Magic Bean Recipe to Prevent SUDs
By Patrick N. Moore LPC
What causes a Substance Use Disorder (SUD)? For lack of a provable scientific explanation, undesirable SUD outcomes continue. Frustration and desperation build. Scientific failure promotes the search for something, anything, even magic bean kind of solutions. Why not? What do we have to lose? This may be a good thing. Magic bean theory promotes outside the box thinking. How would we know if the new recipe was effective and exposed causality? Use scientific research methods. If we were to test old theories against new bean theories it would look like the following.
Identify conventional pattern. Make a better bean. Test the bean. Compare patterns. Conclusions. Separate correlation from causation. APPLY Y=f(X).
First, let's examine conventional patterns of SUD prevention and treatment.
Identify conventional pattern.
Patterns we have already (Babor, Higgins-Biddle, 2001, p.33). Most SUD research is really epidemiology - looking for patterns in the population. Regarding SUDS, in a random sample, we know: 75% are low risk for SUDs. 20% are high risk. 5% are severe risk. Most deaths and hospitalizations happen to the low risk. Change due to treatment is challenging in the 5%. Failure is all too common. Many die. Year after year, decade after decade, not much changes. Hence the disappointment in past prevention and treatment modalities. Here’s the thing. This frequency distribution is valid, reliable and measurable in random samples. The whole sample. One pattern. Like a control group.
Make a better bean.
Making a better bean is easier when conventional bean strengths and weaknesses are analyzed. First the strengths. The “beans” used to study and treat SUDs are assessments and interventions based on frequency, quantity and consequences. These beans are reliable and valid. They are based on measures of behavior and outcomes. Can’t argue with “evidence”. These are “fact” based beans. Every now and then a little magic dust in the form of genetic predisposition or social and psychological observations spice up the recipe. These are strong beans. Like many things in life, examining the greatest strengths will reveal the greatest weakness.
Selection bias is the curse of conventional beans. Every single attribute of conventional assessment, prevention and treatment theory is derived from the 5% severe end of the addiction continuum. These beans ignore 95% of the population. The path to a better bean from this perspective is crystal clear. How to include all people in a sample? What do all humans on the continuum have in common that is related to behavior and SUDs?
The latest best answer is the human risk response system. This system explains why, for better or worse, we do the things we do, no matter where on the continuum anybody stands (Ropeik, 2010). We all have highly evolved, constantly developing risk response systems. Sometimes these systems have gaps (Ropeik, 2010).
More good news. The attributes that make up the risk response system fit right into current patterns (Moore, 2016, pp. 25-58). These attributes have names like risk/benefit, new/familiar, good/bad, control, commitment and social proof. This bean is beyond illustrating the disease end of the SUD continuum. This bean explains why. At any risk level. For any outcome. A very special bean indeed.
Test the bean.
How to test the bean? Put it in the current recipe. Add a risk response column in a conventional model that accounts for all risk levels. (Moore, 2016, pp. 45-58). Like an upside down assessment. Rather than ask a lot of questions in order to interpret a risk level back to a client, offer the client stages of risk, behavior, outcomes and risk response combinations so they can self assess. A static measurement is nice. A measurement over time, before and after the intervention would be even better. We need a time machine bean.
To make a time machine bean ask three questions based on the stage assessment (Moore, 2016, pp. 58-67). What stage were you at? What stage are you at? What stage will you be at? Past behavior is measurable (where were you to where you are) and the impact of the intervention (where you are to where you are going). Best served anonymously to random or at least convenient samples. This is necessary to avoid selection bias and compare patterns.
Compare patterns.
Pattern comparisons look like this. 1. Virtually the same 75%, 20% and 5% risk distribution, a very high correlation (r=.9999). 2. 20% of the sample will reduce their risk level response to stage 1 or 0, from a higher stage, after the intervention. 3. When analyzing (Moore, 2016, pp. 70-84) the distribution of risk with two variables (Risk (high and low) and Benefit (high and low) where the control variable is the stage frequency of risk the following is revealed. 75% of the population develops a high benefit pattern, 25% of the population develops a low benefit pattern. What explains these different patterns? The answer is benefit / risk (Y) outcomes are a function (f) of new / familiar (X). The scientific notation is Y=f(X) (https://vimeo.com/326933952).
Conclusions
Simply put, addiction is mistaking dependence for autonomy.
( https://vimeo.com/ondemand/prehabknowyourpattern) This explains all challenges, failures and successes in prevention and treatment efforts. This means students can be educated (Moore, 2016, pp. 94-99) about risk response development before acute accidents caused by misinterpreting their own development or peer development. The invisible high risk students can be identified and intervened on before symptoms. Resistance to treatment is reduced for the severe risk students. If you ever wondered how effective an intervention actually is across a classroom, school or community - you can instantly calculate change after the self assessment with this method. BOOM go the beans.
Separate correlation from causation.
Is the outcome of Y really the function of X or just a coincidence or correlation? Nomothetic criteria is the scientific test - which means answer these three questions (Babbie, 2004, pp. 90-91).
- Is there a high correlation between old bean and new bean patterns? Oh yes. Nearly perfect at r=.9999. If there was no correlation or low correlation we could stop here.
- Does the outcome of Y happen after the function of X? Yes. X (patterns; new to familiar) develop long before the risk/benefit outcome of Y. If the answer was no we could stop here.
- Can another variable explain the outcome? Possible. Not at this time. I must concede more will be revealed. Given the time, effort and cost of this discovery, not for a long while. Meeting these three criteria establishes that Y=f(X) is causation as opposed to correlation. SUD outcomes are caused by risk response system development. Change risk response development changes outcomes. Sooner is better.
There is another test. The necessary and sufficient test used in the following manner (Babbie, 2004, pp. 92-93).
- Is high or low benefit development necessary to cause dependence or its opposite, autonomy? Yes, because either result is unlikely without a pattern strong enough to counter hundreds of other variables with thousands of attributes.
- Is high or low benefit development sufficient to cause dependence or its opposite, autonomy? Yes, because results are not dependent on anything other than the developing or developed pattern.
There are three targets and objectives for maximum effect.
- Educate the 75% low risk.
- Identify and Intervene on the 20% high risk.
- Lower resistance to change for the 5% severe risk .
These targets and objectives can be met in various high school and college classrooms.
If you know someone interested in the next level of SUD assessments, they can start getting results and make a difference today simply by renting or buying the educational video at: https://vimeo.com/ondemand/prehabknowyourpattern
There are several other videos at the same link at no charge. Other information like the Prehab book, journal article and research poster can be found at www.duncanparkpress.com. Please contact me if you have further questions or comments through Linkedin or [email protected].
Bibliography
Babbie, Earl. (2004). The Practice of Social Research. (10th ed.). Belmont, CA. Wadsworth/Thomson Learning.
Babor, Thomas F. Higgins-Biddle, John C. (2001). Brief Intervention For Hazardous and Harmful Drinking, A Manual for Use in Primary Care. World Health Organization, Department of Mental Health and Substance Dependence. WHO/MSD/MSB/01.6b
Moore, Patrick N. (2016) PREHAB Leveraging Perception to End Substance Abuse. Duncan Park Press LLC. Roswell, GA.
Ropeik, David. (2010). How Risky Is It, Really? Why Our Fears Don’t Always Match the Facts. New York, New York: McGraw Hill.
You can't make a better bean unless you plant them and fertilize the soil. You have to encourage growth with water and sunlight, and patiently let the bean grow into a vine. If the growth is disturbed, the water and sunlight not enough, the bean plant will go into a desparate survival mode. It will try to grow at all costs, but may produce weak fruit. It will get "spindally" and have skinny, long shoots and may have poor blossums. It could even die. It might pull moisture from toxic substances if around. Then, you would not want to use it in any recipe. So the answer for making a better bean may be soil and water purity, adequate but not overwhelming sunlight/temperature and stable wind, rain etc., in an area where the plant won't be battered. Testing bean recipes can only occur at harvest. So in human terms it would happen with areas conducive for nurturance and growth with prevention of ACEs (adverse childhood events) and Trauma.
Encouraging Autonomy Combats Demoralization
5 年You will help many Sarkis Sarkisian.??
Substance Abuse Counselor
5 年Wow, I've had the similar lines of thinking and always felt there are other clinicians who feel this way. I've worked in a few different modalities and all have the same standards. I feel if we assist with out if the box assessing. I'm currently in drug court and many that come through the system are not high risk. I've been restricted with agency stanards with two placements level II&III groups. due to the politics involved. Nonetheless, I've made adjustments to my assesments and placements. I have colleagues that feel otherwise, however, I feel they are not out the box thinkers to standardized in thinking. Great research and read!!!