Addiction research has produced a lot of data covering adolescent addiction, from onset variables to post treatment factors. Because the research becomes dated quickly, it is important to stay current with literature.  

Research tells us that 80 percent of U.S. adolescents have used alcohol, and about five percent meet the criteria for substance use disorder (Tarter, 2002). Genetic predisposition is a well known risk factor –some adolescents are more prone to addiction than others, and substance abuse runs in certain families. Not having a cohesive family unit is yet another risk factor for adolescent substance abuse, as are substance using peers and communities with a strong drug trade (Kinney, 2009).

Also, risk-taking behavior is often found in delinquent behavior, and both are associated with strong substance abuse risk factors (Perkinson, 2008). Not having strong emotions toward events and others during child development, and not having one’s behavior regulated, when combined, predisposes such individuals to substance abuse (Tarter, 2002). One study (Ohannessian & Hasselbrock, 2008), followed 200 adolescents who had paternal alcoholism into early adulthood, and found that these individuals exhibited hostility, sadness, conduct problems and risk-­taking play, which in turn, predicted substance abuse. Additionally, earlier onset of drug/alcohol use increases the chance of abuse problems just a few short years later (Peleg-Oren, et. al., 2009; Zucker, et. al. 2009).

Adolescents with dual disorders are often found to have less successful outcomes; drop out of treatment more often; and relapse more frequently than those without associated disorders. Conduct disorders, depression and attention deficit hyperactivity disorder all predicted higher chemical abuse at treatment intake, as well as less successful outcomes at one year. Longer treatment is usually associated with better outcomes, but not always. Psychiatric services and psychotherapy are associated with better substance abuse and mental health outcomes. Boys have worse outcomes; and adolescents who entered treatment at an older age tended not to do as well following treatment. Lastly, research is revealing how severe substance abuse at admission plays a role in outcome. Essen­tially, clients with more severe abuse upon admission experienced worse outcomes in the years just after treatment (Sterling, Chi, Campbell & Weisner, 2009).

Many of these studies looked at adolescent addiction and mental health issues, but outcome research is limited. Outcome research on a group of 630 subjects ages 13–18 uncovered two key findings (Sterling, Chi, Campbell & Weisner, 2009). First, a treatment experience that contributes to an adolescent’s sobriety at one year also contributes to better mental health and sobriety at three years. A mark of this better outcome was that a good engagement was established between client and counselor. Second, it is important to provide continuing care for this population following treatment. This continuing care often consists of ongoing groups, activities and individual sessions.

There has been little research on the relationship between adolescent development and its effect on treatment outcome. A few studies found that age was not related to treatment outcome, but this included no difference between those who completed treatment and those who did not. Additionally, better indicator data could be one based on grade level, or at least addressing the developmental stage of substance abusing adolescents.

Some research indicates that early onset of puberty is associated with substance abuse in boys, as is early menarche associated with substance abuse in girls (Wagner, 2009). In addition, developmental transitions such as working outside the home and the changing relationship of the adolescent to their parents are indicative of substance abuse. To further complicate matters, none of the factors listed act in isolation of one another; rather, they are interrelated, making the comprehension of substance abuse onset even more difficult.

Try it
The following are practical assessment ideas you can use from the adolescent development research. An assessment process, Developmentally Informed Research on the Effectiveness of Clinical Trails (DIRECT), is divided into four broad categories that are summarized below, with a few prospective questions you might consider using with your adolescent populations (Wagner, 2009).

• Biological Factors. Consider assessing and examining two factors for this element. First, you may want to account for the client’s pubertal standing (e.g., onset of menarche); hormonal changes that are taking place; and how such changes might effect treatment. Another develop­mental marker would be the ability of the adolescent to make rational decisions, and the effect this might have on selecting a treatment modality such as cognitive behavi­oral treatment over another possible treatment. In this case you are looking to see if the client thinks more concretely versus abstractly.
• Psychological Factors. Here you would be looking to see if the client had formed or is in the process of forming an identity and what effect that might have on treatment. Also, assess if the client has the skills to adequately solve problems—if they don’t, that will have profound implications on treatment. The same goes for self-regulation in terms of the client’s ability and to self regulate. It makes sense not to expose clients to certain treatment functions and interactions if they can’t control themselves. And, if need be, teach them how to regulate.
• Social Factors. Here you want to assess for the various levels of parental or peer influences on the client. Is the client capable of interpersonal negotiation and social problem solving? Depending on the client’s varying abilities in these areas, the information will tip you off as to what you need to address sooner rather than later. Lastly, determine from what media the client usually obtains his/her sources of information, and use that information to your advantage.
• Transitions. Here you want to know key transition points such as moving from one level of education (elementary school) to another (middle school) and, in terms of treatment, adjust reading assignments accordingly.

There are development issues not listed here that could be important in your assessment. This information can help determine treatment focus and direction.

Research you can do
Consider getting a snapshot of develop­mental variables in the treatment population you now serve—this is your convenience sample. In our case, you are interested how your adolescent places in what particular DIRECT category.

The following short survey will give you an idea of what a development survey might look like. Feel free to add or modify the questions for your treatment population.

Start with demographics: age, sex, school grade, etc.
1. I prefer to make my important decisions based on (Circle one):
My emotion side
My rational side

2. I have a good idea of who I am and where I’m going in life
(Choose one):
Yes I do  or  I Don’t have a clue

3. In terms of self control, I tend to (choose one):
Keep myself in control  in most situations
Often lose control in most situations

4. The people in this world I most trust most are (choose one):
    My friends
    My family
    My siblings

5.  I found moving from one school grade to another to be (choose one):
Easy  or  Difficult

These questions might also serve as good assessment questions. This survey approach allows you to see patterns and make treatment adjustments accordingly. Conduct the same survey every six months or so, and you begin see other patterns, all of which can supply you with valuable information. As always, I am interested in your results.

Mike Taleff has written numerous articles, several books (most recent—Advanced Ethics for Addiction Pro­fessionals), teaches at the college level, and conducts trainings and workshops (e.g., Critical Think­ing, Advanced Ethics, and the latest treatment practices). He can be contacted at This e-mail address is being protected from spambots. You need JavaScript enabled to view it , or ­ This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

Olivia Harkins-Pignolet is a Licensed Clinical Social Worker whose practice focuses on women’s issues, dual diagnosis, children and families.

References
Kinney, J. (2009). Loosening the grip: A handbook of alcohol information, (9th Ed). Boston: McGraw Hill. hannessian, C.M. & Hasselbrock, V.C. (2008). Paternal alcoholism and youth substance abuse: The indirect effects of negative affect, conduct problems, and risk taking. Journal of Adolescent Health, 42, 2, 198–200.
Peleg-Oren, N., Saint-Jean, G., Gardenas, G.A., Tammara, H., & Pierre, C. (2009). Drinking alcohol before age 13 and negative outcomes in late adolescence. Alcoholism: Clinical and Experimental Research, 33, 11, 1966–1972.
Perkinson, R.R. (2008). Chemical dependency counseling: A practical guide (3rd Ed.). Los Angeles, CA: Sage. Sterling, S., Chi, F., Campbell, C., & Weisner, C. (2009). Three-year chemical dependency and mental health outcomes among adolescents: The role of continuing care. Alcoholism: Clinical and Experimental Research, 33, 8,1417–1429.
Tarter, R.E. (2002). Etiology of adolescent substance abuse. A developmental perspective. American Journal on Addictions, 11, 3, 171–191.
Wagner, E.F. (2009). Improving treatment through research: Directing attention to the role of development in adolescent treatment. Alcohol Research & Health, 32, 1, 67–75.
Zucker, R.A., Donovan, J.E., Masten, A.S., Mattson, M.E., Moss, H.B. (2009). Developmental process and mechanism: Ages 0–10. Alcohol Research & Health, 32, 1,16–29

Addiction research has produced a lot of data covering adolescent addiction, from onset variables to post treatment factors. Because the research becomes dated quickly, it is important to stay current with literature.  

Research tells us that 80 percent of U.S. adolescents have used alcohol, and about five percent meet the criteria for substance use disorder (Tarter, 2002). Genetic predisposition is a well known risk factor –some adolescents are more prone to addiction than others, and substance abuse runs in certain families. Not having a cohesive family unit is yet another risk factor for adolescent substance abuse, as are substance using peers and communities with a strong drug trade (Kinney, 2009).

Also, risk-taking behavior is often found in delinquent behavior, and both are associated with strong substance abuse risk factors (Perkinson, 2008). Not having strong emotions toward events and others during child development, and not having one’s behavior regulated, when combined, predisposes such individuals to substance abuse (Tarter, 2002). One study (Ohannessian & Hasselbrock, 2008), followed 200 adolescents who had paternal alcoholism into early adulthood, and found that these individuals exhibited hostility, sadness, conduct problems and risk-­taking play, which in turn, predicted substance abuse. Additionally, earlier onset of drug/alcohol use increases the chance of abuse problems just a few short years later (Peleg-Oren, et. al., 2009; Zucker, et. al. 2009).

Adolescents with dual disorders are often found to have less successful outcomes; drop out of treatment more often; and relapse more frequently than those without associated disorders. Conduct disorders, depression and attention deficit hyperactivity disorder all predicted higher chemical abuse at treatment intake, as well as less successful outcomes at one year. Longer treatment is usually associated with better outcomes, but not always. Psychiatric services and psychotherapy are associated with better substance abuse and mental health outcomes. Boys have worse outcomes; and adolescents who entered treatment at an older age tended not to do as well following treatment. Lastly, research is revealing how severe substance abuse at admission plays a role in outcome. Essen­tially, clients with more severe abuse upon admission experienced worse outcomes in the years just after treatment (Sterling, Chi, Campbell & Weisner, 2009).

Many of these studies looked at adolescent addiction and mental health issues, but outcome research is limited. Outcome research on a group of 630 subjects ages 13–18 uncovered two key findings (Sterling, Chi, Campbell & Weisner, 2009). First, a treatment experience that contributes to an adolescent’s sobriety at one year also contributes to better mental health and sobriety at three years. A mark of this better outcome was that a good engagement was established between client and counselor. Second, it is important to provide continuing care for this population following treatment. This continuing care often consists of ongoing groups, activities and individual sessions.

There has been little research on the relationship between adolescent development and its effect on treatment outcome. A few studies found that age was not related to treatment outcome, but this included no difference between those who completed treatment and those who did not. Additionally, better indicator data could be one based on grade level, or at least addressing the developmental stage of substance abusing adolescents.

Some research indicates that early onset of puberty is associated with substance abuse in boys, as is early menarche associated with substance abuse in girls (Wagner, 2009). In addition, developmental transitions such as working
outside the home and the changing relationship of the adolescent to their parents are indicative of substance abuse. To further complicate matters, none of the factors listed act in isolation of one another; rather, they are interrelated, making the comprehension of substance abuse onset even more difficult.

Try it
The following are practical assessment ideas you can use from the adolescent development research. An assessment process, Developmentally Informed Research on the Effectiveness of Clinical Trails (DIRECT), is divided into four broad categories that are summarized below, with a few prospective questions you might consider using with your adolescent populations (Wagner, 2009).
• Biological Factors. Consider assessing and examining two factors for this element. First, you may want to account for the client’s pubertal standing (e.g., onset of menarche); hormonal changes that are taking place; and how such changes might effect treatment. Another develop­mental marker would be the ability
of the adolescent to make rational decisions, and the effect this might have on selecting a treatment modality such as cognitive
behavi­oral treatment over another possible treatment. In this case you are looking to see if the client thinks more concretely versus abstractly.
• Psychological Factors. Here you would be looking to see if the client had formed or is in the process of forming an identity and what effect that might have on treatment. Also, assess if the client has the skills to adequately solve problems—if they don’t, that will have profound implications on treatment. The same goes for self-regulation in terms of the client’s ability and to self regulate. It makes
sense not to expose clients to certain treatment functions and interactions if they can’t control themselves. And, if need be, teach them how to regulate.
• Social Factors. Here you want to assess for the various levels of parental or peer influences on the client. Is the client capable of interpersonal negotiation and social problem solving? Depending on the client’s varying abilities in these areas, the information will tip you
off as to what you need to address sooner rather than later. Lastly, determine from what media the client usually obtains his/her sources of information, and use that information to your advantage.
• Transitions. Here you want to know key transition points such as
moving from one level of education (elementary school) to another (middle school) and, in terms of treatment, adjust reading assignments accordingly.

There are development issues not listed here that could be important in your assessment. This information can help determine treatment focus and direction.

Research you can do
Consider getting a snapshot of develop­mental variables in the treatment population you now serve—this is your convenience sample. In our case, you are interested how your adolescent places in what particular DIRECT category.

The following short survey will give you an idea of what a development survey might look like. Feel free to add or modify the questions for your treatment population.

Start with demographics: age, sex, school grade, etc.
1. I prefer to make my important decisions based on (Circle one):
My emotion side
My rational side
2. I have a good idea of who I am and where I’m going in life
(Choose one):
Yes I do  or  I Don’t have a clue
3. In terms of self control, I tend to (choose one):
• Keep myself in control  in most situations
    • Often lose control in most situations
4. The people in this world I most trust most are (choose one):
    My friends
    My family
    My siblings
5.  I found moving from one school grade to another to be (choose one):
Easy  or  Difficult

These questions might also serve as good assessment questions. This survey approach allows you to see patterns and make treatment adjustments accordingly. Conduct the same survey every six months or so, and you begin see other patterns, all of which can supply you with valuable information. As always, I am interested in your results.

Mike Taleff has written numerous articles, several books (most recent—Advanced Ethics for Addiction Pro­fessionals), teaches at the college level, and conducts trainings and workshops (e.g., Critical Think­ing, Advanced Ethics, and the latest treatment practices). He can be contacted at This e-mail address is being protected from spambots. You need JavaScript enabled to view it , or ­ This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

Olivia Harkins-Pignolet is a Licensed Clinical Social Worker whose practice focuses on women’s issues, dual diagnosis, children and families.

References
Kinney, J. (2009). Loosening the grip: A handbook of alcohol information, (9th Ed). Boston: McGraw Hill.
Ohannessian, C.M. & Hasselbrock, V.C. (2008). Paternal alcoholism and outh substance abuse: The indirect effects of negative affect, conduct problems, and risk taking. Journal of Adolescent Health, 42, 2, 198–200.
Peleg-Oren, N., Saint-Jean, G., Gardenas, G.A., Tammara, H., & Pierre, C. (2009). Drinking alcohol before age 13 and negative outcomes in late adolescence. Alcoholism: Clinical and Experimental Research, 33, 11, 1966–1972.
Perkinson, R.R. (2008). Chemical dependency counseling: A practical guide (3rd Ed.). Los Angeles, CA: Sage.
Sterling, S., Chi, F., Campbell, C., & Weisner, C. (2009). Three-year chemical dependency and mental health outcomes among adolescents: The role of continuing care. Alcoholism: Clinical and Experimental Research, 33, 8,1417–1429.
Tarter, R.E. (2002). Etiology of adolescent substance abuse. A developmental perspective. American Journal on Addictions, 11, 3, 171–191.
Wagner, E.F. (2009). Improving treatment through research: Directing attention to the role of development in adolescent treatment. Alcohol Research & Health, 32, 1, 67–75.
Zucker, R.A., Donovan, J.E., Masten, A.S., Mattson, M.E., Moss, H.B. (2009). Developmental process and mechanism: Ages 0–10. Alcohol Research & Health, 32, 1,16–29

In the first part of this column, I pointed out some unsettling research implications for our field. Sooner or later, these implications will have a direct impact on your everyday work.

In a nutshell, the past research methods have not been up to the task of answering our ever complex treatment questions. The traditional technology model of psychotherapy research doesn’t seem able to give us clear answers as to what parts of various addiction therapies work as they are supposed to work. For example, why does Cognitive Behavior Therapy (CBT) work with one client and not another? The same can be said for Motivational Interviewing (MI), Behavior Couples and 12-Step oriented treatments. You now need to recall mediators and moderators, those pesky co-variants that screw-up everything.

The current research methods  seemingly cannot do the job. Yet, we shouldn’t  abandon the old ways; rather, we need new methods to peer deeper into addiction treatment. Specifically, the technology model of psychotherapy is limited by what is called its linear, dose-response assumptions. Basically, the research model assumes you give a quantity of treatment in a straight, uniform manner. However, that cannot account for all client and counselor variables which not only confuse the technology model, but also yield confusing results.  

We need something new—something that better explains the interactions that take place, such as rapport, bond, relationship (non-specific effects); and specific effects (change talk, refusal skills, belief in spirituality, etc.), as well as the back and forth mix between them.
Morganstern and McKay (2007) discuss three such approaches. The first two—critical sessions and patient-focused research—focus on new research strategies to model client response to various effects. They are still in development, and what they presently offer is limited. The third approach—generic model of psychotherapy—studies the interaction between non-specific and specific factors.

Have you ever noticed how one client session can produce sudden—and sometimes noticeably more significant —changes in a client? Further­more, these changes don’t die out in a week, but seem to continue. Some data indicates that just prior to these sudden changes, certain clients began to process what is going on in therapy better. Often, good adherence to treatment goals and good alliance in early sessions set the stage for these sudden breakthroughs. In addition, key mediators, such as increase in self-efficacy (e.g., thinking “I can do this”), or the ability to better handle a craving, may lay the ground work for a sudden gain. Basically, critical session models point toward finding subgroups of clients who respond well to specific treatments. Instead of applying one treatment, such as CBT, to all your clients, you may want to apply parts of the treatment to selected clients. The idea is to find who responds to what and use more of the application on this subgroup. For instance, research indicates that MI seems to get better results from clients who have high levels of anger. However, that does not mean you should use MI with all your angry substance abuse clients. It does mean you may have to become more sophisticated in your application of treatments to clients, and not just apply one treatment across-the-board. This approach requires more thought, more assessment and more work, but the end product would be potentially higher levels of recovery.

While interesting and promising, not much is available from this perspective at this time which remains in development.
This approach, which is also known as treatment algorithms, claims that there is sizeable variability in the way clients respond to treatment. Thus, it is very difficult to predict how clients will respond to any one intervention. Therefore, you have to collect data after each and every session (not just progress notes). Based on that data, you can make modifications to your treatment direction. In terms of addiction, this research model is still very much in development. We do know from all that data collected from treatment sessions may emerge decision rules (algorithms) which can guide or modify your treatment. Some of the data collected could include: daily or weekly substance use levels; 12-step group attendance (daily/weekly); and changes in a client’s level of self-efficacy. As stated, how all this comes together is still in the development process, but if created, such protocols

The genetic model of psychotherapy views therapy as dynamic, evolving and ordered (Orlinsky, Rønnestad and Willutzki, 2004), and focuses on the interaction between non-specific and specific factors. From this perspective, you are to pay attention to critical components of treatment that include what’s inside a session and what happens across many sessions. There are five components:
• The therapeutic contract, or roles played by client and counselor, whether treatment is conducted individually or in a group, as well
as treatment model and session schedule, among others.
• Therapeutic operations, which include how the client presents his complaints and problems; how he thinks; how the counselor under­stands the client (e.g., diagnosis, case formulation); the strategy used (e.g., 12-step model); and how the client responds or cooperates with the interventions.
• Therapeutic bond, or the quality of involvement and rapport between client and counselor
• In-session impacts, or therapeutic realizations, such as insights vs. confusion, relief vs. distress, as
well as the counselor impact,
such as frustration vs. feeling
good about a session.
• Temporal patterns, or distinctive moments of facilitation as well as total number of sessions.

Efforts are being made to figure out how to code these components for a session, so that counselors can assess actual progress with clients, rather than relying on hunches. After several of these coding sessions you are able to effectively correlate what contributed to a client’s positive or negative outcome. Such specific information could hold more weight as to why clients don’t change, versus blatantly faulting denial or client laziness.

The bottom line to these new research approaches is that they tell us treatment is more complex then simply applying a treatment model to a client. Truth be told, addiction treatment always was complex. It was never about simply apply treatment “X” and get “Y” results.
Since the newer research models need more time to evolve, only the briefest application ideas are outlined here. In terms of critical sessions,  you can  be observant of what seems to precede critical sessions, be it the application of a certain therapy with a certain type of client; an increase of trust in the client-counselor relationship; a flash of client confidence; or whatever. The point is to really pay attention to what preceded a sudden breakthrough.

The practical application of the patient-focused model is a little tricky. The main idea is to collect data (e.g., days of sobriety, session attendance, a client’s self-proclaimed level of motivation) not just write the run-of-the-mill progress notes. The patient-focused approach requires a bit more work, but, in turn, will give you more information to work with. For example, once you have collected a month’s worth of data, you might be able to establish “a decision rule” to a client. A very primitive example might be, “When you attend your scheduled sessions on time, and you and I trust one another, you do not drink at all or as much. So, a decision for you to use might be to keep all your scheduled sessions, and work on keeping our trust strong.”

In terms of the generic model, one useful idea is just to code two of the five items listed above, such as therapeutic bond and in-session impacts. This code will be subjective, and can be as simple as a three point scale—high, nominal, none. Following each session, ask the client to select (high, nominal, none) for how he rates the therapeutic bond between you, and if the session made an impact on his treatment goals. After a few of these ratings, you should get an idea if your counseling is making a difference in the recovery process. If the ratings are high, you may be on the right track. If the ratings are low, that might signal that you need to improve your professional relationship. In addition, if the ratings are low you would need to adjust.

Keep in mind that past research methods are showing their limitations as we get deeper into the 21st century. These promising new research methods may well have the potential to reveal new and meaningful ideas for addiction treatment strategies.  

Mike Taleff has written numerous articles, several books, teaches at the college level, and conducts trainings and workshops (e.g., the latest treatment practices, Critical Thinking and Advanced Ethics). He can be contacted at This e-mail address is being protected from spambots. You need JavaScript enabled to view it , or This e-mail address is being protected from spambots. You need JavaScript enabled to view it .


References
Baron, R.M. & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psycholoical research: Conceptual, strategic, and statistical considersations. Journal of Personality and Social Psychology, 51, 6, 1173–1182.
Morgenstern, J. & McKay, J.R. (2007). Rethinking the paradigms that inform behavioral treatment research for substance use disorders. Addiction, 102, 1377–1389.
Orlinsky, D.E., Rønnestad, M.H. & Willutzti, U. (2004). Fifty years of psychotherapy process-outcome research: Continuity and change. In M. Lambert (Ed.). Handbook of psychotherapy and behavior change. (5th ed., pp: 307-389). New York: John Wiley & Sons.
Wigner, D.E., & Solberg, K.B. (2001). Tracking mental health outcomes: A therapist’s guide to measuring client progress, analyzing data, and improving your practice. New York: John Wiley & Sons

This article is published in Counselor, The Magazine for Addiction Professionals, February 2010, v.11, n.1, pp.16-17. 

Those who use research-based treatment practices or recognize the most basic of research methods would voice familiarity with the concept—randomized control trial (RCT). It is the strength behind the dominant, most reliable treatment research methods used in the last three decades called the technology model of psychotherapy research. Most folks know it by the term evidence-based treatment. When you see it you know you’re getting quality.

Basically, RCT is a scientific procedure in which subjects are randomly assigned within an experiment. In good addiction treatment research, subjects are indiscriminately assigned to a group that receives a certain treatment (treatment group), or a group that does not receive the treatment (non-treatment group). When you get enough subjects for both groups, you can then compare the differences between the groups in terms of outcome. The strength of the RCT is that subjects have an equal chance of being assigned to either group. What does that do? It improves the chances that your independent variable (e.g., a treatment administered to one group, and not the other) will be considered the difference or the cause of any differences between the groups (groups in this case are the dependent variables). In research parlance, RCT improves the reliability of your data and decreases error; and who doesn’t want that?

On the whole, the technology model of psychotherapy research and RCT is behind all the empirically-based treatments being touted these days. Without it, we have to resort to educated guesses. Not good. However, cracks in this gold standard have begun to appear.

Problems on the horizon
It always happens. Just when you think you have a good thing in hand (the technology model) something comes along to challenge your theory. Or to put it another way, ugly facts will dispute beautiful theories. Case in point: for decades researchers assumed that by following the technology model, we would eventually discover some consistent, reliable and detailed addiction counseling techniques for us to use in our therapy sessions, but that has not happened.

The central problem seems to be that the technology model may well be showing its limitations. According to Morgenstern and McKay (2007), we cannot extract better information from it than we already have. That’s why we only get general information about addiction treatment outcomes. We cannot seem to find specific mechanisms within various therapies that constantly succeed across a spectrum of clients. Essentially, we cannot identify what it is about treatment specifics that make them work (see examples below). In addition, the technology model is giving us conflicting results. Many research studies often give varying results from the same treatment. I often see this when I review the research literature for this column. And ta’ boot, sometimes the treatment works with one client and is a total bust on another. So, what’s up?

To answer that question we need to ask a few questions:
• What are treatment specifics?
• Or in research terms, what are efficacy, moderator and mediator effects?

Treatment specifics—efficacy and moderator effects
Efficacy, or the efficacy research method, compares a specific treatment (e.g., Motivational Interviewing) against a control group under tight research conditions to determine if the treatment is indeed causing a desired outcome (Wigner & Solberg, 2001). Gener­ally, all the treatments examined in the Morgenstern and McKay (2007) study indicated some level of efficacy. The treatments reviewed included Moti­vational Interviewing (MI), Cognitive Behavioral Therapy (CBT), Behavioral couples treatment (convincing evidence for this) and 12-Step treatment (TST).
Moderators are effects such as sex, age, race or the strength of a reward that can affect the direction and/or strength of a relation between a independent variable on a dependent one. Yes, believe it or not, all such moderator effects influence treatment outcome. (Begin to see the problem here?) (Baron & Kenny, 1986). Morgenstern and McKay (2007) report­ed mixed outcomes across all therapies reviewed.

Treatment specifics—mediator effects
Mediators describe how, not when, ­certain internal variables beside the independent variable impact the dependent variable (Baron & Kenny, 1986). For example, in MI there are supposedly vital mediators that make change happen. One particular mediator is the client’s commitment language, or the client using more change language. Using such language, as suggested by the MI model, should bring about more readiness for change. How­ever, eight studies cited by Morgenstern and McKay (2007) found little evidence for such an effect on readiness to change. Other studies they reviewed indicated essentially mixed results using MI with patients who had low readiness to change.
But what about other therapies? Well, consider that the main tools of CBT are the acquisition of coping tools or skills (e.g., correcting inaccurate self thoughts, dealing with cravings). Those things are suppose to mediate outcome, right? Morgenstern and McKay’s analysis found no support for the idea that CBT works via skill acquisition. In many of the studies reviewed, the authors found skill increase was unrelated to outcome.
Okay, what about behavioral couples treatment? While no full test of mediator effects exists for this approach, it looks like behavioral contracting had the best efficacy evidence.

Finally, what about TST? The same investigators looked at three mediators specific to 12-Step programs—spirituality, endorsement of abstinence and commitment to Alcoholics Anonymous practices. Only the commitment to AA practices mediator made a difference, but not the belief in powerlessness.

Having found all that, the authors are quick to point out that they are not willing to say there are no specific effects to be had from these therapies. The problem may not be with the mediators, but in the technology model itself.

It’s the model, Silly
It is beginning to look like technology applied research is giving us mixed results. Why? According to our authors, the technology model has problems. Two big knotty issues have become evident. One is that clients are diverse and do not uniformly respond to general treatment approaches. Such a criticism has been leveled at various treatments for years, but the point is that our clients are different. They vary in the way they think, feel, and certainly in terms of their culture and individual history. The one knotty problem is how then can one therapy account or treat such variably?  More­over, treatment is dynamic or changeable. Anyone who has conducted therapy will attest to the fact that treatment not only changes from session to session, but will sometimes change significantly from one minute of a session to another. The current state of the technology model has a difficult time accounting for these changes and the many client differences you and I see all the time. Even with the best RCT design, all these factors mentioned cannot be tracked well and we end up with mixed results.*
Knotty problem number two is the relationship between specific and non-specific factors. It is assumed that all one needs is a bare minimum level of non-specific factors or what we call “engagement” (rapport) between a client and counselor for the specific therapy factors (e.g., mediator factors mentioned above) to kick in. In the first place, this is a simplistic assumption because attaining a relationship with a client is a complex, challenging and dynamic process. The relationship itself fluctuates over time and even within a session. So, what chance does a treatment specific technique have in such a variable environment?

In summary, the problem, to some degree, seems to be in the way we research our treatments. That recognition calls for change.

Next time
Given the importance of this subject, this column needs to be completed in two sections. For now, know that there are problems with our research design gold standards (Technology Model of Psycho­therapy Research). However, hope is also on the horizon. We address those promising possibilities next time in Part II.

*We are not going to get into the new statistical models floating around out here like Structured Equation Modeling.

Michael J. Taleff, PhD, CSAC, MAC, has written numerous articles, several books, teaches at the college level, and conducts trainings and workshops (e.g., the latest treatment practices, Critical Thinking and Advanced Ethics). He can be contacted at This e-mail address is being protected from spambots. You need JavaScript enabled to view it , or This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

References
Baron, R.M. & Kenny, D.A. (1986). The moderator-mediator variable distinction in social psycholoical research: Conceptual, strategic, and statistical considersations. Journal of Personality and Social Psychology, 51, 6, 1173–1182.
Morgenstern, J. & McKay, J.R. (2007). Rethinking the paradigms that inform behavioral treatment research for substance use disorders. Addiction, 102, 1377–1389.
Wigner, D.E., & Solberg, K.B. (2001). Tracking mental health outcomes: A therapist’s guide to measuring client progress, analyzing data, and improving your practice. New York: John Wiley & Sons

This article is published in Counselor, The Magazine for Addiction Professionals, December 2009, v.10, n.6, pp.16-17. 

For the first time in this column, we will examine the research on continuing care. Our literature review uncovered one particular article that did a commendable job of summarizing the research to date on continuing care (McKay, 2009). We outline the key points of that research, tease out a few findings that have practical value and encourage a crack at a small research project for the bold.

One small forewarning: addiction re­search usually presents a mixed bag of results. That mixed bag of results will be reflected here. However, there are a number of moderately solid findings that, if implemented, may well assist the recovery process for your clients.

Continuing care research
It was once referred to as aftercare, but the common term these days is continuing care. This is a phase of treatment that generally follows an intense level of addiction treatment such as inpatient addiction rehabilitation. For a long time, many inpatient programs offered limited ongoing care or simply referred clients to local outpatient programs. Recently, things have begun to change a bit and continuing care is getting the attention it deserves. If there is indeed more attention being given to continuing care, the question becomes, “what is effective at this level of treatment?”

One noteworthy finding indicates that continuing care is not the simple standard approach so often provided by treatment programs, but actually as with other parts of modern addiction treatment, is actually quite complex (McKay, 2009). Another noteworthy finding was that in terms of overall effectiveness, the research indicated that continuing care conducted over longer periods of time holds advantages over shorter durations of intervention, provided the clients remain engaged.

Continuing care interventions that use more direct and active attempts to bring treatment to the client through aggressive outreach (i.e., taking the treatment to the client by visiting the home, or inviting a spouse to session, or through lower burden delivery systems such as using the telephone) all had clear advantages over the old traditional approaches. The traditional formats generally expect clients to show-up-for-a-group session. All the research projects reviewed in McKay’s study used a variety of the aggressive methods mentioned above; none used the show up for a session method. An interesting finding was the aggressive continuing care methods generally ran for 12 months or longer.

One of the mixed bag findings was a clear recognition that despite using the so-called effective interventions, clients varied in their response to continuing care. Some did well, some didn’t. More­over, many didn’t wish to engage in continuing care even when it was available. This was true even with the best of continuing care programs. In most studies, about one-third of the continuing care programs had very good outcomes; about one-third had mixed outcomes; and about one-third did poorly.

There were two other findings that offered a mixed bag of outcomes. First, while many clients wanted to come into residential care, they also wanted to finish, and stop coming to a continuing care clinic for regular sessions. Second, many clients were unable to come to continuing care sessions because of family responsibilities, transportation problems or similar issues. Often, clinicians make generalized and sometimes incorrect assumptions about why clients are not coming to continuing care treatment, such as because they are resisting treatment or are in denial.

Lastly, no particular theoretical approach, such as cognitive behavior therapy or motivational enhancement therapy, appeared to influence continuing care effectiveness. From all the examined research, only a weak effect was noted for the more intense (meaning many sessions) interventions over the not so intense varieties.

McKay also looked at future promising interventions, and noted three things. First, new models of treatment are being developed that may use algorithms to guide counselors in response to progress or lack of progress. Second, medications to reduce relapse, such as Naltrexone, may become an important part of continuing care interventions. Third, there seems to be energy growing around what works and what does not work when it comes to continuing care—a career thought, for a young researcher or enterprising addiction counselor.

Try it
In this section, we apply the research cited above to practical applications you could use without much effort. There seems to be a number of things you can immediately employ.

First, if you are associated with an inpatient rehabilitation program and don’t have a continuing care component, start one. Rely on the methods listed just below, especially the aggressive methods that bring treatment to the client. Second, if you are associated with an inpatient rehabilitation program and have a continuing care component, take some time to examine if you are using any of the methods noted above that have been found to effective. If not, consider using some of the methods outlined in this quick summary:
• Bring your continuing care treatment to the client (aggressive method).
• Don’t put all your theoretical eggs in one basket. That is, do not rely on a single treatment approach.
• If a client stops coming to continuing care sessions, don’t jump to the conclusion that they are in denial. They could well be busy with work, family or any number of life situations.
• Consider the use of Naltrexone.
• If continuing care is becoming more complex, as suggested by McKay, that means placing more emphasis on tailor-made treatment, because simple approaches tend to over generalize address complex issues.

Third, consider that many enterprising individuals who wish to start a program of their own focus on those clients who are early in treatment, or what is commonly referred to as the precontemplation stage of change. The new thought is why not shift direction and start treatment programs that meet the needs of those coming out of intensive inpatient programs. Many rehab programs only offer limited levels of continuing care. Why not start programs that concentrate on this important phase of treatment? Consider creating a national franchise of such programs that exclusively meet the needs of clients fresh out of intensive treatment. It might actually work.

Research you can do
If you have a continuing care component to your program, you probably know the approximate percentage of clients maintain their recovery in one form or another after they leave your program. This simple experiment would be to introduce changes suggested from McKay into your present program and simply note any changes in variables of sobriety from the day you start making your changes. Nothing fancy, just change a few things and see what, if anything, happens.

Of importance in such a project might be an anonymous client survey that would potentially give you vital information. Toward the end of your continuing care program, consider asking your clients to complete a short survey of what they found helpful, not helpful, or liked and what they didn’t like. Be sure to include the following issues:
• the usual demographics (age, gender, married, in a relationship, employed, etc.)
• chemical of choice, how long used
• how many previous treatment programs they attended
• if they had to pick one key component of treatment that made the biggest impact on their new recovery, what it might be
As always, I remain interested in your findings, especially with this subject area.

Mike Taleff has written numerous articles, several books, teaches at the college level, and conducts trainings and workshops (e.g., the latest treatment practices, Critical Thinking and Advanced Ethics). He can be contacted at This e-mail address is being protected from spambots. You need JavaScript enabled to view it , or This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

Jamie Irvine is job placement specialist who is presently attending Leeward Community College studying to be a
certified substance abuse counselor. She has two adult children also attending college in Hawaii.

References
McKay, J.R. (2009). Continuing care research: What we have learned and where we are going. Journal of Substance Abuse Counseling, 36, 2,131–145

This article is published in Counselor, The Magazine for Addiction Professionals, October 2009, v.10, n.5, pp.36-37.

At the risk of sounding stale, our field is advancing at an ever-increasing pace, especially on the genetics front. At the risk of sounding threatening, such advancements require that counselors keep pace.

Epigenetics, as it applies to the addiction field, is so new that not much has translated to practice yet. So, why spend time on something that has little realistic treatment value? First, knowing epigenetics helps you to better understand mechanisms of addiction. Profoundly knowing addiction makes it possible to move away from the myth and baseless assumptions that often surround it. Second, understanding research that stands in opposition to the addiction myths and assumptions will only improve clinical assessment, treatment selection and, hopefully, outcome.

Most of us are aware that a family history of alcoholism increases a person’s risk of addiction. While having a family history of drug and alcohol problems does not doom one to addiction, the history increases the probability (Hesselbrock, Hesselbrock & Epstein, 1999). This is just one of many potential family variables that can increase the risk. Among several well-known family predisposition variables, one in particular may not have received the press it deserves. It is called density.

Before discussing density, it is important to review what we know about familial alcoholism. This review illustrates how density fits into the overall familial risk factors.