1We note that in this chapter the phrases “individuals who entered and completed

treatment” and “treated individuals” refers to the joint conditional probability of entering and

competing treatment. These terms are not to be confused with other studies where researchers

compared actual treatment completers to drop-outs, comparison subjects, etc. We refer readers to

our discussion of the analytic strategies in Chapter 8 and to our discussion in Chapter 7 of the

probit model for estimating the probability of entering and completing treatment. 

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CHAPTER 9: SUMMARY AND CONCLUSIONS

The importance of rigorous evaluation of prison-based drug treatment programs is crucial given

an increasingly larger population of incarcerated drug users in the recent decade as well as a

paucity of outcome evaluation results. This report showed that treatment programs in prison,

when properly implemented, can work. The findings showed that offenders who completed the

residential drug abuse treatment program and had been released to the community for three years

were less likely to recidivate or use drugs. Our findings consistently showed that the residential

Drug Abuse Treatment Programs (DAP’s) in the BOP contributed to a reduced likelihood of

post-release failure among men, when failure was defined as arrest for new offense or revocation

and when failure was defined as return to drug use. Among female inmates, while the effect of

treatment was not statistically significant, the failure rate for recidivism and drug use of treated

inmates compared with untreated inmates suggested a positive effect for treatment.

1

 Our findings

also showed positive effects of in-prison treatment on employment among women. In addition to

providing information on the effectiveness of prison-based intensive drug-treatment on 3-year

post-release outcomes, this report described the federal substance abusing population and

provided information on what types of incarcerated drug users are more likely to volunteer for

and enter treatment. 

Our 3-year outcomes analyses improved upon the methodological rigor used in previous prisonbased evaluation studies in several ways. First and foremost, we addressed the issue of selection

bias, the most commonly neglected methodological problem in other drug treatment evaluation

studies. Not only did we address the issue of selection bias but we conducted sensitivity analyses

by comparing the results using two different methods of addressing selection bias. While this has

added significantly to the materials presented in this report, it has also provided greater

confidence in our results and in the conclusions we have drawn because of the increased rigor

and scrutiny we brought to bear on our empirical analyses. We used an instrumental variable

approach which provides unbiased (at least in terms of individual selection bias), albeit

conservative, results. The results are conservative since they represent the effects of having

treatment available and not a direct effect of being in treatment. Our second approach, the

Heckman approach, not only addressed the problem of selection bias but also provided

information on whether selection bias occurred and, if so, the nature of the selection bias. 

We used a third approach which did not address selection bias, which we referred to as the

unadjusted model. By comparing the treatment effect in the unadjusted model to the solutions
2Right censoring occurred for study participants who had not experienced the post-release

outcome in question. These subjects remained at risk of experiencing the event, and our event

history models took the censoring of the observations at the 36-month point into account. 

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addressing selection bias, we gained an understanding of the direction and level of selection bias. 

Despite some similarity of our unadjusted model to models used in previous studies, we

improved upon these previous studies in several ways. First, our models included a

comprehensive set of control variables. Second, we used event history techniques that most

adequately controlled for the right censoring of data (Allison, 1984; Blossfeld and Rohwer,

1995).2

 Third, our study was multi-site. Previous evaluations of prison-based drug treatment

programs are primarily based on one site. The multi-site nature of our study increased the

generalizability of our findings.

In addition to providing results which improved upon the methodological rigor of previous

studies, our study also represented a step in increasing the understanding of gender differences in

the treatment process. We presented separate models for men and women both for our analyses

of treatment entry and retention as well as all our outcome analyses. Furthermore, we included

predictors of treatment entry and post-release outcomes that were not based solely upon studies

of male drug users, but also studies of female drug users. We also provided descriptive statistics

which examined gender differences in an incarcerated population with a history of drug use. 

A last commentary on the important contributions of this study pertains to treatment entry and

retention. A number of researchers have found that among those entering treatment, those who

do not stay in treatment have less favorable post-treatment outcomes (Hubbard et al., 1989;

Gerstein and Harwood, 1990; Simpson, Joe, and Brown, 1997; Simpson, Joe, and Rowan-Szal,

1997). However, treatment retention processes may vary across different populations because of

differences in the type of individuals who enter treatment. As suggested by some studies on

treatment seeking behavior within non-incarcerated populations, individuals with more serious

problems were more likely to enter treatment programs. Our study provided the first study of

treatment entry within an incarcerated population. In doing so, we also examined gender

differences in the treatment entry process. 

Summary of Findings

We begin the summary of our findings by discussing the results pertaining to treatment entry and

treatment retention. We then present our findings for post-release outcomes and focus upon the

effect that selection bias had on the different outcomes of interest: a new arrest, a new arrest or a

revocation, evidence of drug use, post-release employment behavior, and CCC placement failure. 

We conclude with a discussion of the consistency in findings across our three modeling

techniques.
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Treatment Entry and Completion

We modeled treatment entry and completion for the methodological purpose of addressing the

problem of selection bias. However, we also modeled treatment entry to provide information on

the types of incarcerated Bureau of Prisons drug users who are being served by the programs. Our

analyses represent the first such analysis within an incarcerated sample. 

Regarding selection into and out of treatment, most of the attention in the drug treatment field

has been on treatment retention. This focus is a result of the well-established correlation between

treatment retention and positive outcomes. However, our understanding of treatment retention,

particularly within a prison setting, is limited without information on what type of offender is

receiving treatment. Are the treatment programs serving individuals with more serious problems

or individuals with less serious problems? 

Our analyses of treatment entry and retention included predictors that were selected based upon a

review of previous research and included predictors to acknowledge possible gender differences.

Our models predicting treatment entry as well as those predicting treatment entry and completion

showed both gender similarities and differences. We consistently found that both men and

women who were motivated to change were more likely to enter and complete treatment. These

findings were important since we controlled for external incentives such as the availability of a

sentence reduction for successful completion of treatment. The results point to the importance of

an individual’s recognition of a drug abuse problem and willingness to address the problem in

volunteering for treatment. It is notable that one of the factors indicative of high risk for

recidivism – prior commitment – was not found to be related to treatment entry or treatment

entry and completion. Our results also showed that women with a lifetime diagnosis of

depression were less likely to enter treatment than women without this diagnosis. Among men,

we did not find any evidence of a relationship between mental health disorders and treatment

entry or retention. 

Our findings confirmed the need to perform separate analyses for men and women because the

pathway by which men and women enter and complete treatment differed in several important

ways. Information on the different processes of treatment entry and retention can assist drug

treatment staff in identifying individuals who are less likely to enter treatment and complete

treatment. They may use this information to consider specific mechanisms for attracting those

types of offenders who are in need of treatment but less likely to volunteer for and complete

treatment. 

Recidivism 

We found that DAP treatment did seem to “work” by lowering the odds of 3-year post-release

recidivism. The positive results of DAP treatment on post-release recidivism and drug use were

statistically significant for men but not for women. We found a positive effect of in-prison drug

abuse treatment in lowering the likelihood of post-release failure for men, where failure was
155

defined as a new arrest or revocation. The probability of arrest or revocation for men released to

supervision who entered and completed treatment was 44.3 percent as compared to a probability

of approximately 52.5 percent for untreated subjects. Thus, men who received and completed inprison treatment were 16 percent less likely to recidivate. Although the results for women were

not statistically significant, the difference between the treated and comparison group suggests

that treatment helped to reduce recidivism among women. Among women who completed

residential drug abuse treatment, 24.5 percent were likely to be arrested for a new offense or have

supervision revoked within 3 years after release compared to 29.7 percent among untreated

inmates; that is, those women who completed residential drug abuse treatment were 18 percent

less likely to recidivate in the first 3 years following release than those who did not receive

treatment.

We modeled post-release recidivism as indicated by a new arrest – either for supervised and

unsupervised subjects or supervised subjects only – or a new arrest or revocation using three

approaches, two of which addressed selection bias. The findings from all three approaches for all

three indicators of recidivism were consistent. There was evidence of selection bias among men

but not women. The selection process was such that men who entered treatment were riskier

prisoners. They were more likely to fail than comparison subjects in the absence of treatment. 

The existence of selection bias and the direction of the bias explains why the treatment effect was

larger in the two models that addressed selection bias. Among men, our unadjusted model served

to mute the effects of treatment. In contrast, with no evidence of selection bias among women,

the treatment effects were similar across the different models.

First Detected Drug Use

Our results for post-release drug use were similar to those we found for recidivism. We found

positive results of DAP treatment on post-release drug use. The results were statistically

significant for men but not for women. Among male inmates who completed residential drug

abuse treatment, 49.9 percent were likely to use drugs within 3 years after release compared to

58.5 percent among untreated inmates. Male inmates who entered and completed in-prison

residential drug abuse treatment were 15 percent less likely to use drugs 3 years following release

than those who did not receive treatment. Among female inmates who completed residential drug

abuse treatment, 35.0 percent were likely to use drugs within 3 years after release compared to

42.6 percent among untreated inmates; that is, female inmates who completed residential drug

abuse treatment were 18 percent less likely to use drugs in the 3 years following release. The

results for women, however, were not statistically significant,

Post-release failure, defined as the first detected drug use, was modeled following the same

strategy used for recidivism, i.e., using all three approaches. Similar to our findings for

recidivism, the findings for drug use showed evidence of selection bias among men but not

women. In the absence of treatment, male prisoners selected for treatment would have been more

likely than comparison subjects to return to drug use upon release. Once again, we found that the

models for men which adjusted for selection bias showed a somewhat larger treatment effect than
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did the unadjusted model. We also found that, with no evidence of selection bias occurring

among women, the treatment effect was statistically insignificant in all three models.

Post-release Employment

As with post-release recidivism and drug use, we found positive effects of DAP on post-release

employment outcomes for two different employment measures. However, unlike the results for

our outcome measures of recidivism and drug use, we found significant and positive effects of

DAP treatment on post-release employment for women but not men. 

The two different indicators of post-release employment were employment rate – the percentage

of time employed during the post-release period – and a categorical variable representing ordinal

levels of employment. The results for employment rate showed that women who entered and

completed in-prison residential treatment were employed 68.6 percent of the post-release period

and untreated women were employed 59.1 percent of the time. In addition, by examining the

different levels of employment, we found that 30.6 percent of the treated women were employed

full-time the entire post-release period as compared with 18.3 percent of the untreated women. 

In contrast, men who entered and completed treatment were employed 70.5 percent of their postrelease period as compared with 68,6 percent of the untreated men. Furthermore, 36 percent of

the treated men were employed full-time the entire post-release period as compared with 38

percent of the untreated men.

We modeled employment rate using all three approaches and we modeled employment level

using two approaches, the unadjusted and the instrumental variable approaches. We did not find

evidence for selection bias effects among either men or women. These results explain why the

parameters for the treatment effect were very similar across all three modeling approaches for

employment rate among both men and women.

CCC Placement Failure

We did not find any effects for DAP treatment on CCC placement failure among either men or

women. Our models of CCC placement failure were limited to the unadjusted approach and the

instrumental variable approach. Thus, we were unable to determine the direction of selection bias

effects, if any. We noted, however, that the results for men suggested that without adjusting for

selection bias as we did through our instrumental approach, we might have concluded that

treatment had a positive effects, when in fact, there was no positive effect. 

Our ability to ascertain the effect of in-prison treatment upon CCC placement failure was

hampered by the fact that approximately one-quarter of the subjects were not released to the

community via a CCC placement. Furthermore, we know that by policy, the most risky offenders

are not allowed to be released to a CCC. This selection process reduces our ability to find

treatment effects on CCC failure. 
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Selection Bias

The substantive results across the two modeling approaches addressing selection bias were

consistent for all of our outcome measures. These findings give us more confidence in our results

and confirm that our findings were not method-dependent. However, the size of the treatment

parameter did vary, with the effect generally being larger in the Heckman models than in the

instrumental models. While it is clear that we cannot assume that two different methods of

addressing selection bias will yield identical results, we would expect, that if there truly is a

treatment effect, the Heckman model results would show stronger treatment effects because of

the inherent inefficiency of the instrumental variable approach. 

We note that the Heckman model was able to provide important information about the nature of

self-selection into treatment. We found that our male treatment subjects were at a higher risk of

experiencing negative outcomes than were individuals not self-selecting into treatment when our

outcome measures were arrest for a new offense, arrest or revocation, and drug use. We did not,

however, find evidence of selection bias among men for our outcome measures of post-release

employment and CCC placement failure. Among women, we did not find evidence of selection

bias for any of our four outcome measures. These findings explain why the effects of treatment

on post-release recidivism and drug use were muted for men but not for women in the unadjusted

models. It is worth noting that in our application, the selection process drew riskier offenders into

the residential drug treatment programs. However, it is not difficult to conceive of other

situations or applications in which a creaming effect occurs. Less risky inmates are selected or

self-select into a treatment program. In that case, without controlling for selection bias, we might

overestimate the effect of an intervention. In the present application, had we not addressed the

selection bias issue, we could have underestimated the effect of residential drug treatment.

Discussion

We begin our discussion highlighting implications of the gender differences in the covariates

found to be significant predictors of post-release outcomes. We continue with a discussion of 

some of the methodological limitations of our outcome measures. 

Gender Differences

As mentioned earlier in our discussion of findings relating to selection bias, we found evidence

of selection bias only among men. This finding along with our findings describing gender

differences in our treatment entry models clearly point to the need to account for gender

differences in treatment processes and outcomes. 

We found significant gender differences both in the background characteristics, post-release

behaviors, treatment entry, treatment entry and retention, and in the effects of treatment. Not only

did the effects of treatment differ between men and women but the factors other than treatment
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which were predictive of post-release outcomes also differed. Our findings suggest that the

pathways to treatment entry and retention, post-release recidivism, drug use and employment

differ across gender.

We saw gender differences in background characteristics both in our models of treatment entry

and retention as well as in the characteristics we used as covariates in our outcome models. In

general, our findings were consistent with previous findings: drug-abusing women have more life

problems than men. Women were more likely to have a lifetime diagnosis of depression, to have

been unemployed before incarceration, to have a spouse with a drug problem, to have a history of

previous mental health treatment, and to have a history of sexual abuse. As has also been

previously found, women were less likely to have a criminal history. 

The difference in the overall post-release recidivism rate of men and women was consistent with

previous findings for overall recidivism of prison releasees. As discussed in our literature review

in Chapter 3, men generally have higher recidivism rates than do women (Chard-Wierschem,

1992; Donnelly and Bala, 1994; Florida Department of Corrections, 1999; Saylor and Gaes,

1995). The findings for our outcome measures of recidivism and drug use were consistent with

the limited literature on gender differences in post-release outcomes: women are found to have

lower overall failure rates than men or to have similar failure rates. In addition, our findings

provide some support for the notion of a “gender paradox” pinpointed by Fiorentine et al. (1997)

and Rounds-Bryant (1999): despite a greater number of life problems among women, the posttreatment or post-release outcomes of women are better than those of men. The one exception

was for employment: women were less likely to be employed both before and after incarceration. 

However, we note that information on women’s employment before and after treatment is even

more sparse than is information for outcome measures such as criminal behavior and drug use.

The results confirm the need to separately analyze men and women. The use of gender as a

covariate in multivariate outcome models is not sufficient for understanding gender differences. 

This is particularly true when samples are comprised mostly of men where the effects will reflect

primarily the effects for men. For example, in our interim 6-month outcome report (Pelissier et

al., 1998) where we were not able to separately analyze recidivism and drug use for men and

women, we found that women had lower failure rates than men. We also found that individuals

living with a spouse after release were less likely to recidivate or use drugs. Yet, when

conducting separate analyses by gender in the present outcome analyses, we found that living

with a spouse was significant for men but not for women. Our inability to detect an effect for

living with a spouse among women is consistent with the literature on gender differences. We

suggest consideration of the following explanation: it is possible that we did not find an effect for

living with a spouse after release among women because some of the women may have been

married to an individual with a drug use problem. We know that a much higher percentage of

women were married to individuals with a drug problem before incarceration than were men. 

Other gender differences point to differences between men and women in the recovery process.

Such differences point to the need for clinicians to pay attention to and address issues specific to
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categories of high-risk individuals, these categories differing by gender. We found that well

known predictors of recidivism – criminal history and age – were associated with a higher

probability of recidivism among men but not women. We also found a relationship between the

type and amount of drugs used on a daily basis before arrest and post-release drug use among

men but not women. The factor that appeared to be important for women was whether or not they

had ever used an illicit drug on a daily basis. We found that age and drug use in the year before

incarceration were predictors of post-release employment among men. In contrast, we did not

find these to be predictors of post-release employment among women. 

Methodological Caveats

There are several measurement issues that limit comparisons of our results to other studies and

which also point to issues which require consideration for future studies We discussed the

problem of limiting our measure of recidivism to arrest for a new offense because revocation for

a violation of a condition of supervision is a competing event. However, our primary measure of

recidivism which includes arrest for a new offense as well as revocation also has its problems.

There is evidence that revocation decisions vary from one district to another due to differences in

treatment philosophies and other factors. A revocation may also reflect the Probation officer’s

discretionary decision that an offender’s behavior pattern is not satisfactory. Post-release

supervision is conducted by Probation officers; however, the supervision system is organized into

94 separate jurisdictions. Each jurisdiction can set policy within discretionary limits of the law.

We looked at the overall percent of written violation reports that resulted in a revocation by

district as an indicator of differences in the probability that a violation of a condition of

supervision will result in a revocation. We found that between 1995 and 1997 these percentages

ranged between a low of 7 percent and a high of 85 percent. Some judicial districts have set

policies that dictate revocation after a certain number of positive urine tests, while others have no

set policies. While we do not yet have clear mechanisms for increasing the precision of our

measures of recidivism, we must recognize the limitations of our measure. 

Our measure of drug use, although not plagued by the limitations of self-report data, fails to

provide a measure of severity of drug use. As is often discussed in the relapse prevention

literature, a lapse – that is, one episode of drug use – should not be viewed as a failure. In fact,

this is seen as helping an individual identify high-risk situations and alternative methods of

coping and thus preventing a full relapse. Thus, it is possible, for example, that individuals who

were referred to additional treatment by their Probation officer after a lapse, in fact, are able to

avoid further use of drugs. Our measure does not distinguish those who had a lapse from those

who had a relapse, that is, returned to regular drug use.
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Forthcoming Report

Effects of Other Components of the Treatment Continuum

While the focus of this report has been upon the effects of in-prison DAP treatment, we

recognize the importance of viewing treatment as a continuum of programs, not limited solely to

services received while subjects are incarcerated. To more fully understand the effects of

treatment services, a second report will examine more broadly the role of treatment across its

entire spectrum, including treatment received during a CCC placement – e.g., transitional drug

treatment – and post-release treatment received while under supervision of a Probation officer. 

Even though transitional drug treatment is automatic for in-prison DAP program graduates who

receive a CCC placement, many of our comparison subjects began receiving drug treatment

services during their CCC placement. Furthermore, not all in-prison DAP program graduates

received treatment after release. Fifty percent of the men and 45 percent of the women did not

receive treatment during their first 6 months of supervised release. To increase our understanding

of the treatment continuum we also need to test for interactive effects between treatments. That

is, we need to address such questions as: “are the effects of treatment additive in nature across

the treatment continuum?” 

The first issue to be addressed in our second report on 3-year follow-up outcomes concerns the

effects of transitional drug treatment. Analyses will be limited to our comparison sample. We

will not include the in-prison treatment sample because the effects of in-prison treatment cannot

be disentangled from the effects of transitional services. By policy, in-prison drug treatment

graduates receive transitional drug treatment only if they receive a CCC placement and thus the 

effects that could be assessed for these subjects are inextricably confounded with receiving a

CCC placement. On the other hand, we can assess the effects of transitional services for our

comparison subjects, among whom only 37 percent of the men and 26 percent of the women

released to a CCC received such services. Offenders generally do not self-select into transitional

drug treatment. Most often they are required to participate because their CCC placement is

housed at a CCC facility which requires participation of all individuals with an assessed need for

treatment, or because the Community Corrections Service manager required treatment as a

condition of CCC placement. In order to address the potential selection bias resulting from the

process of staff selection into treatment, our analyses will include models of selection into

transitional drug treatment. 

Another topic of our second report pertains to the effects of post-release treatment services

provided while under supervision of a U.S. Probation officer on post-release behaviors. Our

analyses will prevent the confounding of selection into and out of in-prison treatment with that of

selection into post-release treatment by separately analyzing DAP treatment subjects and 

comparison subjects – DAP comparisons and non-DAP controls. Our analyses will also include

models of the selection into post-release treatment.

The last issue to be addressed in the forthcoming report pertains to inter-institutional effects. As
161

done in our 6-month interim report, we will determine whether there is significant interinstitutional variation in outcome. If such variation is found, we will identify, from the available

information, factors which may account for these inter-institutional variations. Measures will

include factors such as program maturity and type of staff. 

Other Future Research Efforts

Women

The findings we report point to the need to further study gender differences in treatment entry

and retention as well as treatment process and outcomes. It would be desirable to conduct another

evaluation study of in-prison residential drug treatment for women with a larger sample size to

better assess treatment effectiveness. Such a study within the BOP may not be feasible since the

rate of volunteering has dramatically increased since the inception of the current evaluation

project. With the changes in the sentencing laws (e.g., VCCLEA), most of the women in need of

treatment receive treatment. This would preclude the ability to sample from a group of women

who do not enter treatment. 

To increase our understanding of gender differences in recovery and the effects of treatment,

future evaluation efforts would need to include additional measures. Information on the first age

of abuse, the frequency of abuse, the criminal activities and drug use behaviors of an individual’s

partner or friends, the selection of husbands and partners after treatment, and the role of children,

would assist in identifying factors which mediate the effects of in-prison treatment.

Proximal Outcomes

A dimension of outcome not included in this report, but important in and of itself, concerns

proximal outcomes. These proximal outcomes represent the intervening mechanism through

which the treatment program affects the ultimate outcomes (i.e., “distal outcome”) such as

recidivism and drug use. 

Each program makes assumptions about the cognitive and behavioral deficiencies of the clientele

served, and programs are designed to ameliorate these deficiencies. Without addressing these

deficiencies, the programs cannot be expected to have any effect on the “distal outcomes,” as

these deficiencies contribute to these outcomes. It is likely that these proximal outcomes (such as

increases in self-efficacy and changes in ways of coping with stressful situations) can also

contribute to our understanding of inter-site differences as well as gender differences.

Although our findings suggested that drug abuse treatment had a positive effect, our study lacked

the programmatic specificity to identify the particular factors that contributed to this successful

outcome. Such specificity would require the identification of intervening mechanisms. An

assessment of the extent to which the population served had the purported deficiencies and the
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extent to which these deficiencies were remedied will help us understand how the treatment

programs work. Furthermore, gender differences in the percent with the purported deficiencies

and in the percent with remedied deficiencies will increase our understanding of gender

differences in the treatment process.

Beyond the theoretical grounding, there is a methodological rationale for examining proximal

outcomes. The causal link between treatment and outcomes is strengthened when a strong

association between treatment and proximal outcomes predicted by theory exists, as well as a

strong association between the proximal outcome and the distal outcome (in this case, arrests and

drug use) (Mohr, 1992). This concept of an intervening mechanism based upon theory will be

examined in future analyses using pre- and post-treatment measures selected because of their

relationships to the theoretical underpinnings of the DAP’s. Structural equation models which

include both proximal and distal outcomes may also be useful in assessing the causal links.

Another rationale for the examination of the proximal outcomes arises from the goal of

generalization in any evaluation research. More recent evaluation research recognizes the limited

utility of research that solely addresses the question of whether a program works (Chen, 1990).

When the response is yes, as appears to be the case here, the successful replication of the

program and its improvement depend upon an understanding of the causal mechanisms that lead

to this “success.” 

A Final Note

A commentary on future analyses pertains to increasing the understanding of the relationship

among the various outcomes of interest. Among men, both recidivism and drug use were affected

by drug treatment. Among women, only employment was affected by drug treatment.

Comparisons of the causal processes by gender may provide another avenue for better

understanding the nature of gender differences in treatment needs and outcomes.

Disentanglement of the causal nature of the relationships between recidivism, drug use and

employment, some of which are most likely to be reciprocal in nature, will require utilizing a

path analytic method. Although path analytic methods do not lend themselves to addressing

selection bias issues, such techniques may assist us in identifying the direct and indirect effects of

treatment.

Another commentary pertains to addressing the question of “what works with whom.” Future

studies could focus on examining the effects of factors other than residential treatment on the

various outcomes. Such studies could no longer assume a linear additive model. Interaction

effects between variables such as treatment and level of drug addiction would need to be

considered. Such interaction effects could identify the variation in treatment effectiveness among

men and women for individuals with different characteristics.

Our last commentary pertains to cost-benefit. In the future, we will consider the cost-savings for

the monies spent given the effect size. For each dollar spent on treatment, how much of a cost
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savings, if any, do we receive?

We have attempted to identify some of the limitations of the conclusions we have drawn, to

identify the issues to be discussed in the second 3-year outcome report, and to identify the

general issues important to future research. Major topics for future research concern gender

differences in treatment process and outcome, the role of proximal outcomes and the relationship

between the various outcome measures.