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. 

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RICHARD C. SUAREZ CRISIS INTER  - counselor   |63.117.245.xxx |2010-03-29 11:55:40 Crisis Intervention Co-occurring disorders. Richard Suarez Reply 0   0

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