as unlikely to benefit from the program or inappropriate for the program (Knight,

Simpson, Chatham, & Camacho, 1997), introducing potential selection bias into the research

design. Researchers separated treatment admissions into aftercare completers (TC + after-

care; n = 169) and aftercare dropouts (TC only; n = 122).

Analysis of 3-year outcome data showed that in-prison treatment followed by aftercare

was most effective for high-risk, high-need offenders (Hiller, Knight, & Simpson, 1999;

Knight, Simpson, & Hiller, 1999). Aftercare completers had a 3-year reincarceration rate of

only 25%, significantly better than the 42% reincarceration rate of the comparison group and

the 64% reincarceration rate of the aftercare dropouts. Neither drug relapse nor employment

were examined. Because the treatment and comparison groups differed significantly on prior

offense and problem severity (and perhaps other unmeasured characteristics), researchers

further broke down the three groups into low-risk and high-risk subgroups (six comparisons

overall, with sample sizes < 100 in four of the six groups). Treatment effects were greatest

for high-risk inmates who completed both TC and aftercare (Hiller et al., 1999).

GAPS IN CURRENT KNOWLEDGE

The effectiveness of prison-based TC drug treatment is less clear than commonly assumed

because of methodological limitations, including selection and attrition biases, dissimilar out-

come measures, few statistical controls, and potentially low statistical power (Austin, 1998;

Fletcher & Tims, 1992; Gaes et al., 1999; Mitchell et al., 2006; Pearson & Lipton, 1999). In

many studies, inmates were allowed to self-select into treatment, they were selected on crite-

ria unrelated to their assessed level of need for treatment, or they dropped out of treatment at

high rates (Simpson, Joe, Broome, et al., 1997; Young, 2002). Dropouts were often excluded

or analyzed as though they were a valid, independent comparison group. In such cases, out-

comes are biased because of uncontrolled selection or attrition processes (Austin, 1998; Gaes

et al., 1999; Pearson & Lipton, 1999). Instead, the entire sample should be included in analy-

ses of outcome, with program dropout factored in as a control variable.

There were inconsistencies in the use of outcome measures across the three studies. Only

one study (Amity) examined postrelease employment, using only self-report measures. Much

research supports a relationship between postrelease employment and recidivism (Uggen,

2000; Western, Kling, & Weiman, 2001), and postrelease employment is often an important

predictor of posttreatment relapse and recidivism (French, Zarkin, Hubbard, & Rachal, 1993;

O’Connell, 2003; Siegal, Li, & Rapp, 2002; Sung, 2001; Wilson, Gallagher, & Mackenzie,

2000). Only two studies (Amity and Delaware) examined drug relapse as an outcome, and

only one (Delaware) used urinalysis to confirm self-reported drug use. Arrestees’ self-reports

underestimate drug use detected by urinalysis by magnitudes of 40% to 60% (Taylor,

Fitzgerald, Hunt, Reardon, & Brownstein, 2001), and other limitations of self-report measures

of criminal behavior are well known (Cantor & Lynch, 2000; Thornberry & Krohn, 2000).

Only two studies (California and Texas) examined reincarceration; one (Texas) excluded all

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felonies and misdemeanors resulting in local jail rather than prison time. Only one study

(Delaware) examined rearrest but without measuring reincarceration. Rearrest may or may

not lead to reincarceration depending on the nature of the offense and the disposition. Both

are important in studies of drug-involved offenders.

Treatment migration is another potential limitation. The wrong treatment may be deliv-

ered to one or more groups, or different treatment conditions may be mixed. This sort of

problem is surprisingly common in evaluation studies, even those that use random assign-

ment (Gartin, 1995). In Delaware, Inciardi et al. (1997, p. 266) noted, “Many of the so-called

no treatment comparison group did get some treatment help.” Similarly, inmates in the

Amity no-treatment comparison group may have received some unknown mix of drug edu-

cation, self-help, or outpatient services: “The control group did not receive any formal sub-

stance abuse treatment during their prison stay, although limited drug education and 12-step

groups were available” (Wexler et al., 1999, p. 325).

Major conclusions about prison TC have been based on studies of three model programs

in three states, all of which received extensive program development funding from the

National Institute on Drug Abuse (Melnick, Hawke, & Wexler, 2004). It is not clear that

these three programs represent the typical prison TC program widely implemented in the

1990s as a result of federal funding for the Residential Substance Abuse Treatment (RSAT)

initiative (L. D. Harrison & Martin, 2003). At a minimum, replication across a greater

number of sites is needed.

It has been argued that prison TC does not produce significant treatment effects unless

the continuum of care is completed by community aftercare treatment (Inciardi et al.,

2004). One key objective of this study, therefore, was to examine the effects of prison TC

drug treatment in a sample that did not receive mandatory community aftercare. A second

objective was to examine to what degree outcomes varied across different measures and

sites. It was predicted that the level of outcomes (rearrest, reincarceration, and drug relapse)

would be better for the treatment group (TC) than the comparison group and that treatment

effects would be consistent across different prisons in the same state using the same TC

framework and philosophy.

METHOD

BACKGROUND

Pennsylvania consistently ranks among the 10 highest prison populations in the country

(P. M. Harrison & Beck, 2006). The Pennsylvania Department of Corrections (PADOC)

operates 25 state correctional institutions, 1 motivational boot camp, and 14 community cor-

rections centers. The department housed 42,446 inmates as of December 31, 2005, with males

representing 95% of the state’s inmate population (PADOC, 2006c). The inmate population

consisted of 51% African Americans, 37% Caucasians, and 11% Hispanics, with 1% percent

accounted for by other ethnicities or races. The average age at commitment was 33 years old.

In fiscal year 2005-2006, PADOC budgeted $26.1 million in state funds and $1.3 million in

federal funds on alcohol and other drug (AOD) treatment (PADOC, 2006b). At year-end

2005, 16,250 inmates were enrolled in AOD treatment programs (PADOC, 2006b).

At the time data were collected, the Department’s AOD programming was grouped into

four major categories: (a) drug and alcohol education programs offered to inmates identified

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as having any level of drug and alcohol involvement; (b) outpatient treatment programs

offered to inmates in need of more intermediate levels of intervention, including individual

and group counseling sessions; (c) TCs offered to inmates identified as needing intensive

substance-abuse intervention; and (d) ancillary groups, such as self-help and peer counsel-

ing, offered to inmates as a supplement to other treatment (PADOC, 2002).

TC inmates moved through three phases of treatment. The first phase, orientation (1 to

3 months duration), consisted of orientation to TC philosophy and concepts, diagnosis, and an

assimilation process. In the second phase, primary treatment (3 to 7 months duration), inmates

took on increased responsibility and involvement in the program, including teaching new

members and assisting in the day-to-day operation of the TC. Through peer interactions as well

as individual and group counseling, primary treatment focused on positive behavior manage-

ment, social and confrontation skills, acceptance of guidance for problem areas, effective

participation in the inmate house structure (e.g., morning meetings, inmate committees), identi-

fication of personal relapse triggers, effective use of recovery tools, trust and relationship issues,

self-identity, and awareness of criminal thinking. During the third phase, reentry (1 to 3 months

duration), inmates strengthened planning and decision-making skills, developed a relapse pre-

vention plan, and designed an individual release plan signed by both the inmate and the treat-

ment specialist. Counselors informed inmates of the availability of 12-step groups, such as

Alcoholics Anonymous and Narcotics Anonymous, in the community and encouraged inmates

to use these services (PADOC, 2002). PADOC also offered an in-prison alumni program for

inmates who completed TC treatment but had not yet qualified for community placement or

parole (PADOC, 2002). In contrast to studies of prison TC in Delaware, California, and Texas,

however, no mandatory community aftercare treatment was provided to TC graduates.

PROCEDURE

Dependent variables. Recidivism data were coded for all sample inmates released from

PADOC custody by the conclusion of the study. Three types of data were collected: (a) rein-

carceration (0 = no, 1 = yes), (b) rearrest (0 = no, 1 = yes), and (c) drug relapse (0 = all neg-

ative drug tests, 1 = at least one positive drug test). Reincarceration data were obtained from

records provided by the Pennsylvania Department of Corrections. Rearrest data, collected by

the Pennsylvania State Police, was available through the Pennsylvania Commission on

Crime and Delinquency (PCCD). The Pennsylvania Board of Probation and Parole (PBPP)

provided drug-testing data for parolees. Eighty-seven percent of all inmates released were

under the jurisdiction of this agency, which employs a state-of-the-art urinalysis program

that calls for frequent, random drug testing.

Independent variables. The TCU Drug Screen II, created by researchers at Texas Christian

University, has been widely used and validated with inmate populations and has evidenced

excellent reliability (Broome, Knight, Joe, & Simpson, 1996; Peters, Greenbaum, & Edens,

1998, Peters et al., 2000; Shearer & Carter, 1999; Simpson, Knight, & Broome, 1997). The

items in this screening tool represent key clinical and diagnostic criteria for substance depen-

dency as they appear in the DSM-IV and the Structured Clinical Interview for DSM-IV (First,

Spitzer, Gibbon, & Williams, 1997). The inmate’s score (0 to 9) helps to determine the level

of need for treatment. According to scoring criteria for the TCU Drug Screen II, score values

of 3 or greater indicate relatively severe drug-related problems and correspond approximately

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to DSM-IV drug-dependence diagnosis. Standardized drug scores were calculated based on

the inmate’s TCU Drug Screen II score, if available, or the inmate’s score on a similar drug

screening instrument called the Pennsylvania Department of Corrections Screening

Instrument (PACSI) if the TCU was not available.

Because some inmates had a score on one

instrument, some inmates had another, and other inmates had both scores, statistical analyses

used only standardized z scores rather than raw scores.

Age and criminal history have consistently predicted recidivism, and time remaining in an

inmate’s sentence is often an important criterion for treatment eligibility (Andrews & Bonta,

2003; Gendreau, Little, & Goggin, 1996). Age at time of admission was calculated by sub-

tracting the inmate’s birth date from the date of program admission. For each inmate, PADOC

also supplied time remaining until minimum release date. PADOC also supplied current and

prior Offense Gravity Scores (OGS), as determined by the Pennsylvania sentencing guide-

lines. These variables provide precise controls for previous and current criminal history.

Four categorical variables were also entered as predictors. The first reflected the treatment

group (0 = TC, 1 = comparison). If TC were effective, we would expect a significant coef-

ficient for TC even when controlling for individual differences. A second categorical vari-

able reflected the institutional setting of the TC program (1 = Prison A, 2 = Prison B, 3 =

Prison C, 4 = Prison D, 0 = Prison E). Contextual effects of TC treatment provided in dif-

ferent settings may influence outcomes, although previous research has examined such

effects only with community rather than prison samples (Simpson, Joe, & Brown, 1997). A

third categorical variable reflected postrelease employment status (1 = full-time, 2 = part-

time, 3 = unemployed but able, and 0 = unemployed and unable to work), using the most

recent employment status available from automated Parole Board data.

A fourth categorical

variable represented whether the inmate successfully completed his treatment program or

not (0 = no, 1 = yes). Previous research suggests that those successfully completing treat-

ment are less likely to recidivate (Hiller et al., 1999; Hiller, Knight, Leukefeld, & Simpson,

2002; Simpson, Joe, & Brown, 1997; Zhang, Friedmann, & Gerstein, 2003).

PARTICIPANTS

All admissions to AOD programs at five Pennsylvania state prisons between January 1, 2000,

and November 30, 2000, were recorded. Each of the five institutions had a full range of AOD

programs including well-established prison TC programs: Prison A (medium security; total

pop. = 1,302; TC beds = 52), Prison B (maximum security; total pop. = 3,638; TC beds = 50),

Prison C (medium security; total pop. = 1,500; TC beds = 120), Prison D (maximum security;

total pop. = 1,668; TC beds = 36), and Prison E (minimum/medium security; total pop. = 1,218;

TC beds = 100). All five institutions housed male inmates only. Although deserving of separate

analysis, treatment programs for female offenders target a distinct population with unique needs

(Bloom, Owen, & Covington, 2003; Lockwood, McCorkel, & Inciardi, 1998).

Previous research demonstrated the therapeutic integrity of the five TC drug-treatment

programs (Welsh, 2002). Qualitative research (inmate and staff interviews, structured observa-

tions, and inspection of treatment files) confirmed the implementation of basic components of

the TC model including appropriate use of sanctions and rewards, a shared sense of commu-

nity and accountability, inmate participation in leadership roles and committees, perceptions of

peer support and trust, and rapport with counselors (ONDCP, 1999). Results from an AOD

program census (Welsh & Zajac, 2004a, 2004b) showed that the TC programs were highly

consistent along dimensions such as primary treatment approach (cognitive– behavioral and

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psychodynamic approaches were commonly used), program content (e.g., emphases on

thinking errors, models of addiction, and problem-solving skills), and treatment duration

(mean = 48 weeks).

Each of the five programs, though consistently implementing the basic TC philosophy,

also exhibited some variations. Two of the five TC units (Prisons C and E) were quite large

(124 and 100 inmates, respectively). The other three had between 36 and 52 beds. Program

duration and intensity varied slightly. Three programs offered 15 hours per week of treat-

ment (individual or group counseling); two programs (Prisons B and D) offered weekly pro-

gramming of 30 hours or more. Three programs lasted 12 months; one (Prison E) lasted 9

months; another (Prison A) lasted 12 to 16 months. Prisons D (10%) and E (5%) evidenced

lower dropout rates than Prisons A (16%), B (20%), or C (21%). It was difficult to estimate

staffing levels precisely, because of the fact that TC staff members were assigned exclusively

to TC in some institutions (Prisons A, B, and E), whereas TC staff in others (Prisons C and

D) also provided education and outpatient programming to the general population. Given

these limitations, staffing ratios (inmates per counselor) ranged from 9:1 (Prison D) to 26:1

(Prison A). Prison E had a ratio of 14:1; Prisons B and C both had ratios of 25:1.

Because a shortage of space precluded intensively treating all inmates assessed with a

high need for drug and alcohol treatment, a large pool of drug-involved offenders was

assigned to less intensive forms of treatment (short-term drug education and outpatient treat-

ment groups). A total of 2,809 inmates participated in AOD programs at the five institutions

during the study period, although relapse and recidivism data were available only for

inmates who had been released from prison by the conclusion of the study. After listwise

deletion, the final sample available for analyses (n = 708) consisted of 217 TC inmates and

491 inmates in the comparison group.

Because all treatment admissions and discharges were recorded, we were able to account

for total treatment exposure for all participants. Monthly tracking throughout the study

recorded treatment outcomes (e.g., successful versus unsuccessful), total treatment exposure

(number of hours of treatment completed), and date of release from prison. As expected, TC

inmates (mean = 912 hours) received more than 13 times the treatment exposure of inmates

in the comparison group (mean = 68 hours) (F [1,704 df] = 1081.6, p < .001), providing a

strong rationale for the quasi-experimental research design. The response–dosage design

used in the current study compares different levels of treatment while statistically control-

ling for inmate selection differences.

Using one-way ANOVA and chi-square analyses, major characteristics for inmates in the

TC and comparison groups were compared (see Table 1). The two groups did not differ sig-

nificantly on age at the time of program admission. However, significant variability was

observed in time remaining until minimum release date. Many inmates were already past

their minimum release date, suggesting that many had already been denied parole at least

once. TC inmates had slightly more serious current and prior offense histories, suggesting

that higher risk inmates were targeted for TC placement. TC inmates had slightly higher

mean standardized drug scores, suggesting that higher need inmates were more likely to be

placed in TC. Most inmates in the sample, however, far surpassed the minimum eligibility

criteria for TC placement (i.e., 87% had a TCU Drug Screen score of 3 or greater or a PACSI

score of 5 or greater). The two groups did not differ significantly on amount of time at risk

since their release from prison (mean = 17 months).

Descriptive results indicated that the majority of inmates in the sample, regardless of

program type, were classified as high-need. A fortunate situation thus existed in terms of

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research design (i.e., many high-need inmates were assigned to programs of dramatically

different treatment dosages) but not treatment responsivity (i.e., there were simply not

enough TC beds to assign all high-need inmates to high-intensity programs, with the result

that many high-need inmates received some form of less intensive treatment). Statistical

controls were used to adjust for initial inmate selection differences.

ANALYTIC APPROACH

Stepwise logistic regressions were used to examine reincarceration, rearrest, and drug-

relapse rates for the TC and comparison groups. These techniques enter into regression equa-

tions only those variables that exceed a specified probability of statistical significance and

remove variables that fail to reach a specified level of significance. These procedures allow

the researcher to estimate models of outcome that reflect only the most robust and signifi-

cant predictors. Logistic regression is useful for examining dichotomous outcomes, includ-

ing analyses of factors influencing reincarceration (or not), rearrest (or not), and drug relapse

(or not). The odds of returning to prison within a given time frame carry enormous implica-

tions for inmate housing, security, treatment, medical, educational, and vocational needs.

Similarly, rates of rearrest and drug relapse affect available criminal justice and treatment

resources, in which demand far exceeds supply (Belenko & Peugh, 2005).

Logistic regression allows the researcher to enter categorical (e.g., treatment completion)

or continuous variables (e.g., age) as predictors of recidivism for the experimental and

comparison groups. Because age and employment have both been associated with offending

(Laub & Sampson, 2003; Uggen, 2000), interaction terms were also examined for Age ×

Comparison Group and Age × Employment Status.

Welsh / PRISON-BASED TC DRUG TREATMENT 1489

TABLE 1: Sample Characteristics

Comparison Group

(

= 491) TC Group (

= 217) Total (

= 708)

% M SD % M SD % M SD

Age at time of admission 33.8 8.7 36.5 8.8 34.6*** 8.8

Time to minimum (months) –9.92 30.5 –2.40 26.6 –7.61** 29.6

Current offense severity (0 to 10) 4.9 2.5 5.5 2.1 5.1** 2.4

Prior offense severity (0 to 10) 4.9 2.8 5.4 2.4 5.1* 2.6

Standardized drug score (

) –0.16 1.0 0.38 0.90 0.0*** 1.0

Time at risk (months) 16.5 4.3 16.6 4.7 16.5 4.4

Institution 100 100 100

Prison A 28.9 7.4 22.3***

Prison B 13.4 12.0 13.0

Prison C 33.6 43.3 36.6*

Prison D 16.5 9.2 14.3**

Prison E 7.5 28.1 13.8***

Dropout rate 17.1 12.4 15.7

Postrelease employment status 100 100 100

Full-time 25.9 39.2 29.9***

Part-time 4.7 3.7 4.4

Unemployed and able 30.3 23.0 28.1*

Unemployed and not able 39.1 34.1 37.6

Note

. TC = therapeutic community

< .05. **

< .01. ***

< .001.

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RESULTS

Reincarceration rates were examined with statistical controls for selection differences

between the TC and comparison groups (see Table 2). Inspections for multicollinearity

revealed no difficulties (e.g., no paired correlations exceeded .50). Variables that signifi-

cantly predicted reincarceration included participation in the comparison rather than TC

group (comparison inmates were 1.6 times more likely to be reincarcerated), age (younger

inmates had a higher rate of reincarceration), drug score (higher need inmates had a higher

rate of reincarceration), and postrelease employment status (inmates employed full-time

had a lower rate of reincarceration). None of the institutional effects were statistically sig-

nificant, suggesting that the impact of TC on reincarceration was invariant across the five

institutions. Other variables, including prior and current offense severity, failed to reach sta-

tistical significance and dropped out of the equation. Neither interaction term (Age ×

Comparison Group, Age × Employment Status) was statistically significant. Figure 1 shows

that TC significantly reduced the probability of reincarceration (30% versus 41%) when

results were adjusted for the effects of all control variables.

1490 CRIMINAL JUSTICE AND BEHAVIOR

TABLE 2: Stepwise Logistic Regression of Reincarceration, Rearrest, and Relapse on Predictor and

Control Variables

Reincarceration Rearrest Drug Relapse

BSE

Exp(

)

BSE

Exp(

)

BSE

Exp(

)

Age –.026* .010 .975 –.034* .010 .967 .021* .010 1.022

Time to minimum — — —

OGS–current — — —

OGS–prior — .120* .034 1.128 —

Drug score (

) .192* .094 1.211 — —

Treatment completion — — —

Program type .477* .206 1.611 .395* .195 1.484 —

Age × Program Type — — —

Prison (1) — — .934* .309 2.544

Prison (2) — — .550 .353 1.734

Prison (3) — — .831* .283 2.295

Prison (4) — — .407 .346 1.502

Employment status (1) –2.216* .228 .109 — –.582* .207 .559

Employment status (2) –1.689* .436 .185 — –.205 .413 .815

Employment status (3) –1.790* .213 .167 — –.285 .205 .752

Age × Employment (1) — –.027* .006 .973 —

Age × Employment (2) — –.004 .012 .996 —

Age × Employment (3) — –.018* .006 .982 —

Constant 1.162* .419 3.197 –.139 .427 .870 –1.78* .536 .169

Chi-square (

) 163.05* (6

) 62.36* (6

) 28.96* (8

)

–2 log likelihood 773.21 815.12 830.30

Nagelkerke

.280 .119 .059

of cases 708 708 650

Note

= standard error; Time to minimum = Time remaining to minimum release date at time of program admis-

sion; OGS = offense gravity score, current (1 to 10) and prior (1 to 10); Treatment completion: 1 =

successfully com-

pleted treatment

, 0 =

unsuccessfully discharged

; Program type: 1 =

comparison group

, 0 =

therapeutic community

Prison: 1 =

Prison A

, 2 =

Prison B

, 3 =

Prison C

, 4 =

Prison D

; Employment status (1) =

full-time employment

employment status (2) =

part-time employment

, employment status (3) =

unemployed and able

. Criteria:

–in (.10),

–out (.10); these terms refer to the criteria of statistical significance (

) required for entering the regression equa-

tion (

–in) or leaving it (

–out).

< .05.

distribution.

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The impact of TC on rearrest rates was examined next, controlling for selection differ-

ences between the TC and comparison groups (see Table 2). Inspections for possible mul-

ticollinearity again revealed no difficulties. Variables that significantly predicted rearrest

included participation in the comparison rather than TC group (comparison group inmates

were 1.5 times more likely to be rearrested), age (younger inmates had a higher rate of rear-

rest), and prior offense gravity score (inmates with a more serious criminal history had a

higher rate of rearrest). Although postrelease employment status by itself did not signifi-

cantly predict rearrest, the interaction between age and employment did. Older inmates

who were employed full-time had a lower rate of rearrest, as did older inmates who were

unemployed but able to work. None of the institutional effects were statistically significant,

suggesting that the impact of TC on rearrest was invariant across the five institutions. Other

variables failed to reach statistical significance and dropped out of the equation. Figure 1

shows that TC significantly reduced the probability of rearrest (24% versus 34%) when

results were adjusted for the effects of all control variables.

Welsh / PRISON-BASED TC DRUG TREATMENT 1491

Figure 1: Estimated Probabilities of Reincarceration, Rearrest, and Drug Relapse for Comparison and

Therapeutic Community (TC) Groups (Adjusted for Control Variables)

Note

. Probabilities of reincarceration for different groups were estimated using logistic regression equations with

all predictor and control variables entered: Prob (event) = (1/(1 + e

-Z

), where Ζ=ΣB

(Hanushek & Jackson,

1977; Lichter, 1989; Norusis, 2005); B = the coefficient for each variable; and X = the mean value for each vari-

able. Probabilities were adjusted for all control variables, using logistic regression coefficients reported in Table 2.

Means with different subscripts were significantly different from each other (

< .05).

distribution.

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The impact of TC on drug relapse rates was examined next, again controlling for selec-

tion differences between the TC and comparison groups (see Table 2). Inspections for mul-

ticollinearity revealed no difficulties. The effect of TC on drug relapse was nonsignificant.

Figure 1 shows that TC (35%) did not significantly reduce the probability of drug relapse

when compared to the comparison group (38%), adjusting for the effects of all control vari-

ables. Overall, 37% of the sample tested positive for at least one drug during the follow-up

period. Positive drug tests occurred most frequently for cocaine (55% of those testing pos-

itive), cannabinoids (25%), opiates (22%), and ethanol (16%).

Variables that significantly predicted drug relapse included age (older inmates had a

higher rate of relapse) and postrelease employment status (inmates employed full-time had

a lower rate of relapse). The log-odds ratios (see Table 2) showed that inmates employed

full-time after release from prison were only about half as likely to relapse as other inmates

in the sample. In contrast to findings reported for reincarceration and rearrest, the impact

of TC on drug relapse depended on which institution the inmate received treatment at. The

log-odds ratios reported in Table 2 show that inmates at Prison A and Prison C were 2.5 and

2.3 times as likely to relapse as other inmates, respectively. Neither interaction term was

statistically significant.

Results suggest that prison-based TC drug treatment reduces the likelihood of postre-

lease rearrest and reincarceration but not substance abuse. It may be the case, however, that

the measure of drug relapse used here (any positive drug test) was not precise enough.

Thus, an alternative measure of postrelease drug use was examined. The proportion of drug

tests resulting in a positive test controls for the number of times that an ex-offender was

tested, and it offers a reasonable proxy for intensity of use. Univariate general linear mod-

eling (GLM) was used to examine the effect of TC treatment because the dependent vari-

able was continuous, whereas independent variables were both categorical and continuous.

All continuous variables were entered as covariates; categorical variables were entered as

fixed factors. GLM results (not shown) confirmed that the TC and comparison groups did

not differ significantly on postrelease drug use, net of all controls. The only significant pre-

dictor was postrelease employment. In contrast to logistic regression findings, institutional

effects in GLM results were invariant. Because the drug relapse results were somewhat sen-

sitive to the dependent measure (any positive drug test versus proportion of tests resulting

in a positive) and the analysis used (logistic regression versus GLM), some caution in inter-

preting these results is called for.

DISCUSSION

Results partially confirmed previous findings regarding the positive effects of prison TC.

Reincarceration and rearrest rates (but not drug relapse rates) were significantly lower for

TC inmates than for comparison inmates. Treatment effects remained even after program

failures were taken into account. Treatment effects were surpassed in magnitude only by

postrelease employment, with inmates employed full-time showing substantially lower rates

of reincarceration and drug relapse. For rearrest, however, only older inmates benefited from

full-time employment.

With the exception of drug relapse, treatment effects were invariant across the five institu-

tions. Weak institutional effects might be expected, because policies governing TC programs

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at all five institutions were set by the same state correctional agency, all five programs previ-

ously evidenced implementation fidelity, and all five programs were of similar duration.

Programs did vary somewhat on dropout rate and other contextual factors, however (see

Methods section).

The nonsignificant finding for drug relapse accords with more mixed findings from prior

research. Only one of the three major prison TC studies (Delaware) examined drug urinaly-

sis as an outcome and found significant treatment effects. In the Amity study, treatment had

no significant effect on self-reported drug use (Prendergast et al., 2004). In Texas, results for

drug relapse were not reported. Although prison TC addresses both addiction and criminal

behavior, the two types of behavior can exist independently, and drug-using behavior appears

more resistant to change.

Indeed, a recent meta-analysis of prison-based drug treatment (boot camp, narcotic main-

tenance, group counseling, and TC programs) found a nonsignificant mean effect size for

drug relapse in contrast to rearrest, reconviction, or reincarceration (Mitchell et al., 2006).

Interestingly, drug relapse was also the only outcome examined for which prison TC alone

(without mandatory aftercare) did not result in a significant mean effect size. In other words,

prison TC alone did result in a significant mean effect size for reincarceration and rearrest

(as in this study) but did so for drug relapse only when mandatory aftercare was provided. It

is likely, therefore, that mandatory aftercare has greater effects on reducing drug relapse than

criminal recidivism.

In the current study, prison TC significantly reduced recidivism on two out of three out-

come measures, even though no mandatory community aftercare treatment was provided.

Researchers in Delaware, California, and Texas have argued that a continuum of care, includ-

ing community aftercare, is necessary for prison TC treatment effects to emerge (Inciardi

et al., 2004; Knight et al., 1999; Prendergast et al., 2004). Previous studies may thus have

overstated the effects of community aftercare and understated the independent effects of

prison TC treatment.

Consistent with previous research, younger offenders had higher rates of rearrest and rein-

carceration. However, older rather than younger offenders had higher rates of drug relapse.

These results are consistent with the findings of Laub and Sampson (2003), who examined

relationships between age and offense type (property, violence, and alcohol or drug offenses)

in a longitudinal analysis of 500 male delinquents up to age 70. In contrast to other offense

types, the peak age for drug offending was later, and the rate of decline in drug offending

over time was slower. The highest incidence rate of alcohol and drug arrests occurred

between the ages of 32 and 39, whereas the highest incidence of arrests for other offenses

occurred between the ages of 17 and 24. In the present study, the positive relationship

observed between age and drug relapse may be related to the fact that many men had not yet

reached the peak age of drug and alcohol offending.

Few studies of prison TC have examined employment as an outcome. In this study, postre-

lease employment significantly reduced reincarceration and drug relapse, whereas postrelease

employment reduced rearrest only for older ex-offenders. Although further studies of the rec-

iprocal relationship between employment and incarceration are needed, employment may

help ex-offenders to rebuild human and social capital following incarceration, resources that

reduce the risk of reoffending (Hagan & Dinovitzer, 1999; Petersilia, 2003; Travis & Visher,

2005; Western et al., 2001).

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Employment may reduce relapse and recidivism in several ways. First, to maintain full-

time employment, the ex-offender’s daily routine activities must be structured around work

to a considerable degree rather than drug use or a criminal lifestyle. Second, full-time

employment changes the nature of one’s peers. One might find positive role models to emu-

late at work, rather than (or in addition to) some of the negative ones present in his or her

neighborhood (Clear, Rose, & Ryder, 2001). Third, full-time employment can be rewarding,

in that it offers highly desired freedom and independence. Finally, full-time employment is

often emphasized as part of an offender’s release plan and recovery from substance abuse.

Many ex-offenders, especially older ones, may see employment as a critical tool to help

achieve meaningful goals (e.g., food and shelter; the potential for rebuilding meaningful

relationships with friends and family; Shover, 1985, 1996).

The finding that postrelease employment reduced rearrests only for older ex-offenders is

consistent with results reported by Uggen (2000). Using event history models to analyze par-

ticipation in a national work program for criminal offenders, Uggen found that age interacted

with employment to affect self-reported recidivism. Older offenders (27-plus) were less likely

to report crime and rearrest when provided with employment opportunities. For younger

offenders, the job program had little effect on crime or rearrest. Like Uggen, we conclude that

work is more likely to be a turning point for older than younger offenders. In the present

study, however, this effect was found for rearrest only and was based on official records rather

than offender self-reports. Further research should explore relationships among age, treatment

participation, postrelease employment, and recidivism, using multiple measures and methods.

Although employment appears to be critical for older ex-offenders, other individual and envi-

ronmental variables may take precedence for younger offenders (Laub & Sampson, 2003).

LIMITATIONS

In the current study, major variables predictive of recidivism were statistically controlled,

constituting a strong alternative to a randomized experiment (Mitchell et al., 2006; Pearson

& Lipton, 1999). It is still possible, however, that unmeasured sources of bias could have

influenced the results. Although the present study addressed numerous limitations of previ-

ous studies (e.g., multiple outcomes assessed across multiple sites, controls for selection and

attrition bias), the follow-up period was shorter than that of the three major studies in this

area, and effects may potentially change over time. As more inmates are released and as time

at risk in the community increases, the conclusions in this report will be revisited.

Although the present study found a significant effect of prison TC treatment, no mandatory

aftercare treatment was provided. More research on how prison-based drug treatment inter-

faces with critical postrelease mechanisms, such as parole supervision, employment and after-

care treatment, would be invaluable. Interest in prisoner reentry in recent years has been

heightened by continued growth in imprisonment rates and the concentrated return of drug

involved offenders to disadvantaged communities (Belenko, 2006; Clear et al., 2001;

Petersilia, 2003; Travis & Visher, 2005). At this time, however, it appears difficult for correc-

tional, parole, and community agencies to reach beyond traditional boundaries for brokering

services in the community (Petersilia, 2003). Glaring differences in definitions and implemen-

tation of services across jurisdictions have also inhibited advances in this area (Lynch, 2006).

Although the measurement of employment preceded the measurement of recidivism in

the present study, more detailed, longitudinal data on pre- and postrelease employment (e.g.,

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type of employment, employee performance, earnings) are needed to examine how nonre-

lapsing or nonrecidivating parolees differ from others. None of the control variables exam-

ined in this study substantially weakened the observed relationships between postrelease

employment and three different measures of recidivism, however, leading to the conclusion

that the effect of postrelease employment is likely robust.

The finding that at least one treatment outcome (drug relapse) varied depending on the spe-

cific program setting deserves further attention. Two of the five TC units studied were quite

large (100-plus inmates), potentially making it difficult to properly supervise and monitor

inmate behavior (ONDCP, 1999). Staffing ratios (inmates per counselor) ranged from 9:1 to

26:1. Although overall program dropout rates were low, two programs evidenced lower rates

than the others. Program duration and intensity also varied slightly. One rarely finds discus-

sion of programmatic variations or their influence on treatment outcomes in studies of prison-

based TC. Programs and individuals interact in complex ways that researchers have barely

begun to assess. To do so, however, more systematic assessments of programs as well as indi-

viduals are needed, as are larger samples of programs (Lowenkamp, Latessa, & Smith, 2006;

Welsh, 2006).

CONCLUSION

Results supported previous findings regarding significant reductions in recidivism because

of participation in prison TC drug treatment. However, in contrast to previous studies, prison

TC exerted strong, significant treatment effects independently of community aftercare. The

effects of prison TC drug treatment were not unqualified. TC significantly lowered the likeli-

hood of reincarceration and rearrest but not drug relapse. Postrelease employment emerged

as the strongest predictor of reincarceration and rearrest, whereas rearrest was reduced only

for older offenders who were employed full-time or unemployed but able to work. Although

the effects of prison TC were invariant across rearrest and reincarceration, the institutional

setting significantly influenced drug relapse. Further research should explore how both indi-

vidual and programmatic variations influence treatment outcomes and explore why prison-

based drug treatment seems to have stronger effects on criminal behavior than drug-using

behavior.

In general, policy-relevant research should further explore more detailed interactions

between inmate characteristics, treatment process, and postrelease outcomes. There is good

reason to believe that prison TC can be a life-altering experience for many drug-involved

offenders, but future evaluation research should incorporate a longitudinal perspective that

includes more detailed assessments of the diverse individual, programmatic, and environ-

mental influences of relapse and recidivism (Prendergast et al., 2004; Welsh, 2006).

NOTES

1. The Department revised its alcohol and other drug (AOD) treatment policy 7.4.1 effective January 19, 2006 (PADOC,

2006a). Stand-alone drug education programs are no longer offered. Instead, drug education is now incorporated into regu-

lar outpatient and therapeutic community (TC) programming.

2. Although individual release plans could include a stated intention by the offender to participate in some type of postre-

lease recovery program, the responsibility for following through on any stated intention resided solely with the inmate. For

example, questions on the form asked, “Where and when will you continue in AOD treatment upon your release from this

Welsh / PRISON-BASED TC DRUG TREATMENT 1495

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institution?” and “What issues do you want to address in continuing care?” When present at all, plans to participate in 12-step

groups only were signed by both the inmate and the treatment specialist.

3. As of January 1, 2001, DOC formally began using the TCU Drug Screen to screen all inmates for AOD treatment needs.

Formerly, DOC used the Pennsylvania Department of Corrections Screening Instrument (PACSI) to initially screen inmates

for substance abuse. The PACSI results in a need for treatment score that ranges from 0 to 10. This screening process was

designed to determine who can benefit from treatment and which general category of substance-abuse treatment was best

suited for each inmate.

4. The “unemployed and unable to work” category (36%) included parolees who were disabled, elderly, full-time students,

or suffering a prolonged illness. This category also included unconvicted parole violators in detention status (19%), parolees

serving county detainers at release (0.4%), parolees residing in a mental-health residential facility (0.6%), and parolees who

had absconded (3.2%).

5. Percentages do not total to 100 because offenders could test positive for more than one type of drug.

REFERENCES

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC:

Author.

Andrews, D. A., & Bonta, J. (2003). The psychology of criminal conduct. Cincinnati, OH: Anderson.

Austin, J. (1998). The limits of prison drug treatment. Corrections Management Quarterly, 2, 66-74.

Belenko, S. (2006). Assessing released inmates for substance-abuse-related service needs. Crime and Delinquency, 52, 94-113.

Belenko, S., & Peugh, J. (2005). Estimating drug treatment needs among state prison inmates. Drug and Alcohol Dependence,

77, 269-281.

Bloom, B., Owen, B., & Covington, S. (2003). Gender-responsive strategies: Research, practice, and guiding principles for

women offenders. Washington, DC: U.S. Department of Justice (USDOJ), National Institute of Corrections. Retrieved

May 10, 2007, from www.nicic.org/pubs/2003/018017.pdf

Borenstein, M., Rothstein, H., & Cohen, J. (2001). Power and precision. Englewood, NJ: Biostat, Inc.

Broome, K. M., Knight, K., Joe, G. W., & Simpson, D.D. (1996). Evaluating the drug-abusing probationer: Clinical interview

versus self-administered assessment. Criminal Justice and Behavior, 23, 593-606.

Cantor, D., & Lynch, J. P. (2000). Self-report surveys as measures of crime and criminal victimization. In D. Duffee (Ed.),

Criminal Justice 2000 (Vol. 4, pp. 85-138). Washington, DC: USDOJ, Office of Justice Programs, National Institute of

Justice (NIJ) (NCJ-182411).

Clear, T. R., Rose, D. R., & Ryder, J. A. (2001). Incarceration and the community: The problem of removing and returning

offenders. Crime and Delinquency, 47, 335-351.

De Leon, G. (2000). The therapeutic community: Theory, model and method. New York: Springer-Verlag.

Eisenberg, M., & Fabelo, T. (1996). Evaluation of the Texas correctional substance abuse treatment initiative: The impact of

policy research. Crime and Delinquency, 42, 296-308.

Farabee, D., Prendergast, M., Cartier, J., Wexler, H., Knight, K., & Anglin, D. (1999). Barriers to implementing effective cor-

rectional drug treatment programs. The Prison Journal, 79, 150-162.

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J.B.W. (1997). Structured Clinical Interview for DSM-IV Axis

I Disorders—Clinical Version (SCID-CV). Washington, DC: American Psychiatric Press.

Fletcher, B. W., & Tims, F. M. (1992). Methodological issues: Drug abuse treatment in prisons and jails. In C. G. Leukefeld

& F. M. Tims (Eds.), Drug abuse treatment in prisons and jails (pp. 246-260). Washington, DC: Government Printing

Office.

French, M. T., Zarkin, G. A., Hubbard, R. L., & Rachal, J. V. (1993). Effects of time in drug abuse treatment and employ-

ment on posttreatment drug use and criminal activity. American Journal of Drug Alcohol Abuse, 19, 19-33.

Gaes, G. G., Flanagan, T. J., Motiuk, L. L., & Stewart, L. (1999). Adult correctional treatment. In M. Tonry & J. Petersilia

(Eds.), Prisons. Crime and justice: A review of research, v. 26 (pp. 361-426). Chicago: University of Chicago Press.

Gartin, P. R. (1995). Dealing with design failures in randomized field experiments: Analytic issues regarding the evaluation

of treatment effects. Journal of Research in Crime and Delinquency, 32, 425-445.

Gendreau, P., Little, T., & Goggin, C. (1996). Meta-analysis of the predictors of adult offender recidivism: What works!

Criminology, 34, 575-607.

Hagan, J., & Dinovitzer, R. (1999). Collateral consequences of imprisonment for children, communities, and prisoners. In M.

Tonry & J. Petersilia (Eds.), Prisons. Crime and justice: A review of research, v. 26 (pp. 121-162). Chicago: University

of Chicago Press.

Hanushek, E. A., & Jackson, J. (1977). Statistical methods for social scientists. New York: Academic Press.

Harrison, L. D., & Martin, S. S. (2003). Residential substance abuse treatment for state prisoners: Implementation lessons

learned (NCJ 195738). Washington, DC: USDOJ, NIJ.

1496 CRIMINAL JUSTICE AND BEHAVIOR

distribution.

at TEMPLE UNIV on October 28, 2007 http://cjb.sagepub.comDownloaded from

Harrison, P. M., & Beck, A. J. (2005). Prisoners in 2004 (NCJ 210677). Washington, DC: USDOJ, Office of Justice

Programs, Bureau of Justice Statistics (BJS). Retrieved November 27, 2006, from the BJS Web site at http://www.ojp

.usdoj.gov/bjs/pub/pdf/p04.pdf

Harrison, P. M., & Beck, A. J. (2006). Prison and jail and inmates at midyear 2005 (NCJ 213133) Washington, DC: USDOJ,

Office of Justice Programs, BJS. Retrieved November 27, 2006, from the BJS Web site at http://www.ojp

.usdoj.gov/bjs/pub/pdf/pjim05.pdf

Hiller, M. L, Knight, K., Leukefeld, C. G., & Simpson, D. D. (2002). Motivation as a predictor of engagement in mandated

residential substance abuse treatment. Criminal Justice and Behavior, 29, 56-75.

Hiller, M. L., Knight, K., & Simpson, D. (1999). Risk factors that predict dropout from corrections-based treatment for drug

abuse. The Prison Journal, 79, 411-430.

Inciardi, J. A., Martin, S. S., & Butzin, C. A. (2004). Five-year outcomes of therapeutic community treatment of drug-

involved offenders after release from prison. Crime and Delinquency, 50, 88-107.

Inciardi, J., Martin, S., Butzin, C., Hooper, R., & Harrison, L. (1997). An effective model of prison-based treatment for drug-

involved offenders. Journal of Drug Issues, 27, 261-278.

Knight, K., Simpson, D., Chatham, L., & Camacho, L. (1997). An assessment of prison-based drug treatment: Texas’ in-

prison therapeutic community program. Journal of Offender Rehabilitation, 24, 75-100.

Knight, K., Simpson, D., & Hiller, M. (1999). Three-year reincarceration outcomes for in-prison therapeutic community treat-

ment in Texas. The Prison Journal, 79, 337-351.

Laub, J., & Sampson, R. J. (2003). Shared beginnings, divergent lives. Delinquent boys to age 70. Cambridge, MA: Harvard

University Press.

Lichter, D. T. (1989). Race, employment hardship, and inequality in the American nonmetropolitan South. American

Sociological Review, 54, 436-446.

Lockwood, D., Inciardi, J. A., & Surratt, H. L. (1997). CREST Outreach Center: A model for blending treatment and cor-

rections. In F. M. Tims, J. A. Inciardi, B. W. Fletcher, & A. M. Horton, Jr. (Eds.), The effectiveness of innovative

approaches in the treatment of drug abuse (pp. 70-82). Westport, CT: Greenwood.

Lockwood, D., McCorkel, J., & Inciardi, J. (1998). Developing comprehensive prison-based therapeutic community treat-

ment for women. Drugs and Society, 13, 193-212.

Lowenkamp, C. T., Latessa, E. J., & Smith, P. (2006). Does correctional program quality really matter? The impact of adher-

ing to the principles of effective intervention. Criminology and Public Policy, 5, 575-594.

Lynch, J. P. (2006). Prisoner reentry: Beyond program evaluation. Criminology and Public Policy, 5, 401-412.

Martin, S., Butzin, C., Saum, C., & Inciardi, J. (1999). Three-year outcomes of therapeutic community treatment for drug-

involved offenders in Delaware: From prison to work release to aftercare. The Prison Journal, 79, 294-320.

Melnick, G., Hawke, J., & Wexler, H. K. (2004). Client perceptions of prison-based therapeutic community drug treatment

programs. The Prison Journal, 84, 121-138.

Mitchell, O., MacKenzie, D. L., & Wilson, D. B. (2006). The effectiveness of incarceration-based drug treatment on crimi-

nal behavior. Paper approved by the Campbell Collaboration, Criminal Justice Review Group. Retrieved December 1,

2006, from http://www.campbellcollaboration.org/doc-pdf/Incarceration-BasedDrugTxSept06final.pdf

Mumola, C. J. (1999). Substance abuse and treatment, state and federal prisoners, 1997 (BJS Special Report NCJ 172871).

Washington, DC: USDOJ, BJS.

Mumola, C. J., & Karberg, J. C. (2006). Drug use and dependence, state and federal prisoners, 2004 (BJS Special Report

NCJ 213530). Washington, DC: USDOJ, BJS.

Nielsen, A. L., Scarpitti, F. R., & Inciardi, J. A. (1996). Integrating the therapeutic community and work release for drug-

involved offenders: The CREST program. Journal of Substance Abuse Treatment, 13, 349-358.

Norusis, M. J. (2005). SPSS 13.0 advanced statistical procedures companion. Upper Saddle River, NJ: Prentice Hall.

O’Connell, D. J. (2003). Investigating latent trait and life course theories as predictors of recidivism among an offender sam-

ple. Journal of Criminal Justice, 31, 455-467.

Office of National Drug Control Policy (ONDCP), Executive Office of the President. (1996). Treatment Protocol

Effectiveness Study. Washington, DC: Author.

ONDCP, Executive Office of the President. (1999). Therapeutic communities in correctional settings: The Prison Based TC

Standards Development Project. Final Report of Phase II (NCJ-179365). Washington, DC: Author.

Pearson, F., & Lipton, D. (1999). A meta-analytic review of the effectiveness of corrections-based treatments for drug abuse.

The Prison Journal, 79, 384-410.

Pennsylvania Department of Corrections (PADOC). (2002). Philosophy and framework: Alcohol and other drug treatment.

Policy 7.4.1. Camp Hill, PA: Author.

PADOC. (2006a). Alcohol and other drug (AOD) abuse treatment programs procedures manual. Camp Hill, PA: PADOC,

Bureau of Inmate Services.

PADOC. (2006b). Alcohol and other drugs abuse treatment. Retrieved November 21, 2006, from http://www.cor.state

.pa.us/stats/lib/stats/da.pdf

PADOC. (2006c). Budget presentation. Retrieved November 21, 2006, from http://www.cor.state.pa.us/stats/lib/stats/

2006budgetpresentation.pdf

Welsh / PRISON-BASED TC DRUG TREATMENT 1497

distribution.

at TEMPLE UNIV on October 28, 2007 http://cjb.sagepub.comDownloaded from

Peters, R. H., Greenbaum, P. E., & Edens, J. F. (1998). Prevalence of DSM-IV substance abuse and dependence disorders

among prison inmates. American Journal of Drug and Alcohol Abuse, 24, 573-587.

Peters, R. H., Greenbaum, P. E., Steinberg, M. L., Carter, C. R., Ortiz, M. M., Fry, B. C., et al. (2000). Effectiveness of

screening instruments in detecting substance use disorders among prisoners. Journal of Substance Abuse Treatment, 18,

349-358.

Petersilia, J. (2003). When prisoners come home: Parole and prisoner reentry. New York: Oxford University Press.

Prendergast, M. L., Hall, E. A., Wexler, H. K., Melnick, G., & Cao, Y. (2004). Amity prison-based therapeutic community:

5-year outcomes. The Prison Journal, 84, 36-60.

Shearer, R. A., & Carter, C. R. (1999). Screening and assessing substance-abusing offenders: Quantity and quality. Federal

Probation, 63, 30-34.

Shover, N. (1985). Aging criminals. Beverly Hills, CA: Sage.

Shover, N. (1996). Great pretenders: Pursuits and careers of persistent thieves. Boulder, CO: Westview.

Siegal, H. A., Li, L., & Rapp, R.C. (2002). Abstinence trajectories among treated crack cocaine users. Addictive Behaviors,

27, 437-449.

Simpson, D. D., Joe, G. W., Broome, K. M., Hiller, M. L., Knight, K., & Rowan-Szal, G. A. (1997). Program diversity and

treatment retention rates in the Drug Abuse Treatment Outcome Study (DATOS). Psychology of Addictive Behaviors, 11,

279-293.

Simpson, D. D., Joe, G. W., & Brown, B. S. (1997). Treatment retention and follow-up outcomes in the Drug Abuse Treatment

Outcome Study (DATOS). Psychology of Addictive Behaviors, 11, 294-307.

Simpson, D. D., Knight, K., & Broome, K. M. (1997). TCU/CJ forms manual: TCU Drug Screen and Initial Assessment. Fort

Worth: Texas Christian University, Institute of Behavioral Research.

Sung, H. E. (2001). Rehabilitating felony drug offenders through job development: A look into a prosecutor-led diversion

program. The Prison Journal, 81, 271-286.

Taylor, B. G., Fitzgerald, N., Hunt, D., Reardon, J. A., & Brownstein, H. H. (2001). ADAM preliminary 2000 findings on

drug use and drug markets—adult male arrestees (NCJ 189101). Washington, DC: USDOJ, NIJ.

Thornberry, T. P., & Krohn, M. D. (2000). The self-report method for measuring delinquency and crime. In D. Duffee (Ed.),

Criminal Justice 2000 (NCJ 182411, Vol. 4, pp. 33-84). Washington, DC: USDOJ, Office of Justice Programs, NIJ.

Travis, J., & Visher, C. (Eds.). (2005). Prisoner reentry and crime in America. New York: Cambridge University Press.

Uggen, C. (2000). Work as a turning point in the life course of criminals: A duration model of age, employment, and recidi-

vism. American Sociological Review, 65, 529-546.

Welsh, W. N. (2002). Building an effective research collaboration between the Center for Public Policy at Temple University

and the Pennsylvania Department of Corrections (Final Report to National Institute of Justice). Philadelphia: Temple

University, Center for Public Policy. Retrieved from http://www.ncjrs.org/pdffiles1/nij/grants/197067.pdf

Welsh, W. N. (2006). The need for a comprehensive approach to program planning, development and evaluation. Criminology

and Public Policy, 5, 603-614.

Welsh, W. N., & Zajac, G. (2004a). Building an effective research partnership between a university and a state correctional

agency: Assessment of drug treatment in Pennsylvania prisons. The Prison Journal, 84, 143-170.

Welsh, W. N., & Zajac, G. (2004b). A census of prison-based drug treatment programs: Implications for programming,

policy and evaluation. Crime and Delinquency, 50, 108-133.

Western, B., Kling, J. R., & Weiman, D. F. (2001). The labor market consequences of incarceration. Crime and Delinquency,

47, 410-427.

Wexler, H., Melnick, G., Lowe, L., & Peters, J. (1999). Three-year reincarceration outcomes for Amity in-prison therapeutic

community and aftercare in California. The Prison Journal, 79, 321-336.

Wilson, D. B., Gallagher, C. A., & Mackenzie, D. L. (2000). A meta-analysis of corrections-based education, vocation, and

work programs for adult offenders. Journal of Research in Crime and Delinquency, 37, 347-368.

Young, D. (2002). Impacts of perceived legal pressure on retention in drug treatment. Criminal Justice and Behavior, 29, 27-55.

Zhang, Z., Friedmann, P. D., & Gerstein, D. R. (2003). Does retention matter? Treatment duration and improvement in drug

use. Addiction, 98, 673-684.

1498 CRIMINAL JUSTICE AND BEHAVIOR

distribution.

at TEMPLE UNIV on October 28, 2007 http://cjb.sagepub.comDownloaded from

Implementing therapeutic community as a rehabilitation intervention for the imprisoned narcotic drug offenders with substance use disorder: special reference to Sri Lanka

Article

Apr 2022

Narcotic offenders with substance use disorder (SUD) have become a severe burden around the world including Sri Lanka. Prisons and other rehabilitation centers are threatened by overcrowding, due to recidivism, rearrest, and re-incarceration of narcotic offenders with SUD and limited capacities available in these centers. The therapeutic community (TC), owing to its comprehensive approach, long history of use, proven efficacy, and more importantly its relatability to Asian culture, has made it more appropriate for narcotic offenders with SUD sentenced to imprisonment, to subdue SUD while serving their prison sentence. TC is a rehabilitation intervention found by Tom Main in 1946 and a proper understanding of its core concept and elements is important when implementing a TC program. However, challenges are inevitable that need to be overcome when implementing a prison TC program. This paper intended to provide an insight to consider TC as a rehabilitation intervention for the imprisoned narcotic drug offenders with SUD. A special reference is provided to Sri Lanka, given its current requirement for an appropriate rehabilitation model for the prison community suffering from SUD.

Perceptions of incarcerated people: prison conditions, public health, and justice in the United States

Article

Full-text available

Jul 2024
J PUBLIC HEALTH POL

Carceral conditions in the United States may serve as a proxy for crises within justice and health systems. This study seeks to consider and measure prison climate from the perspective of incarcerated people. By examining within-facility differences in carceral experiences, results shed light on the complex nexus between the carceral context, health, and justice. We administered the Prison Climate Questionnaire (PCQ) to the complete population of incarcerated men in a correctional facility located in the Eastern United States. In this facility, housing units hold distinct populations, fulfill different functions, and can offer unique programming. We regress select items from the PCQ on a set of dummies corresponding to different residential units within the facility. Responses indicate low but relatively uniform perceptions of overall personal health, as well as access to, and satisfaction with, medical care. Between-unit differences emerge regarding staff relationships, experiences of discrimination, and levels of isolation. The perspectives of incarcerated people can, and should, play a role in understanding and conceptualizing the nature of the prison environment. Policy responses, especially those that impact the health and well-being of currently and formerly incarcerated people, can be informed by these perspectives.

Drug Addiction and Incarceration: A Call for Research and Transparency Among Prison-Based Substance Abuse Treatment Programs

Article

Jun 2023
INT J OFFENDER THER

As drug-related offense and illicit drug overdose rates continue to grow in the United States, criminologists have begun to pay more attention to factors influencing illicit drug use as well as effective methods of promoting drug abstinence in treatment programs across the nation. Although much scholarly attention is given to community-based substance abuse treatment programs, a considerably smaller focus of research is devoted to substance abuse treatment programs that are prison-based. Moreover, some of the most effective methods of treating inmates who are addicted to an illicit drug (such as Cognitive Behavioral Therapy, Therapeutic Community, etc.), although praised for their initiative and theoretical effectiveness, are often demonstrated via individualized empirical study that the expected advantages of such programmatic forms of treatment fail to emerge. The present study explores what scholars have discovered regarding the effectiveness of prison-based substance abuse treatment programs, how such findings appear to contradict one another, and why state prison systems should be more transparent regarding their in-house drug treatment programs in their publicly accessible reports that are formulated into cumulative reports on each states' Bureau of Corrections websites.

Strength‐based Approaches with Offenders with Mental Illness

Chapter

Jun 2022

This chapter provides a succinct review of the literature demonstrating that the general mental health literature has largely integrated the principles of positive psychology and that the importance of including strengths is firmly entrenched in mental health as standard, evidence‐based practice. It considers the importance of integrating strength‐based approaches as well as the alignment of existing models (such as RNR) to facilitate desistance from aggression and crime. The Safewards Model, Recovery Model, Reasoning and Rehabilitation, Enhanced Thinking Skills, Tidal Model, Therapeutic Communities, and Changing Lives and Changing Outcomes are outlined. In addition, Promising practices such as art therapy, mindfulness, meditation and yoga are reviewed. The chapter concludes that there is a growing body of solid research evidence to support the use of strength‐based approaches for the treatment of offenders with mental illness.

Correctional Therapeutic Communities

Chapter

Full-text available

Aug 2023

Matthew L Hiller

Therapeutic communities (TCs) for substance use disorders originated in the late 1950s and quickly became a major treatment modality during the 1960s. Psychological theory, practice, and research were instrumental to this, as well as to its importation into correctional settings. The first prison-based TCs were implemented beginning in the 1960s, but their heyday was during the 1990s when several states, including California, Pennsylvania, and Texas, initiated a system-wide implementation of multiple correctional TCs. Also during the 1990s, several prominent research groups were involved in evaluating the effectiveness of these programs. Work continues on correctional TCs, with some of it directed at evaluating one or multiple programs or delving into the “black box” of the treatment process. This research is needed, but understanding the treatment process as it relates to long-term outcomes like postrelease drug use and criminality may show the most promise for advancing correctional TC practice and research. Although considerable research (most of it reviewed herein) on TCs has been accomplished, myriad opportunities remain for psychologists interested in substance use disorders to make meaningful contributions to this field.KeywordsSubstance use disorderTreatment modalityCorrectional therapeutic communityLiterature review and integrationHistory of therapeutic communitiesTreatment effectivenessTreatment processMental illness and co-occurring disordersPrisons

Alcohol Use in Forensic Mental Health and Criminal Justice Settings

Chapter

Feb 2023

For those within secure settings such as forensic mental health services and the criminal justice system (CJS), problematic alcohol use is known to have higher prevalence rates compared to the general population. While assessment and treatment options for alcohol problems exist, research and funding tend to disproportionately focus on illicit drug use. Managing problematic alcohol use can be complex and challenging within secure settings, particularly at points of discharge or release. Therefore, it is important to ensure services provide robust assessment and effective, ethical, person-centred treatment approaches (which include family members where possible) to help individuals manage their alcohol use and recovery. This chapter will provide a brief overview of assessment and some of the alcohol treatment options available in secure settings. It will also engage in discussion around working with families and outline some of the complexities, challenges and ethical considerations when supporting individuals with alcohol use problems.KeywordsCriminal justice systemForensic mental health servicesAlcohol-related offendingFamiliesTherapeutic treatment

Drug Use Disorders before, during, and after Imprisonment

Article

Sep 2022
CRIME JUSTICE

Ojmarrh Mitchell

Drug-involved offenders have been long overrepresented in prisons. Intensified drug law enforcement in many countries increased both incarceration rates, especially for drug offenses, and numbers of drug-involved prisoners. This is attributable to four features of drugs and drug markets. Drug use disorders typically are not the only problems facing drug-involved prisoners. A high proportion exhibit severe mental health problems such as major depression, personality disorders, and psychotic disorders. Before incarceration, many drug-involved prisoners have unstable housing and are at high risk of homelessness; incarceration increases that risk. The high concentration of drug-involved offenders in prisons presents numerous challenges. After release, the most obvious are high rates of drug relapse and recidivism. Former prisoners have extraordinarily high risks of drug overdoses shortly after release; in the long term, diseases acquired during imprisonment are transmitted in the community. Effective prison-based drug treatment holds promise to break this cycle and mitigate physical and mental health problems of drug-involved prisoners. Unfortunately, access to treatment modalities proven to be effective in reducing drug use and recidivism is limited. A paradigm shift in drug enforcement and treatment is needed to meet the challenges presented by individuals with drug use disorders more effectively. © 2022 The University of Chicago. All rights reserved. Published by The University of Chicago Press.

Effects of a Residential Multimodal Psychological Treatment in an Addicted Population, at 6 and 12 Months: Differences Between Men and Women

Article

Full-text available

Jun 2022

The aim of this study is to explore the effects of a residential multimodal treatment intervention for an addict population. We gathered participants from the “Programa Base” (n = 166) of the Solidarity and Reinsertion Foundation of Murcia, and assessed the various problematic areas with the EuropASI at baseline level, 6 months and 12 months of treatment. We found improved outcomes in every area except for Legal Status. In addition, we found differences between male and female participants in their baseline evaluation, as well as between completers and non-completers. In conclusion, this data shows us some changes which occurred in individuals with problematic drug use during treatment, going further into the complex social reality which causes great suffering and damage to people and their families.

EVADRUG Final Report

Technical Report

Full-text available

Dec 2021

This current study ‘EVADRUG’, as conducted between 2020 and 2021, presents a general evaluation of the Belgian drug policy, as defined by the Federal Drug Note of 2001 and the Joint Declaration of 2010. Evaluating a drug policy provides some indications about the success but also the shortcomings of policy, and contributes to transparency, accountability and a better planning of resources. It is no coincidence that the importance of systematically monitoring and evaluating (national) drug policy has been repeatedly stressed in several policy documents at international, European and national level. As a result, the past two decades spurred an increasing amount of countries to evaluate their national drug policy, mostly focusing on process evaluations verifying whether the objectives and actions described in one’s drug policy have been implemented. These types of (process) evaluations are indispensable to policy evaluation and effective policy making, because they can indicate whether a policy action is fully or properly implemented, how the results of a policy are achieved, and what the limitations of a policy encounter.

Less Is More: The Effect of a Short-term Substance Use Disorder Treatment Program on Recidivism

Article

Jan 2022

Valerie A. Clark

Most people behind bars suffer from substance use disorders (SUDs), but very few of them participate in treatment prior to leaving prison. Minnesota implemented a shorter-term therapeutic community-based treatment program to target higher custody individuals serving relatively short terms of incarceration. The purpose of this research is to determine whether this program reduced the likelihood of recidivism. This study used propensity score matching to compare 351 men who participated in the short-term SUD treatment program to 351 men who left prison without participating in any treatment. Cox regression was used to predict four different types of recidivism. This research found that participation in the short-term program significantly reduced the likelihood of three out of four measures of recidivism. A medium level of treatment exposure (four to five months) significantly reduced the likelihood of all four types of recidivism relative to individuals who did not participate in any treatment. This research contributes to a growing body of evidence showing that evidence-based SUD treatment programs do not need to be long to be effective. By reducing the length of treatment, prisons can increase their treatment capacity, ensuring that fewer individuals leave prison without treatment.

The Therapeutic Community

Book

Apr 2000

George De Leon

Crest outreach center: A model for blending treatment and corrections

Article

Jan 1997

When Prisoners Come Home: Parole and Prisoner Reentry

Article

Dec 2003

Every year, hundreds of thousands of jailed Americans leave prison and return to society. Largely uneducated, unskilled, often without family support, and with the stigma of a prison record hanging over them, many, if not most, will experience serious social and psychological problems after release. Fewer than one in three prisoners receive substance abuse or mental health treatment while incarcerated, and each year fewer and fewer participate in the dwindling number of vocational or educational pre-release programs, leaving many all but unemployable. Not surprisingly, the great majority is rearrested, most within six months of their release. As long as there have been prisons, society has struggled with how best to help prisoners reintegrate once released. But the current situation is unprecedented. As a result of the quadrupling of the American prison population in the last quarter century, the number of returning offenders dwarfs anything in America's history. A crisis looms, and the criminal justice and social welfare system is wholly unprepared to confront it. Drawing on dozens of interviews with inmates, former prisoners, and prison officials, the book shows us how the current system is failing, and failing badly. Unwilling merely to sound the alarm, it explores the harsh realities of prisoner re-entry and offers specific solutions to prepare inmates for release, reduce recidivism, and restore them to full citizenship, while never losing sight of the demands of public safety. As the number of ex-convicts in America continues to grow, their systemic marginalization threatens the very society their imprisonment was meant to protect.

Treatment Protocol Effectiveness Study

Article

Jul 1996
J SUBST ABUSE TREAT

Screening and assessing substance-abusing offenders: Quantity and quality

Article

Jun 1999

The sheer number of offenders with substance abuse problems continues to be a major concern for the criminal justice system. Screening and assessment is the beginning of the substance abuse treatment process. Authors Robert A. Shearer and Chris R. Carter discuss the importance of proper screening and assessment in creating effective treatment plans and in using scarce treatment resources wisely. They address such issues as using interviews versus self-reports, screening instrument accuracy, screening offenders for psychopathy, and readiness screening.

Integrating the therapeutic community and work release for drug-involved offenders the CREST program

Article

Jan 1996

D. Wilmington

As the nation's first therapeutic community (TC) and work release center for drug involved offenders, CREST combines the basic elements of both modalities into an effective agent for behavioral change. This article explores the ways in which these elements are integrated and applied, and the outcome of such treatment as determined by subsequent substance abuse and criminal activity. Clients entering the program from prison progress through several phases of counseling, group interaction, confrontation, and education before they enter the work release phase, where they gain realistic experience and can implement what they learned in the TC concerning living drug free. Follow-up data collected at 6 and 18 months after entry into the program indicate that CREST clients have significantly lower relapse and recidivism rates than a comparable comparison group. CREST has similar effects on relapse and recidivism across sexes, racial/ethnic groups, and different age categories, although length of time in treatment and whether clients graduated do impact outcome variables.

An Effective Model of Prison-Based Treatment for Drug-Involved Offenders

Article

Mar 1997

A multistage therapeutic community treatment system has been instituted in the Delaware correctional system, and its effectiveness has captured the attention of the National Institutes of Health, the Department of Justice, members of Congress, and the White House. Treatment occurs in a three-stage system, with each phase corresponding to the client's changing correctional status-incarceration, work release, and parole. In this paper, 18 month follow-up data are analyzed for those who received treatment in: (1) a prison-based therapeutic community only, (2) a work release therapeutic community followed by aftercare, and (3) the prison-based therapeutic community followed by the work release therapeutic community and aftercare. These groups are compared with a no-treatment group. Those receiving treatment in the two-stage (work release and aftercare) and three-stage (prison, work release, and aftercare) models had significantly lower rates of drug relapse and criminal recidivism, even when adjusted for other risk factors. The results support the effectiveness of a multistage therapeutic community model for drug-involved offenders, and the importance era work release transitional therapeutic community as a component of this model.

Shared Beginnings, Divergent Lives: Delinquent Boys To Age 70

Article